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+The Brainomics/Localizer dataset is a subset of the Functional Localizer dataset.
+
+For more details have a look at the dataset DOI landing page:
+https://doi.org/10.25720/1ca1-0sfd

dataset_description.json

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+{
+    "BIDSVersion": "1.0.2", 
+    "Name": "Brainomics/Localizer",
+    "License": "Creative Commons Attribution-NonCommercial 4.0 International",
+    "Authors": [
+        "Dimitri Papadopoulos Orfanos",
+        "Vincent Michel",
+        "Yannick Schwartza",
+        "Philippe Pinel",
+        "Antonio Moreno",
+        "Denis Le Bihan",
+        "Vincent Frouin"
+    ],
+    "Acknowledgements": "We thank Stanislas Dehaene for his participation to the creation of the Localizer database and Bernadette Martins for helping us navigate through regulatory rules. We thank the Inria-CEA Parietal team and in particular Virgile Fritsch for the Localizer data fetcher in NiLearn.",
+    "HowToAcknowledge": "Papadopoulos Orfanos, D., Michel, V., Schwartz, Y., Pinel, P., Moreno, A., Le Bihan, D., & Frouin, V. (2017). The Brainomics/Localizer database. NeuroImage, 144, 309\u2013314. doi:10.1016/j.neuroimage.2015.09.052",
+    "Funding": [
+        "ANR-10-BINF-04"
+    ],
+    "ReferencesAndLinks": [
+        "Papadopoulos Orfanos, D., Michel, V., Schwartz, Y., Pinel, P., Moreno, A., Le Bihan, D., & Frouin, V. (2017). The Brainomics/Localizer database. NeuroImage, 144, 309\u2013314. doi:10.1016/j.neuroimage.2015.09.052",
+        "Pinel, P., Thirion, B., Meriaux, S., Jobert, A., Serres, J., Le Bihan, D., Poline, J.-B., & Dehaene, S. (2007). Fast reproducible identification and large-scale databasing of individual functional cognitive networks BMC Neurosci., 8 (1) (2007), p. 91, 10.1186/1471-2202-8-91"
+    ]
+}

derivatives/fmriprep/dataset_description.json

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+{
+    "Name": "fMRIPrep - fMRI PREProcessing workflow",
+    "BIDSVersion": "1.1.1",
+    "PipelineDescription": {
+        "Name": "fMRIPrep",
+        "Version": "20.0.6",
+        "CodeURL": "https://github.com/poldracklab/fmriprep/archive/20.0.6.tar.gz"
+    },
+    "CodeURL": "https://github.com/poldracklab/fmriprep",
+    "HowToAcknowledge": "Please cite our paper (https://doi.org/10.1038/s41592-018-0235-4), and include the generated citation boilerplate within the Methods section of the text."
+}

derivatives/fmriprep/logs/CITATION.bib

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+@article{fmriprep1,
+    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
+    year = {2018},
+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
+    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
+    doi = {10.3389/fninf.2011.00013},
+    journal = {Frontiers in Neuroinformatics},
+    pages = 13,
+    shorttitle = {Nipype},
+    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
+    volume = 5,
+    year = 2011
+}
+
+@article{nipype2,
+    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{n4,
+    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
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+    year = 2010
+}
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+
+@article{mni152nlin6asym,
+    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
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+    doi = {10.1016/j.neuroimage.2012.01.024},
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+    pages = {911--922},
+    year = 2012
+}
+
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+    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
+    doi = {10.1016/j.media.2007.06.004},
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+    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
+    volume = 12,
+    year = 2008
+}
+
+@article{fsl_fast,
+    author = {Zhang, Y. and Brady, M. and Smith, S.},
+    doi = {10.1109/42.906424},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
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+
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+    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
+    doi = {10.3389/fninf.2017.00017},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
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+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
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+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
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+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
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+    doi = {10.1016/S1361-8415(01)00036-6},
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+    urldate = {2018-07-27},
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+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
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+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
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+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
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+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
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+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
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+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
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derivatives/fmriprep/logs/CITATION.html

@@ -0,0 +1,83 @@
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+  <meta charset="utf-8" />
+  <meta name="generator" content="pandoc" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
+  <title>fMRIPrep citation boilerplate</title>
+  <style type="text/css">
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+<body>
+<p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div>
+</body>
+</html>

derivatives/fmriprep/logs/CITATION.md

@@ -0,0 +1,110 @@
+
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+

derivatives/fmriprep/logs/CITATION.tex

@@ -0,0 +1,166 @@
+\PassOptionsToPackage{unicode=true}{hyperref} % options for packages loaded elsewhere
+\PassOptionsToPackage{hyphens}{url}
+%
+\documentclass[]{article}
+\usepackage{lmodern}
+\usepackage{amssymb,amsmath}
+\usepackage{ifxetex,ifluatex}
+\usepackage{fixltx2e} % provides \textsubscript
+\ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex
+  \usepackage[T1]{fontenc}
+  \usepackage[utf8]{inputenc}
+  \usepackage{textcomp} % provides euro and other symbols
+\else % if luatex or xelatex
+  \usepackage{unicode-math}
+  \defaultfontfeatures{Ligatures=TeX,Scale=MatchLowercase}
+\fi
+% use upquote if available, for straight quotes in verbatim environments
+\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
+% use microtype if available
+\IfFileExists{microtype.sty}{%
+\usepackage[]{microtype}
+\UseMicrotypeSet[protrusion]{basicmath} % disable protrusion for tt fonts
+}{}
+\IfFileExists{parskip.sty}{%
+\usepackage{parskip}
+}{% else
+\setlength{\parindent}{0pt}
+\setlength{\parskip}{6pt plus 2pt minus 1pt}
+}
+\usepackage{hyperref}
+\hypersetup{
+            pdfborder={0 0 0},
+            breaklinks=true}
+\urlstyle{same}  % don't use monospace font for urls
+\setlength{\emergencystretch}{3em}  % prevent overfull lines
+\providecommand{\tightlist}{%
+  \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}
+\setcounter{secnumdepth}{0}
+% Redefines (sub)paragraphs to behave more like sections
+\ifx\paragraph\undefined\else
+\let\oldparagraph\paragraph
+\renewcommand{\paragraph}[1]{\oldparagraph{#1}\mbox{}}
+\fi
+\ifx\subparagraph\undefined\else
+\let\oldsubparagraph\subparagraph
+\renewcommand{\subparagraph}[1]{\oldsubparagraph{#1}\mbox{}}
+\fi
+
+% set default figure placement to htbp
+\makeatletter
+\def\fps@figure{htbp}
+\makeatother
+
+\usepackage[]{natbib}
+\bibliographystyle{plainnat}
+
+\date{}
+
+\begin{document}
+
+Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}
+
+\end{document}

derivatives/fmriprep/sub-S01.html

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+  width: 100%;
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+    width: 100%;
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+.boiler-html {
+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
+    padding: 10px;
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+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
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+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S01</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S01/figures/sub-S01_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S01/figures/sub-S01_dseg.svg" target="_blank">sub-S01/figures/sub-S01_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S01/figures/sub-S01_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S01/figures/sub-S01_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S01/figures/sub-S01_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S01/figures/sub-S01_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S01/figures/sub-S01_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S01/figures/sub-S01_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S01/figures/sub-S01_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S01/figures/sub-S01_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S01/figures/sub-S01_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S01/figures/sub-S01_task-localizer_desc-rois_bold.svg" target="_blank">sub-S01/figures/sub-S01_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S01/figures/sub-S01_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S01/figures/sub-S01_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S01/figures/sub-S01_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S01/figures/sub-S01_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S01/figures/sub-S01_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S01/figures/sub-S01_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S01/figures/sub-S01_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S01/figures/sub-S01_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S01/figures/sub-S01_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S01 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 14:16:11 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
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+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
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+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
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+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
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+    year = 2011
+}
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+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{n4,
+    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
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+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 6,
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+    shorttitle = {N4ITK},
+    title = {N4ITK: Improved N3 Bias Correction},
+    volume = 29,
+    year = 2010
+}
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+    journal = {PLOS Computational Biology},
+    number = 2,
+    pages = {e1005350},
+    title = {Mindboggling morphometry of human brains},
+    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
+    volume = 13,
+    year = 2017
+}
+
+@article{mni152lin,
+    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
+    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
+    volume = {2},
+    issn = {1053-8119},
+    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
+    doi = {10.1006/nimg.1995.1012},
+    number = {2, Part A},
+    journal = {NeuroImage},
+    year = {1995},
+    pages = {89--101}
+}
+
+@article{mni152nlin2009casym,
+    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
+    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
+    doi = {10.1016/S1053-8119(09)70884-5},
+    journal = {NeuroImage},
+    pages = {S102},
+    volume = {47, Supplement 1},
+    year = 2009
+}
+
+@article{mni152nlin6asym,
+    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
+    title = {Brain templates and atlases},
+    doi = {10.1016/j.neuroimage.2012.01.024},
+    journal = {NeuroImage},
+    volume = {62},
+    number = {2},
+    pages = {911--922},
+    year = 2012
+}
+
+@article{ants,
+    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
+    doi = {10.1016/j.media.2007.06.004},
+    issn = {1361-8415},
+    journal = {Medical Image Analysis},
+    number = 1,
+    pages = {26-41},
+    shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
+    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
+    volume = 12,
+    year = 2008
+}
+
+@article{fsl_fast,
+    author = {Zhang, Y. and Brady, M. and Smith, S.},
+    doi = {10.1109/42.906424},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 1,
+    pages = {45-57},
+    title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
+    volume = 20,
+    year = 2001
+}
+
+
+@article{fieldmapless1,
+    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
+    doi = {10.3389/fninf.2017.00017},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
+    volume = 11,
+    year = 2017
+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S02.html

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+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
+<title></title>
+<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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+<style type="text/css">
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+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
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+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+#errors div, #errors p {
+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S02</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S02/figures/sub-S02_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S02/figures/sub-S02_dseg.svg" target="_blank">sub-S02/figures/sub-S02_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S02/figures/sub-S02_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S02/figures/sub-S02_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S02/figures/sub-S02_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S02/figures/sub-S02_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S02/figures/sub-S02_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S02/figures/sub-S02_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S02/figures/sub-S02_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S02/figures/sub-S02_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S02/figures/sub-S02_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S02/figures/sub-S02_task-localizer_desc-rois_bold.svg" target="_blank">sub-S02/figures/sub-S02_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S02/figures/sub-S02_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S02/figures/sub-S02_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S02/figures/sub-S02_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S02/figures/sub-S02_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S02/figures/sub-S02_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S02/figures/sub-S02_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S02/figures/sub-S02_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S02/figures/sub-S02_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S02/figures/sub-S02_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S02 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 15:44:42 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
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+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S03.html

@@ -0,0 +1,867 @@
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+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
+<title></title>
+<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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+<style type="text/css">
+.sub-report-title {}
+.run-title {}
+
+h1 { padding-top: 35px; }
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+h3 { padding-top: 15px; }
+
+.elem-desc {}
+.elem-caption {
+    margin-top: 15px
+    margin-bottom: 0;
+}
+.elem-filename {}
+
+div.elem-image {
+  width: 100%;
+  page-break-before:always;
+}
+
+.elem-image object.svg-reportlet {
+    width: 100%;
+    padding-bottom: 5px;
+}
+body {
+    padding: 65px 10px 10px;
+}
+
+.boiler-html {
+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
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+
+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+#errors div, #errors p {
+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S03</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S03/figures/sub-S03_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S03/figures/sub-S03_dseg.svg" target="_blank">sub-S03/figures/sub-S03_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S03/figures/sub-S03_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S03/figures/sub-S03_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S03/figures/sub-S03_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S03/figures/sub-S03_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S03/figures/sub-S03_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S03/figures/sub-S03_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S03/figures/sub-S03_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S03/figures/sub-S03_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S03/figures/sub-S03_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S03/figures/sub-S03_task-localizer_desc-rois_bold.svg" target="_blank">sub-S03/figures/sub-S03_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S03/figures/sub-S03_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S03/figures/sub-S03_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S03/figures/sub-S03_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S03/figures/sub-S03_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S03/figures/sub-S03_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S03/figures/sub-S03_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S03/figures/sub-S03_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S03/figures/sub-S03_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S03/figures/sub-S03_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S03 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 16:44:59 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
+    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
+    year = {2018},
+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
+    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
+    doi = {10.3389/fninf.2011.00013},
+    journal = {Frontiers in Neuroinformatics},
+    pages = 13,
+    shorttitle = {Nipype},
+    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
+    volume = 5,
+    year = 2011
+}
+
+@article{nipype2,
+    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{n4,
+    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
+    doi = {10.1109/TMI.2010.2046908},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 6,
+    pages = {1310-1320},
+    shorttitle = {N4ITK},
+    title = {N4ITK: Improved N3 Bias Correction},
+    volume = 29,
+    year = 2010
+}
+
+@article{fs_reconall,
+    author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
+    doi = {10.1006/nimg.1998.0395},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {179-194},
+    shorttitle = {Cortical Surface-Based Analysis},
+    title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
+    volume = 9,
+    year = 1999
+}
+
+
+
+@article{mindboggle,
+    author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
+    doi = {10.1371/journal.pcbi.1005350},
+    issn = {1553-7358},
+    journal = {PLOS Computational Biology},
+    number = 2,
+    pages = {e1005350},
+    title = {Mindboggling morphometry of human brains},
+    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
+    volume = 13,
+    year = 2017
+}
+
+@article{mni152lin,
+    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
+    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
+    volume = {2},
+    issn = {1053-8119},
+    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
+    doi = {10.1006/nimg.1995.1012},
+    number = {2, Part A},
+    journal = {NeuroImage},
+    year = {1995},
+    pages = {89--101}
+}
+
+@article{mni152nlin2009casym,
+    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
+    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
+    doi = {10.1016/S1053-8119(09)70884-5},
+    journal = {NeuroImage},
+    pages = {S102},
+    volume = {47, Supplement 1},
+    year = 2009
+}
+
+@article{mni152nlin6asym,
+    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
+    title = {Brain templates and atlases},
+    doi = {10.1016/j.neuroimage.2012.01.024},
+    journal = {NeuroImage},
+    volume = {62},
+    number = {2},
+    pages = {911--922},
+    year = 2012
+}
+
+@article{ants,
+    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
+    doi = {10.1016/j.media.2007.06.004},
+    issn = {1361-8415},
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+    number = 1,
+    pages = {26-41},
+    shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
+    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
+    volume = 12,
+    year = 2008
+}
+
+@article{fsl_fast,
+    author = {Zhang, Y. and Brady, M. and Smith, S.},
+    doi = {10.1109/42.906424},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
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+    volume = 20,
+    year = 2001
+}
+
+
+@article{fieldmapless1,
+    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
+    doi = {10.3389/fninf.2017.00017},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
+    volume = 11,
+    year = 2017
+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
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+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
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+    volume = 10,
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+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
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+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S04.html

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+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S04</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 160x240x256</li>
+			<li>Output voxel size: 1.1mm x 1mm x 1mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S04/figures/sub-S04_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S04/figures/sub-S04_dseg.svg" target="_blank">sub-S04/figures/sub-S04_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S04/figures/sub-S04_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S04/figures/sub-S04_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S04/figures/sub-S04_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S04/figures/sub-S04_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S04/figures/sub-S04_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S04/figures/sub-S04_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S04/figures/sub-S04_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S04/figures/sub-S04_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S04/figures/sub-S04_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S04/figures/sub-S04_task-localizer_desc-rois_bold.svg" target="_blank">sub-S04/figures/sub-S04_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S04/figures/sub-S04_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S04/figures/sub-S04_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S04/figures/sub-S04_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S04/figures/sub-S04_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S04/figures/sub-S04_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S04/figures/sub-S04_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S04/figures/sub-S04_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S04/figures/sub-S04_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S04/figures/sub-S04_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S04 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 17:13:23 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
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+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S05.html

@@ -0,0 +1,859 @@
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+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
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+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
+<title></title>
+<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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+<style type="text/css">
+.sub-report-title {}
+.run-title {}
+
+h1 { padding-top: 35px; }
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+
+.elem-desc {}
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+    margin-top: 15px
+    margin-bottom: 0;
+}
+.elem-filename {}
+
+div.elem-image {
+  width: 100%;
+  page-break-before:always;
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+
+.elem-image object.svg-reportlet {
+    width: 100%;
+    padding-bottom: 5px;
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+body {
+    padding: 65px 10px 10px;
+}
+
+.boiler-html {
+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
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+
+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+#errors div, #errors p {
+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S05</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 160x240x256</li>
+			<li>Output voxel size: 1.1mm x 1mm x 1mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S05/figures/sub-S05_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S05/figures/sub-S05_dseg.svg" target="_blank">sub-S05/figures/sub-S05_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S05/figures/sub-S05_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S05/figures/sub-S05_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S05/figures/sub-S05_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S05/figures/sub-S05_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S05/figures/sub-S05_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S05/figures/sub-S05_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S05/figures/sub-S05_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S05/figures/sub-S05_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S05/figures/sub-S05_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S05/figures/sub-S05_task-localizer_desc-rois_bold.svg" target="_blank">sub-S05/figures/sub-S05_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S05/figures/sub-S05_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S05/figures/sub-S05_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S05/figures/sub-S05_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S05/figures/sub-S05_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S05/figures/sub-S05_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S05/figures/sub-S05_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S05/figures/sub-S05_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S05/figures/sub-S05_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S05/figures/sub-S05_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S05 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 17:16:28 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
+    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
+    year = {2018},
+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
+    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
+    doi = {10.3389/fninf.2011.00013},
+    journal = {Frontiers in Neuroinformatics},
+    pages = 13,
+    shorttitle = {Nipype},
+    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
+    volume = 5,
+    year = 2011
+}
+
+@article{nipype2,
+    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
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+    year = 2010
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+    number = 2,
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+    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
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+
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+    author = {Zhang, Y. and Brady, M. and Smith, S.},
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+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
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+    year = 2017
+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
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+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
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+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
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+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
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+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
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+    journal = {Frontiers in Neuroinformatics},
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+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
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+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
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+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
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+
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+
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+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S06.html

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+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S06</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 160x240x256</li>
+			<li>Output voxel size: 1.1mm x 1mm x 1mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S06/figures/sub-S06_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S06/figures/sub-S06_dseg.svg" target="_blank">sub-S06/figures/sub-S06_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S06/figures/sub-S06_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S06/figures/sub-S06_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S06/figures/sub-S06_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S06/figures/sub-S06_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S06/figures/sub-S06_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S06/figures/sub-S06_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S06/figures/sub-S06_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S06/figures/sub-S06_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S06/figures/sub-S06_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S06/figures/sub-S06_task-localizer_desc-rois_bold.svg" target="_blank">sub-S06/figures/sub-S06_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S06/figures/sub-S06_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S06/figures/sub-S06_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S06/figures/sub-S06_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S06/figures/sub-S06_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S06/figures/sub-S06_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S06/figures/sub-S06_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S06/figures/sub-S06_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S06/figures/sub-S06_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S06/figures/sub-S06_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S06 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 17:19:02 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
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+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S07.html

@@ -0,0 +1,859 @@
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+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
+<title></title>
+<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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+<style type="text/css">
+.sub-report-title {}
+.run-title {}
+
+h1 { padding-top: 35px; }
+h2 { padding-top: 20px; }
+h3 { padding-top: 15px; }
+
+.elem-desc {}
+.elem-caption {
+    margin-top: 15px
+    margin-bottom: 0;
+}
+.elem-filename {}
+
+div.elem-image {
+  width: 100%;
+  page-break-before:always;
+}
+
+.elem-image object.svg-reportlet {
+    width: 100%;
+    padding-bottom: 5px;
+}
+body {
+    padding: 65px 10px 10px;
+}
+
+.boiler-html {
+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+#errors div, #errors p {
+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S07</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 160x240x256</li>
+			<li>Output voxel size: 1.1mm x 1mm x 1mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S07/figures/sub-S07_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S07/figures/sub-S07_dseg.svg" target="_blank">sub-S07/figures/sub-S07_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S07/figures/sub-S07_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S07/figures/sub-S07_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S07/figures/sub-S07_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S07/figures/sub-S07_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S07/figures/sub-S07_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S07/figures/sub-S07_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S07/figures/sub-S07_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S07/figures/sub-S07_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S07/figures/sub-S07_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S07/figures/sub-S07_task-localizer_desc-rois_bold.svg" target="_blank">sub-S07/figures/sub-S07_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S07/figures/sub-S07_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S07/figures/sub-S07_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S07/figures/sub-S07_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S07/figures/sub-S07_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S07/figures/sub-S07_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S07/figures/sub-S07_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S07/figures/sub-S07_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S07/figures/sub-S07_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S07/figures/sub-S07_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S07 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 18:03:39 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
+    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
+    year = {2018},
+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
+    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
+    doi = {10.3389/fninf.2011.00013},
+    journal = {Frontiers in Neuroinformatics},
+    pages = 13,
+    shorttitle = {Nipype},
+    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
+    volume = 5,
+    year = 2011
+}
+
+@article{nipype2,
+    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{n4,
+    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
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+    shorttitle = {N4ITK},
+    title = {N4ITK: Improved N3 Bias Correction},
+    volume = 29,
+    year = 2010
+}
+
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+    author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
+    doi = {10.1006/nimg.1998.0395},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {179-194},
+    shorttitle = {Cortical Surface-Based Analysis},
+    title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
+    volume = 9,
+    year = 1999
+}
+
+
+
+@article{mindboggle,
+    author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
+    doi = {10.1371/journal.pcbi.1005350},
+    issn = {1553-7358},
+    journal = {PLOS Computational Biology},
+    number = 2,
+    pages = {e1005350},
+    title = {Mindboggling morphometry of human brains},
+    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
+    volume = 13,
+    year = 2017
+}
+
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+    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
+    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
+    volume = {2},
+    issn = {1053-8119},
+    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
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+    number = {2, Part A},
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+    pages = {89--101}
+}
+
+@article{mni152nlin2009casym,
+    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
+    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
+    doi = {10.1016/S1053-8119(09)70884-5},
+    journal = {NeuroImage},
+    pages = {S102},
+    volume = {47, Supplement 1},
+    year = 2009
+}
+
+@article{mni152nlin6asym,
+    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
+    title = {Brain templates and atlases},
+    doi = {10.1016/j.neuroimage.2012.01.024},
+    journal = {NeuroImage},
+    volume = {62},
+    number = {2},
+    pages = {911--922},
+    year = 2012
+}
+
+@article{ants,
+    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
+    doi = {10.1016/j.media.2007.06.004},
+    issn = {1361-8415},
+    journal = {Medical Image Analysis},
+    number = 1,
+    pages = {26-41},
+    shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
+    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
+    volume = 12,
+    year = 2008
+}
+
+@article{fsl_fast,
+    author = {Zhang, Y. and Brady, M. and Smith, S.},
+    doi = {10.1109/42.906424},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 1,
+    pages = {45-57},
+    title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
+    volume = 20,
+    year = 2001
+}
+
+
+@article{fieldmapless1,
+    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
+    doi = {10.3389/fninf.2017.00017},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
+    volume = 11,
+    year = 2017
+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
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+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

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+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S08</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 176x256x256</li>
+			<li>Output voxel size: 1mm x 1mm x 1mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S08/figures/sub-S08_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S08/figures/sub-S08_dseg.svg" target="_blank">sub-S08/figures/sub-S08_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S08/figures/sub-S08_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S08/figures/sub-S08_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S08/figures/sub-S08_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S08/figures/sub-S08_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S08/figures/sub-S08_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S08/figures/sub-S08_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S08/figures/sub-S08_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S08/figures/sub-S08_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S08/figures/sub-S08_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S08/figures/sub-S08_task-localizer_desc-rois_bold.svg" target="_blank">sub-S08/figures/sub-S08_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S08/figures/sub-S08_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S08/figures/sub-S08_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S08/figures/sub-S08_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S08/figures/sub-S08_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S08/figures/sub-S08_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S08/figures/sub-S08_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S08/figures/sub-S08_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S08/figures/sub-S08_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S08/figures/sub-S08_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S08 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 18:20:36 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
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+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S09.html

@@ -0,0 +1,859 @@
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+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
+<title></title>
+<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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+<style type="text/css">
+.sub-report-title {}
+.run-title {}
+
+h1 { padding-top: 35px; }
+h2 { padding-top: 20px; }
+h3 { padding-top: 15px; }
+
+.elem-desc {}
+.elem-caption {
+    margin-top: 15px
+    margin-bottom: 0;
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+.elem-filename {}
+
+div.elem-image {
+  width: 100%;
+  page-break-before:always;
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+
+.elem-image object.svg-reportlet {
+    width: 100%;
+    padding-bottom: 5px;
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+body {
+    padding: 65px 10px 10px;
+}
+
+.boiler-html {
+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
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+
+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+#errors div, #errors p {
+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S09</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 160x240x256</li>
+			<li>Output voxel size: 1.1mm x 1mm x 1mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S09/figures/sub-S09_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S09/figures/sub-S09_dseg.svg" target="_blank">sub-S09/figures/sub-S09_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S09/figures/sub-S09_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S09/figures/sub-S09_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S09/figures/sub-S09_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S09/figures/sub-S09_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S09/figures/sub-S09_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S09/figures/sub-S09_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S09/figures/sub-S09_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S09/figures/sub-S09_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S09/figures/sub-S09_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S09/figures/sub-S09_task-localizer_desc-rois_bold.svg" target="_blank">sub-S09/figures/sub-S09_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S09/figures/sub-S09_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S09/figures/sub-S09_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S09/figures/sub-S09_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S09/figures/sub-S09_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S09/figures/sub-S09_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S09/figures/sub-S09_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S09/figures/sub-S09_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S09/figures/sub-S09_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S09/figures/sub-S09_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S09 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 18:30:55 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
+    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
+    year = {2018},
+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
+    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
+    doi = {10.3389/fninf.2011.00013},
+    journal = {Frontiers in Neuroinformatics},
+    pages = 13,
+    shorttitle = {Nipype},
+    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
+    volume = 5,
+    year = 2011
+}
+
+@article{nipype2,
+    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{n4,
+    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
+    doi = {10.1109/TMI.2010.2046908},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 6,
+    pages = {1310-1320},
+    shorttitle = {N4ITK},
+    title = {N4ITK: Improved N3 Bias Correction},
+    volume = 29,
+    year = 2010
+}
+
+@article{fs_reconall,
+    author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
+    doi = {10.1006/nimg.1998.0395},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {179-194},
+    shorttitle = {Cortical Surface-Based Analysis},
+    title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
+    volume = 9,
+    year = 1999
+}
+
+
+
+@article{mindboggle,
+    author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
+    doi = {10.1371/journal.pcbi.1005350},
+    issn = {1553-7358},
+    journal = {PLOS Computational Biology},
+    number = 2,
+    pages = {e1005350},
+    title = {Mindboggling morphometry of human brains},
+    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
+    volume = 13,
+    year = 2017
+}
+
+@article{mni152lin,
+    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
+    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
+    volume = {2},
+    issn = {1053-8119},
+    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
+    doi = {10.1006/nimg.1995.1012},
+    number = {2, Part A},
+    journal = {NeuroImage},
+    year = {1995},
+    pages = {89--101}
+}
+
+@article{mni152nlin2009casym,
+    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
+    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
+    doi = {10.1016/S1053-8119(09)70884-5},
+    journal = {NeuroImage},
+    pages = {S102},
+    volume = {47, Supplement 1},
+    year = 2009
+}
+
+@article{mni152nlin6asym,
+    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
+    title = {Brain templates and atlases},
+    doi = {10.1016/j.neuroimage.2012.01.024},
+    journal = {NeuroImage},
+    volume = {62},
+    number = {2},
+    pages = {911--922},
+    year = 2012
+}
+
+@article{ants,
+    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
+    doi = {10.1016/j.media.2007.06.004},
+    issn = {1361-8415},
+    journal = {Medical Image Analysis},
+    number = 1,
+    pages = {26-41},
+    shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
+    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
+    volume = 12,
+    year = 2008
+}
+
+@article{fsl_fast,
+    author = {Zhang, Y. and Brady, M. and Smith, S.},
+    doi = {10.1109/42.906424},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 1,
+    pages = {45-57},
+    title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
+    volume = 20,
+    year = 2001
+}
+
+
+@article{fieldmapless1,
+    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
+    doi = {10.3389/fninf.2017.00017},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
+    volume = 11,
+    year = 2017
+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
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+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
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+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
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+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S10</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S10/figures/sub-S10_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S10/figures/sub-S10_dseg.svg" target="_blank">sub-S10/figures/sub-S10_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S10/figures/sub-S10_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S10/figures/sub-S10_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S10/figures/sub-S10_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S10/figures/sub-S10_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S10/figures/sub-S10_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S10/figures/sub-S10_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S10/figures/sub-S10_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S10/figures/sub-S10_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S10/figures/sub-S10_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S10/figures/sub-S10_task-localizer_desc-rois_bold.svg" target="_blank">sub-S10/figures/sub-S10_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S10/figures/sub-S10_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S10/figures/sub-S10_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S10/figures/sub-S10_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S10/figures/sub-S10_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S10/figures/sub-S10_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S10/figures/sub-S10_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S10/figures/sub-S10_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S10/figures/sub-S10_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S10/figures/sub-S10_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S10 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 11:39:43 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
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+    title = {Nipype},
+    year = 2018,
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+    publisher = {Zenodo},
+    journal = {Software}
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+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S11.html

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+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
+<title></title>
+<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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+<style type="text/css">
+.sub-report-title {}
+.run-title {}
+
+h1 { padding-top: 35px; }
+h2 { padding-top: 20px; }
+h3 { padding-top: 15px; }
+
+.elem-desc {}
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+    margin-top: 15px
+    margin-bottom: 0;
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+
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+  width: 100%;
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+    width: 100%;
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+    padding: 65px 10px 10px;
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+
+.boiler-html {
+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
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+
+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+#errors div, #errors p {
+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S11</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S11/figures/sub-S11_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S11/figures/sub-S11_dseg.svg" target="_blank">sub-S11/figures/sub-S11_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S11/figures/sub-S11_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S11/figures/sub-S11_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S11/figures/sub-S11_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S11/figures/sub-S11_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S11/figures/sub-S11_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S11/figures/sub-S11_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S11/figures/sub-S11_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S11/figures/sub-S11_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S11/figures/sub-S11_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S11/figures/sub-S11_task-localizer_desc-rois_bold.svg" target="_blank">sub-S11/figures/sub-S11_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S11/figures/sub-S11_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S11/figures/sub-S11_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S11/figures/sub-S11_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S11/figures/sub-S11_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S11/figures/sub-S11_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S11/figures/sub-S11_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S11/figures/sub-S11_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S11/figures/sub-S11_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S11/figures/sub-S11_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S11 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-11 15:31:55 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
+    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
+    year = {2018},
+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
+    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
+    doi = {10.3389/fninf.2011.00013},
+    journal = {Frontiers in Neuroinformatics},
+    pages = 13,
+    shorttitle = {Nipype},
+    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
+    volume = 5,
+    year = 2011
+}
+
+@article{nipype2,
+    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{n4,
+    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
+    doi = {10.1109/TMI.2010.2046908},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 6,
+    pages = {1310-1320},
+    shorttitle = {N4ITK},
+    title = {N4ITK: Improved N3 Bias Correction},
+    volume = 29,
+    year = 2010
+}
+
+@article{fs_reconall,
+    author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
+    doi = {10.1006/nimg.1998.0395},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {179-194},
+    shorttitle = {Cortical Surface-Based Analysis},
+    title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
+    volume = 9,
+    year = 1999
+}
+
+
+
+@article{mindboggle,
+    author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
+    doi = {10.1371/journal.pcbi.1005350},
+    issn = {1553-7358},
+    journal = {PLOS Computational Biology},
+    number = 2,
+    pages = {e1005350},
+    title = {Mindboggling morphometry of human brains},
+    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
+    volume = 13,
+    year = 2017
+}
+
+@article{mni152lin,
+    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
+    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
+    volume = {2},
+    issn = {1053-8119},
+    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
+    doi = {10.1006/nimg.1995.1012},
+    number = {2, Part A},
+    journal = {NeuroImage},
+    year = {1995},
+    pages = {89--101}
+}
+
+@article{mni152nlin2009casym,
+    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
+    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
+    doi = {10.1016/S1053-8119(09)70884-5},
+    journal = {NeuroImage},
+    pages = {S102},
+    volume = {47, Supplement 1},
+    year = 2009
+}
+
+@article{mni152nlin6asym,
+    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
+    title = {Brain templates and atlases},
+    doi = {10.1016/j.neuroimage.2012.01.024},
+    journal = {NeuroImage},
+    volume = {62},
+    number = {2},
+    pages = {911--922},
+    year = 2012
+}
+
+@article{ants,
+    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
+    doi = {10.1016/j.media.2007.06.004},
+    issn = {1361-8415},
+    journal = {Medical Image Analysis},
+    number = 1,
+    pages = {26-41},
+    shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
+    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
+    volume = 12,
+    year = 2008
+}
+
+@article{fsl_fast,
+    author = {Zhang, Y. and Brady, M. and Smith, S.},
+    doi = {10.1109/42.906424},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 1,
+    pages = {45-57},
+    title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
+    volume = 20,
+    year = 2001
+}
+
+
+@article{fieldmapless1,
+    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
+    doi = {10.3389/fninf.2017.00017},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
+    volume = 11,
+    year = 2017
+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
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+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
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+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
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+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S12</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S12/figures/sub-S12_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S12/figures/sub-S12_dseg.svg" target="_blank">sub-S12/figures/sub-S12_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S12/figures/sub-S12_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S12/figures/sub-S12_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S12/figures/sub-S12_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S12/figures/sub-S12_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, cosine00, cosine01, cosine02, non_steady_state_outlier00, non_steady_state_outlier01, non_steady_state_outlier02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, motion_outlier11, motion_outlier12, motion_outlier13, motion_outlier14, motion_outlier15, motion_outlier16, motion_outlier17, motion_outlier18, motion_outlier19, motion_outlier20, motion_outlier21, motion_outlier22, motion_outlier23</li>
+			<li>Non-steady-state volumes: 3</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S12/figures/sub-S12_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S12/figures/sub-S12_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S12/figures/sub-S12_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S12/figures/sub-S12_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S12/figures/sub-S12_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S12/figures/sub-S12_task-localizer_desc-rois_bold.svg" target="_blank">sub-S12/figures/sub-S12_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S12/figures/sub-S12_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S12/figures/sub-S12_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S12/figures/sub-S12_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S12/figures/sub-S12_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S12/figures/sub-S12_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S12/figures/sub-S12_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S12/figures/sub-S12_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S12/figures/sub-S12_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S12/figures/sub-S12_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S12 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-11 16:57:42 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
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+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S13.html

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+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
+<title></title>
+<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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+<style type="text/css">
+.sub-report-title {}
+.run-title {}
+
+h1 { padding-top: 35px; }
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+
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+    margin-top: 15px
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+div.elem-image {
+  width: 100%;
+  page-break-before:always;
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+    width: 100%;
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+body {
+    padding: 65px 10px 10px;
+}
+
+.boiler-html {
+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
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+
+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+#errors div, #errors p {
+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S13</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 160x240x256</li>
+			<li>Output voxel size: 1.1mm x 1mm x 1mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S13/figures/sub-S13_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S13/figures/sub-S13_dseg.svg" target="_blank">sub-S13/figures/sub-S13_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S13/figures/sub-S13_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S13/figures/sub-S13_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S13/figures/sub-S13_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S13/figures/sub-S13_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, cosine00, cosine01, cosine02, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01</li>
+			<li>Non-steady-state volumes: 1</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S13/figures/sub-S13_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S13/figures/sub-S13_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S13/figures/sub-S13_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S13/figures/sub-S13_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S13/figures/sub-S13_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S13/figures/sub-S13_task-localizer_desc-rois_bold.svg" target="_blank">sub-S13/figures/sub-S13_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S13/figures/sub-S13_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S13/figures/sub-S13_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S13/figures/sub-S13_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S13/figures/sub-S13_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S13/figures/sub-S13_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S13/figures/sub-S13_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S13/figures/sub-S13_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S13/figures/sub-S13_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S13/figures/sub-S13_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S13 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-12 13:47:46 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
+    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
+    year = {2018},
+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
+    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
+    doi = {10.3389/fninf.2011.00013},
+    journal = {Frontiers in Neuroinformatics},
+    pages = 13,
+    shorttitle = {Nipype},
+    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
+    volume = 5,
+    year = 2011
+}
+
+@article{nipype2,
+    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{n4,
+    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
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+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 6,
+    pages = {1310-1320},
+    shorttitle = {N4ITK},
+    title = {N4ITK: Improved N3 Bias Correction},
+    volume = 29,
+    year = 2010
+}
+
+@article{fs_reconall,
+    author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
+    doi = {10.1006/nimg.1998.0395},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {179-194},
+    shorttitle = {Cortical Surface-Based Analysis},
+    title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
+    volume = 9,
+    year = 1999
+}
+
+
+
+@article{mindboggle,
+    author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
+    doi = {10.1371/journal.pcbi.1005350},
+    issn = {1553-7358},
+    journal = {PLOS Computational Biology},
+    number = 2,
+    pages = {e1005350},
+    title = {Mindboggling morphometry of human brains},
+    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
+    volume = 13,
+    year = 2017
+}
+
+@article{mni152lin,
+    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
+    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
+    volume = {2},
+    issn = {1053-8119},
+    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
+    doi = {10.1006/nimg.1995.1012},
+    number = {2, Part A},
+    journal = {NeuroImage},
+    year = {1995},
+    pages = {89--101}
+}
+
+@article{mni152nlin2009casym,
+    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
+    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
+    doi = {10.1016/S1053-8119(09)70884-5},
+    journal = {NeuroImage},
+    pages = {S102},
+    volume = {47, Supplement 1},
+    year = 2009
+}
+
+@article{mni152nlin6asym,
+    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
+    title = {Brain templates and atlases},
+    doi = {10.1016/j.neuroimage.2012.01.024},
+    journal = {NeuroImage},
+    volume = {62},
+    number = {2},
+    pages = {911--922},
+    year = 2012
+}
+
+@article{ants,
+    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
+    doi = {10.1016/j.media.2007.06.004},
+    issn = {1361-8415},
+    journal = {Medical Image Analysis},
+    number = 1,
+    pages = {26-41},
+    shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
+    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
+    volume = 12,
+    year = 2008
+}
+
+@article{fsl_fast,
+    author = {Zhang, Y. and Brady, M. and Smith, S.},
+    doi = {10.1109/42.906424},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 1,
+    pages = {45-57},
+    title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
+    volume = 20,
+    year = 2001
+}
+
+
+@article{fieldmapless1,
+    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
+    doi = {10.3389/fninf.2017.00017},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
+    volume = 11,
+    year = 2017
+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
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+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
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+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S14</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S14/figures/sub-S14_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S14/figures/sub-S14_dseg.svg" target="_blank">sub-S14/figures/sub-S14_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S14/figures/sub-S14_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S14/figures/sub-S14_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S14/figures/sub-S14_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S14/figures/sub-S14_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, motion_outlier11, motion_outlier12, motion_outlier13, motion_outlier14, motion_outlier15, motion_outlier16, motion_outlier17, motion_outlier18, motion_outlier19</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S14/figures/sub-S14_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S14/figures/sub-S14_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S14/figures/sub-S14_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S14/figures/sub-S14_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S14/figures/sub-S14_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S14/figures/sub-S14_task-localizer_desc-rois_bold.svg" target="_blank">sub-S14/figures/sub-S14_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S14/figures/sub-S14_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S14/figures/sub-S14_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S14/figures/sub-S14_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S14/figures/sub-S14_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S14/figures/sub-S14_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S14/figures/sub-S14_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S14/figures/sub-S14_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S14/figures/sub-S14_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S14/figures/sub-S14_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S14 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-12 13:47:45 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
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+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S15.html

@@ -0,0 +1,867 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
+<title></title>
+<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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+<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/css/bootstrap.min.css" integrity="sha384-MCw98/SFnGE8fJT3GXwEOngsV7Zt27NXFoaoApmYm81iuXoPkFOJwJ8ERdknLPMO" crossorigin="anonymous">
+<style type="text/css">
+.sub-report-title {}
+.run-title {}
+
+h1 { padding-top: 35px; }
+h2 { padding-top: 20px; }
+h3 { padding-top: 15px; }
+
+.elem-desc {}
+.elem-caption {
+    margin-top: 15px
+    margin-bottom: 0;
+}
+.elem-filename {}
+
+div.elem-image {
+  width: 100%;
+  page-break-before:always;
+}
+
+.elem-image object.svg-reportlet {
+    width: 100%;
+    padding-bottom: 5px;
+}
+body {
+    padding: 65px 10px 10px;
+}
+
+.boiler-html {
+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+#errors div, #errors p {
+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S15</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S15/figures/sub-S15_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S15/figures/sub-S15_dseg.svg" target="_blank">sub-S15/figures/sub-S15_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S15/figures/sub-S15_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S15/figures/sub-S15_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S15/figures/sub-S15_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S15/figures/sub-S15_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, motion_outlier11, motion_outlier12, motion_outlier13, motion_outlier14, motion_outlier15, motion_outlier16</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S15/figures/sub-S15_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S15/figures/sub-S15_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S15/figures/sub-S15_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S15/figures/sub-S15_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S15/figures/sub-S15_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S15/figures/sub-S15_task-localizer_desc-rois_bold.svg" target="_blank">sub-S15/figures/sub-S15_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S15/figures/sub-S15_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S15/figures/sub-S15_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S15/figures/sub-S15_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S15/figures/sub-S15_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S15/figures/sub-S15_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S15/figures/sub-S15_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S15/figures/sub-S15_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S15/figures/sub-S15_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S15/figures/sub-S15_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S15 --nthreads 8 --omp-nthreads 8 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-12 13:42:50 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
+    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
+    year = {2018},
+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
+    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
+    doi = {10.3389/fninf.2011.00013},
+    journal = {Frontiers in Neuroinformatics},
+    pages = 13,
+    shorttitle = {Nipype},
+    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
+    volume = 5,
+    year = 2011
+}
+
+@article{nipype2,
+    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{n4,
+    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
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+    shorttitle = {N4ITK},
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+    volume = 29,
+    year = 2010
+}
+
+@article{fs_reconall,
+    author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
+    doi = {10.1006/nimg.1998.0395},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {179-194},
+    shorttitle = {Cortical Surface-Based Analysis},
+    title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
+    volume = 9,
+    year = 1999
+}
+
+
+
+@article{mindboggle,
+    author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
+    doi = {10.1371/journal.pcbi.1005350},
+    issn = {1553-7358},
+    journal = {PLOS Computational Biology},
+    number = 2,
+    pages = {e1005350},
+    title = {Mindboggling morphometry of human brains},
+    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
+    volume = 13,
+    year = 2017
+}
+
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+    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
+    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
+    volume = {2},
+    issn = {1053-8119},
+    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
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+    number = {2, Part A},
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+    year = {1995},
+    pages = {89--101}
+}
+
+@article{mni152nlin2009casym,
+    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
+    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
+    doi = {10.1016/S1053-8119(09)70884-5},
+    journal = {NeuroImage},
+    pages = {S102},
+    volume = {47, Supplement 1},
+    year = 2009
+}
+
+@article{mni152nlin6asym,
+    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
+    title = {Brain templates and atlases},
+    doi = {10.1016/j.neuroimage.2012.01.024},
+    journal = {NeuroImage},
+    volume = {62},
+    number = {2},
+    pages = {911--922},
+    year = 2012
+}
+
+@article{ants,
+    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
+    doi = {10.1016/j.media.2007.06.004},
+    issn = {1361-8415},
+    journal = {Medical Image Analysis},
+    number = 1,
+    pages = {26-41},
+    shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
+    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
+    volume = 12,
+    year = 2008
+}
+
+@article{fsl_fast,
+    author = {Zhang, Y. and Brady, M. and Smith, S.},
+    doi = {10.1109/42.906424},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 1,
+    pages = {45-57},
+    title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
+    volume = 20,
+    year = 2001
+}
+
+
+@article{fieldmapless1,
+    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
+    doi = {10.3389/fninf.2017.00017},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
+    volume = 11,
+    year = 2017
+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
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+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
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+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
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+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

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+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
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+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
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+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S16</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S16/figures/sub-S16_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S16/figures/sub-S16_dseg.svg" target="_blank">sub-S16/figures/sub-S16_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S16/figures/sub-S16_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S16/figures/sub-S16_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S16/figures/sub-S16_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S16/figures/sub-S16_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S16/figures/sub-S16_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S16/figures/sub-S16_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S16/figures/sub-S16_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S16/figures/sub-S16_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S16/figures/sub-S16_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S16/figures/sub-S16_task-localizer_desc-rois_bold.svg" target="_blank">sub-S16/figures/sub-S16_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S16/figures/sub-S16_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S16/figures/sub-S16_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S16/figures/sub-S16_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S16/figures/sub-S16_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S16/figures/sub-S16_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S16/figures/sub-S16_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S16/figures/sub-S16_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S16/figures/sub-S16_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S16/figures/sub-S16_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S16 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-12 16:04:04 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
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+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S17.html

@@ -0,0 +1,867 @@
+<?xml version="1.0" encoding="utf-8" ?>
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
+<title></title>
+<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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+<style type="text/css">
+.sub-report-title {}
+.run-title {}
+
+h1 { padding-top: 35px; }
+h2 { padding-top: 20px; }
+h3 { padding-top: 15px; }
+
+.elem-desc {}
+.elem-caption {
+    margin-top: 15px
+    margin-bottom: 0;
+}
+.elem-filename {}
+
+div.elem-image {
+  width: 100%;
+  page-break-before:always;
+}
+
+.elem-image object.svg-reportlet {
+    width: 100%;
+    padding-bottom: 5px;
+}
+body {
+    padding: 65px 10px 10px;
+}
+
+.boiler-html {
+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+#errors div, #errors p {
+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S17</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S17/figures/sub-S17_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S17/figures/sub-S17_dseg.svg" target="_blank">sub-S17/figures/sub-S17_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S17/figures/sub-S17_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S17/figures/sub-S17_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S17/figures/sub-S17_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S17/figures/sub-S17_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S17/figures/sub-S17_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S17/figures/sub-S17_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S17/figures/sub-S17_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S17/figures/sub-S17_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S17/figures/sub-S17_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S17/figures/sub-S17_task-localizer_desc-rois_bold.svg" target="_blank">sub-S17/figures/sub-S17_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S17/figures/sub-S17_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S17/figures/sub-S17_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S17/figures/sub-S17_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S17/figures/sub-S17_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S17/figures/sub-S17_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S17/figures/sub-S17_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S17/figures/sub-S17_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S17/figures/sub-S17_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S17/figures/sub-S17_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S17 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-13 18:40:28 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
+    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
+    year = {2018},
+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
+    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
+    doi = {10.3389/fninf.2011.00013},
+    journal = {Frontiers in Neuroinformatics},
+    pages = 13,
+    shorttitle = {Nipype},
+    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
+    volume = 5,
+    year = 2011
+}
+
+@article{nipype2,
+    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{n4,
+    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
+    doi = {10.1109/TMI.2010.2046908},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 6,
+    pages = {1310-1320},
+    shorttitle = {N4ITK},
+    title = {N4ITK: Improved N3 Bias Correction},
+    volume = 29,
+    year = 2010
+}
+
+@article{fs_reconall,
+    author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
+    doi = {10.1006/nimg.1998.0395},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {179-194},
+    shorttitle = {Cortical Surface-Based Analysis},
+    title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
+    volume = 9,
+    year = 1999
+}
+
+
+
+@article{mindboggle,
+    author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
+    doi = {10.1371/journal.pcbi.1005350},
+    issn = {1553-7358},
+    journal = {PLOS Computational Biology},
+    number = 2,
+    pages = {e1005350},
+    title = {Mindboggling morphometry of human brains},
+    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
+    volume = 13,
+    year = 2017
+}
+
+@article{mni152lin,
+    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
+    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
+    volume = {2},
+    issn = {1053-8119},
+    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
+    doi = {10.1006/nimg.1995.1012},
+    number = {2, Part A},
+    journal = {NeuroImage},
+    year = {1995},
+    pages = {89--101}
+}
+
+@article{mni152nlin2009casym,
+    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
+    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
+    doi = {10.1016/S1053-8119(09)70884-5},
+    journal = {NeuroImage},
+    pages = {S102},
+    volume = {47, Supplement 1},
+    year = 2009
+}
+
+@article{mni152nlin6asym,
+    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
+    title = {Brain templates and atlases},
+    doi = {10.1016/j.neuroimage.2012.01.024},
+    journal = {NeuroImage},
+    volume = {62},
+    number = {2},
+    pages = {911--922},
+    year = 2012
+}
+
+@article{ants,
+    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
+    doi = {10.1016/j.media.2007.06.004},
+    issn = {1361-8415},
+    journal = {Medical Image Analysis},
+    number = 1,
+    pages = {26-41},
+    shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
+    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
+    volume = 12,
+    year = 2008
+}
+
+@article{fsl_fast,
+    author = {Zhang, Y. and Brady, M. and Smith, S.},
+    doi = {10.1109/42.906424},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 1,
+    pages = {45-57},
+    title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
+    volume = 20,
+    year = 2001
+}
+
+
+@article{fieldmapless1,
+    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
+    doi = {10.3389/fninf.2017.00017},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
+    volume = 11,
+    year = 2017
+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

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+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S18</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 160x240x256</li>
+			<li>Output voxel size: 1.1mm x 1mm x 1mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S18/figures/sub-S18_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S18/figures/sub-S18_dseg.svg" target="_blank">sub-S18/figures/sub-S18_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S18/figures/sub-S18_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S18/figures/sub-S18_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S18/figures/sub-S18_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S18/figures/sub-S18_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S18/figures/sub-S18_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S18/figures/sub-S18_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S18/figures/sub-S18_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S18/figures/sub-S18_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S18/figures/sub-S18_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S18/figures/sub-S18_task-localizer_desc-rois_bold.svg" target="_blank">sub-S18/figures/sub-S18_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S18/figures/sub-S18_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S18/figures/sub-S18_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S18/figures/sub-S18_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S18/figures/sub-S18_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S18/figures/sub-S18_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S18/figures/sub-S18_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S18/figures/sub-S18_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S18/figures/sub-S18_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S18/figures/sub-S18_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S18 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-12 13:47:46 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
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+}
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+}
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+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

derivatives/fmriprep/sub-S19.html

@@ -0,0 +1,867 @@
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+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="Docutils 0.12: http://docutils.sourceforge.net/" />
+<title></title>
+<script src="https://code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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+<style type="text/css">
+.sub-report-title {}
+.run-title {}
+
+h1 { padding-top: 35px; }
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+    margin-top: 15px
+    margin-bottom: 0;
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+  width: 100%;
+  page-break-before:always;
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+    width: 100%;
+    padding-bottom: 5px;
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+body {
+    padding: 65px 10px 10px;
+}
+
+.boiler-html {
+    font-family: "Bitstream Charter", "Georgia", Times;
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
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+
+div#boilerplate pre {
+    margin: 20px 25px;
+    padding: 10px;
+    background-color: #F8F9FA;
+}
+
+#errors div, #errors p {
+    padding-left: 1em;
+}
+</style>
+</head>
+<body>
+
+
+<nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
+<div class="collapse navbar-collapse">
+    <ul class="navbar-nav">
+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S19</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S19/figures/sub-S19_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S19/figures/sub-S19_dseg.svg" target="_blank">sub-S19/figures/sub-S19_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S19/figures/sub-S19_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S19/figures/sub-S19_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S19/figures/sub-S19_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S19/figures/sub-S19_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S19/figures/sub-S19_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S19/figures/sub-S19_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S19/figures/sub-S19_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S19/figures/sub-S19_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S19/figures/sub-S19_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S19/figures/sub-S19_task-localizer_desc-rois_bold.svg" target="_blank">sub-S19/figures/sub-S19_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S19/figures/sub-S19_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S19/figures/sub-S19_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S19/figures/sub-S19_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S19/figures/sub-S19_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S19/figures/sub-S19_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S19/figures/sub-S19_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S19/figures/sub-S19_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S19/figures/sub-S19_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S19/figures/sub-S19_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S19 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-12 13:47:46 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
+    author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
+    title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
+    year = {2018},
+    doi = {10.1038/s41592-018-0235-4},
+    journal = {Nature Methods}
+}
+
+@article{fmriprep2,
+    author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
+    title = {fMRIPrep},
+    year = 2018,
+    doi = {10.5281/zenodo.852659},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{nipype1,
+    author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
+    doi = {10.3389/fninf.2011.00013},
+    journal = {Frontiers in Neuroinformatics},
+    pages = 13,
+    shorttitle = {Nipype},
+    title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
+    volume = 5,
+    year = 2011
+}
+
+@article{nipype2,
+    author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
+    title = {Nipype},
+    year = 2018,
+    doi = {10.5281/zenodo.596855},
+    publisher = {Zenodo},
+    journal = {Software}
+}
+
+@article{n4,
+    author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
+    doi = {10.1109/TMI.2010.2046908},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 6,
+    pages = {1310-1320},
+    shorttitle = {N4ITK},
+    title = {N4ITK: Improved N3 Bias Correction},
+    volume = 29,
+    year = 2010
+}
+
+@article{fs_reconall,
+    author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
+    doi = {10.1006/nimg.1998.0395},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {179-194},
+    shorttitle = {Cortical Surface-Based Analysis},
+    title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
+    volume = 9,
+    year = 1999
+}
+
+
+
+@article{mindboggle,
+    author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
+    doi = {10.1371/journal.pcbi.1005350},
+    issn = {1553-7358},
+    journal = {PLOS Computational Biology},
+    number = 2,
+    pages = {e1005350},
+    title = {Mindboggling morphometry of human brains},
+    url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
+    volume = 13,
+    year = 2017
+}
+
+@article{mni152lin,
+    title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
+    author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
+    volume = {2},
+    issn = {1053-8119},
+    shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
+    doi = {10.1006/nimg.1995.1012},
+    number = {2, Part A},
+    journal = {NeuroImage},
+    year = {1995},
+    pages = {89--101}
+}
+
+@article{mni152nlin2009casym,
+    title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
+    author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
+    doi = {10.1016/S1053-8119(09)70884-5},
+    journal = {NeuroImage},
+    pages = {S102},
+    volume = {47, Supplement 1},
+    year = 2009
+}
+
+@article{mni152nlin6asym,
+    author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
+    title = {Brain templates and atlases},
+    doi = {10.1016/j.neuroimage.2012.01.024},
+    journal = {NeuroImage},
+    volume = {62},
+    number = {2},
+    pages = {911--922},
+    year = 2012
+}
+
+@article{ants,
+    author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
+    doi = {10.1016/j.media.2007.06.004},
+    issn = {1361-8415},
+    journal = {Medical Image Analysis},
+    number = 1,
+    pages = {26-41},
+    shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
+    title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
+    volume = 12,
+    year = 2008
+}
+
+@article{fsl_fast,
+    author = {Zhang, Y. and Brady, M. and Smith, S.},
+    doi = {10.1109/42.906424},
+    issn = {0278-0062},
+    journal = {IEEE Transactions on Medical Imaging},
+    number = 1,
+    pages = {45-57},
+    title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
+    volume = 20,
+    year = 2001
+}
+
+
+@article{fieldmapless1,
+    author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
+    doi = {10.3389/fninf.2017.00017},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
+    url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
+    volume = 11,
+    year = 2017
+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
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+        <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
+        <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
+        <li class="nav-item dropdown">
+            <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
+            <div class="dropdown-menu" aria-labelledby="navbarFunctional">
+                    <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
+            </div>
+        </li>
+        <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
+        <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
+        <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
+    </ul>
+</div>
+</nav>
+<noscript>
+    <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
+</noscript>
+
+    <div id="Summary">
+    <h1 class="sub-report-title">Summary</h1>
+        <div id="datatype-anat_desc-summary_suffix-T1w">
+                    <ul class="elem-desc">
+		<li>Subject ID: S20</li>
+		<li>Structural images: 1 T1-weighted </li>
+		<li>Functional series: 1</li>
+		<ul class="elem-desc">
+			<li>Task: localizer (1 run)</li>
+		</ul>
+		<li>Standard output spaces: MNI152NLin2009cAsym</li>
+		<li>Non-standard output spaces: </li>
+		<li>FreeSurfer reconstruction: Not run</li>
+	</ul>
+        </div>
+    </div>
+    <div id="Anatomical">
+    <h1 class="sub-report-title">Anatomical</h1>
+        <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
+                    <h3 class="elem-title">Anatomical Conformation</h3>
+		<ul class="elem-desc">
+			<li>Input T1w images: 1</li>
+			<li>Output orientation: RAS</li>
+			<li>Output dimensions: 192x256x128</li>
+			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
+			<li>Discarded images: 0</li>
+
+		</ul>
+        </div>
+        <div id="datatype-anat_suffix-dseg">
+<h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.</p>                    <img class="svg-reportlet" src="./sub-S20/figures/sub-S20_dseg.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S20/figures/sub-S20_dseg.svg" target="_blank">sub-S20/figures/sub-S20_dseg.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
+<h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S20/figures/sub-S20_space-MNI152NLin2009cAsym_T1w.svg">
+Problem loading figure sub-S20/figures/sub-S20_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S20/figures/sub-S20_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S20/figures/sub-S20_space-MNI152NLin2009cAsym_T1w.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="Functional">
+    <h1 class="sub-report-title">Functional</h1>
+        <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
+<h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
+		<ul class="elem-desc">
+			<li>Repetition time (TR): 2.4s</li>
+			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
+			<li>Slice timing correction: Not applied</li>
+			<li>Susceptibility distortion correction: None</li>
+			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
+			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2</li>
+			<li>Non-steady-state volumes: 0</li>
+		</ul>
+        </div>
+        <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
+                    <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
+			    <p class="elem-desc">
+			    The qform has been copied from sform.
+			    The difference in angle is 0.
+			    The difference in translation is 0.
+			    </p>
+        </div>
+        <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
+<h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-S20/figures/sub-S20_task-localizer_desc-flirtbbr_bold.svg">
+Problem loading figure sub-S20/figures/sub-S20_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S20/figures/sub-S20_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S20/figures/sub-S20_task-localizer_desc-flirtbbr_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
+<h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-S20/figures/sub-S20_task-localizer_desc-rois_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S20/figures/sub-S20_task-localizer_desc-rois_bold.svg" target="_blank">sub-S20/figures/sub-S20_task-localizer_desc-rois_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
+<h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.</p>                    <img class="svg-reportlet" src="./sub-S20/figures/sub-S20_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S20/figures/sub-S20_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S20/figures/sub-S20_task-localizer_desc-compcorvar_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
+<h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-S20/figures/sub-S20_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S20/figures/sub-S20_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S20/figures/sub-S20_task-localizer_desc-carpetplot_bold.svg</a>
+</div>
+
+        </div>
+        <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
+<h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
+(Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
+Right: magnitude of the correlation between each confound time series and the
+mean global signal. Strong correlations might be indicative of partial volume
+effects and can inform decisions about feature orthogonalization prior to
+confound regression.
+</p>                    <img class="svg-reportlet" src="./sub-S20/figures/sub-S20_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
+</div>
+<div class="elem-filename">
+    Get figure file: <a href="./sub-S20/figures/sub-S20_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S20/figures/sub-S20_task-localizer_desc-confoundcorr_bold.svg</a>
+</div>
+
+        </div>
+    </div>
+    <div id="About">
+    <h1 class="sub-report-title">About</h1>
+        <div id="datatype-anat_desc-about_suffix-T1w">
+                    <ul>
+		<li>fMRIPrep version: 20.0.6</li>
+		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S20 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
+		<li>Date preprocessed: 2020-05-12 16:10:02 -0400</li>
+	</ul>
+</div>
+        </div>
+    </div>
+
+<div id="boilerplate">
+    <h1 class="sub-report-title">Methods</h1>
+    <p>We kindly ask to report results preprocessed with this tool using the following
+       boilerplate.</p>
+    <ul class="nav nav-tabs" id="myTab" role="tablist">
+        <li class="nav-item">
+            <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
+        </li>
+        <li class="nav-item">
+            <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
+        </li>
+    </ul>
+    <div class="tab-content" id="myTabContent">
+      <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
+<dl>
+<dt>Anatomical data preprocessing</dt>
+<dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
+</dd>
+<dt>Functional data preprocessing</dt>
+<dd><p>For each of the 1 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
+</dd>
+</dl>
+<p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
+<h3 id="copyright-waiver">Copyright Waiver</h3>
+<p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
+<h3 id="references" class="unnumbered">References</h3>
+<div id="refs" class="references">
+<div id="ref-nilearn">
+<p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
+</div>
+<div id="ref-ants">
+<p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
+</div>
+<div id="ref-compcor">
+<p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
+</div>
+<div id="ref-fmriprep2">
+<p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
+</div>
+<div id="ref-fmriprep1">
+<p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
+</div>
+<div id="ref-mni152nlin2009casym">
+<p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
+</div>
+<div id="ref-nipype1">
+<p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
+</div>
+<div id="ref-nipype2">
+<p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
+</div>
+<div id="ref-bbr">
+<p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
+</div>
+<div id="ref-mcflirt">
+<p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
+</div>
+<div id="ref-flirt">
+<p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
+</div>
+<div id="ref-lanczos">
+<p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
+</div>
+<div id="ref-power_fd_dvars">
+<p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
+</div>
+<div id="ref-confounds_satterthwaite_2013">
+<p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
+</div>
+<div id="ref-n4">
+<p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
+</div>
+<div id="ref-fsl_fast">
+<p>Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
+</div>
+</div></div></div>
+      <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
+Results included in this manuscript come from preprocessing
+performed using *fMRIPrep* 20.0.6
+(@fmriprep1; @fmriprep2; RRID:SCR_016216),
+which is based on *Nipype* 1.4.2
+(@nipype1; @nipype2; RRID:SCR_002502).
+
+Anatomical data preprocessing
+
+: The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
+with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
+The T1w-reference was then skull-stripped with a *Nipype* implementation of
+the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
+as target template.
+Brain tissue segmentation of cerebrospinal fluid (CSF),
+white-matter (WM) and gray-matter (GM) was performed on
+the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
+@fsl_fast].
+Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
+nonlinear registration with `antsRegistration` (ANTs 2.2.0),
+using brain-extracted versions of both T1w reference and the T1w template.
+The following template was selected for spatial normalization:
+*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
+
+Functional data preprocessing
+
+: For each of the 1 BOLD runs found per subject (across all
+tasks and sessions), the following preprocessing was performed.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Susceptibility distortion correction (SDC) was omitted.
+The BOLD reference was then co-registered to the T1w reference using
+`flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
+cost-function.
+Co-registration was configured with nine degrees of freedom to account
+for distortions remaining in the BOLD reference.
+Head-motion parameters with respect to the BOLD reference
+(transformation matrices, and six corresponding rotation and translation
+parameters) are estimated before any spatiotemporal filtering using
+`mcflirt` [FSL 5.0.9, @mcflirt].
+The BOLD time-series (including slice-timing correction when applied)
+were resampled onto their original, native space by applying
+the transforms to correct for head-motion.
+These resampled BOLD time-series will be referred to as *preprocessed
+BOLD in original space*, or just *preprocessed BOLD*.
+The BOLD time-series were resampled into standard space,
+generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
+First, a reference volume and its skull-stripped version were generated
+using a custom methodology of *fMRIPrep*.
+Several confounding time-series were calculated based on the
+*preprocessed BOLD*: framewise displacement (FD), DVARS and
+three region-wise global signals.
+FD and DVARS are calculated for each functional run, both using their
+implementations in *Nipype* [following the definitions by @power_fd_dvars].
+The three global signals are extracted within the CSF, the WM, and
+the whole-brain masks.
+Additionally, a set of physiological regressors were extracted to
+allow for component-based noise correction [*CompCor*, @compcor].
+Principal components are estimated after high-pass filtering the
+*preprocessed BOLD* time-series (using a discrete cosine filter with
+128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
+and anatomical (aCompCor).
+tCompCor components are then calculated from the top 5% variable
+voxels within a mask covering the subcortical regions.
+This subcortical mask is obtained by heavily eroding the brain mask,
+which ensures it does not include cortical GM regions.
+For aCompCor, components are calculated within the intersection of
+the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation). Components
+are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the *k* components with the largest singular
+values are retained, such that the retained components' time series are
+sufficient to explain 50 percent of variance across the nuisance mask (CSF,
+WM, combined, or temporal). The remaining components are dropped from
+consideration.
+The head-motion estimates calculated in the correction step were also
+placed within the corresponding confounds file.
+The confound time series derived from head motion estimates and global
+signals were expanded with the inclusion of temporal derivatives and
+quadratic terms for each [@confounds_satterthwaite_2013].
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers.
+All resamplings can be performed with *a single interpolation
+step* by composing all the pertinent transformations (i.e. head-motion
+transform matrices, susceptibility distortion correction when available,
+and co-registrations to anatomical and output spaces).
+Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
+configured with Lanczos interpolation to minimize the smoothing
+effects of other kernels [@lanczos].
+Non-gridded (surface) resamplings were performed using `mri_vol2surf`
+(FreeSurfer).
+
+
+Many internal operations of *fMRIPrep* use
+*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
+mostly within the functional processing workflow.
+For more details of the pipeline, see [the section corresponding
+to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
+
+
+### Copyright Waiver
+
+The above boilerplate text was automatically generated by fMRIPrep
+with the express intention that users should copy and paste this
+text into their manuscripts *unchanged*.
+It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
+
+### References
+
+</pre>
+</div>
+      <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
+using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
+RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
+(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
+
+\begin{description}
+\item[Anatomical data preprocessing]
+The T1-weighted (T1w) image was corrected for intensity non-uniformity
+(INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
+ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
+throughout the workflow. The T1w-reference was then skull-stripped with
+a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
+workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
+segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
+gray-matter (GM) was performed on the brain-extracted T1w using
+\texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
+Volume-based spatial normalization to one standard space
+(MNI152NLin2009cAsym) was performed through nonlinear registration with
+\texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
+of both T1w reference and the T1w template. The following template was
+selected for spatial normalization: \emph{ICBM 152 Nonlinear
+Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
+RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
+\item[Functional data preprocessing]
+For each of the 1 BOLD runs found per subject (across all tasks and
+sessions), the following preprocessing was performed. First, a reference
+volume and its skull-stripped version were generated using a custom
+methodology of \emph{fMRIPrep}. Susceptibility distortion correction
+(SDC) was omitted. The BOLD reference was then co-registered to the T1w
+reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
+boundary-based registration \citep{bbr} cost-function. Co-registration
+was configured with nine degrees of freedom to account for distortions
+remaining in the BOLD reference. Head-motion parameters with respect to
+the BOLD reference (transformation matrices, and six corresponding
+rotation and translation parameters) are estimated before any
+spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
+5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
+correction when applied) were resampled onto their original, native
+space by applying the transforms to correct for head-motion. These
+resampled BOLD time-series will be referred to as \emph{preprocessed
+BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
+time-series were resampled into standard space, generating a
+\emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
+reference volume and its skull-stripped version were generated using a
+custom methodology of \emph{fMRIPrep}. Several confounding time-series
+were calculated based on the \emph{preprocessed BOLD}: framewise
+displacement (FD), DVARS and three region-wise global signals. FD and
+DVARS are calculated for each functional run, both using their
+implementations in \emph{Nipype} \citep[following the definitions
+by][]{power_fd_dvars}. The three global signals are extracted within the
+CSF, the WM, and the whole-brain masks. Additionally, a set of
+physiological regressors were extracted to allow for component-based
+noise correction \citep[\emph{CompCor},][]{compcor}. Principal
+components are estimated after high-pass filtering the
+\emph{preprocessed BOLD} time-series (using a discrete cosine filter
+with 128s cut-off) for the two \emph{CompCor} variants: temporal
+(tCompCor) and anatomical (aCompCor). tCompCor components are then
+calculated from the top 5\% variable voxels within a mask covering the
+subcortical regions. This subcortical mask is obtained by heavily
+eroding the brain mask, which ensures it does not include cortical GM
+regions. For aCompCor, components are calculated within the intersection
+of the aforementioned mask and the union of CSF and WM masks calculated
+in T1w space, after their projection to the native space of each
+functional run (using the inverse BOLD-to-T1w transformation).
+Components are also calculated separately within the WM and CSF masks.
+For each CompCor decomposition, the \emph{k} components with the largest
+singular values are retained, such that the retained components' time
+series are sufficient to explain 50 percent of variance across the
+nuisance mask (CSF, WM, combined, or temporal). The remaining components
+are dropped from consideration. The head-motion estimates calculated in
+the correction step were also placed within the corresponding confounds
+file. The confound time series derived from head motion estimates and
+global signals were expanded with the inclusion of temporal derivatives
+and quadratic terms for each \citep{confounds_satterthwaite_2013}.
+Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
+were annotated as motion outliers. All resamplings can be performed with
+\emph{a single interpolation step} by composing all the pertinent
+transformations (i.e.~head-motion transform matrices, susceptibility
+distortion correction when available, and co-registrations to anatomical
+and output spaces). Gridded (volumetric) resamplings were performed
+using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
+interpolation to minimize the smoothing effects of other kernels
+\citep{lanczos}. Non-gridded (surface) resamplings were performed using
+\texttt{mri\_vol2surf} (FreeSurfer).
+\end{description}
+
+Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
+\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
+processing workflow. For more details of the pipeline, see
+\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
+section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
+
+\hypertarget{copyright-waiver}{%
+\subsubsection{Copyright Waiver}\label{copyright-waiver}}
+
+The above boilerplate text was automatically generated by fMRIPrep with
+the express intention that users should copy and paste this text into
+their manuscripts \emph{unchanged}. It is released under the
+\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
+
+\hypertarget{references}{%
+\subsubsection{References}\label{references}}
+
+\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
+<h3>Bibliography</h3>
+<pre>@article{fmriprep1,
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+    title = {Nipype},
+    year = 2018,
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+    journal = {Software}
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+}
+
+@phdthesis{fieldmapless2,
+    address = {Berlin},
+    author = {Huntenburg, Julia M.},
+    language = {eng},
+    school = {Freie Universität},
+    title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
+    type = {Master's Thesis},
+    url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
+    year = 2014
+}
+
+@article{fieldmapless3,
+    author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
+    doi = {10.1371/journal.pone.0152472},
+    issn = {1932-6203},
+    journal = {PLOS ONE},
+    number = 3,
+    pages = {e0152472},
+    title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
+    url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
+    volume = 11,
+    year = 2016
+}
+
+@article{flirt,
+    title = {A global optimisation method for robust affine registration of brain images},
+    volume = {5},
+    issn = {1361-8415},
+    url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
+    doi = {10.1016/S1361-8415(01)00036-6},
+    number = {2},
+    urldate = {2018-07-27},
+    journal = {Medical Image Analysis},
+    author = {Jenkinson, Mark and Smith, Stephen},
+    year = {2001},
+    keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
+    pages = {143--156}
+}
+
+@article{mcflirt,
+    author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
+    doi = {10.1006/nimg.2002.1132},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 2,
+    pages = {825-841},
+    title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
+    volume = 17,
+    year = 2002
+}
+
+@article{bbr,
+    author = {Greve, Douglas N and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2009.06.060},
+    issn = {1095-9572},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {63-72},
+    title = {Accurate and robust brain image alignment using boundary-based registration},
+    volume = 48,
+    year = 2009
+}
+
+@article{aroma,
+    author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
+    doi = {10.1016/j.neuroimage.2015.02.064},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {267-277},
+    shorttitle = {ICA-AROMA},
+    title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
+    volume = 112,
+    year = 2015
+}
+
+@article{power_fd_dvars,
+    author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
+    doi = {10.1016/j.neuroimage.2013.08.048},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = {Supplement C},
+    pages = {320-341},
+    title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
+    volume = 84,
+    year = 2014
+}
+
+@article{confounds_satterthwaite_2013,
+    author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
+    doi = {10.1016/j.neuroimage.2012.08.052},
+    issn = {10538119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {240--256},
+    title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
+    url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
+    volume = 64,
+    year = 2013
+}
+
+
+@article{nilearn,
+    author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
+    doi = {10.3389/fninf.2014.00014},
+    issn = {1662-5196},
+    journal = {Frontiers in Neuroinformatics},
+    language = {English},
+    title = {Machine learning for neuroimaging with scikit-learn},
+    url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
+    volume = 8,
+    year = 2014
+}
+
+@article{lanczos,
+    author = {Lanczos, C.},
+    doi = {10.1137/0701007},
+    issn = {0887-459X},
+    journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
+    number = 1,
+    pages = {76-85},
+    title = {Evaluation of Noisy Data},
+    url = {http://epubs.siam.org/doi/10.1137/0701007},
+    volume = 1,
+    year = 1964
+}
+
+@article{compcor,
+    author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
+    doi = {10.1016/j.neuroimage.2007.04.042},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    number = 1,
+    pages = {90-101},
+    title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
+    volume = 37,
+    year = 2007
+}
+
+@article{hcppipelines,
+    author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
+    doi = {10.1016/j.neuroimage.2013.04.127},
+    issn = {1053-8119},
+    journal = {NeuroImage},
+    pages = {105-124},
+    series = {Mapping the Connectome},
+    title = {The minimal preprocessing pipelines for the Human Connectome Project},
+    url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
+    volume = 80,
+    year = 2013
+}
+
+@article{fs_template,
+    author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
+    doi = {10.1016/j.neuroimage.2010.07.020},
+    journal = {NeuroImage},
+    number = 4,
+    pages = {1181-1196},
+    title = {Highly accurate inverse consistent registration: A robust approach},
+    volume = 53,
+    year = 2010
+}
+
+@article{afni,
+    author = {Cox, Robert W. and Hyde, James S.},
+    doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
+    journal = {NMR in Biomedicine},
+    number = {4-5},
+    pages = {171-178},
+    title = {Software tools for analysis and visualization of fMRI data},
+    volume = 10,
+    year = 1997
+}
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}
+</pre>
+</div>
+    </div>
+</div>
+
+<div id="errors">
+    <h1 class="sub-report-title">Errors</h1>
+    <p>No errors to report!</p>
+</div>
+
+
+<script type="text/javascript">
+    function toggle(id) {
+        var element = document.getElementById(id);
+        if(element.style.display == 'block')
+            element.style.display = 'none';
+        else
+            element.style.display = 'block';
+    }
+</script>
+</body>
+</html>

participants.json

@@ -0,0 +1,22 @@
+{
+    "age": {
+        "Description": "Age of the participant",
+        "Units": "years"
+    },
+    "sex": {
+        "Description": "Biological sex of the participant",
+        "Levels": {
+            "M": "male",
+            "F": "female"
+        }
+    },
+    "site": {
+        "Description": "Assessment site"
+    },
+    "family": {
+        "Description": "Identifier of the participant's family"
+    },
+    "language": {
+        "Description": "Participant's mother-tongue"
+    }
+}

participants.tsv

@@ -0,0 +1,21 @@
+participant_id	age	sex	site	family	language
+S01	24	M	SHFJ	F01	French
+S02	20	M	SHFJ	F02	French
+S03	22	F	SHFJ	F03	French
+S04	20	M	Neurospin	F04	French
+S05	23	F	Neurospin	F05	French
+S06	19	M	Neurospin	F06	French
+S07	26	F	Neurospin	F07	French
+S08	27	M	Neurospin	F89	French
+S09	21	F	Neurospin	F09	French
+S10	24	F	SHFJ	F10	French
+S11	47	F	SHFJ	F11	French
+S12	23	F	SHFJ	F12	French
+S13	19	M	Neurospin	F13	French
+S14	21	F	SHFJ	F14	French
+S15	22	F	SHFJ	F15	French
+S16	24	F	SHFJ	F16	French
+S17	22	M	SHFJ	F17	French
+S18	20	F	Neurospin	F18	French
+S19	18	M	SHFJ	F52	French
+S20	25	F	SHFJ	F20	French

phenotype/behavioural.tsv

@@ -0,0 +1,95 @@
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+S07	60.0	0.887654	1.0	1.0	False	-0.013893	2.0	False	3.0	1.0	1.0	10.0	0.923668	2.0	False	True	1.0	0.7	3.8	300.0	1.0	11.0	True	16.5	7.0	0.666667	50.0	15.0	80.0	False	0.25	9.0	2.0	2.0	0.967964	10.0	0.0	10.0	0.909775	3.0	30.0	3.0	62.0	40.0	1.0	6.0	0.0	3.0	aprentissage lecture , difference d/t , p/b a 6ans duree 1ans	False	4.0	True	5.0	0.994361	1.30039	False	50.0	False	False	2.0	False	1.32119	n/a	10.0	40.0	30.0	100.0	80.0	100.0	d	30.0	100.0	2.0	True	0.0	True	6.0	False	3.0	False	1.0	0.884219	False	False	1.0	4.0	10.0	1.09569	2.0	n/a	11.0	0.0	40.0	0	n/a	True	1.03761	True	2.0	1.0	0.333333	1.0	LIEUX	4.0	30.0	333	n/a	8.0	1.1032	5.84722	6.0	5.0	40.0	10.0	30.0	False	True	True	False	2.0	False	22.0	0.958523	1.0	False
+S08	30.0	0.378301	n/a	n/a	False	0.0574613	3.0	False	4.0	n/a	2.0	5.0	0.914426	1.35714	False	False	2.0	0.833333	7.0	350.0	n/a	14.0	False	16.5	64.0	0.416667	20.0	15.0	35.0	False	0.0	10.0	0.0	3.0	0.657123	80.0	2.0	10.0	0.971887	3.0	32.0	4.0	63.0	42.0	n/a	8.0	2.0	1.0	0	False	4.0	False	5.0	0.999458	0.551338	False	100.0	False	False	3.0	True	0.563067	n/a	10.0	20.0	18.0	35.0	40.0	50.0	u	15.0	50.0	4.0	False	0.0	True	2.0	False	5.0	False	1.0	0.721875	False	True	1.0	20.0	10.0	0.74894	4.0	LIEUX	5.0	0.0	30.0	0	n/a	False	1.00225	False	0.0	1.5	0.151515	2.0	PHYSIONOMIE	4.0	100.0	290	n/a	16.0	0.748933	4.84722	8.0	4.0	90.0	10.0	80.0	False	True	False	False	1.9	False	19.0	0.959285	3.0	False
+S09	100.0	1.29591	n/a	n/a	False	-0.0193175	4.0	False	3.0	n/a	1.0	12.0	0.943651	1.47826	False	True	2.0	0.533333	2.9	800.0	n/a	11.5	False	14.25	10.0	0.5	80.0	15.0	100.0	False	0.2	10.0	0.0	2.0	1.03413	200.0	2.0	10.0	0.924333	3.0	35.0	3.0	55.0	42.0	n/a	6.0	0.0	3.0	0	False	3.0	True	2.0	0.988621	1.94941	False	50.0	False	False	1.0	False	1.92885	n/a	20.0	50.0	35.0	110.0	130.0	160.0	u	25.0	120.0	3.0	False	0.0	True	2.0	False	4.0	False	1.0	0.85892	False	False	1.0	15.0	8.0	1.18304	0.0	n/a	5.5	0.0	50.0	0	n/a	False	1.03538	False	2.0	3.0	0.192982	1.0	LIEUX	2.0	300.0	336	n/a	18.0	1.17861	7.5	4.0	2.0	45.0	14.0	30.0	False	True	True	False	1.5	False	17.0	1.22631	1.0	False
+S10	43.0	0.534045	n/a	n/a	False	0.0017111	4.5	False	3.0	n/a	1.0	40.0	0.973282	1.66667	False	True	2.0	0.6	4.2	500.0	0.0	9.0	True	12.0	10.0	0.583333	27.0	12.0	39.0	False	0.25	8.0	4.0	2.0	0.780877	50.0	2.0	10.0	0.974993	3.0	30.0	3.0	66.0	43.0	n/a	7.0	1.0	3.0	APPRENTISSAGE LECTURE/DYSLEXIE/BEGAIEMENT ZEZAIEMENT	False	4.0	True	5.0	0.994773	0.792601	False	70.0	True	False	2.0	False	0.794878	n/a	12.0	25.0	25.0	48.0	58.0	70.0	d	19.0	60.0	4.0	True	2.0	True	4.0	True	3.0	True	2.0	0.944997	False	False	1.0	12.0	11.0	0.890339	2.33333	animaux	6.0	0.0	50.0	0	n/a	True	1.05757	True	1.0	3.0	0.25	3.0	lieux	3.0	80.0	163	n/a	19.0	0.889978	8.06944	6.0	3.0	70.0	10.0	100.0	False	True	False	False	3.0	False	15.0	0.999617	2.0	False
+S11	50.0	0.501391	n/a	n/a	False	-0.155042	3.5	False	4.0	n/a	1.0	25.0	0.877283	1.25	True	True	2.0	0.466667	1.6	400.0	n/a	8.0	False	9.0	8.0	0.583333	30.0	10.0	40.0	False	0.0	10.0	0.0	2.0	0.786632	200.0	2.0	7.0	0.722241	3.0	30.0	5.0	70.0	41.0	n/a	6.0	0.0	3.0	MATHS	False	2.0	True	1.0	0.994483	0.767568	False	80.0	False	True	1.0	True	0.746275	n/a	16.0	38.0	20.0	75.0	55.0	70.0	d	16.0	40.0	2.0	False	3.0	True	1.0	False	4.0	False	2.0	0.918997	False	True	1.0	15.0	11.0	0.901555	2.66667	0	2.0	3.0	46.0	Lettres, chiffres, sons, temps, en espace et en couleur	n/a	False	0.973921	False	2.0	n/a	0.111111	3.0	visages, lieux	1.0	90.0	176	n/a	17.0	0.896537	4.81944	6.0	1.0	80.0	16.0	80.0	False	True	n/a	False	3.0	True	10.0	1.03446	2.0	False
+S12	40.0	0.45318	n/a	n/a	False	-0.0294667	0.5	False	4.0	n/a	2.0	5.0	0.923912	1.75	False	True	1.0	0.633333	3.2	500.0	n/a	12.0	False	16.5	8.0	0.5	35.0	12.0	37.0	False	0.25	8.0	2.0	3.0	0.722995	20.0	1.0	7.0	0.894445	3.0	32.0	4.0	60.0	45.0	n/a	8.0	1.0	3.0	FRANCAIS ORTHOGRAPHE LECTURE	False	3.0	True	3.0	0.999026	0.676939	False	100.0	False	False	3.0	False	0.674518	n/a	12.0	30.0	25.0	35.0	50.0	70.0	u	15.0	50.0	2.0	True	0.0	True	4.0	False	3.0	False	1.0	0.883083	False	False	1.0	8.0	15.0	0.822753	3.33333	0	9.0	0.0	56.0	n/a	n/a	True	1.00245	False	1.0	1.5	0.272727	2.0	0	4.0	40.0	182	n/a	7.0	0.824009	5.91667	6.0	4.0	50.0	11.0	80.0	True	True	True	True	2.0	False	21.0	1.08707	2.0	True
+S13	30.0	0.553522	1.0	1.0	False	-0.0442015	3.5	False	3.0	1.0	1.0	12.0	0.962172	1.54545	False	True	2.0	1.0	3.8	350.0	1.0	11.0	False	14.0	10.0	0.666667	25.0	10.0	50.0	False	0.2	10.0	0.0	4.0	0.840788	80.0	2.0	10.0	0.91797	3.0	33.0	4.0	70.0	44.0	1.0	10.0	0.0	3.0	0	False	3.0	True	4.0	0.999519	0.797884	False	75.0	False	False	3.0	False	0.823867	n/a	12.0	30.0	20.0	59.0	55.0	60.0	d	20.0	70.0	2.0	False	0.0	True	2.0	False	5.0	False	1.0	0.96621	False	False	1.0	10.0	15.0	0.94617	5.0	n/a	6.0	0.0	60.0	0	n/a	False	1.02888	False	2.0	1.5	0.214286	1.0	LIEUX	5.0	100.0	318	n/a	17.0	0.958259	6.84722	4.0	5.0	85.0	12.0	80.0	False	True	False	False	2.5	False	17.0	1.04223	2.0	False
+S14	40.0	0.536215	n/a	n/a	False	-0.0167022	1.5	False	4.0	n/a	1.0	7.0	0.982381	1.90909	False	False	1.0	0.8	3.4	350.0	n/a	11.0	False	16.0	9.0	0.75	25.0	10.0	50.0	False	0.11	10.0	0.0	3.0	0.842689	50.0	1.0	10.0	0.965678	3.0	36.0	4.0	62.0	45.0	n/a	9.0	0.0	3.0	0	False	3.0	True	3.0	0.997089	0.788024	False	60.0	False	False	3.0	False	0.798107	n/a	12.0	30.0	20.0	40.0	50.0	70.0	d	15.0	60.0	4.0	False	0.0	True	2.0	False	4.0	False	2.0	0.910639	False	False	1.0	20.0	10.0	0.955723	3.33333	0	10.0	0.0	50.0	n/a	n/a	False	1.01764	False	2.0	2.5	0.3125	1.0	LIEUX VISAGES	4.0	100.0	219	n/a	9.0	0.960426	7.14583	5.0	3.0	60.0	10.0	30.0	False	True	True	n/a	4.0	False	21.0	0.927176	2.0	False
+S15	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	366	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
+S16	55.0	0.432409	n/a	n/a	False	-0.114653	2.0	False	3.0	n/a	1.0	40.0	0.930288	2.0	False	False	1.0	0.633333	3.4	350.0	n/a	7.0	False	10.5	8.0	0.666667	30.0	10.0	55.0	False	0.0	9.0	0.0	2.0	0.735609	50.0	1.0	8.0	0.815635	3.0	30.0	4.0	67.0	43.0	n/a	6.0	0.0	3.0	0	False	4.0	True	3.0	0.99974	0.651471	False	100.0	False	False	2.0	False	0.643601	n/a	15.0	35.0	25.0	45.0	50.0	60.0	d	20.0	50.0	4.0	False	0.0	True	3.0	False	3.0	False	2.0	0.896679	False	False	1.0	15.0	15.0	0.835647	2.33333	0	7.0	2.0	50.0	n/a	n/a	False	1.01069	False	1.0	3.5	0.333333	2.0	physionomie	3.0	40.0	194	n/a	10.0	0.838385	7.74306	7.0	3.0	45.0	10.0	80.0	False	True	n/a	False	2.0	True	14.0	0.951493	2.0	False
+S17	50.0	1.90848	n/a	n/a	False	0.00262361	5.0	False	2.0	n/a	1.0	20.0	0.934938	2.0	True	True	2.0	0.266667	1.8	200.0	n/a	11.0	False	16.5	8.0	0.416667	20.0	10.0	100.0	False	0.0	8.0	0.0	n/a	1.48161	30.0	2.0	7.0	0.937562	3.0	30.0	5.0	60.0	40.0	n/a	0.0	1.0	3.0	0	False	3.0	True	4.0	0.995747	2.7377	False	200.0	False	False	2.0	False	2.8406	n/a	10.0	50.0	20.0	100.0	130.0	200.0	u	10.0	200.0	3.0	False	0.0	True	2.0	False	3.0	False	2.0	0.744411	False	False	1.0	50.0	8.0	1.67618	2.0	0	11.0	2.0	57.0	0	n/a	False	0.944038	False	1.0	3.0	0.333333	3.0	Visages	3.0	150.0	205	n/a	20.0	1.68861	8.18056	4.0	3.0	50.0	11.0	100.0	False	True	False	False	2.0	False	22.0	0.940993	3.0	False
+S18	40.0	2.15291	1.0	1.0	False	-0.169848	3.5	False	3.0	1.0	1.0	10.0	0.890863	1.41667	False	True	2.0	0.466667	3.0	300.0	1.0	12.0	False	14.5	9.0	0.583333	35.0	7.0	40.0	False	0.5	10.0	1.0	2.0	1.41522	20.0	2.0	10.0	0.721015	3.0	30.0	4.0	63.0	43.0	1.0	4.0	0.0	1.0	0	False	2.0	True	3.0	1.0	2.99655	False	10.0	False	False	2.0	False	3.20441	n/a	10.0	35.0	25.0	70.0	150.0	200.0	u	20.0	300.0	3.0	True	0.0	True	2.0	False	5.0	False	2.0	0.764588	False	False	1.0	4.0	15.0	1.59256	0.0	n/a	5.0	0.0	50.0	0	n/a	True	1.0	False	1.0	3.0	0.172414	2.0	VISAGES	1.0	10.0	332	n/a	17.0	1.61294	5.98611	6.0	2.0	80.0	12.0	50.0	False	True	True	True	1.5	False	17.0	0.944911	2.0	False
+S19	40.0	0.396693	n/a	n/a	False	-0.018355	1.0	True	3.0	n/a	2.0	10.0	0.958646	1.64286	True	False	1.0	0.5	3.4	600.0	n/a	14.0	False	18.5	8.5	0.666667	30.0	14.0	45.0	False	0.0	6.0	4.0	2.0	0.623431	50.0	0.0	8.0	0.940291	3.0	25.0	4.0	59.0	42.0	n/a	5.0	0.0	3.0	DYSLEXIE DYSORTHOGRAPHIE PB EXPRESSION	False	2.0	True	4.0	0.997793	0.589586	False	60.0	True	True	2.0	False	0.590441	n/a	15.0	35.0	20.0	45.0	50.0	58.0	d	25.0	50.0	1.0	True	1.0	True	3.0	False	4.0	True	1.0	0.969703	False	False	1.0	12.0	11.0	0.708503	2.0	0	9.0	2.0	40.0	0	n/a	False	0.970941	True	2.0	4.0	0.243243	1.0	lieux	2.0	110.0	183	n/a	6.0	0.710534	8.53472	5.0	2.0	50.0	10.0	60.0	False	True	False	True	1.5	False	23.0	1.14744	3.0	True
+S20	65.0	0.580366	n/a	n/a	False	-0.0676551	2.5	False	4.0	n/a	1.0	15.0	0.938926	1.875	False	True	1.0	0.833333	2.0	300.0	n/a	8.0	False	11.5	8.0	0.666	30.0	15.0	45.0	False	0.0	9.0	0.0	3.0	0.757288	100.0	1.0	9.0	0.871271	3.0	30.0	5.0	63.0	45.0	n/a	8.0	0.0	3.0	0	False	4.0	True	2.0	0.996139	0.879881	False	50.0	False	False	2.0	False	0.863822	n/a	15.0	40.0	25.0	50.0	70.0	80.0	d	20.0	60.0	3.0	False	0.0	True	2.0	False	5.0	False	2.0	0.675853	False	True	1.0	10.0	20.0	0.867901	3.5	0	7.0	0.0	50.0	n/a	n/a	False	0.96733	False	1.0	3.0	0.304348	2.0	Visages	3.0	25.0	166	n/a	11.0	0.863093	7.51333	4.0	3.0	70.0	13.0	500.0	False	True	False	False	7.0	False	15.0	0.861326	2.0	False
+S21	75.0	0.827894	n/a	n/a	False	-0.0550406	1.0	False	3.0	n/a	3.0	10.0	0.933896	1.5	False	True	1.0	0.6	2.8	500.0	n/a	10.0	False	12.5	11.0	0.25	25.0	10.0	60.0	False	0.2	6.0	0.0	3.0	0.97081	30.0	0.0	9.0	0.878856	3.0	30.0	5.0	60.0	40.0	n/a	4.0	0.0	1.0	n/a	False	4.0	True	2.0	0.995747	1.23959	False	20.0	False	False	3.0	False	1.23225	n/a	15.0	40.0	25.0	45.0	90.0	100.0	d	20.0	85.0	3.0	False	0.0	True	2.0	False	4.0	False	2.0	0.885648	False	False	0.0	10.0	13.0	1.10749	3.0	lieux	5.0	0.0	100.0	n/a	n/a	False	0.944038	False	0.0	0.0	0.2	1.0	visage	3.0	40.0	104	n/a	4.0	1.10645	1.875	12.0	3.0	60.0	11.0	80.0	False	True	True	False	3.0	False	15.0	0.975003	2.0	False
+S22	50.0	0.310351	n/a	n/a	False	-0.249045	4.0	False	5.0	n/a	1.0	20.0	0.783854	1.5	False	False	2.0	0.866667	4.4	300.0	n/a	12.0	False	15.0	7.0	0.666667	20.0	10.0	35.0	False	0.0	9.0	0.0	4.0	0.695147	50.0	2.0	10.0	0.534809	3.0	28.0	4.0	62.0	45.0	n/a	10.0	0.0	3.0	0	False	4.0	True	5.0	0.999154	0.471502	False	40.0	False	False	3.0	False	0.461929	n/a	10.0	50.0	20.0	30.0	40.0	50.0	d	15.0	35.0	4.0	False	0.0	True	2.0	False	5.0	False	2.0	0.977155	False	True	1.0	15.0	10.0	0.786574	4.33333	0	6.0	0.0	40.0	0	n/a	False	0.997375	False	0.0	4.0	0.2	3.0	Visages	4.0	80.0	188	n/a	18.0	0.79227	9.20139	5.0	5.0	70.0	10.0	50.0	False	True	False	False	2.5	False	18.0	0.955999	3.0	False
+S23	50.0	0.508968	1.0	1.0	False	-0.0350254	2.5	False	3.0	1.0	1.0	10.0	0.871767	1.54545	False	True	1.0	0.433333	2.8	800.0	1.0	11.0	False	14.0	5.0	0.5	20.0	10.0	30.0	False	0.0	10.0	0.0	2.0	0.800143	15.0	0.0	10.0	0.836741	3.0	23.0	4.0	64.0	44.0	1.0	3.0	0.0	3.0	0	False	4.0	True	2.0	0.992409	0.7681	False	30.0	False	True	2.0	False	0.757553	n/a	15.0	40.0	15.0	40.0	60.0	70.0	u	15.0	50.0	2.0	False	0.0	True	3.0	False	4.0	False	1.0	0.855351	False	False	1.0	10.0	10.0	0.916341	0.0	n/a	6.0	0.0	40.0	0	n/a	False	0.984198	False	1.0	2.0	0.214286	2.0	n/a	2.0	40.0	323	n/a	9.0	0.911936	6.14583	7.0	2.0	50.0	10.0	200.0	False	True	True	False	2.0	False	17.0	1.14246	2.0	False
+S24	60.0	0.671362	1.0	1.0	False	-0.0573619	3.0	False	3.0	1.0	2.0	25.0	0.944763	0.947368	True	True	2.0	0.5	3.2	300.0	1.0	19.0	False	18.5	8.0	0.333333	35.0	10.0	75.0	False	0.0	9.0	0.0	2.0	0.951386	600.0	1.0	9.0	0.887401	3.0	34.0	3.0	57.0	45.0	1.0	6.0	0.0	1.0	0	False	2.0	True	2.0	0.990636	1.00301	False	45.0	False	True	1.0	False	0.999262	n/a	10.0	35.0	25.0	60.0	70.0	80.0	u	25.0	70.0	2.0	False	0.0	True	2.0	False	3.0	False	0.0	0.673082	False	False	1.0	20.0	20.0	1.08045	0.0	VISAGES	-1.0	2.0	30.0	0	n/a	False	1.04792	False	2.0	1.5	-0.027027	1.0	LIEUX	3.0	500.0	335	n/a	14.0	1.08431	4.13194	7.0	4.0	90.0	10.0	300.0	False	True	True	True	3.0	False	18.0	1.06108	2.0	False
+S25	50.0	0.688949	1.0	1.0	False	-0.0489149	1.0	False	4.0	1.0	1.0	20.0	0.948051	2.0	False	True	0.0	0.566667	3.0	500.0	0.0	10.0	False	15.0	8.5	0.666667	40.0	10.0	60.0	False	0.0	10.0	0.0	3.0	0.875224	800.0	1.0	10.0	0.899136	3.0	40.0	5.0	76.0	35.0	1.0	7.0	0.0	3.0	n/a	False	2.0	True	2.0	0.979872	1.04007	False	70.0	False	False	2.0	False	1.02544	n/a	15.0	30.0	25.0	50.0	70.0	100.0	u	20.0	60.0	4.0	False	0.0	True	1.0	False	4.0	False	1.0	0.575015	False	False	1.0	4.0	15.0	1.00105	3.33333	animaux	10.0	2.0	50.0	0	n/a	False	0.991503	False	1.0	2.5	0.333333	2.0	n/a	3.0	10.0	9	n/a	6.0	0.997507	7.05556	4.0	3.0	50.0	16.0	6.0	False	True	False	False	1.2	True	20.0	1.09846	3.0	False
+S26	35.0	0.455105	n/a	n/a	False	-0.00552304	1.0	False	2.0	n/a	2.0	5.0	0.96991	2.07692	True	True	1.0	0.433333	1.8	240.0	n/a	13.0	True	20.0	12.0	0.25	20.0	10.0	35.0	False	0.2	9.0	3.0	1.0	0.801922	5.0	0.0	10.0	0.964387	3.0	32.0	5.0	64.0	43.0	n/a	4.0	1.0	3.0	dyslexie, pblm de: lecture, inversion de syllabes,dorganisation, emploi du tps	False	4.0	True	4.0	0.996225	0.68226	False	10.0	True	False	1.0	False	0.677383	n/a	10.0	20.0	17.0	40.0	50.0	60.0	d	15.0	45.0	2.0	True	0.0	True	3.0	False	4.0	True	2.0	0.610165	False	False	1.0	6.0	7.0	0.919298	0.0	n/a	14.0	3.0	50.0	0	n/a	False	0.969552	True	2.0	2.0	0.35	3.0	VISAGES LIEUX	3.0	15.0	347	n/a	6.0	0.913964	6.52083	5.0	3.0	60.0	10.0	4.0	False	True	True	True	1.5	True	27.0	0.830617	3.0	False
+S27	30.0	0.400822	1.0	1.0	False	-0.0253511	2.0	False	4.0	1.0	2.0	4.0	0.941145	1.38462	False	False	0.0	0.966667	4.2	300.0	1.0	13.0	False	15.5	8.0	0.75	20.0	10.0	40.0	False	0.0	10.0	0.0	3.0	0.676112	60.0	1.0	10.0	0.915794	3.0	27.0	4.0	65.0	42.0	1.0	10.0	0.0	3.0	0	False	4.0	True	4.0	0.998677	0.60153	False	80.0	False	False	3.0	False	0.596588	n/a	15.0	30.0	20.0	40.0	50.0	60.0	d	20.0	40.0	4.0	False	0.0	True	1.0	False	4.0	False	2.0	0.870445	False	True	1.0	15.0	16.0	0.768781	3.0	n/a	5.0	0.0	80.0	0	n/a	False	0.993971	False	1.0	1.5	0.16129	1.0	VISAGES	4.0	100.0	331	n/a	8.0	0.770575	6.22917	7.0	4.0	50.0	11.0	100.0	False	True	False	False	2.0	False	18.0	1.06635	2.0	False
+S28	53.0	1.08503	n/a	n/a	False	-0.0193036	4.0	False	3.0	n/a	3.0	15.0	0.918141	1.77778	True	False	2.0	0.666667	3.2	600.0	n/a	9.0	False	12.5	8.0	0.583333	20.0	10.0	55.0	False	0.0	10.0	0.0	3.0	1.09877	25.0	2.0	9.0	0.898837	3.0	25.0	4.0	65.0	42.0	n/a	7.0	0.0	3.0	0	False	2.0	True	5.0	0.99648	1.60522	False	30.0	False	False	2.0	False	1.61497	n/a	15.0	25.0	25.0	105.0	76.0	135.0	u	15.0	90.0	3.0	False	0.0	True	2.0	False	4.0	False	2.0	0.814187	False	False	1.0	9.0	16.0	1.25582	2.0	n/a	7.0	2.0	50.0	0	n/a	False	0.987764	False	1.0	1.5	0.28	3.0	VIGAGE	5.0	50.0	294	n/a	18.0	1.25228	6.56944	8.0	5.0	70.0	13.0	50.0	False	True	False	True	12.0	False	16.0	0.81182	2.0	False
+S29	34.0	0.403552	n/a	n/a	False	-0.032888	3.5	False	3.0	n/a	2.0	5.0	0.915869	1.8	False	True	2.0	0.433333	3.0	800.0	2.0	10.0	False	14.0	10.0	0.666667	15.0	10.0	30.0	False	0.0	8.0	0.0	3.0	0.737564	500.0	2.0	10.0	0.882981	3.0	33.0	4.0	57.0	42.0	1.0	5.0	0.0	3.0	0	False	2.0	True	4.0	0.997662	0.613726	False	40.0	False	False	2.0	False	0.600651	n/a	10.0	20.0	20.0	35.0	47.0	60.0	d	15.0	35.0	4.0	False	0.0	True	1.0	False	4.0	False	2.0	0.404527	False	False	1.0	10.0	15.0	0.84771	0.0	n/a	8.0	0.0	40.0	0	n/a	False	0.987342	False	1.0	1.5	0.285714	3.0	VISAGES ANIMAUX	4.0	500.0	330	n/a	17.0	0.840613	7.01389	4.0	4.0	1.0	11.0	300.0	False	True	False	False	3.0	False	18.0	1.08016	3.0	False
+S30	60.0	0.46732	n/a	n/a	False	-0.281506	1.0	False	4.0	n/a	1.0	50.0	0.818848	1.88889	False	False	0.0	0.566667	3.6	400.0	n/a	9.0	False	13.0	10.0	0.75	30.0	10.0	40.0	False	0.5	9.0	0.0	3.0	0.757308	70.0	1.0	8.0	0.537342	3.0	25.0	5.0	62.0	42.0	n/a	7.0	0.0	3.0	0	False	4.0	True	4.0	0.9921	0.72291	False	250.0	False	False	2.0	False	0.695564	n/a	15.0	40.0	30.0	90.0	60.0	60.0	u	20.0	40.0	3.0	False	0.0	True	3.0	False	3.0	False	2.0	0.78383	False	False	1.0	15.0	15.0	0.864595	3.33333	0	8.0	0.0	50.0	nombre / espace	n/a	False	0.941167	False	1.0	4.0	0.307692	1.0	Visages	3.0	60.0	177	n/a	6.0	0.863115	8.60417	5.0	2.0	50.0	10.0	20.0	False	True	False	False	1.0	False	17.0	1.05261	3.0	False
+S31	40.0	0.580558	n/a	n/a	False	0.0131732	2.5	False	4.0	n/a	2.0	5.0	0.967509	1.46154	False	True	1.0	0.533333	4.4	800.0	n/a	13.0	False	16.0	11.0	0.666667	20.0	11.0	40.0	False	0.0	10.0	0.0	1.0	0.868769	200.0	1.0	10.0	0.980682	3.0	30.0	4.0	63.0	45.0	n/a	4.0	0.0	2.0	0	False	4.0	True	3.0	0.999827	0.86083	False	600.0	False	False	2.0	False	0.864108	n/a	12.0	25.0	18.0	50.0	60.0	70.0	d	14.0	60.0	4.0	False	0.0	True	5.0	False	4.0	False	0.0	0.789305	False	False	1.0	7.0	12.0	0.993075	4.0	VISAGE	6.0	0.0	50.0	0	n/a	False	1.00572	False	2.0	3.0	0.1875	2.0	LIEUX	3.0	150.0	295	n/a	11.0	0.99015	6.74306	5.0	4.0	80.0	10.0	300.0	False	True	True	False	2.5	False	19.0	1.33373	2.0	False
+S32	60.0	1.50179	n/a	n/a	False	-0.0628016	0.0	False	4.0	n/a	1.0	10.0	0.937975	1.57143	False	True	0.0	0.833333	3.6	500.0	0.0	14.0	False	18.0	7.0	n/a	40.0	15.0	120.0	False	0.0	10.0	0.0	3.0	1.12305	20.0	0.0	8.0	0.875174	3.0	27.0	4.0	64.0	43.0	n/a	9.0	0.0	1.0	0	False	4.0	True	5.0	0.998799	2.15269	False	60.0	False	False	3.0	False	2.23528	n/a	15.0	40.0	30.0	150.0	100.0	150.0	d	30.0	170.0	4.0	False	1.0	True	0.0	False	4.0	False	0.0	0.866485	False	False	1.0	10.0	15.0	1.26973	3.33333	Visages animaux	8.0	0.0	40.0	0	n/a	False	0.994238	False	2.0	2.5	0.222222	1.0	lieux	5.0	150.0	185	n/a	2.0	1.27996	4.5625	5.0	5.0	80.0	14.0	15.0	True	True	False	False	2.0	False	22.0	1.06178	n/a	False
+S33	80.0	1.00954	n/a	n/a	False	-0.037087	2.0	False	3.0	n/a	3.0	6.0	0.970922	1.875	True	True	1.0	0.733333	2.8	400.0	n/a	8.0	True	11.5	11.0	0.583333	35.0	10.0	90.0	False	0.2	7.0	2.0	4.0	1.07425	30.0	2.0	9.0	0.933835	3.0	32.0	4.0	65.0	42.0	n/a	10.0	0.0	3.0	apprentissage lecture	False	3.0	True	4.0	0.999484	1.50266	False	80.0	False	False	2.0	False	1.5026	n/a	15.0	50.0	25.0	90.0	100.0	110.0	u	25.0	100.0	3.0	True	0.0	True	1.0	False	5.0	False	1.0	0.882616	False	False	1.0	20.0	15.0	1.22089	3.33333	0	7.0	2.0	100.0	0	n/a	True	1.00767	False	1.0	2.0	0.304348	2.0	0	3.0	150.0	216	n/a	12.0	1.22434	6.61111	6.0	4.0	80.0	10.0	70.0	False	True	False	False	2.5	False	15.0	1.04328	1.0	False
+S34	39.0	0.546959	n/a	n/a	False	-0.0169346	3.5	False	2.0	n/a	1.0	20.0	0.978523	1.77778	False	False	2.0	0.766667	3.0	350.0	n/a	9.0	False	12.5	8.0	1.0	29.0	13.0	55.0	False	0.33	10.0	0.0	2.0	0.790664	50.0	2.0	10.0	0.961589	3.0	33.0	4.0	72.0	44.0	n/a	9.0	0.0	3.0	timbre de voix	False	3.0	True	n/a	0.999336	0.808399	False	90.0	False	False	2.0	False	0.8141	n/a	13.0	36.0	21.0	50.0	60.0	70.0	d	18.0	60.0	4.0	False	0.0	True	0.0	False	3.0	False	1.0	0.95104	True	False	1.0	17.0	13.0	0.897307	1.66667	0	7.0	0.0	40.0	n/a	n/a	False	1.03377	True	2.0	3.0	0.28	2.0	lieux	2.0	50.0	191	n/a	17.0	0.901132	7.875	4.0	2.0	75.0	16.0	100.0	False	True	True	False	3.0	False	16.0	0.952927	3.0	False
+S35	45.0	0.739662	n/a	n/a	False	-0.0104976	3.0	False	4.0	n/a	1.0	4.0	0.956899	2.1	True	False	1.0	0.533333	2.4	350.0	n/a	10.0	False	15.5	9.0	0.833333	30.0	12.0	50.0	False	0.6	7.0	0.0	1.0	0.966636	30.0	0.0	8.0	0.946401	3.0	32.0	4.0	70.0	43.0	n/a	3.0	0.0	1.0	positionement main gauche	False	4.0	True	4.0	0.999391	1.07854	False	8.0	False	False	2.0	False	1.10092	n/a	10.0	30.0	30.0	75.0	70.0	80.0	d	15.0	85.0	3.0	False	1.0	True	2.0	False	4.0	False	1.0	0.814216	True	False	1.0	5.0	6.0	1.09427	2.66667	0	11.0	2.0	60.0	0	n/a	False	1.02351	True	1.0	4.0	0.354839	1.0	0	4.0	60.0	192	n/a	10.0	1.10169	6.90278	6.0	1.0	50.0	13.0	20.0	True	True	False	False	2.5	False	21.0	0.995345	2.0	False
+S36	24.0	0.373996	n/a	n/a	False	0.0331708	0.5	False	4.0	n/a	1.0	4.0	0.885147	2.22222	False	True	0.0	0.8	2.8	450.0	n/a	9.0	False	14.5	7.0	0.583333	20.0	11.0	39.0	False	0.5	9.0	0.0	3.0	0.668698	167.0	0.0	9.0	0.918318	3.0	28.0	4.0	55.0	43.0	n/a	7.0	0.0	3.0	0	False	4.0	True	2.0	0.998932	0.556324	False	30.0	False	False	3.0	False	0.556659	n/a	13.0	15.0	16.0	35.0	45.0	50.0	u	15.0	41.0	4.0	False	0.0	True	2.0	False	4.0	False	1.0	0.856854	False	True	1.0	7.0	11.0	0.768194	3.66667	0	11.0	0.0	45.0	0	n/a	False	0.970847	False	1.0	1.0	0.37931	2.0	tout	2.0	30.0	200	n/a	3.0	0.762126	5.50694	5.0	2.0	70.0	12.0	40.0	False	True	True	False	2.5	False	20.0	1.08659	3.0	False
+S37	60.0	1.20749	n/a	n/a	False	0.00848409	4.0	False	0.0	n/a	3.0	20.0	0.911086	1.33333	False	False	2.0	0.733333	3.8	300.0	n/a	9.0	False	10.5	9.0	0.833333	45.0	15.0	85.0	False	0.0	6.0	0.0	0.0	0.988676	60.0	2.0	10.0	0.91957	3.0	30.0	4.0	64.0	44.0	n/a	8.0	0.0	3.0	0	False	4.0	True	3.0	0.999972	1.76399	False	45.0	False	False	3.0	False	1.79724	n/a	16.0	26.0	30.0	60.0	100.0	150.0	d	25.0	130.0	4.0	False	0.0	True	5.0	False	0.0	False	1.0	0.957402	False	True	1.0	15.0	12.0	1.12971	0.0	n/a	3.0	0.0	50.0	0	n/a	False	1.00563	False	2.0	4.0	0.142857	1.0	LIEUX,VISAGE	5.0	35.0	291	n/a	18.0	1.12681	8.63194	7.0	2.0	90.0	13.0	80.0	False	True	True	False	2.5	False	12.0	0.938097	2.0	False
+S38	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	368	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
+S39	50.0	0.621052	n/a	n/a	False	-0.0259647	0.5	False	5.0	n/a	1.0	25.0	0.923161	1.58333	False	False	0.0	0.733333	2.8	800.0	n/a	12.0	False	15.5	10.0	0.666667	25.0	10.0	30.0	False	0.0	9.0	0.0	4.0	0.840421	110.0	0.0	10.0	0.897196	3.0	30.0	5.0	61.0	41.0	n/a	10.0	0.0	3.0	 LANGUES	False	4.0	True	5.0	0.995976	0.934155	False	50.0	False	False	2.0	False	0.924379	n/a	15.0	30.0	20.0	60.0	70.0	80.0	d	20.0	60.0	2.0	False	0.0	True	2.0	False	4.0	False	2.0	0.947885	True	False	1.0	13.0	10.0	0.962968	4.0	0	7.0	0.0	58.0	n/a	n/a	False	0.950016	False	2.0	4.0	0.225806	1.0	visages, lieux	4.0	100.0	139	n/a	3.0	0.957841	8.68056	4.0	5.0	80.0	10.0	30.0	False	True	False	True	2.0	False	19.0	1.13179	3.0	False
+S40	60.0	0.669262	n/a	n/a	False	-0.120663	3.0	False	4.0	n/a	1.0	6.0	0.869703	1.26667	False	True	2.0	0.85	2.8	400.0	n/a	15.0	False	17.0	6.0	0.75	30.0	12.0	35.0	False	0.29	10.0	0.0	3.0	0.798569	80.0	1.0	10.0	0.74904	3.0	22.0	3.0	61.0	42.0	0.0	7.0	0.0	2.0	0	False	4.0	True	2.0	0.988815	1.03409	False	100.0	False	False	1.0	True	0.996137	n/a	15.0	30.0	28.0	50.0	70.0	110.0	d	18.0	45.0	4.0	False	0.0	True	0.0	False	3.0	False	2.0	0.917762	False	False	1.0	7.0	18.0	0.923717	3.5	lieux	4.0	0.0	60.0	Chiffres lettres, noms, mots / couleurs, prenoms / gout, toucher, nombres / temps	n/a	False	1.01591	False	1.0	2.0	0.117647	2.0	Visages	3.0	50.0	197	n/a	14.0	0.910141	5.83333	6.0	2.0	70.0	10.0	90.0	False	True	False	False	2.0	False	19.0	1.1343	2.0	False
+S41	50.0	0.634911	n/a	n/a	False	-0.123459	1.5	False	4.0	n/a	3.0	6.0	0.911603	1.33333	False	True	0.0	0.8	5.0	400.0	n/a	12.0	False	14.0	10.0	0.583333	25.0	10.0	40.0	False	0.0	10.0	0.0	3.0	0.892371	60.0	1.0	10.0	0.788143	3.0	27.0	3.0	63.0	41.0	n/a	10.0	0.0	3.0	0	False	2.0	True	2.0	0.994467	0.985329	False	75.0	False	False	2.0	False	0.945008	n/a	12.0	30.0	20.0	50.0	80.0	90.0	d	15.0	40.0	4.0	False	0.0	True	1.0	False	5.0	False	1.0	0.910247	False	False	1.0	20.0	10.0	1.03135	2.0	n/a	4.0	0.0	50.0	0	n/a	False	1.035	False	1.0	1.0	0.142857	1.0	n/a	4.0	80.0	348	n/a	7.0	1.01705	5.29861	7.0	4.0	40.0	11.0	50.0	False	True	True	False	3.0	False	16.0	0.979685	2.0	False
+S42	70.0	0.971599	n/a	n/a	False	-0.0145183	3.0	False	3.0	n/a	3.0	20.0	0.93927	1.83333	False	False	1.0	0.9	3.4	500.0	n/a	12.0	False	17.0	10.0	0.666667	30.0	15.0	77.0	False	0.0	10.0	0.0	2.0	0.955744	50.0	1.0	10.0	0.924752	3.0	32.0	4.0	65.0	44.0	2.0	10.0	0.0	3.0	0	True	4.0	True	4.0	0.99983	1.46297	False	250.0	False	False	3.0	False	1.44614	n/a	15.0	40.0	20.0	70.0	90.0	130.0	d	25.0	80.0	4.0	False	0.0	True	0.0	False	4.0	False	2.0	0.893647	False	False	1.0	15.0	15.0	1.09782	2.0	n/a	10.0	0.0	70.0	0	n/a	False	1.01353	False	2.0	2.0	0.294118	3.0	VISAGE, LIEUX,PHYSIONOMIE	5.0	130.0	324	n/a	12.0	1.08928	6.53472	9.0	4.0	65.0	11.0	50.0	False	True	False	False	4.0	False	22.0	1.00161	2.0	False
+S43	50.0	0.444483	n/a	n/a	False	-0.0592845	2.5	False	3.0	n/a	2.0	35.0	0.889422	1.91667	False	False	2.0	0.933333	3.2	600.0	n/a	12.0	False	17.5	7.0	0.916667	25.0	15.0	40.0	False	0.0	10.0	0.0	2.0	0.608799	40.0	1.0	10.0	0.830138	3.0	22.0	4.0	68.0	43.0	n/a	9.0	1.0	3.0	0	False	2.0	True	3.0	0.988576	0.669578	False	100.0	False	False	2.0	False	0.661573	n/a	20.0	50.0	25.0	50.0	60.0	70.0	u	25.0	50.0	4.0	False	0.0	True	1.0	False	4.0	False	0.0	0.8933	False	False	1.0	12.0	18.0	0.692751	3.0	VISAGES	11.0	0.0	50.0	0	n/a	False	0.988251	False	2.0	3.0	0.314286	1.0	LIEUX	5.0	40.0	349	n/a	13.0	0.693858	7.93056	6.0	4.0	60.0	12.0	25.0	False	True	False	True	2.5	False	23.0	0.986973	3.0	False
+S44	60.0	1.05413	n/a	n/a	False	-0.0291271	5.0	False	4.0	n/a	2.0	10.0	0.913654	1.07692	False	True	2.0	0.666667	2.4	600.0	n/a	13.0	False	13.5	10.0	0.833333	50.0	15.0	65.0	False	0.0	10.0	0.0	3.0	0.863541	20.0	2.0	7.0	0.884527	3.0	25.0	5.0	55.0	42.0	n/a	10.0	0.0	3.0	0	False	4.0	True	2.0	0.99285	1.59484	False	100.0	False	True	2.0	True	1.56897	n/a	25.0	40.0	25.0	78.0	115.0	150.0	u	30.0	85.0	3.0	False	1.0	True	2.0	False	3.0	False	2.0	0.6925	False	False	1.0	6.0	15.0	0.99406	0.0	n/a	1.0	0.0	50.0	0	n/a	False	0.920358	False	2.0	1.5	0.037037	3.0	VISAGE ET LIEUX	2.0	15.0	293	n/a	20.0	0.984191	6.73611	6.0	1.0	80.0	13.0	6.0	False	True	False	False	3.0	False	14.0	0.943294	3.0	False
+S45	30.0	0.287899	n/a	n/a	False	-0.0142111	1.5	False	4.0	n/a	3.0	30.0	0.89745	2.4	False	True	1.0	0.733333	3.4	600.0	n/a	10.0	False	17.0	6.0	0.583333	20.0	6.0	20.0	False	0.25	9.0	0.0	3.0	0.706925	80.0	1.0	9.0	0.883239	3.0	27.0	5.0	70.0	45.0	n/a	7.0	0.0	3.0	0	False	4.0	True	3.0	0.992768	0.419834	False	30.0	False	False	2.0	False	0.428512	n/a	12.0	25.0	15.0	30.0	30.0	40.0	u	12.0	40.0	2.0	False	0.0	True	2.0	False	5.0	False	1.0	0.912762	False	False	1.0	8.0	12.0	0.796829	3.33333	0	14.0	0.0	40.0	0	n/a	False	0.977134	False	2.0	2.5	0.411765	1.0	lieux	3.0	60.0	170	n/a	9.0	0.805694	7.23611	6.0	3.0	50.0	10.0	100.0	False	True	False	False	3.0	False	24.0	1.04265	4.0	False
+S46	50.0	0.719223	n/a	n/a	False	-0.0376729	3.0	False	2.0	n/a	1.0	150.0	0.917437	1.69231	True	True	2.0	0.733333	2.6	400.0	n/a	13.0	False	17.5	6.0	0.5	25.0	15.0	80.0	False	0.0	10.0	0.0	2.0	0.927479	40.0	1.0	10.0	0.879764	3.0	30.0	5.0	52.0	40.0	0.0	8.0	0.0	3.0	0	False	4.0	True	4.0	0.994182	1.05988	False	200.0	False	False	2.0	False	1.0705	n/a	10.0	30.0	20.0	70.0	65.0	80.0	d	20.0	75.0	1.0	False	0.0	True	4.0	False	3.0	False	2.0	0.743006	False	False	1.0	20.0	13.0	1.05708	1.0	FLEURS	9.0	2.0	50.0	0	n/a	False	0.919182	False	2.0	2.5	0.257143	1.0	VISAGES LIEUX	4.0	150.0	337	n/a	14.0	1.05706	7.20833	6.0	4.0	80.0	15.0	100.0	False	True	True	True	3.0	False	22.0	0.976954	2.0	False
+S47	50.0	0.747239	n/a	n/a	False	-0.0657451	4.0	False	4.0	n/a	2.0	n/a	0.91669	1.81818	False	True	2.0	0.533333	3.6	n/a	n/a	11.0	False	15.5	n/a	0.916667	25.0	12.0	45.0	False	0.0	6.0	0.0	2.0	0.839307	n/a	2.0	10.0	0.850945	3.0	30.0	4.0	60.0	45.0	n/a	9.0	0.0	3.0	0	False	2.0	True	2.0	0.999447	1.11427	False	n/a	False	False	3.0	False	1.1122	n/a	18.0	40.0	30.0	45.0	100.0	90.0	d	20.0	80.0	1.0	False	2.0	True	1.0	False	4.0	False	1.0	n/a	False	False	1.0	n/a	n/a	0.95479	2.66667	0	9.0	0.0	n/a	n/a	n/a	False	0.997244	False	1.0	2.5	0.290323	2.0	0	4.0	n/a	218	n/a	18.0	0.956572	7.24306	9.0	4.0	n/a	10.0	n/a	False	True	False	False	n/a	False	20.0	n/a	3.0	False
+S48	30.0	0.507131	n/a	n/a	False	-0.00956098	0.5	False	2.0	n/a	1.0	20.0	0.939729	2.77778	False	True	0.0	0.733333	4.0	500.0	n/a	9.0	False	17.0	7.0	0.833333	20.0	10.0	40.0	False	0.0	10.0	0.0	3.0	0.824241	40.0	1.0	10.0	0.930168	3.0	30.0	5.0	65.0	43.0	n/a	9.0	0.0	2.0	0	False	4.0	True	4.0	0.996104	0.752623	False	20.0	False	False	2.0	True	0.754819	n/a	10.0	20.0	15.0	30.0	50.0	70.0	d	15.0	50.0	4.0	False	0.0	True	0.0	False	4.0	False	1.0	0.917877	False	False	1.0	15.0	7.0	0.943009	2.33333	lieux	16.0	0.0	52.0	n/a	n/a	False	0.96704	False	0.0	0.0	0.470588	1.0	Visages	4.0	70.0	168	n/a	5.0	0.9394	4.09028	7.0	5.0	50.0	13.0	30.0	False	True	True	False	3.0	False	25.0	0.966885	2.0	False
+S49	30.0	0.329163	n/a	n/a	False	-0.0534856	2.0	False	3.0	n/a	1.0	7.0	0.781903	2.09091	False	False	1.0	0.8	3.0	400.0	n/a	11.0	True	17.0	10.0	0.833333	20.0	10.0	25.0	False	0.0	9.0	3.0	3.0	0.616801	30.0	2.0	9.0	0.728418	3.0	27.0	4.0	62.0	42.0	n/a	7.0	0.0	3.0	APPRENTISSAGE LECTURE / DYSLEXIE	False	4.0	True	3.0	0.999214	0.50128	False	50.0	False	False	2.0	False	0.489929	n/a	10.0	20.0	30.0	25.0	50.0	55.0	u	20.0	35.0	2.0	True	0.0	True	1.0	True	3.0	True	2.0	0.942988	False	False	1.0	8.0	10.0	0.704073	3.0	0	12.0	0.0	40.0	0	n/a	True	0.985763	True	1.0	3.5	0.352941	2.0	Visages	3.0	100.0	171	n/a	12.0	0.702978	8.17361	5.0	4.0	45.0	10.0	60.0	False	True	False	False	1.2	False	23.0	1.1189	2.0	False
+S50	60.0	0.801137	n/a	n/a	False	-0.0291117	0.5	False	4.0	n/a	1.0	50.0	0.98375	1.66667	False	True	0.5	0.666667	2.6	n/a	n/a	9.0	False	12.0	10.0	0.666667	40.0	15.0	80.0	False	0.25	10.0	0.0	3.0	0.826063	60.0	0.0	10.0	0.954638	3.0	25.0	5.0	63.0	43.0	n/a	7.0	1.0	3.0	n/a	False	4.0	True	3.0	0.991647	1.16622	False	30.0	False	False	2.0	False	1.19242	n/a	20.0	50.0	30.0	70.0	80.0	95.0	u	27.0	100.0	4.0	False	0.0	True	1.0	False	4.0	False	0.0	n/a	False	False	1.0	8.0	12.5	0.933209	3.33333	visage	6.0	0.0	70.0	n/a	n/a	False	0.946907	False	1.0	1.5	0.25	1.0	n/a	3.0	150.0	117	n/a	4.0	0.941477	6.13889	4.0	3.0	75.0	10.0	250.0	False	False	True	True	1.5	False	15.0	n/a	2.0	False
+S51	120.0	0.902599	n/a	n/a	False	-0.231889	4.0	False	3.0	n/a	2.0	10.0	0.752758	1.71429	False	True	1.0	0.833333	2.6	700.0	n/a	7.0	False	9.5	7.0	0.833333	50.0	17.0	54.0	False	0.17	10.0	0.0	2.0	0.812932	30.0	2.0	10.0	0.520869	3.0	30.0	4.0	60.0	40.0	n/a	7.0	0.0	2.0	0	False	4.0	True	3.0	0.999712	1.25798	False	200.0	False	False	2.0	False	1.34344	n/a	20.0	80.0	42.0	120.0	50.0	90.0	d	30.0	160.0	4.0	False	0.0	True	2.0	False	4.0	False	1.0	0.89375	False	False	1.0	10.0	18.0	0.90344	2.66667	0	5.0	0.0	40.0	n/a	n/a	False	0.982427	False	1.0	1.5	0.263158	2.0	0	2.0	100.0	173	n/a	16.0	0.926512	5.69444	8.0	3.0	60.0	11.0	115.0	False	True	True	False	2.0	False	12.0	1.1715	3.0	False
+S52	55.0	0.87824	n/a	n/a	False	-0.00832569	2.0	False	5.0	n/a	1.0	20.0	0.971116	1.77778	True	True	1.0	0.766667	2.6	400.0	n/a	9.0	False	12.5	7.0	0.5	35.0	10.0	75.0	False	0.0	8.0	0.0	2.0	1.0228	75.0	1.0	7.0	0.96279	3.0	25.0	4.0	61.0	42.0	n/a	9.0	0.0	1.0	0	False	4.0	True	1.0	0.997485	1.29562	False	150.0	False	False	1.0	True	1.30718	n/a	12.0	30.0	25.0	80.0	80.0	100.0	u	25.0	90.0	3.0	False	0.0	True	2.0	False	4.0	False	2.0	0.855701	False	False	1.0	20.0	12.0	1.16281	3.66667	0	7.0	2.0	60.0	0	n/a	False	0.976732	False	1.0	2.0	0.28	2.0	Visages	3.0	100.0	206	n/a	10.0	1.1657	4.66667	4.0	5.0	60.0	10.0	15.0	False	True	False	False	2.0	False	16.0	1.02892	2.0	False
+S53	57.0	0.721953	1.0	1.0	False	-0.038159	2.0	False	3.0	1.0	2.0	21.0	0.949123	2.0	False	True	1.0	0.6	2.0	400.0	1.0	10.0	False	15.0	7.0	0.666667	18.0	8.0	47.0	False	0.5	10.0	0.0	2.0	1.04459	500.0	2.0	9.0	0.910964	3.0	25.0	4.0	53.0	34.0	1.0	8.0	0.0	3.0	"confusion entre ""p""et""b"",""a"" et""d"",""v""et""f"" a9ans pendant3-4mois "	True	2.0	False	3.0	0.999318	1.09054	False	40.0	False	False	2.0	False	1.07456	n/a	11.0	36.0	22.0	52.0	78.0	89.0	u	13.0	61.0	2.0	False	1.0	True	6.0	True	3.0	False	1.0	0.727899	False	False	1.0	9.0	17.0	1.19193	0.0	n/a	10.0	0.0	40.0	0	n/a	False	0.92573	True	1.0	1.5	0.333333	1.0	n/a	4.0	60.0	299	n/a	12.0	1.19054	6.53472	5.0	4.0	70.0	12.0	12.0	False	True	False	False	1.8	False	20.0	1.04877	3.0	False
+S54	38.0	0.383918	n/a	0.0	False	-0.103711	1.0	False	3.0	n/a	3.0	5.0	0.86639	1.8	True	True	1.0	0.666667	2.8	300.0	n/a	10.0	False	14.0	12.0	0.75	20.0	10.0	30.0	False	0.33	8.0	1.0	3.0	0.731965	10.0	1.0	9.0	0.76268	3.0	30.0	4.0	64.0	42.0	n/a	9.0	0.0	2.0	DYSORTHOGRAPHIE DYSLEXIE	False	3.0	True	2.0	0.999901	0.594111	False	20.0	False	False	2.0	False	0.571427	n/a	10.0	25.0	15.0	25.0	50.0	60.0	u	12.0	30.0	4.0	True	0.0	True	1.0	True	4.0	True	2.0	0.799681	False	False	0.0	4.0	8.0	0.846727	3.0	noms	8.0	2.0	45.0	n/a	n/a	False	0.999775	True	1.0	1.5	0.285714	1.0	Visages	2.0	12.0	179	n/a	6.0	0.834232	4.625	12.0	2.0	40.0	10.0	50.0	False	True	True	False	1.5	False	18.0	0.95225	3.0	False
+S55	38.0	0.351037	n/a	n/a	False	-0.0959172	2.5	False	2.0	n/a	2.0	15.0	0.874188	1.4	True	True	2.0	0.53	2.6	500.0	n/a	10.0	False	12.0	8.0	0.5	19.0	9.0	25.0	False	0.0	10.0	0.0	2.0	0.670693	75.0	1.0	7.0	0.778271	3.0	28.0	4.0	75.0	45.0	n/a	5.0	1.0	3.0	0	False	3.0	True	1.0	0.997022	0.542072	False	35.0	False	False	1.0	True	0.522487	n/a	13.0	27.0	20.0	39.0	45.0	57.0	d	15.0	29.0	4.0	False	0.0	True	2.0	False	3.0	False	1.0	0.969419	False	False	1.0	10.0	13.0	0.771524	2.0	n/a	4.0	3.0	45.0	0	n/a	False	1.03044	False	2.0	3.0	0.166667	3.0	LIEUX	2.0	50.0	297	n/a	13.0	0.764399	7.08333	6.0	1.0	55.0	10.0	75.0	False	True	True	False	2.5	True	14.0	1.02732	2.0	False
+S56	50.0	0.728235	1.0	1.0	False	-0.0200327	2.5	False	3.0	1.0	2.0	13.0	0.958526	1.55556	False	False	1.0	0.7	3.0	450.0	1.0	9.0	False	11.5	9.0	0.666667	30.0	10.0	40.0	False	0.0	8.0	0.0	2.0	0.891965	35.0	2.0	9.0	0.938493	3.0	32.0	5.0	62.0	44.0	1.0	6.0	0.0	3.0	zozottement	False	3.0	True	4.0	0.996171	1.08598	False	45.0	False	False	2.0	False	1.08391	n/a	15.0	30.0	20.0	50.0	70.0	100.0	d	20.0	70.0	0.0	False	1.0	True	1.0	False	4.0	False	2.0	0.976141	True	False	1.0	13.0	14.0	1.02026	2.66667	n/a	5.0	0.0	45.0	0	n/a	False	0.966929	True	1.0	1.0	0.217391	1.0	visage	3.0	75.0	39	n/a	13.0	1.01659	6.15972	5.0	3.0	80.0	13.0	35.0	False	True	True	True	3.0	False	14.0	0.967209	2.0	False
+S57	45.0	0.543197	n/a	n/a	False	-0.036005	0.5	False	4.0	n/a	1.0	20.0	0.969654	2.0	False	False	0.0	0.766667	2.4	700.0	n/a	9.0	False	13.5	6.5	0.583333	30.0	10.0	60.0	False	0.13	10.0	0.0	1.0	0.817328	30.0	1.0	10.0	0.933649	3.0	25.0	4.0	60.0	45.0	n/a	10.0	0.0	3.0	0	False	3.0	True	4.0	0.996329	0.804771	False	50.0	False	False	2.0	False	0.8085	n/a	15.0	30.0	25.0	50.0	55.0	70.0	d	17.0	60.0	1.0	False	0.0	True	0.0	False	4.0	False	2.0	0.930744	False	False	0.0	10.0	15.0	0.927787	2.33333	0	9.0	0.0	50.0	0	n/a	False	0.98254	False	2.0	3.0	0.333333	2.0	visages, lieux	4.0	25.0	181	n/a	3.0	0.931521	7.61111	4.0	2.0	80.0	12.0	80.0	False	True	True	False	1.2	False	18.0	1.14636	3.0	False
+S58	49.0	0.482002	n/a	n/a	False	-0.0484288	1.5	False	5.0	n/a	1.0	10.0	0.95826	2.5	False	False	1.0	0.933333	4.2	450.0	n/a	8.0	False	14.0	6.0	0.833333	30.0	10.0	50.0	False	0.2	9.0	0.0	4.0	0.770706	30.0	1.0	9.0	0.909831	3.0	35.0	4.0	62.0	45.0	n/a	9.0	0.0	3.0	0	False	4.0	True	4.0	0.997881	0.72737	False	50.0	False	False	3.0	False	0.717416	n/a	15.0	30.0	20.0	50.0	55.0	65.0	u	20.0	50.0	4.0	False	0.0	True	4.0	False	5.0	False	1.0	0.950643	False	True	1.0	14.0	12.0	0.880939	4.33333	0	12.0	0.0	50.0	0	n/a	False	1.01537	False	1.0	3.0	0.428571	2.0	0	4.0	30.0	201	n/a	9.0	0.878385	8.06944	4.0	4.0	100.0	10.0	40.0	False	True	True	False	2.5	False	20.0	1.01326	3.0	False
+S59	80.0	1.21402	n/a	n/a	False	-0.0345568	0.0	False	3.0	n/a	1.0	15.0	0.975677	1.46154	False	False	1.0	0.6	4.2	400.0	n/a	13.0	False	16.0	8.0	0.583333	40.0	10.0	100.0	False	0.0	8.0	0.0	4.0	1.14589	100.0	0.0	8.0	0.94112	3.0	35.0	5.0	60.0	40.0	n/a	4.0	0.0	3.0	0	False	4.0	True	3.0	0.992796	1.81669	False	50.0	False	False	3.0	False	1.80695	n/a	15.0	40.0	30.0	100.0	100.0	150.0	d	20.0	100.0	3.0	False	0.0	True	1.0	False	3.0	False	1.0	0.748922	False	False	0.0	20.0	100.0	1.30699	3.5	0	6.0	0.0	50.0	0	n/a	False	0.956471	False	1.0	3.0	0.1875	3.0	0	3.5	60.0	203	n/a	2.0	1.30599	7.65278	n/a	4.0	100.0	15.0	30.0	False	True	True	False	5.0	False	19.0	0.806267	2.0	False
+S60	45.0	0.705189	1.0	1.0	False	-0.0101898	0.0	False	3.0	1.0	1.0	7.0	0.974984	1.55556	False	False	0.0	0.933333	3.6	380.0	1.0	9.0	False	11.5	8.5	0.416667	30.0	12.0	55.0	False	0.333333	10.0	0.0	3.0	0.828774	10.0	0.0	9.0	0.964794	3.0	30.0	5.0	64.0	45.0	1.0	10.0	0.0	2.0	n/a	False	4.0	True	2.0	0.996178	1.04281	False	20.0	False	n/a	3.0	False	1.04961	n/a	15.0	35.0	30.0	50.0	75.0	95.0	d	25.0	75.0	3.0	False	0.0	True	1.0	False	5.0	False	2.0	0.680142	False	False	0.0	12.0	12.0	0.939798	3.0	lieux	5.0	0.0	50.0	0	n/a	False	0.970066	False	0.0	2.5	0.217391	1.0	visage	3.0	40.0	132	n/a	0.0	0.944566	5.28472	7.0	5.0	50.0	12.0	10.0	False	True	True	False	7.0	False	14.0	0.738193	2.0	False
+S61	30.0	0.420859	n/a	n/a	False	-0.0346947	1.0	False	3.0	n/a	3.0	17.5	0.946556	2.45455	False	False	2.0	0.5	3.4	400.0	n/a	11.0	False	19.0	8.0	0.833333	20.0	10.0	30.0	False	0.666667	9.0	0.0	2.0	0.753665	100.0	1.0	8.0	0.911862	3.0	29.0	3.0	60.0	45.0	n/a	1.0	1.0	3.0	0	False	2.0	True	3.0	0.994075	0.603338	False	40.0	False	False	1.0	False	0.626411	n/a	10.0	20.0	15.0	30.0	40.0	50.0	u	15.0	60.0	1.0	False	0.0	True	3.0	False	3.0	False	0.0	0.853415	False	False	1.0	10.0	15.0	0.854203	2.5	physionomie	16.0	0.0	52.0	n/a	n/a	False	1.044	False	2.0	4.0	0.421053	2.0	lieux	3.0	25.0	199	n/a	10.0	0.858964	8.25694	9.0	3.0	50.0	11.0	200.0	False	True	False	False	2.0	False	27.0	1.02602	3.0	False
+S62	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	367	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
+S63	40.0	0.710036	1.0	1.0	False	-0.00756445	1.0	False	4.0	1.0	3.0	5.0	0.952403	1.85714	False	True	0.0	0.833333	2.4	1000.0	1.0	7.0	False	10.0	8.0	0.583333	25.0	10.0	60.0	False	0.0	10.0	0.0	2.0	0.912774	800.0	1.0	10.0	0.944839	3.0	25.0	3.5	60.0	42.0	1.0	9.0	0.0	2.0	0	False	2.0	True	1.0	0.997006	1.03295	False	400.0	False	True	2.0	False	1.05682	n/a	15.0	25.0	20.0	70.0	60.0	80.0	u	20.0	80.0	4.0	False	0.0	True	3.0	False	4.0	False	2.0	0.235942	False	False	0.0	4.0	8.0	1.03665	3.0	LIEUX	6.0	0.0	50.0	0	n/a	False	0.996833	False	0.0	1.0	0.3	1.0	VISAGES	2.0	200.0	325	n/a	4.0	1.0403	3.75694	11.0	1.0	60.0	14.0	500.0	False	True	True	False	500.0	False	13.0	0.782561	2.0	False
+S64	40.0	0.526118	n/a	n/a	False	-0.000933116	4.0	False	4.0	n/a	1.0	50.0	0.971842	1.7	False	False	2.0	0.4	2.4	600.0	0.0	10.0	False	13.5	7.0	0.75	20.0	10.0	40.0	False	0.75	8.0	1.0	3.0	0.88201	30.0	2.0	7.0	0.970909	3.0	36.0	5.0	60.0	40.0	n/a	4.0	0.0	3.0	0	False	2.0	True	1.0	0.991759	0.772525	False	10.0	False	False	1.0	False	0.783079	n/a	10.0	25.0	15.0	50.0	50.0	60.0	u	15.0	60.0	1.0	True	0.0	True	3.0	False	4.0	False	2.0	0.726744	False	False	1.0	3.0	20.0	1.00469	0.0	ESPECES ANIMALES	7.0	0.0	45.0	0	n/a	True	0.958743	False	1.0	2.5	0.259259	2.0	VISAGE	1.0	40.0	296	n/a	18.0	1.00524	7.35417	5.0	1.0	50.0	12.0	20.0	False	True	False	False	2.0	False	17.0	0.975383	3.0	False
+S65	30.0	0.422172	1.0	1.0	False	-0.0217241	3.5	False	3.0	1.0	1.0	10.0	0.956833	1.58333	False	False	2.0	0.866667	3.8	500.0	1.0	12.0	False	15.5	7.0	0.916667	20.0	10.0	40.0	False	0.0	10.0	0.0	4.0	0.757414	40.0	2.0	10.0	0.935109	3.0	30.0	5.0	64.0	43.0	1.0	8.0	0.0	3.0	0	False	3.0	True	4.0	0.996173	0.615527	False	60.0	False	False	3.0	False	0.628364	n/a	10.0	30.0	20.0	30.0	40.0	60.0	d	15.0	50.0	4.0	False	0.0	True	1.0	False	4.0	False	1.0	0.939091	False	False	1.0	30.0	12.0	0.853278	2.0	n/a	7.0	0.0	55.0	0	n/a	False	0.964347	False	1.0	2.5	0.225806	2.0	n/a	4.0	70.0	327	n/a	17.0	0.863237	8.05556	4.0	4.0	80.0	10.0	40.0	False	True	False	False	3.0	False	19.0	0.994149	3.0	False
+S66	50.0	0.602292	n/a	n/a	False	-0.0338606	1.5	False	4.0	n/a	1.0	20.0	0.934056	2.11111	False	False	0.0	0.6	3.2	400.0	n/a	9.0	False	14.0	9.0	0.5	15.0	7.0	35.0	False	0.0	10.0	0.0	3.0	1.0295	25.0	1.0	9.0	0.900195	3.0	25.0	3.0	65.0	42.0	n/a	8.0	0.0	3.0	0	False	4.0	True	3.0	0.992627	0.904788	False	35.0	False	False	3.0	False	0.896458	n/a	10.0	30.0	15.0	50.0	50.0	80.0	u	10.0	50.0	4.0	False	0.0	True	1.0	False	5.0	False	2.0	0.894138	False	False	1.0	20.0	15.0	1.17632	0.0	n/a	10.0	0.0	50.0	0	n/a	False	1.03726	False	2.0	3.0	0.357143	1.0	VISAGES LIEUX	3.0	90.0	344	n/a	7.0	1.17333	7.27083	5.0	3.0	80.0	12.0	20.0	False	True	False	True	2.0	False	19.0	0.942724	2.0	False
+S67	50.0	0.760311	n/a	n/a	False	-0.090743	0.5	False	3.0	n/a	2.0	40.0	0.957141	1.75	False	False	0.0	0.7	2.8	300.0	n/a	8.0	False	11.0	7.0	0.166667	40.0	12.0	60.0	False	0.25	8.0	0.0	2.0	0.895573	50.0	0.0	10.0	0.866398	3.0	32.0	5.0	65.0	44.0	2.0	5.0	0.0	3.0	0	False	3.0	True	2.0	0.996432	1.11832	False	150.0	False	False	2.0	True	1.13165	n/a	15.0	45.0	25.0	100.0	70.0	90.0	u	25.0	80.0	3.0	False	0.0	True	2.0	False	5.0	False	1.0	0.719087	False	False	1.0	3.0	12.0	1.01291	2.33333	0	6.0	0.0	58.0	Chiffres, voyelles en couleur	n/a	False	0.975137	False	2.0	3.0	0.272727	1.0	Lieux, itineraires	2.0	40.0	215	n/a	3.0	1.0207	6.49306	5.0	2.0	80.0	10.0	8.0	False	True	True	False	2.0	False	14.0	1.00441	1.0	False
+S68	42.0	0.518261	n/a	n/a	False	-0.0204349	2.0	False	5.0	n/a	1.0	5.0	0.936235	1.88889	False	False	1.0	1.0	4.2	700.0	n/a	9.0	False	13.0	7.0	0.833333	27.0	12.0	47.0	False	0.666667	10.0	1.0	3.0	0.753259	15.0	1.0	10.0	0.9158	3.0	33.0	4.0	63.0	43.0	n/a	10.0	0.0	3.0	n/a	False	4.0	True	5.0	0.999161	0.76944	False	50.0	True	False	3.0	False	0.771384	n/a	12.0	27.0	23.0	32.0	58.0	72.0	u	21.0	59.0	1.0	True	0.0	True	0.0	False	5.0	False	1.0	0.850019	False	False	1.0	8.0	15.0	0.858164	4.0	n/a	8.0	0.0	40.0	0	n/a	False	1.00769	False	2.0	4.0	0.307692	1.0	lieux	5.0	50.0	94	n/a	10.0	0.858501	8.90278	6.0	5.0	60.0	16.0	130.0	False	True	False	False	3.0	False	17.0	1.08324	3.0	False
+S69	40.0	0.320483	n/a	n/a	False	-0.0823311	4.0	False	4.0	n/a	1.0	32.5	0.906829	2.14286	False	False	2.0	0.7	4.2	450.0	n/a	7.0	False	11.0	9.0	0.5	20.0	13.0	40.0	False	0.0	7.0	0.0	4.0	0.601481	150.0	2.0	9.0	0.824498	3.0	25.0	5.0	60.0	48.0	n/a	8.0	0.0	3.0	n/a	False	4.0	True	4.0	0.989427	0.488542	False	2500.0	False	False	2.0	True	0.47701	n/a	12.0	30.0	25.0	40.0	45.0	50.0	u	20.0	35.0	4.0	False	0.0	True	3.0	False	5.0	False	2.0	0.554342	False	False	1.0	8.0	15.0	0.68626	4.0	n/a	8.0	0.0	35.0	couleur, mot, espace	n/a	False	0.95227	False	2.0	4.0	0.363636	2.0	visage, lieux	4.0	200.0	105	n/a	18.0	0.685518	9.0	4.0	5.0	70.0	13.0	10.0	False	True	False	False	2.5	False	15.0	1.11756	3.0	False
+S70	350.0	4.37116	n/a	n/a	False	-0.082708	3.0	False	4.0	n/a	1.0	15.0	0.923019	1.5	False	False	2.0	0.766667	3.8	500.0	n/a	10.0	False	12.5	9.0	0.833333	50.0	15.0	250.0	False	0.0	8.0	0.0	3.0	1.71511	20.0	2.0	10.0	0.840311	3.0	25.0	4.0	68.0	43.0	n/a	8.0	0.0	3.0	0	False	4.0	True	4.0	0.995391	6.66643	False	200.0	False	False	3.0	False	6.50608	n/a	15.0	100.0	30.0	200.0	400.0	500.0	d	20.0	300.0	2.0	False	0.0	True	1.0	False	5.0	False	2.0	0.832625	False	False	1.0	5.0	6.0	1.97093	3.33333	0	5.0	0.0	40.0	Odeurs, images : souvenirs	n/a	False	0.998561	False	0.0	3.0	0.2	1.0	Visages	5.0	70.0	187	n/a	16.0	1.95474	8.42361	5.0	5.0	40.0	14.0	50.0	False	True	True	False	3.0	False	15.0	1.0604	4.0	False
+S71	20.0	0.212617	n/a	n/a	False	-0.0629865	1.5	False	n/a	n/a	2.0	15.0	0.830302	1.55556	False	False	0.0	0.5	3.2	400.0	n/a	9.0	False	11.5	8.0	0.5	15.0	10.0	20.0	False	0.0	6.0	0.0	n/a	0.516706	40.0	1.0	7.0	0.767316	3.0	25.0	4.0	60.0	42.0	n/a	3.0	0.0	3.0	0	False	3.0	True	2.0	0.997657	0.325419	False	50.0	False	False	2.0	False	0.316461	n/a	10.0	20.0	15.0	20.0	30.0	40.0	u	10.0	20.0	1.0	False	0.0	True	2.0	False	n/a	False	2.0	0.968262	False	False	1.0	15.0	10.0	0.594702	n/a	0	5.0	0.0	50.0	n/a	n/a	False	0.97386	False	1.0	3.0	0.217391	1.0	Visages	2.0	60.0	159	n/a	7.0	0.588898	7.16667	6.0	3.0	100.0	10.0	100.0	False	True	True	False	2.0	False	14.0	1.05611	3.0	False
+S72	60.0	0.715723	1.0	1.0	False	-0.0551373	2.0	False	2.0	2.0	1.0	40.0	0.958627	1.33333	True	True	1.0	0.433333	3.6	450.0	1.0	15.0	False	17.5	7.0	0.416667	30.0	10.0	70.0	False	0.0	8.0	2.0	0.0	1.0041	30.0	1.0	7.0	0.90349	3.0	21.0	3.0	67.0	45.0	1.0	5.0	0.0	3.0	pbl de deglutition a9ans(suce son pouce)	False	3.0	True	3.0	0.98314	1.07017	False	50.0	False	False	1.0	False	1.06529	n/a	10.0	30.0	20.0	70.0	70.0	80.0	d	20.0	70.0	2.0	True	0.0	True	2.0	False	3.0	False	0.0	0.830847	False	False	1.0	20.0	14.0	1.14585	0.0	PHYSIONOMIE	5.0	3.0	60.0	0	n/a	True	1.03714	True	1.0	2.5	0.142857	1.0	ESPECES VEGETALES	3.0	110.0	329	n/a	10.0	1.14439	6.59722	8.0	2.0	50.0	10.0	20.0	False	True	True	False	5.0	True	20.0	0.841678	2.0	True
+S73	40.0	0.927448	1.0	1.0	False	-0.00182578	n/a	False	n/a	1.0	3.0	15.0	0.979823	1.90476	False	False	n/a	0.766667	3.2	300.0	0.0	8.4	False	12.2	8.0	0.916667	25.0	10.0	50.0	False	0.0	9.0	0.0	n/a	1.07666	60.0	n/a	10.0	0.977997	3.0	30.0	3.0	63.0	41.0	1.0	10.0	0.0	3.0	n/a	n/a	2.0	True	4.0	0.994564	1.36044	False	70.0	False	False	n/a	False	1.38042	n/a	10.0	30.0	20.0	70.0	80.0	110.0	n/a	18.0	90.0	4.0	False	0.0	True	3.0	False	n/a	False	2.0	0.979557	False	False	n/a	15.0	13.0	1.22344	n/a	espece animal	7.6	0.0	60.0	n/a	n/a	False	1.0435	False	1.0	1.0	0.311475	1.0	visage	3.0	60.0	365	n/a	n/a	1.22709	n/a	7.0	3.0	50.0	10.0	55.0	False	True	n/a	False	3.0	False	16.0	0.929294	3.0	False
+S74	30.0	0.382676	n/a	n/a	False	-0.0665277	2.5	True	4.0	n/a	1.0	40.0	0.920906	1.66667	False	True	1.0	0.8	3.6	250.0	n/a	9.0	False	12.0	12.0	0.833333	30.0	12.0	50.0	False	0.2	9.0	1.0	3.0	0.637316	20.0	1.0	10.0	0.854378	3.0	32.0	4.0	62.0	42.0	n/a	7.0	0.0	3.0	EXPRESSION ORALE	False	4.0	True	5.0	0.999392	0.562843	False	30.0	False	False	3.0	False	0.569578	n/a	15.0	30.0	20.0	50.0	50.0	50.0	U?	25.0	50.0	1.0	True	2.0	True	3.0	True	4.0	False	2.0	0.859567	False	False	1.0	10.0	10.0	0.720039	3.5	lieux	6.0	2.0	60.0	0	n/a	False	0.999466	True	0.0	0.0	0.25	1.0	Visages	5.0	50.0	207	n/a	11.0	0.726359	5.81944	4.0	5.0	50.0	10.0	30.0	False	True	True	False	2.5	True	15.0	0.819761	3.0	False
+S75	90.0	1.17674	1.0	1.0	False	-0.0322937	1.5	False	4.0	1.0	1.0	30.0	0.962962	1.5	False	False	1.0	0.966667	5.0	300.0	1.0	10.0	False	12.5	7.0	0.666667	40.0	10.0	80.0	False	0.2	10.0	0.0	5.0	1.06134	15.0	1.0	10.0	0.930669	3.0	32.0	4.0	64.0	43.0	1.0	10.0	0.0	3.0	0	False	3.0	True	3.0	0.999677	1.77613	False	40.0	False	False	3.0	False	1.75148	n/a	20.0	50.0	30.0	80.0	120.0	150.0	u	25.0	100.0	3.0	False	0.0	True	1.0	False	5.0	False	2.0	0.843897	False	False	0.0	7.0	10.0	1.2123	4.0	n/a	5.0	0.0	60.0	0	n/a	False	1.00794	False	2.0	3.5	0.2	2.0	LIEUX VISAGES	3.0	60.0	319	n/a	7.0	1.20962	7.72222	6.0	5.0	70.0	13.0	120.0	False	True	True	True	3.0	False	15.0	0.945441	2.0	False
+S76	50.0	0.611865	n/a	n/a	False	-0.0818007	3.5	False	4.0	n/a	1.0	20.0	0.921049	1.66667	False	False	2.0	0.833333	2.6	500.0	n/a	9.0	False	12.0	7.0	0.25	30.0	10.0	35.0	False	0.0	10.0	0.0	3.0	0.873615	150.0	2.0	10.0	0.839249	3.0	33.0	4.0	66.0	45.0	n/a	6.0	0.0	3.0	0	False	4.0	True	3.0	0.999685	0.93416	False	200.0	False	False	2.0	True	0.910705	n/a	10.0	30.0	20.0	40.0	70.0	90.0	u	20.0	50.0	3.0	False	0.0	True	3.0	False	3.0	False	2.0	0.825401	False	True	1.0	50.0	15.0	1.0031	3.33333	0	6.0	0.0	30.0	Couleur /  mots	n/a	False	1.02149	False	2.0	3.5	0.25	2.0	visages, lieux	3.0	200.0	172	n/a	17.0	0.995672	7.95833	6.0	3.0	65.0	10.0	100.0	False	True	False	False	2.0	False	15.0	1.09488	3.0	False
+S77	45.0	0.689299	n/a	n/a	False	-0.0180914	1.0	False	5.0	n/a	2.0	10.0	0.968552	2.11111	True	True	0.0	0.966667	3.0	500.0	n/a	9.0	False	14.0	11.0	0.416667	20.0	10.0	40.0	False	0.0	9.0	0.0	4.0	0.981267	15.0	1.0	10.0	0.950461	3.0	30.0	4.0	64.0	43.0	n/a	10.0	n/a	3.0	n/a	False	4.0	True	3.0	0.999983	1.01458	False	500.0	False	False	3.0	False	1.02596	n/a	10.0	30.0	20.0	70.0	60.0	80.0	u	15.0	70.0	2.0	False	1.0	True	3.0	False	5.0	False	2.0	0.671399	False	False	1.0	7.0	10.0	1.11533	4.33333	lieux	10.0	3.0	60.0	0	n/a	False	1.00274	False	1.0	0.0	0.357143	2.0	visage	4.0	700.0	115	n/a	6.0	1.11837	4.65972	5.0	4.0	80.0	15.0	20.0	False	True	True	False	2.0	True	19.0	1.19359	2.0	False
+S78	40.0	0.548867	n/a	n/a	False	-0.0193456	3.0	False	4.0	n/a	2.0	25.0	0.950317	1.7	False	False	2.0	0.7	3.8	600.0	n/a	10.0	False	13.5	15.0	0.916667	40.0	10.0	50.0	False	0.0	10.0	0.0	3.0	0.813199	60.0	1.0	10.0	0.930971	3.0	30.0	4.0	62.0	41.0	1.0	7.0	2.0	3.0	0	False	2.0	True	3.0	0.999859	0.815318	False	45.0	False	False	2.0	False	0.816939	n/a	15.0	30.0	20.0	60.0	60.0	70.0	u	20.0	60.0	2.0	False	0.0	True	2.0	False	4.0	False	2.0	0.905331	False	False	1.0	10.0	15.0	0.925195	2.0	n/a	7.0	0.0	50.0	0	n/a	False	0.991229	False	1.0	2.5	0.259259	1.0	VISAGES	1.0	30.0	328	n/a	14.0	0.926816	6.74306	9.0	2.0	60.0	16.0	112.0	False	True	False	False	3.0	False	17.0	0.960007	n/a	False
+S79	42.0	0.449873	n/a	0.0	False	0.00970278	4.0	False	3.0	n/a	1.0	12.0	0.905277	1.4	False	False	2.0	0.566667	1.8	350.0	n/a	10.0	False	12.0	7.0	0.666667	28.0	16.0	30.0	False	0.25	9.0	0.0	1.0	0.603731	30.0	2.0	9.0	0.914979	3.0	27.0	4.0	68.0	45.0	n/a	6.0	0.0	3.0	0	False	4.0	True	1.0	0.997786	0.671734	False	100.0	False	True	1.0	False	0.669595	n/a	18.0	28.0	21.0	50.0	59.0	63.0	d	22.0	55.0	3.0	False	0.0	True	3.0	True	5.0	False	1.0	0.917245	False	False	1.0	20.0	12.0	0.693804	1.66667	Mettre nom sur un visage	4.0	0.0	70.0	Chiffres ds espace	n/a	False	1.01049	False	1.0	3.5	0.166667	3.0	tout	1.0	80.0	198	n/a	18.0	0.688082	8.22222	4.0	1.0	50.0	12.0	100.0	False	False	False	False	2.0	False	14.0	1.03246	3.0	False
+S80	48.0	0.634019	n/a	n/a	False	-0.00810036	2.5	False	4.0	n/a	2.0	30.0	0.948885	1.5	False	True	1.0	0.733333	2.6	530.0	n/a	10.0	False	12.5	13.0	0.5	31.0	8.0	41.0	False	0.0	7.0	0.0	2.0	0.91701	250.0	0.0	10.0	0.940784	3.0	25.0	4.0	62.0	43.0	n/a	10.0	0.0	3.0	0	False	3.0	True	3.0	0.996996	0.946805	False	20.0	False	False	2.0	False	0.94368	n/a	12.0	35.0	34.0	56.0	68.0	86.0	u	19.0	63.0	2.0	False	1.0	True	1.0	False	5.0	False	2.0	0.798489	False	False	1.0	6.0	18.0	1.03757	3.33333	0	5.0	0.0	62.0	0	n/a	False	0.982516	False	2.0	2.0	0.2	1.0	visages, lieux	3.0	45.0	180	n/a	9.0	1.04513	6.375	8.0	3.0	75.0	10.0	50.0	False	True	True	False	3.0	False	15.0	0.987773	3.0	False
+S81	n/a	n/a	n/a	n/a	False	n/a	n/a	False	n/a	n/a	n/a	15.0	n/a	n/a	n/a	n/a	n/a	n/a	n/a	500.0	n/a	n/a	False	n/a	8.0	n/a	n/a	n/a	n/a	False	n/a	n/a	n/a	n/a	n/a	40.0	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	False	40.0	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	1.0	0.979913	False	n/a	n/a	10.0	15.0	n/a	n/a	n/a	n/a	n/a	55.0	n/a	n/a	n/a	n/a	n/a	1.0	n/a	n/a	n/a	n/a	n/a	50.0	217	n/a	n/a	n/a	n/a	n/a	n/a	90.0	n/a	60.0	n/a	n/a	n/a	n/a	3.0	n/a	n/a	1.00486	n/a	False
+S82	55.0	0.611077	n/a	n/a	False	-0.0475203	3.5	False	3.0	n/a	1.0	15.0	0.963192	2.0	True	True	1.0	0.85	2.4	1000.0	n/a	10.0	False	15.0	8.0	0.583333	25.0	11.0	45.0	False	0.33	10.0	0.0	3.0	0.877608	15.0	2.0	10.0	0.915672	3.0	30.0	3.0	66.0	43.0	n/a	7.0	0.0	1.0	0	False	3.0	True	3.0	0.994773	0.92664	False	25.0	False	False	2.0	False	0.909533	n/a	12.0	30.0	20.0	50.0	68.0	80.0	u	17.0	55.0	2.0	False	0.0	True	2.0	False	4.0	False	2.0	0.745845	False	False	1.0	8.0	12.0	1.0073	3.0	lieux	10.0	2.0	50.0	0	n/a	False	1.05757	False	0.0	2.0	0.333333	2.0	Visages	3.0	20.0	195	n/a	15.0	1.00022	5.27778	4.0	4.0	50.0	13.0	6.0	False	True	False	False	2.0	False	20.0	1.00696	2.0	False
+S83	55.0	0.752174	n/a	n/a	False	-0.0102645	3.0	False	3.0	n/a	1.0	7.0	0.983114	1.25	False	True	1.0	1.0	3.4	600.0	n/a	12.0	False	13.5	8.0	0.75	30.0	10.0	60.0	False	0.0	10.0	0.0	3.0	0.946814	15.0	2.0	10.0	0.972849	3.0	35.0	3.0	65.0	43.0	n/a	10.0	0.0	3.0	0	False	3.0	True	4.0	0.992436	1.10866	False	100.0	False	False	3.0	False	1.11954	n/a	13.0	40.0	25.0	75.0	70.0	90.0	d	25.0	80.0	4.0	False	0.0	True	1.0	False	4.0	False	2.0	0.777745	False	False	1.0	8.0	12.0	1.07195	2.66667	lieux	3.0	0.0	50.0	0	n/a	False	1.06735	False	0.0	1.0	0.111111	3.0	Visages	4.0	30.0	184	n/a	14.0	1.0791	6.4375	5.0	4.0	60.0	11.0	50.0	False	True	False	False	8.0	False	15.0	0.904109	3.0	False
+S84	70.0	0.918418	n/a	n/a	False	0.053325	1.0	False	4.0	n/a	1.0	7.0	0.888236	1.54545	False	False	1.0	0.8	3.4	450.0	n/a	11.0	False	14.0	11.0	0.75	40.0	12.0	75.0	False	0.25	10.0	0.0	3.0	1.15816	80.0	1.0	10.0	0.941561	3.0	33.0	5.0	63.0	42.0	n/a	9.0	0.0	3.0	0	False	4.0	True	3.0	0.995588	1.37754	False	70.0	False	False	3.0	False	1.36698	n/a	5.0	40.0	35.0	70.0	95.0	110.0	d	20.0	85.0	4.0	False	0.0	True	0.0	False	4.0	False	2.0	0.818761	False	False	1.0	5.0	14.0	1.3146	3.33333	0	6.0	0.0	45.0	0	n/a	False	0.966338	False	2.0	2.5	0.214286	2.0	visages, lieux	3.0	25.0	193	n/a	8.0	1.31998	7.10417	5.0	3.0	60.0	10.0	9.0	False	True	True	False	1.5	False	17.0	1.05465	2.0	False
+S85	25.0	0.278677	n/a	n/a	False	-0.022051	3.0	False	4.0	n/a	2.0	20.0	0.94183	2.0	False	False	1.0	0.6	4.0	500.0	0.0	9.0	False	13.5	9.0	0.833333	25.0	10.0	30.0	False	0.33	8.0	0.0	2.0	0.562786	60.0	2.0	9.0	0.919779	3.0	30.0	5.0	60.0	40.0	n/a	9.0	0.0	3.0	0	False	3.0	True	3.0	0.995747	0.400515	False	80.0	False	False	2.0	False	0.414786	n/a	15.0	25.0	20.0	30.0	35.0	40.0	u	15.0	45.0	4.0	False	0.0	True	3.0	False	4.0	False	2.0	0.962147	False	False	1.0	10.0	12.0	0.631332	3.0	animaux	9.0	0.0	65.0	0	n/a	False	0.944038	False	1.0	n/a	0.333333	2.0	Visages	3.0	50.0	169	n/a	14.0	0.641416	5.56944	6.0	4.0	60.0	13.0	30.0	False	True	n/a	False	2.0	False	18.0	1.04717	4.0	False
+S86	40.0	0.780401	1.0	1.0	False	-0.0458562	1.5	False	4.0	1.0	1.0	50.0	0.878883	1.75	False	True	0.0	0.5	3.4	750.0	1.0	10.0	False	13.75	9.0	0.416667	40.0	12.0	90.0	True	0.0	9.0	3.0	3.0	0.993049	90.0	2.0	10.0	0.833027	3.0	32.0	4.0	63.0	45.0	1.0	6.0	0.0	3.0	dyslexie, confusion che/jeu, f/v	False	4.0	True	4.0	0.999649	1.14484	True	30.0	False	False	2.0	False	1.16156	n/a	10.0	20.0	28.0	70.0	65.0	90.0	u	15.0	80.0	4.0	True	0.0	True	2.0	True	4.0	True	1.0	0.89167	False	False	1.0	12.0	11.0	1.13006	0.0	n/a	7.5	0.0	60.0	0	n/a	False	1.01092	True	1.0	1.5	0.272727	1.0	n/a	2.0	80.0	346	n/a	9.0	1.13179	6.26389	6.0	4.0	70.0	13.0	30.0	False	True	n/a	True	2.5	False	17.5	1.04276	3.0	False
+S87	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	369	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
+S88	45.0	0.525943	n/a	0.0	False	-0.0442923	0.0	False	4.0	n/a	1.0	35.0	0.958444	1.875	False	False	0.0	0.6	3.6	500.0	n/a	8.0	True	11.5	10.0	0.75	25.0	10.0	40.0	False	0.5	8.0	2.0	2.0	0.825556	60.0	1.0	10.0	0.914152	3.0	32.0	5.0	60.0	42.0	n/a	8.0	0.0	3.0	APPRENTISSAGE LECTURE, math	False	3.0	True	3.0	0.995658	0.77895	False	100.0	True	True	1.0	False	0.782818	n/a	15.0	35.0	20.0	60.0	60.0	60.0	d	15.0	60.0	3.0	True	1.0	True	0.0	False	3.0	False	2.0	0.860257	False	True	0.0	20.0	15.0	0.93793	2.33333	noms	7.0	0.0	60.0	nombre / espace, gouts et sons	n/a	False	0.955381	True	2.0	3.0	0.304348	1.0	visages, lieux	1.0	150.0	178	n/a	2.0	0.940899	7.375	4.0	1.0	80.0	10.0	400.0	False	True	True	False	3.0	False	15.0	1.05283	2.0	False
+S89	70.0	1.09973	n/a	n/a	False	-0.0425949	1.0	False	5.0	n/a	3.0	40.0	0.927972	2.55556	False	True	0.0	0.666667	3.0	200.0	n/a	9.0	False	16.0	20.0	0.583333	40.0	11.0	60.0	False	0.0	10.0	0.0	3.0	1.01285	150.0	0.0	10.0	0.885377	3.0	24.0	3.0	62.0	38.0	n/a	7.0	0.0	2.0	0	True	4.0	True	3.0	0.992471	1.64304	False	40.0	False	False	3.0	False	1.63685	n/a	15.0	30.0	25.0	50.0	100.0	150.0	d	25.0	100.0	2.0	False	0.0	True	1.0	False	5.0	False	2.0	0.00909366	False	True	0.0	15.0	10.0	1.16136	4.0	LIEUX	14.0	0.0	5.0	0	n/a	False	1.01316	False	0.0	0.0	0.4375	1.0	VISAGE	5.0	1000.0	289	n/a	2.0	1.15436	3.38194	9.0	3.0	50.0	18.0	300.0	False	True	True	False	1000.0	False	23.0	0.126279	2.0	False
+S90	80.0	1.19538	n/a	n/a	False	-0.00869327	3.0	False	2.0	n/a	3.0	15.0	0.894658	1.71429	False	False	2.0	0.7	2.8	400.0	n/a	7.0	False	9.5	7.0	0.666667	60.0	10.0	40.0	False	0.33	10.0	1.0	1.0	1.11558	20.0	2.0	10.0	0.885965	3.0	22.0	5.0	62.0	45.0	n/a	7.0	0.0	3.0	en histoire	False	4.0	True	2.0	0.983444	1.75143	False	30.0	True	False	1.0	False	1.77922	n/a	15.0	60.0	30.0	80.0	100.0	150.0	d	15.0	130.0	2.0	True	1.0	True	0.0	False	4.0	False	1.0	0.905508	False	False	1.0	8.0	10.0	1.26396	1.66667	lieux	5.0	0.0	60.0	n/a	n/a	False	0.939536	False	0.0	1.5	0.263158	1.0	0	3.0	20.0	213	n/a	16.0	1.27144	5.84722	10.0	2.0	80.0	10.0	30.0	False	True	True	False	2.5	False	12.0	0.953995	2.0	False
+S91	50.0	0.611427	1.0	1.0	False	-0.00681096	3.5	False	3.0	1.0	2.0	20.0	0.966239	1.6	False	False	2.0	0.9	4.2	500.0	1.0	10.0	False	13.0	7.0	0.833333	20.0	10.0	50.0	False	0.0	10.0	0.0	3.0	0.920033	40.0	2.0	10.0	0.959428	3.0	23.0	5.0	61.0	43.0	1.0	10.0	0.0	3.0	deglutition a 15ans pendant 4mois	False	4.0	True	4.0	0.987777	0.899697	False	120.0	False	False	2.0	False	0.910054	n/a	10.0	30.0	20.0	50.0	60.0	70.0	u	20.0	70.0	2.0	False	0.0	True	1.0	False	4.0	False	1.0	n/a	False	False	1.0	15.0	10.0	1.04483	2.0	n/a	6.0	0.0	60.0	0	n/a	False	0.934578	True	2.0	3.0	0.230769	2.0	LIEUX	4.0	60.0	334	n/a	17.0	1.04858	8.25694	7.0	3.0	n/a	10.0	140.0	False	True	False	True	2.0	False	16.0	n/a	4.0	False
+S92	38.0	0.475912	n/a	n/a	False	-0.0850252	1.5	False	5.0	n/a	1.0	6.0	0.785702	1.44444	False	True	1.0	0.8	3.2	500.0	n/a	9.0	False	11.0	9.0	0.75	25.0	12.0	29.0	False	0.0	10.0	0.0	3.0	0.696258	120.0	1.0	9.0	0.700677	3.0	32.0	5.0	65.0	43.0	n/a	8.0	2.0	3.0	0	False	4.0	True	4.0	0.99624	0.744678	False	200.0	False	False	3.0	False	0.708352	n/a	15.0	25.0	24.0	44.0	66.0	77.0	u	12.0	29.0	4.0	False	0.0	True	n/a	False	5.0	False	1.0	0.815552	False	True	1.0	15.0	12.0	0.810939	3.66667	0	4.0	0.0	52.0	Suites ds espaCe et couleurs	n/a	False	0.972245	False	1.0	2.0	0.181818	2.0	0	5.0	20.0	196	n/a	9.0	0.793536	6.95833	6.0	5.0	80.0	10.0	50.0	False	True	False	False	3.0	False	13.0	1.04952	3.0	False
+S93	50.0	0.717351	n/a	0.0	False	-0.0384674	3.5	False	3.0	n/a	1.0	20.0	0.959921	2.08333	False	False	1.0	1.0	4.8	280.0	n/a	12.0	False	18.5	8.0	0.916667	40.0	12.0	70.0	False	0.0	10.0	0.0	3.0	0.856346	30.0	1.0	10.0	0.921454	3.0	35.0	4.0	60.0	43.0	n/a	10.0	0.0	3.0	0	False	3.0	True	4.0	0.997048	1.0501	False	30.0	False	False	3.0	False	1.06771	n/a	15.0	40.0	30.0	50.0	60.0	100.0	d	20.0	80.0	4.0	False	0.0	True	1.0	False	4.0	False	1.0	0.907422	False	False	1.0	10.0	12.0	0.967146	3.0	NOMS	13.0	0.0	50.0	NUM DE TELEPHONE 	n/a	False	1.00396	False	2.0	3.0	0.351351	1.0	LIEUX CHIFFRES	4.0	120.0	343	n/a	13.0	0.975992	8.03472	5.0	5.0	50.0	12.0	100.0	False	True	n/a	False	2.0	False	25.0	0.980029	2.0	False
+S94	53.0	0.913518	n/a	n/a	False	-0.0127455	4.0	False	3.0	n/a	1.0	17.0	0.992355	2.1	False	False	1.0	0.9	4.7	250.0	n/a	10.0	False	15.5	6.5	1.0	30.0	10.0	60.0	False	0.0	9.0	0.0	3.0	1.03356	50.0	2.0	10.0	0.97961	3.0	36.0	5.0	64.0	43.0	n/a	9.0	0.0	3.0	0	False	1.0	True	5.0	0.994238	1.35131	False	65.0	False	False	3.0	False	1.35969	n/a	13.0	38.0	25.0	60.0	90.0	110.0	d	18.0	90.0	4.0	False	0.0	True	5.0	False	4.0	False	1.0	0.937513	False	False	1.0	15.0	15.0	1.17454	3.33333	0	11.0	0.0	50.0	nombre / espace	n/a	False	0.979051	False	1.0	4.0	0.354839	3.0	0	4.0	55.0	189	n/a	16.0	1.17797	9.66667	4.0	4.0	60.0	10.0	120.0	False	True	n/a	False	2.75	False	21.0	0.933082	4.0	False

phenotype/subject.tsv

@@ -0,0 +1,95 @@
+participant_id	localizer_short_easy	protocol	scanner	family	motor_error	laterality	site	sex	schizophrenic	id	dyslexic	father	comments	tr	excluded	basic	localizer_long_complex	type	sound_problem	epi_problem	statistics_spm5	exam	localizer_short_complex	anatomy	dyscalculic	language	localizer_long_easy	age	video_problem	synaesthete	mother	acquisition
+S01	n/a	UNICOG	3	F01	n/a	Right handed	SHFJ	M	n/a	404	n/a	0	Localizer not done	n/a	n/a	True	n/a	Basic	n/a	n/a	n/a	S01	n/a	n/a	n/a	French	n/a	24	n/a	n/a	0	0
+S02	False	UNICOG	3	F02	n/a	Right handed	SHFJ	M	n/a	191	n/a	0	son a 50% audible pour les histoires	2.4	False	True	False	Basic	True	n/a	True	S02	True	n/a	n/a	French	False	20	n/a	n/a	0	1
+S03	False	UNICOG	3	F03	n/a	Right handed	SHFJ	F	n/a	228	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S03	True	n/a	n/a	French	False	22	n/a	n/a	0	1
+S04	False	UNICOG	3	F04	False	Right handed	Neurospin	M	n/a	320	n/a	0	Substractions difficulties	2.4	False	True	False	Basic	False	n/a	True	S04	True	Ok	n/a	French	False	20	True	n/a	0	1
+S05	False	UNICOG	3	F05	False	Right handed	Neurospin	F	n/a	341	n/a	0	Substractions were difficult for him/her	2.4	n/a	True	False	Basic	False	n/a	n/a	S05	True	Ok	n/a	French	False	23	False	n/a	0	1
+S06	False	UNICOG	3	F06	True	Right handed	Neurospin	M	n/a	311	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S06	True	Ok	n/a	French	False	19	True	n/a	0	1
+S07	False	UNICOG	3	F07	True	Right handed	Neurospin	F	n/a	316	n/a	0	Substractions difficulties, pushing mistake 1 time	2.4	False	True	False	Basic	False	n/a	True	S07	True	Ok	n/a	French	False	26	True	n/a	0	1
+S08	False	UNICOG	3	F89	True	Right handed	Neurospin	M	n/a	347	n/a	0	Rare pushing mistakes	2.4	n/a	True	False	Basic	False	n/a	n/a	S08	True	Ok	n/a	French	False	27	False	n/a	0	1
+S09	False	UNICOG	3	F09	False	Right handed	Neurospin	F	n/a	326	n/a	0	Right side more activated in audio contrast	2.4	False	True	False	Basic	False	n/a	n/a	S09	True	Ok	n/a	French	False	21	False	n/a	0	1
+S10	False	UNICOG	3	F10	n/a	Right handed	SHFJ	F	n/a	176	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S10	True	n/a	n/a	French	False	24	n/a	n/a	0	1
+S11	False	UNICOG	3	F11	n/a	Right handed	SHFJ	F	n/a	193	n/a	0	son trop bas, plusieurs pharses inaudibles	2.4	False	True	False	Basic	True	n/a	True	S11	True	n/a	n/a	French	False	47	n/a	True	0	1
+S12	False	UNICOG	3	F12	n/a	Right handed	SHFJ	F	n/a	201	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S12	True	n/a	n/a	French	False	23	n/a	n/a	0	1
+S13	False	UNICOG	3	F13	False	Right handed	Neurospin	M	n/a	365	n/a	0	n/a	2.4	True	True	False	Basic	False	n/a	True	S13	True	Ok	n/a	French	False	19	False	n/a	0	1
+S14	False	UNICOG	3	F14	n/a	Right handed	SHFJ	F	n/a	247	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S14	True	n/a	n/a	French	False	21	n/a	n/a	0	1
+S15	False	UNICOG	3	F15	n/a	Right handed	SHFJ	F	n/a	11	n/a	0	6 sessions+seq.Fovea	2.4	False	True	False	Basic	n/a	n/a	True	S15	True	ok	n/a	French	False	22	n/a	n/a	0	1
+S16	False	UNICOG	3	F16	n/a	Right handed	SHFJ	F	n/a	218	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S16	True	n/a	n/a	French	False	24	n/a	n/a	0	1
+S17	n/a	UNICOG	3	F17	n/a	Right handed	SHFJ	M	n/a	351	n/a	0	Localizer not done	n/a	n/a	True	n/a	Basic	n/a	n/a	n/a	S17	n/a	n/a	n/a	French	n/a	22	n/a	n/a	0	0
+S18	False	UNICOG	3	F18	True	Right handed	Neurospin	F	n/a	310	n/a	0	Sound too high, substractions difficulties, pushing errors	2.4	False	True	False	Basic	True	n/a	True	S18	True	Ok	n/a	French	False	20	True	n/a	0	1
+S19	False	UNICOG	3	F52	n/a	Right handed	SHFJ	M	n/a	204	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S19	True	n/a	n/a	French	False	18	n/a	n/a	0	1
+S20	False	UNICOG	3	F20	n/a	Right handed	SHFJ	F	n/a	179	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S20	True	n/a	n/a	French	False	25	n/a	n/a	0	1
+S21	False	UNICOG	3	F21	n/a	Right handed	SHFJ	M	n/a	150	n/a	0	meme que S21 et <sanitized>	2.4	False	True	False	Basic	n/a	n/a	True	S21	True	n/a	n/a	French	False	21	n/a	n/a	0	3
+S22	False	UNICOG	3	F22	n/a	Right handed	SHFJ	M	n/a	209	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S22	True	n/a	n/a	French	False	40	n/a	n/a	0	1
+S23	False	UNICOG	3	F23	False	Right handed	Neurospin	F	n/a	334	n/a	0	Substrations difficulties	2.4	False	True	False	Basic	False	n/a	n/a	S23	True	Ok	n/a	French	False	24	True	n/a	0	1
+S24	False	UNICOG	3	F24	False	Right handed	Neurospin	F	n/a	323	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	n/a	S24	True	Ok	n/a	French	False	18	False	n/a	0	1
+S25	False	UNICOG	3	F25	n/a	Right handed	SHFJ	F	n/a	202	n/a	0	meme que S25	2.4	False	True	False	Basic	False	n/a	True	S25	True	n/a	n/a	French	False	24	n/a	n/a	0	2
+S26	False	UNICOG	3	F26	False	Right handed	Neurospin	F	n/a	349	n/a	0	Some substraction errors. Voxel size (localizer) : 2x2x3 mm^3	2.4	n/a	True	False	Basic	False	n/a	True	S26	True	axial+artefacts	n/a	French	False	24	True	n/a	0	1
+S27	False	UNICOG	3	F27	False	Right handed	Neurospin	M	n/a	330	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	n/a	S27	True	Ok	n/a	French	False	30	False	n/a	0	1
+S28	False	UNICOG	3	F28	True	Right handed	Neurospin	F	n/a	345	n/a	0	Pushing mistakes 3 times	2.4	n/a	True	False	Basic	False	n/a	n/a	S28	True	Ok	n/a	French	False	18	False	n/a	0	1
+S29	False	UNICOG	3	F29	False	Right handed	Neurospin	F	n/a	339	n/a	0	Weak activation in Checkboard contrast	2.4	False	True	False	Basic	False	n/a	n/a	S29	True	Ok	n/a	French	False	21	False	n/a	0	1
+S30	False	UNICOG	3	F30	n/a	Right handed	SHFJ	F	n/a	194	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S30	True	n/a	n/a	French	False	34	n/a	n/a	0	1
+S31	False	UNICOG	3	F31	False	Right handed	Neurospin	F	n/a	344	n/a	0	n/a	2.4	n/a	True	False	Basic	False	n/a	n/a	S31	True	Ok	n/a	French	False	22	False	n/a	0	1
+S32	False	UNICOG	3	F32	n/a	Right handed	SHFJ	F	n/a	206	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S32	True	n/a	n/a	French	False	26	n/a	n/a	0	1
+S33	False	UNICOG	3	F33	n/a	Right handed	SHFJ	F	n/a	240	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S33	True	n/a	n/a	French	False	21	n/a	n/a	0	1
+S34	False	UNICOG	3	F34	n/a	Right handed	SHFJ	M	n/a	214	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S34	True	n/a	n/a	French	False	21	n/a	n/a	0	1
+S35	False	UNICOG	3	F35	n/a	Right handed	SHFJ	F	n/a	246	n/a	0	meme que S35 - reglage shim mauvais selon Antoinette	2.4	False	True	False	Basic	False	True	True	S35	True	n/a	n/a	French	False	24	n/a	n/a	0	2
+S36	False	UNICOG	3	F36	n/a	Right handed	SHFJ	F	n/a	224	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S36	True	n/a	n/a	French	False	25	n/a	n/a	0	1
+S37	False	UNICOG	3	F37	False	Right handed	Neurospin	M	n/a	362	n/a	0	n/a	2.4	False	True	False	Basic	False	False	True	S37	True	Ok	n/a	French	False	22	False	n/a	0	1
+S38	False	UNICOG	3	F38	n/a	Right handed	SHFJ	M	n/a	213	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S38	True	n/a	n/a	French	False	n/a	n/a	n/a	0	1
+S39	False	UNICOG	3	F39	n/a	Right handed	SHFJ	M	n/a	243	n/a	0	meme que S39	2.4	False	True	False	Basic	False	n/a	True	S39	True	n/a	n/a	French	False	22	n/a	n/a	0	2
+S40	False	UNICOG	3	F40	n/a	Right handed	SHFJ	F	n/a	221	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S40	True	n/a	n/a	French	False	24	n/a	True	0	1
+S41	False	UNICOG	3	F41	n/a	Right handed	Neurospin	M	n/a	738	n/a	0	n/a	2.4	n/a	True	False	Basic	n/a	n/a	True	S41	True	n/a	n/a	French	False	18	n/a	n/a	0	4
+S42	False	UNICOG	3	F42	False	Right handed	Neurospin	M	n/a	318	n/a	0	Problemes de normalisation des images (centre des images trop loin de l'axe CA-CP). Resolu a la main	2.4	False	True	False	Basic	False	n/a	True	S42	True	Ok	n/a	French	False	26	False	n/a	0	1
+S43	False	UNICOG	3	F43	False	Right handed	Neurospin	M	n/a	358	n/a	0	Sound problems (head phones). Voxel size (localizer) : 2x2x3 mm^3	2.4	n/a	True	False	Basic	True	n/a	True	S43	True	n/a	n/a	French	False	49	False	n/a	0	1
+S44	False	UNICOG	3	F44	n/a	Right handed	SHFJ	F	n/a	203	n/a	0	meme que <sanitized>	2.4	False	True	False	Basic	False	n/a	True	S44	True	n/a	n/a	French	False	34	n/a	n/a	0	2
+S45	False	UNICOG	3	F45	n/a	Right handed	SHFJ	M	n/a	184	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S45	True	n/a	n/a	French	False	21	n/a	n/a	0	1
+S46	False	UNICOG	3	F46	False	Right handed	Neurospin	F	n/a	315	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S46	True	Ok	n/a	French	False	24	False	n/a	0	1
+S47	False	UNICOG	3	F47	n/a	Right handed	SHFJ	M	n/a	244	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S47	True	n/a	n/a	French	False	19	n/a	n/a	0	1
+S48	False	UNICOG	3	F48	n/a	Right handed	SHFJ	F	n/a	182	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S48	True	n/a	n/a	French	False	21	n/a	n/a	0	1
+S49	False	UNICOG	3	F49	n/a	Right handed	SHFJ	F	n/a	186	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S49	True	n/a	n/a	French	False	22	n/a	n/a	0	1
+S50	False	UNICOG	3	F50	n/a	Right handed	SHFJ	F	n/a	352	n/a	0	Localizer not done	2.4	n/a	True	False	Basic	n/a	n/a	n/a	S50	False	n/a	n/a	French	False	32	n/a	n/a	0	2
+S51	False	UNICOG	3	F51	n/a	Right handed	SHFJ	M	n/a	189	n/a	0	son trop bas, plusieurs pharses inaudibles	2.4	False	True	False	Basic	True	n/a	True	S51	True	n/a	n/a	French	False	30	n/a	n/a	0	1
+S52	False	UNICOG	3	F52	n/a	Right handed	SHFJ	F	n/a	230	n/a	0	anat avec un echo lumineux	2.4	False	True	False	Basic	False	n/a	True	S52	True	n/a	n/a	French	False	22	n/a	n/a	0	1
+S53	False	UNICOG	3	F53	False	Right handed	Neurospin	F	n/a	340	n/a	0	Little difficulty to read the sentences ?	2.4	n/a	True	False	Basic	False	n/a	True	S53	True	Ok	n/a	French	False	18	False	n/a	0	1
+S54	False	UNICOG	3	F54	n/a	Right handed	SHFJ	F	n/a	197	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S54	True	n/a	n/a	French	False	20	n/a	n/a	0	1
+S55	False	UNICOG	3	F55	False	Right handed	Neurospin	F	n/a	342	n/a	0	Substractions were difficult for him/her	2.4	n/a	True	False	Basic	False	n/a	n/a	S55	True	Ok	n/a	French	False	21	False	n/a	0	1
+S56	False	UNICOG	3	F56	False	Right handed	Neurospin	M	n/a	336	n/a	0	Acquisition of anatomy in axial (pilote, the rest are in sagittal)	2.4	False	True	False	Basic	False	n/a	n/a	S56	True	Ok	n/a	French	False	23	True	n/a	0	4
+S57	False	UNICOG	3	F57	n/a	Right handed	SHFJ	F	n/a	199	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S57	True	n/a	n/a	French	False	20	n/a	n/a	0	1
+S58	False	UNICOG	3	F58	n/a	Right handed	SHFJ	M	n/a	225	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S58	True	n/a	n/a	French	False	34	n/a	n/a	0	1
+S59	False	UNICOG	3	F59	n/a	Right handed	SHFJ	M	n/a	227	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S59	True	n/a	n/a	French	False	30	n/a	n/a	0	1
+S60	n/a	UNICOG	3	F60	n/a	Right handed	Neurospin	M	n/a	357	n/a	0	Localizer not done	n/a	n/a	True	n/a	Basic	n/a	n/a	n/a	S60	n/a	n/a	n/a	French	n/a	21	n/a	n/a	0	0
+S61	False	UNICOG	3	F61	n/a	Right handed	SHFJ	M	n/a	223	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S61	True	n/a	n/a	French	False	23	n/a	n/a	0	1
+S62	False	UNICOG	3	F62	n/a	Right handed	SHFJ	F	n/a	211	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S62	True	n/a	n/a	French	False	n/a	n/a	True	0	1
+S63	False	UNICOG	3	F63	True	Right handed	Neurospin	F	n/a	312	n/a	0	Pushing mistakes 2 or 3 times	2.4	False	True	False	Basic	False	n/a	True	S63	True	Ok	n/a	French	False	19	False	n/a	0	1
+S64	False	UNICOG	3	F64	False	Right handed	Neurospin	F	n/a	343	n/a	0	Substractions were difficult for him/her	2.4	n/a	True	False	Basic	False	n/a	n/a	S64	True	Ok	n/a	French	False	21	False	n/a	0	1
+S65	False	UNICOG	3	F65	True	Right handed	Neurospin	M	n/a	314	n/a	0	Pushing mistakes 3 times	2.4	False	True	False	Basic	False	n/a	True	S65	True	Ok	n/a	French	False	19	False	n/a	0	1
+S66	False	UNICOG	3	F66	True	Right handed	Neurospin	M	n/a	366	n/a	0	Pushing mistakes 1 or 2 times. Voxel size (localizer) : 2x2x2 mm^3	2.4	n/a	True	False	Basic	False	n/a	True	S66	True	axial+artefacts	n/a	French	False	26	False	n/a	0	1
+S67	False	UNICOG	3	F67	n/a	Right handed	SHFJ	F	n/a	239	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S67	True	n/a	n/a	French	False	44	n/a	True	0	1
+S68	False	UNICOG	3	F68	n/a	Right handed	SHFJ	M	n/a	245	n/a	0	meme que S68	2.4	False	True	False	Basic	False	n/a	True	S68	True	n/a	n/a	French	False	20	n/a	n/a	0	3
+S69	n/a	UNICOG	3	F69	n/a	Right handed	SHFJ	F	n/a	353	n/a	0	Localizer not done	n/a	n/a	True	n/a	Basic	n/a	n/a	n/a	S69	n/a	n/a	n/a	French	n/a	21	n/a	n/a	0	0
+S70	False	UNICOG	3	F59	n/a	Right handed	SHFJ	M	n/a	208	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S70	True	n/a	n/a	French	False	25	n/a	n/a	0	1
+S71	False	UNICOG	3	F71	n/a	Right handed	SHFJ	M	n/a	185	n/a	0	meme que <sanitized>	2.4	False	True	False	Basic	False	n/a	True	S71	True	n/a	n/a	French	False	21	n/a	n/a	0	2
+S72	False	UNICOG	3	F72	False	Right handed	Neurospin	F	n/a	325	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	n/a	S72	True	Ok	n/a	French	False	37	False	n/a	0	1
+S73	False	UNICOG	3	F73	False	Right handed	Neurospin	M	n/a	348	n/a	0	n/a	2.4	n/a	True	False	Basic	False	n/a	n/a	S73	True	Ok	n/a	French	False	19	False	n/a	0	1
+S74	False	UNICOG	3	F74	n/a	Right handed	SHFJ	M	n/a	231	n/a	0	anat pourris mais existe au 1.5T	2.4	False	True	False	Basic	False	n/a	True	S74	True	n/a	n/a	French	False	21	n/a	n/a	0	1
+S75	False	UNICOG	3	F75	False	Right handed	Neurospin	M	n/a	328	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S75	True	Ok	n/a	French	False	20	False	n/a	0	1
+S76	False	UNICOG	3	F76	n/a	Right handed	SHFJ	F	n/a	187	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S76	True	n/a	n/a	French	False	28	n/a	n/a	0	1
+S77	False	UNICOG	3	F77	n/a	Right handed	SHFJ	F	n/a	120	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S77	True	n/a	n/a	French	False	23	n/a	n/a	0	1
+S78	False	UNICOG	3	F78	False	Right handed	Neurospin	M	n/a	321	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S78	True	Ok	n/a	French	False	29	False	n/a	0	1
+S79	False	UNICOG	3	F79	n/a	Right handed	SHFJ	F	n/a	222	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S79	True	n/a	n/a	French	False	43	n/a	True	0	1
+S80	False	UNICOG	3	F80	n/a	Right handed	SHFJ	F	n/a	198	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S80	True	n/a	n/a	French	False	19	n/a	n/a	0	1
+S81	False	UNICOG	3	F81	n/a	Right handed	SHFJ	M	n/a	241	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S81	True	n/a	n/a	French	False	n/a	n/a	n/a	0	1
+S82	False	UNICOG	3	F82	n/a	Right handed	SHFJ	F	n/a	219	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S82	True	n/a	n/a	French	False	20	n/a	n/a	0	1
+S83	False	UNICOG	3	F83	False	Right handed	Neurospin	F	n/a	331	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	n/a	S83	True	Ok	n/a	French	False	20	False	n/a	0	2
+S84	False	UNICOG	3	F84	n/a	Right handed	SHFJ	F	n/a	217	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S84	True	n/a	n/a	French	False	23	n/a	n/a	0	1
+S85	False	UNICOG	3	F85	n/a	Right handed	SHFJ	M	n/a	183	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S85	True	n/a	n/a	French	False	21	n/a	n/a	0	1
+S86	False	UNICOG	3	F86	False	Right handed	Neurospin	M	n/a	370	n/a	0	Some substraction errors. Voxel size (localizer) : 2x2x2 mm^3	2.4	n/a	True	False	Basic	False	n/a	True	S86	True	axial+artefacts	n/a	French	False	31	True	n/a	0	1
+S87	False	UNICOG	3	F87	n/a	Right handed	SHFJ	M	n/a	216	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S87	True	n/a	n/a	French	False	n/a	n/a	n/a	0	1
+S88	False	UNICOG	3	F88	n/a	Right handed	SHFJ	M	n/a	196	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S88	True	n/a	n/a	French	False	19	n/a	True	0	1
+S89	False	UNICOG	3	F89	True	Right handed	Neurospin	M	n/a	346	n/a	0	Rare pushing mistakes	2.4	n/a	True	False	Basic	False	n/a	n/a	S89	True	Ok	n/a	French	False	29	False	n/a	0	1
+S90	False	UNICOG	3	F90	n/a	Right handed	SHFJ	M	n/a	237	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S90	True	n/a	n/a	French	False	21	n/a	n/a	0	1
+S91	False	UNICOG	3	F91	False	Right handed	Neurospin	M	n/a	338	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	n/a	S91	True	Ok	n/a	French	False	23	False	n/a	0	1
+S92	False	UNICOG	3	F92	n/a	Right handed	SHFJ	F	n/a	220	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S92	True	n/a	n/a	French	False	26	n/a	True	0	1
+S93	False	UNICOG	3	F93	False	Right handed	Neurospin	M	n/a	332	n/a	0	Acquisition of anatomy in axial (pilote, the rest are in sagittal)	2.4	False	True	False	Basic	False	n/a	n/a	S93	True	Ok	n/a	French	False	36	False	n/a	0	1
+S94	False	UNICOG	3	F94	n/a	Right handed	SHFJ	M	n/a	210	n/a	0	n/a	2.4	False	True	False	Basic	False	n/a	True	S94	True	n/a	n/a	French	False	41	n/a	True	0	1

sub-S02/func/sub-S02_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
+0.0	1	video_computation
+2.4	1	video_computation
+8.7	1	horizontal_checkerboard
+11.4	1	audio_right_hand
+15.0	1	audio_sentence
+18.0	1	video_right_hand
+20.7	1	audio_sentence
+23.7	1	audio_left_hand
+26.7	1	video_left_hand
+29.7	1	audio_sentence
+33.0	1	vertical_checkerboard
+35.4	1	audio_computation
+39.0	1	video_sentence
+41.7	1	video_sentence
+44.7	1	audio_computation
+48.0	1	audio_computation
+56.4	1	video_sentence
+59.7	1	horizontal_checkerboard
+62.4	1	audio_left_hand
+69.0	1	video_right_hand
+71.4	1	video_left_hand
+75.0	1	video_sentence
+83.4	1	audio_computation
+87.0	1	audio_right_hand
+89.7	1	audio_sentence
+96.0	1	vertical_checkerboard
+108.0	1	audio_computation
+116.7	1	video_left_hand
+119.4	1	audio_sentence
+122.7	1	vertical_checkerboard
+125.4	1	video_computation
+131.4	1	video_sentence
+135.0	1	audio_computation
+137.7	1	audio_computation
+140.4	1	vertical_checkerboard
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+293.4	1	audio_sentence
+296.7	1	audio_sentence

sub-S03/func/sub-S03_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
+0.0	1	video_computation
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+11.4	1	audio_right_hand
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+296.7	1	audio_sentence

sub-S04/func/sub-S04_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
+0.0	1	video_computation
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+11.4	1	audio_right_hand
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sub-S05/func/sub-S05_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
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sub-S10/func/sub-S10_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
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sub-S11/func/sub-S11_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
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sub-S12/func/sub-S12_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
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sub-S13/func/sub-S13_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
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+293.4	1	audio_sentence
+296.7	1	audio_sentence

sub-S14/func/sub-S14_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
+0.0	1	video_computation
+2.4	1	video_computation
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+11.4	1	audio_right_hand
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+293.4	1	audio_sentence
+296.7	1	audio_sentence

sub-S15/func/sub-S15_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
+0.0	1	video_computation
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+8.7	1	horizontal_checkerboard
+11.4	1	audio_right_hand
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+293.4	1	audio_sentence
+296.7	1	audio_sentence

sub-S16/func/sub-S16_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
+0.0	1	video_computation
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+11.4	1	audio_right_hand
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+296.7	1	audio_sentence

sub-S17/func/sub-S17_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
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+296.7	1	audio_sentence

sub-S18/func/sub-S18_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
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+296.7	1	audio_sentence

sub-S19/func/sub-S19_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
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+296.7	1	audio_sentence

sub-S20/func/sub-S20_task-localizer_events.tsv

@@ -0,0 +1,81 @@
+onset	duration	trial_type
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+296.7	1	audio_sentence

task-localizer_bold.json

@@ -0,0 +1,9 @@
+{
+     "TaskName": "Localizer",
+     "Manufacturer": "Brucker",
+     "MagneticFieldStrength": "3T",
+     "PulseSequenceType": "EPI",
+     "EchoTime": 0.03,
+     "RepetitionTime": 2.4,
+     "SliceThickness": "4"
+}