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dartbrains
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localizer

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Image Classification
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neuroscience
neuroimaging
fmri
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brain
nifti
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ljchang commited on
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Add fmriprep derivatives for subjects S91-S94

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  1. derivatives/fmriprep/sub-S91.html +859 -0
  2. derivatives/fmriprep/sub-S91/anat/sub-S91_desc-brain_mask.json +6 -0
  3. derivatives/fmriprep/sub-S91/anat/sub-S91_desc-brain_mask.nii.gz +3 -0
  4. derivatives/fmriprep/sub-S91/anat/sub-S91_desc-preproc_T1w.json +3 -0
  5. derivatives/fmriprep/sub-S91/anat/sub-S91_desc-preproc_T1w.nii.gz +3 -0
  6. derivatives/fmriprep/sub-S91/anat/sub-S91_dseg.nii.gz +3 -0
  7. derivatives/fmriprep/sub-S91/anat/sub-S91_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5 +3 -0
  8. derivatives/fmriprep/sub-S91/anat/sub-S91_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5 +3 -0
  9. derivatives/fmriprep/sub-S91/anat/sub-S91_label-CSF_probseg.nii.gz +3 -0
  10. derivatives/fmriprep/sub-S91/anat/sub-S91_label-GM_probseg.nii.gz +3 -0
  11. derivatives/fmriprep/sub-S91/anat/sub-S91_label-WM_probseg.nii.gz +3 -0
  12. derivatives/fmriprep/sub-S91/anat/sub-S91_space-MNI152NLin2009cAsym_desc-brain_mask.json +4 -0
  13. derivatives/fmriprep/sub-S91/anat/sub-S91_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz +3 -0
  14. derivatives/fmriprep/sub-S91/anat/sub-S91_space-MNI152NLin2009cAsym_desc-preproc_T1w.json +3 -0
  15. derivatives/fmriprep/sub-S91/anat/sub-S91_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz +3 -0
  16. derivatives/fmriprep/sub-S91/anat/sub-S91_space-MNI152NLin2009cAsym_dseg.nii.gz +3 -0
  17. derivatives/fmriprep/sub-S91/anat/sub-S91_space-MNI152NLin2009cAsym_label-CSF_probseg.nii.gz +3 -0
  18. derivatives/fmriprep/sub-S91/anat/sub-S91_space-MNI152NLin2009cAsym_label-GM_probseg.nii.gz +3 -0
  19. derivatives/fmriprep/sub-S91/anat/sub-S91_space-MNI152NLin2009cAsym_label-WM_probseg.nii.gz +3 -0
  20. derivatives/fmriprep/sub-S91/figures/sub-S91_dseg.svg +0 -0
  21. derivatives/fmriprep/sub-S91/figures/sub-S91_space-MNI152NLin2009cAsym_T1w.svg +0 -0
  22. derivatives/fmriprep/sub-S91/figures/sub-S91_task-localizer_desc-carpetplot_bold.svg +0 -0
  23. derivatives/fmriprep/sub-S91/figures/sub-S91_task-localizer_desc-compcorvar_bold.svg +0 -0
  24. derivatives/fmriprep/sub-S91/figures/sub-S91_task-localizer_desc-confoundcorr_bold.svg +0 -0
  25. derivatives/fmriprep/sub-S91/figures/sub-S91_task-localizer_desc-flirtbbr_bold.svg +0 -0
  26. derivatives/fmriprep/sub-S91/figures/sub-S91_task-localizer_desc-rois_bold.svg +0 -0
  27. derivatives/fmriprep/sub-S91/func/sub-S91_task-localizer_desc-confounds_regressors.json +3118 -0
  28. derivatives/fmriprep/sub-S91/func/sub-S91_task-localizer_desc-confounds_regressors.tsv +0 -0
  29. derivatives/fmriprep/sub-S91/func/sub-S91_task-localizer_space-MNI152NLin2009cAsym_boldref.nii.gz +3 -0
  30. derivatives/fmriprep/sub-S91/func/sub-S91_task-localizer_space-MNI152NLin2009cAsym_desc-brain_mask.json +5 -0
  31. derivatives/fmriprep/sub-S91/func/sub-S91_task-localizer_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz +3 -0
  32. derivatives/fmriprep/sub-S91/func/sub-S91_task-localizer_space-MNI152NLin2009cAsym_desc-preproc_bold.json +5 -0
  33. derivatives/fmriprep/sub-S91/func/sub-S91_task-localizer_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz +3 -0
  34. derivatives/fmriprep/sub-S92.html +867 -0
  35. derivatives/fmriprep/sub-S92/anat/sub-S92_desc-brain_mask.json +6 -0
  36. derivatives/fmriprep/sub-S92/anat/sub-S92_desc-brain_mask.nii.gz +3 -0
  37. derivatives/fmriprep/sub-S92/anat/sub-S92_desc-preproc_T1w.json +3 -0
  38. derivatives/fmriprep/sub-S92/anat/sub-S92_desc-preproc_T1w.nii.gz +3 -0
  39. derivatives/fmriprep/sub-S92/anat/sub-S92_dseg.nii.gz +3 -0
  40. derivatives/fmriprep/sub-S92/anat/sub-S92_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5 +3 -0
  41. derivatives/fmriprep/sub-S92/anat/sub-S92_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5 +3 -0
  42. derivatives/fmriprep/sub-S92/anat/sub-S92_label-CSF_probseg.nii.gz +3 -0
  43. derivatives/fmriprep/sub-S92/anat/sub-S92_label-GM_probseg.nii.gz +3 -0
  44. derivatives/fmriprep/sub-S92/anat/sub-S92_label-WM_probseg.nii.gz +3 -0
  45. derivatives/fmriprep/sub-S92/anat/sub-S92_space-MNI152NLin2009cAsym_desc-brain_mask.json +4 -0
  46. derivatives/fmriprep/sub-S92/anat/sub-S92_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz +3 -0
  47. derivatives/fmriprep/sub-S92/anat/sub-S92_space-MNI152NLin2009cAsym_desc-preproc_T1w.json +3 -0
  48. derivatives/fmriprep/sub-S92/anat/sub-S92_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz +3 -0
  49. derivatives/fmriprep/sub-S92/anat/sub-S92_space-MNI152NLin2009cAsym_dseg.nii.gz +3 -0
  50. derivatives/fmriprep/sub-S92/anat/sub-S92_space-MNI152NLin2009cAsym_label-CSF_probseg.nii.gz +3 -0
derivatives/fmriprep/sub-S91.html ADDED
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+ <body>
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+
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+
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+ <nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
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+ <div class="collapse navbar-collapse">
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+ <ul class="navbar-nav">
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+ <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
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+ <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
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+ <li class="nav-item dropdown">
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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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+ <div class="dropdown-menu" aria-labelledby="navbarFunctional">
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+ <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
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+ </div>
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+ </li>
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+ <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
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+ <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
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+ <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
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+ </ul>
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+ </div>
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+ </nav>
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+ <noscript>
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+ <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
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+ </noscript>
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+
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+ <div id="Summary">
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+ <h1 class="sub-report-title">Summary</h1>
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+ <div id="datatype-anat_desc-summary_suffix-T1w">
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+ <ul class="elem-desc">
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+ <li>Subject ID: S91</li>
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+ <li>Structural images: 1 T1-weighted </li>
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+ <li>Functional series: 1</li>
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+ <ul class="elem-desc">
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+ <li>Task: localizer (1 run)</li>
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+ </ul>
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+ <li>Standard output spaces: MNI152NLin2009cAsym</li>
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+ <li>Non-standard output spaces: </li>
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+ <li>FreeSurfer reconstruction: Not run</li>
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+ </ul>
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+ </div>
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+ </div>
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+ <div id="Anatomical">
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+ <h1 class="sub-report-title">Anatomical</h1>
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+ <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
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+ <h3 class="elem-title">Anatomical Conformation</h3>
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+ <ul class="elem-desc">
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+ <li>Input T1w images: 1</li>
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+ <li>Output orientation: RAS</li>
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+ <li>Output dimensions: 160x240x256</li>
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+ <li>Output voxel size: 1.1mm x 1mm x 1mm</li>
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+ <li>Discarded images: 0</li>
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+
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+ </ul>
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+ </div>
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+ <div id="datatype-anat_suffix-dseg">
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+ <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-S91/figures/sub-S91_dseg.svg" style="width: 100%" />
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S91/figures/sub-S91_dseg.svg" target="_blank">sub-S91/figures/sub-S91_dseg.svg</a>
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+ </div>
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+
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+ </div>
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+ <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
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+ <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-S91/figures/sub-S91_space-MNI152NLin2009cAsym_T1w.svg">
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+ Problem loading figure sub-S91/figures/sub-S91_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S91/figures/sub-S91_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S91/figures/sub-S91_space-MNI152NLin2009cAsym_T1w.svg</a>
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+ </div>
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+
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+ </div>
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+ </div>
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+ <div id="Functional">
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+ <h1 class="sub-report-title">Functional</h1>
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+ <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
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+ <h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2> <h3 class="elem-title">Summary</h3>
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+ <ul class="elem-desc">
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+ <li>Repetition time (TR): 2.4s</li>
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+ <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
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+ <li>Slice timing correction: Not applied</li>
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+ <li>Susceptibility distortion correction: None</li>
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+ <li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
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+ <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, 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, 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_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</li>
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+ <li>Non-steady-state volumes: 0</li>
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+ </ul>
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+ </div>
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+ <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
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+ <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-S91/figures/sub-S91_task-localizer_desc-flirtbbr_bold.svg">
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+ Problem loading figure sub-S91/figures/sub-S91_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S91/figures/sub-S91_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S91/figures/sub-S91_task-localizer_desc-flirtbbr_bold.svg</a>
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+ </div>
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+
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+ </div>
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+ <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
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+ <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-S91/figures/sub-S91_task-localizer_desc-rois_bold.svg" style="width: 100%" />
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S91/figures/sub-S91_task-localizer_desc-rois_bold.svg" target="_blank">sub-S91/figures/sub-S91_task-localizer_desc-rois_bold.svg</a>
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+ </div>
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+
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+ </div>
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+ <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
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+ <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-S91/figures/sub-S91_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
161
+ </div>
162
+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S91/figures/sub-S91_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S91/figures/sub-S91_task-localizer_desc-compcorvar_bold.svg</a>
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+ </div>
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+
166
+ </div>
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+ <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
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+ <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-S91/figures/sub-S91_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
169
+ </div>
170
+ <div class="elem-filename">
171
+ Get figure file: <a href="./sub-S91/figures/sub-S91_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S91/figures/sub-S91_task-localizer_desc-carpetplot_bold.svg</a>
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+ </div>
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+
174
+ </div>
175
+ <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
176
+ <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
177
+ (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
178
+ Right: magnitude of the correlation between each confound time series and the
179
+ mean global signal. Strong correlations might be indicative of partial volume
180
+ effects and can inform decisions about feature orthogonalization prior to
181
+ confound regression.
182
+ </p> <img class="svg-reportlet" src="./sub-S91/figures/sub-S91_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
183
+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S91/figures/sub-S91_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S91/figures/sub-S91_task-localizer_desc-confoundcorr_bold.svg</a>
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+ </div>
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+
188
+ </div>
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+ </div>
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+ <div id="About">
191
+ <h1 class="sub-report-title">About</h1>
192
+ <div id="datatype-anat_desc-about_suffix-T1w">
193
+ <ul>
194
+ <li>fMRIPrep version: 20.0.6</li>
195
+ <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-S91 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
196
+ <li>Date preprocessed: 2020-05-14 14:35:47 -0400</li>
197
+ </ul>
198
+ </div>
199
+ </div>
200
+ </div>
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+
202
+ <div id="boilerplate">
203
+ <h1 class="sub-report-title">Methods</h1>
204
+ <p>We kindly ask to report results preprocessed with this tool using the following
205
+ boilerplate.</p>
206
+ <ul class="nav nav-tabs" id="myTab" role="tablist">
207
+ <li class="nav-item">
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+ <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
209
+ </li>
210
+ <li class="nav-item">
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+ <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
212
+ </li>
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+ <li class="nav-item">
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+ <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
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+ </li>
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+ </ul>
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+ <div class="tab-content" id="myTabContent">
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+ <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>
219
+ <dl>
220
+ <dt>Anatomical data preprocessing</dt>
221
+ <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>
222
+ </dd>
223
+ <dt>Functional data preprocessing</dt>
224
+ <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>
225
+ </dd>
226
+ </dl>
227
+ <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>
228
+ <h3 id="copyright-waiver">Copyright Waiver</h3>
229
+ <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>
230
+ <h3 id="references" class="unnumbered">References</h3>
231
+ <div id="refs" class="references">
232
+ <div id="ref-nilearn">
233
+ <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>
234
+ </div>
235
+ <div id="ref-ants">
236
+ <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>
237
+ </div>
238
+ <div id="ref-compcor">
239
+ <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>
240
+ </div>
241
+ <div id="ref-fmriprep2">
242
+ <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>
243
+ </div>
244
+ <div id="ref-fmriprep1">
245
+ <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>
246
+ </div>
247
+ <div id="ref-mni152nlin2009casym">
248
+ <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>
249
+ </div>
250
+ <div id="ref-nipype1">
251
+ <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>
252
+ </div>
253
+ <div id="ref-nipype2">
254
+ <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>
255
+ </div>
256
+ <div id="ref-bbr">
257
+ <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>
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+ </div>
259
+ <div id="ref-mcflirt">
260
+ <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>
261
+ </div>
262
+ <div id="ref-flirt">
263
+ <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>
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+ </div>
265
+ <div id="ref-lanczos">
266
+ <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>
267
+ </div>
268
+ <div id="ref-power_fd_dvars">
269
+ <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>
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+ </div>
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+ <div id="ref-confounds_satterthwaite_2013">
272
+ <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>
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+ </div>
274
+ <div id="ref-n4">
275
+ <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>
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+ </div>
277
+ <div id="ref-fsl_fast">
278
+ <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>
279
+ </div>
280
+ </div></div></div>
281
+ <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
282
+ Results included in this manuscript come from preprocessing
283
+ performed using *fMRIPrep* 20.0.6
284
+ (@fmriprep1; @fmriprep2; RRID:SCR_016216),
285
+ which is based on *Nipype* 1.4.2
286
+ (@nipype1; @nipype2; RRID:SCR_002502).
287
+
288
+ Anatomical data preprocessing
289
+
290
+ : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
291
+ with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
292
+ The T1w-reference was then skull-stripped with a *Nipype* implementation of
293
+ the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
294
+ as target template.
295
+ Brain tissue segmentation of cerebrospinal fluid (CSF),
296
+ white-matter (WM) and gray-matter (GM) was performed on
297
+ the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
298
+ @fsl_fast].
299
+ Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
300
+ nonlinear registration with `antsRegistration` (ANTs 2.2.0),
301
+ using brain-extracted versions of both T1w reference and the T1w template.
302
+ The following template was selected for spatial normalization:
303
+ *ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],
304
+
305
+ Functional data preprocessing
306
+
307
+ : For each of the 1 BOLD runs found per subject (across all
308
+ tasks and sessions), the following preprocessing was performed.
309
+ First, a reference volume and its skull-stripped version were generated
310
+ using a custom methodology of *fMRIPrep*.
311
+ Susceptibility distortion correction (SDC) was omitted.
312
+ The BOLD reference was then co-registered to the T1w reference using
313
+ `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
314
+ cost-function.
315
+ Co-registration was configured with nine degrees of freedom to account
316
+ for distortions remaining in the BOLD reference.
317
+ Head-motion parameters with respect to the BOLD reference
318
+ (transformation matrices, and six corresponding rotation and translation
319
+ parameters) are estimated before any spatiotemporal filtering using
320
+ `mcflirt` [FSL 5.0.9, @mcflirt].
321
+ The BOLD time-series (including slice-timing correction when applied)
322
+ were resampled onto their original, native space by applying
323
+ the transforms to correct for head-motion.
324
+ These resampled BOLD time-series will be referred to as *preprocessed
325
+ BOLD in original space*, or just *preprocessed BOLD*.
326
+ The BOLD time-series were resampled into standard space,
327
+ generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
328
+ First, a reference volume and its skull-stripped version were generated
329
+ using a custom methodology of *fMRIPrep*.
330
+ Several confounding time-series were calculated based on the
331
+ *preprocessed BOLD*: framewise displacement (FD), DVARS and
332
+ three region-wise global signals.
333
+ FD and DVARS are calculated for each functional run, both using their
334
+ implementations in *Nipype* [following the definitions by @power_fd_dvars].
335
+ The three global signals are extracted within the CSF, the WM, and
336
+ the whole-brain masks.
337
+ Additionally, a set of physiological regressors were extracted to
338
+ allow for component-based noise correction [*CompCor*, @compcor].
339
+ Principal components are estimated after high-pass filtering the
340
+ *preprocessed BOLD* time-series (using a discrete cosine filter with
341
+ 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
342
+ and anatomical (aCompCor).
343
+ tCompCor components are then calculated from the top 5% variable
344
+ voxels within a mask covering the subcortical regions.
345
+ This subcortical mask is obtained by heavily eroding the brain mask,
346
+ which ensures it does not include cortical GM regions.
347
+ For aCompCor, components are calculated within the intersection of
348
+ the aforementioned mask and the union of CSF and WM masks calculated
349
+ in T1w space, after their projection to the native space of each
350
+ functional run (using the inverse BOLD-to-T1w transformation). Components
351
+ are also calculated separately within the WM and CSF masks.
352
+ For each CompCor decomposition, the *k* components with the largest singular
353
+ values are retained, such that the retained components' time series are
354
+ sufficient to explain 50 percent of variance across the nuisance mask (CSF,
355
+ WM, combined, or temporal). The remaining components are dropped from
356
+ consideration.
357
+ The head-motion estimates calculated in the correction step were also
358
+ placed within the corresponding confounds file.
359
+ The confound time series derived from head motion estimates and global
360
+ signals were expanded with the inclusion of temporal derivatives and
361
+ quadratic terms for each [@confounds_satterthwaite_2013].
362
+ Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
363
+ were annotated as motion outliers.
364
+ All resamplings can be performed with *a single interpolation
365
+ step* by composing all the pertinent transformations (i.e. head-motion
366
+ transform matrices, susceptibility distortion correction when available,
367
+ and co-registrations to anatomical and output spaces).
368
+ Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
369
+ configured with Lanczos interpolation to minimize the smoothing
370
+ effects of other kernels [@lanczos].
371
+ Non-gridded (surface) resamplings were performed using `mri_vol2surf`
372
+ (FreeSurfer).
373
+
374
+
375
+ Many internal operations of *fMRIPrep* use
376
+ *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
377
+ mostly within the functional processing workflow.
378
+ For more details of the pipeline, see [the section corresponding
379
+ to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
380
+
381
+
382
+ ### Copyright Waiver
383
+
384
+ The above boilerplate text was automatically generated by fMRIPrep
385
+ with the express intention that users should copy and paste this
386
+ text into their manuscripts *unchanged*.
387
+ It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
388
+
389
+ ### References
390
+
391
+ </pre>
392
+ </div>
393
+ <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
394
+ using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
395
+ RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
396
+ (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
397
+
398
+ \begin{description}
399
+ \item[Anatomical data preprocessing]
400
+ The T1-weighted (T1w) image was corrected for intensity non-uniformity
401
+ (INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
402
+ ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
403
+ throughout the workflow. The T1w-reference was then skull-stripped with
404
+ a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
405
+ workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
406
+ segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
407
+ gray-matter (GM) was performed on the brain-extracted T1w using
408
+ \texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
409
+ Volume-based spatial normalization to one standard space
410
+ (MNI152NLin2009cAsym) was performed through nonlinear registration with
411
+ \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
412
+ of both T1w reference and the T1w template. The following template was
413
+ selected for spatial normalization: \emph{ICBM 152 Nonlinear
414
+ Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
415
+ RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
416
+ \item[Functional data preprocessing]
417
+ For each of the 1 BOLD runs found per subject (across all tasks and
418
+ sessions), the following preprocessing was performed. First, a reference
419
+ volume and its skull-stripped version were generated using a custom
420
+ methodology of \emph{fMRIPrep}. Susceptibility distortion correction
421
+ (SDC) was omitted. The BOLD reference was then co-registered to the T1w
422
+ reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
423
+ boundary-based registration \citep{bbr} cost-function. Co-registration
424
+ was configured with nine degrees of freedom to account for distortions
425
+ remaining in the BOLD reference. Head-motion parameters with respect to
426
+ the BOLD reference (transformation matrices, and six corresponding
427
+ rotation and translation parameters) are estimated before any
428
+ spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
429
+ 5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
430
+ correction when applied) were resampled onto their original, native
431
+ space by applying the transforms to correct for head-motion. These
432
+ resampled BOLD time-series will be referred to as \emph{preprocessed
433
+ BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
434
+ time-series were resampled into standard space, generating a
435
+ \emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
436
+ reference volume and its skull-stripped version were generated using a
437
+ custom methodology of \emph{fMRIPrep}. Several confounding time-series
438
+ were calculated based on the \emph{preprocessed BOLD}: framewise
439
+ displacement (FD), DVARS and three region-wise global signals. FD and
440
+ DVARS are calculated for each functional run, both using their
441
+ implementations in \emph{Nipype} \citep[following the definitions
442
+ by][]{power_fd_dvars}. The three global signals are extracted within the
443
+ CSF, the WM, and the whole-brain masks. Additionally, a set of
444
+ physiological regressors were extracted to allow for component-based
445
+ noise correction \citep[\emph{CompCor},][]{compcor}. Principal
446
+ components are estimated after high-pass filtering the
447
+ \emph{preprocessed BOLD} time-series (using a discrete cosine filter
448
+ with 128s cut-off) for the two \emph{CompCor} variants: temporal
449
+ (tCompCor) and anatomical (aCompCor). tCompCor components are then
450
+ calculated from the top 5\% variable voxels within a mask covering the
451
+ subcortical regions. This subcortical mask is obtained by heavily
452
+ eroding the brain mask, which ensures it does not include cortical GM
453
+ regions. For aCompCor, components are calculated within the intersection
454
+ of the aforementioned mask and the union of CSF and WM masks calculated
455
+ in T1w space, after their projection to the native space of each
456
+ functional run (using the inverse BOLD-to-T1w transformation).
457
+ Components are also calculated separately within the WM and CSF masks.
458
+ For each CompCor decomposition, the \emph{k} components with the largest
459
+ singular values are retained, such that the retained components' time
460
+ series are sufficient to explain 50 percent of variance across the
461
+ nuisance mask (CSF, WM, combined, or temporal). The remaining components
462
+ are dropped from consideration. The head-motion estimates calculated in
463
+ the correction step were also placed within the corresponding confounds
464
+ file. The confound time series derived from head motion estimates and
465
+ global signals were expanded with the inclusion of temporal derivatives
466
+ and quadratic terms for each \citep{confounds_satterthwaite_2013}.
467
+ Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
468
+ were annotated as motion outliers. All resamplings can be performed with
469
+ \emph{a single interpolation step} by composing all the pertinent
470
+ transformations (i.e.~head-motion transform matrices, susceptibility
471
+ distortion correction when available, and co-registrations to anatomical
472
+ and output spaces). Gridded (volumetric) resamplings were performed
473
+ using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
474
+ interpolation to minimize the smoothing effects of other kernels
475
+ \citep{lanczos}. Non-gridded (surface) resamplings were performed using
476
+ \texttt{mri\_vol2surf} (FreeSurfer).
477
+ \end{description}
478
+
479
+ Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
480
+ \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
481
+ processing workflow. For more details of the pipeline, see
482
+ \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
483
+ section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
484
+
485
+ \hypertarget{copyright-waiver}{%
486
+ \subsubsection{Copyright Waiver}\label{copyright-waiver}}
487
+
488
+ The above boilerplate text was automatically generated by fMRIPrep with
489
+ the express intention that users should copy and paste this text into
490
+ their manuscripts \emph{unchanged}. It is released under the
491
+ \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
492
+
493
+ \hypertarget{references}{%
494
+ \subsubsection{References}\label{references}}
495
+
496
+ \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
497
+ <h3>Bibliography</h3>
498
+ <pre>@article{fmriprep1,
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+ 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},
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+ title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
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+ year = {2018},
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+ doi = {10.1038/s41592-018-0235-4},
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+ journal = {Nature Methods}
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+ }
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+
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+ @article{fmriprep2,
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+ 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.},
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+ title = {fMRIPrep},
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+ year = 2018,
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+ doi = {10.5281/zenodo.852659},
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+ publisher = {Zenodo},
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+ }
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+ @article{nipype1,
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+ @article{nipype2,
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+ 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},
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+ title = {Nipype},
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+ year = 2018,
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+ publisher = {Zenodo},
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+ }
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+ }
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+
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+ @article{power_fd_dvars,
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+ year = 2014
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+ }
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+
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+ @article{confounds_satterthwaite_2013,
742
+ 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.},
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+ title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
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+ url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
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+ volume = 64,
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+ year = 2013
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+ }
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+
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+
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+ @article{nilearn,
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+ 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},
757
+ doi = {10.3389/fninf.2014.00014},
758
+ issn = {1662-5196},
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+ journal = {Frontiers in Neuroinformatics},
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+ language = {English},
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+ title = {Machine learning for neuroimaging with scikit-learn},
762
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+ .run-title {}
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+ .elem-desc {}
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+ div.elem-image {
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+ width: 100%;
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+ }
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+ .elem-image object.svg-reportlet {
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+ padding-bottom: 5px;
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+ }
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+ .boiler-html {
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+ font-family: "Bitstream Charter", "Georgia", Times;
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+ margin: 20px 25px;
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+ padding: 10px;
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+ background-color: #F8F9FA;
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+ }
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+
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+ div#boilerplate pre {
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+ margin: 20px 25px;
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+ padding: 10px;
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+ background-color: #F8F9FA;
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+ }
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+
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+ #errors div, #errors p {
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+ padding-left: 1em;
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+ }
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+ </style>
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+ </head>
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+ <body>
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+
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+
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+ <nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
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+ <div class="collapse navbar-collapse">
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+ <ul class="navbar-nav">
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+ <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
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+ <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
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+ <li class="nav-item dropdown">
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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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+ <div class="dropdown-menu" aria-labelledby="navbarFunctional">
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+ <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
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+ </div>
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+ </li>
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+ <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
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+ <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
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+ <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
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+ </ul>
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+ </div>
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+ </nav>
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+ <noscript>
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+ <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
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+ </noscript>
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+
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+ <div id="Summary">
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+ <h1 class="sub-report-title">Summary</h1>
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+ <div id="datatype-anat_desc-summary_suffix-T1w">
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+ <ul class="elem-desc">
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+ <li>Subject ID: S92</li>
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+ <li>Structural images: 1 T1-weighted </li>
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+ <li>Functional series: 1</li>
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+ <ul class="elem-desc">
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+ <li>Task: localizer (1 run)</li>
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+ </ul>
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+ <li>Standard output spaces: MNI152NLin2009cAsym</li>
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+ <li>Non-standard output spaces: </li>
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+ <li>FreeSurfer reconstruction: Not run</li>
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+ </ul>
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+ </div>
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+ </div>
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+ <div id="Anatomical">
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+ <h1 class="sub-report-title">Anatomical</h1>
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+ <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
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+ <h3 class="elem-title">Anatomical Conformation</h3>
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+ <ul class="elem-desc">
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+ <li>Input T1w images: 1</li>
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+ <li>Output orientation: RAS</li>
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+ <li>Output dimensions: 192x256x128</li>
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+ <li>Output voxel size: 1mm x 1mm x 1.2mm</li>
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+ <li>Discarded images: 0</li>
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+
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+ </ul>
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+ </div>
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+ <div id="datatype-anat_suffix-dseg">
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+ <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-S92/figures/sub-S92_dseg.svg" style="width: 100%" />
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S92/figures/sub-S92_dseg.svg" target="_blank">sub-S92/figures/sub-S92_dseg.svg</a>
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+ </div>
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+
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+ </div>
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+ <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
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+ <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-S92/figures/sub-S92_space-MNI152NLin2009cAsym_T1w.svg">
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+ Problem loading figure sub-S92/figures/sub-S92_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S92/figures/sub-S92_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S92/figures/sub-S92_space-MNI152NLin2009cAsym_T1w.svg</a>
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+ </div>
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+
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+ </div>
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+ </div>
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+ <div id="Functional">
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+ <h1 class="sub-report-title">Functional</h1>
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+ <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
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+ <h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2> <h3 class="elem-title">Summary</h3>
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+ <ul class="elem-desc">
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+ <li>Repetition time (TR): 2.4s</li>
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+ <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
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+ <li>Slice timing correction: Not applied</li>
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+ <li>Susceptibility distortion correction: None</li>
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+ <li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
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+ <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_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_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01</li>
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+ <li>Non-steady-state volumes: 0</li>
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+ </ul>
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+ </div>
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+ <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
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+ <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
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+ <p class="elem-desc">
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+ The qform has been copied from sform.
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+ The difference in angle is 0.
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+ The difference in translation is 0.
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+ </p>
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+ </div>
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+ <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
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+ <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-S92/figures/sub-S92_task-localizer_desc-flirtbbr_bold.svg">
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+ Problem loading figure sub-S92/figures/sub-S92_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S92/figures/sub-S92_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S92/figures/sub-S92_task-localizer_desc-flirtbbr_bold.svg</a>
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+ </div>
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+
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+ </div>
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+ <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
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+ <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-S92/figures/sub-S92_task-localizer_desc-rois_bold.svg" style="width: 100%" />
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S92/figures/sub-S92_task-localizer_desc-rois_bold.svg" target="_blank">sub-S92/figures/sub-S92_task-localizer_desc-rois_bold.svg</a>
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+ </div>
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+
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+ </div>
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+ <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
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+ <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-S92/figures/sub-S92_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S92/figures/sub-S92_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S92/figures/sub-S92_task-localizer_desc-compcorvar_bold.svg</a>
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+ </div>
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+
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+ </div>
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+ <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
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+ <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-S92/figures/sub-S92_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S92/figures/sub-S92_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S92/figures/sub-S92_task-localizer_desc-carpetplot_bold.svg</a>
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+ </div>
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+
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+ </div>
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+ <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
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+ <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
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+ (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
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+ Right: magnitude of the correlation between each confound time series and the
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+ mean global signal. Strong correlations might be indicative of partial volume
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+ effects and can inform decisions about feature orthogonalization prior to
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+ confound regression.
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+ </p> <img class="svg-reportlet" src="./sub-S92/figures/sub-S92_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
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+ </div>
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+ <div class="elem-filename">
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+ Get figure file: <a href="./sub-S92/figures/sub-S92_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S92/figures/sub-S92_task-localizer_desc-confoundcorr_bold.svg</a>
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+ </div>
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+
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+ </div>
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+ </div>
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+ <div id="About">
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+ <h1 class="sub-report-title">About</h1>
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+ <div id="datatype-anat_desc-about_suffix-T1w">
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+ <ul>
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+ <li>fMRIPrep version: 20.0.6</li>
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+ <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-S92 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
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+ <li>Date preprocessed: 2020-05-14 17:54:45 -0400</li>
205
+ </ul>
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+ </div>
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+ </div>
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+ </div>
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+
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+ <div id="boilerplate">
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+ <h1 class="sub-report-title">Methods</h1>
212
+ <p>We kindly ask to report results preprocessed with this tool using the following
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+ boilerplate.</p>
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+ <ul class="nav nav-tabs" id="myTab" role="tablist">
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+ <li class="nav-item">
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+ <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
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+ </li>
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+ <li class="nav-item">
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+ <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
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+ </li>
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+ <li class="nav-item">
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+ <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
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+ </li>
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+ </ul>
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+ <div class="tab-content" id="myTabContent">
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+ <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>
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+ <dl>
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+ <dt>Anatomical data preprocessing</dt>
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+ <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>
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+ </dd>
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+ <dt>Functional data preprocessing</dt>
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+ <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>
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+ </dd>
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+ </dl>
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+ <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>
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+ <h3 id="copyright-waiver">Copyright Waiver</h3>
237
+ <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>
238
+ <h3 id="references" class="unnumbered">References</h3>
239
+ <div id="refs" class="references">
240
+ <div id="ref-nilearn">
241
+ <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>
242
+ </div>
243
+ <div id="ref-ants">
244
+ <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>
245
+ </div>
246
+ <div id="ref-compcor">
247
+ <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>
248
+ </div>
249
+ <div id="ref-fmriprep2">
250
+ <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>
251
+ </div>
252
+ <div id="ref-fmriprep1">
253
+ <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>
254
+ </div>
255
+ <div id="ref-mni152nlin2009casym">
256
+ <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>
257
+ </div>
258
+ <div id="ref-nipype1">
259
+ <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>
260
+ </div>
261
+ <div id="ref-nipype2">
262
+ <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>
263
+ </div>
264
+ <div id="ref-bbr">
265
+ <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>
266
+ </div>
267
+ <div id="ref-mcflirt">
268
+ <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>
269
+ </div>
270
+ <div id="ref-flirt">
271
+ <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>
272
+ </div>
273
+ <div id="ref-lanczos">
274
+ <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>
275
+ </div>
276
+ <div id="ref-power_fd_dvars">
277
+ <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>
278
+ </div>
279
+ <div id="ref-confounds_satterthwaite_2013">
280
+ <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>
281
+ </div>
282
+ <div id="ref-n4">
283
+ <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>
284
+ </div>
285
+ <div id="ref-fsl_fast">
286
+ <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>
287
+ </div>
288
+ </div></div></div>
289
+ <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
290
+ Results included in this manuscript come from preprocessing
291
+ performed using *fMRIPrep* 20.0.6
292
+ (@fmriprep1; @fmriprep2; RRID:SCR_016216),
293
+ which is based on *Nipype* 1.4.2
294
+ (@nipype1; @nipype2; RRID:SCR_002502).
295
+
296
+ Anatomical data preprocessing
297
+
298
+ : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
299
+ with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
300
+ The T1w-reference was then skull-stripped with a *Nipype* implementation of
301
+ the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
302
+ as target template.
303
+ Brain tissue segmentation of cerebrospinal fluid (CSF),
304
+ white-matter (WM) and gray-matter (GM) was performed on
305
+ the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
306
+ @fsl_fast].
307
+ Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
308
+ nonlinear registration with `antsRegistration` (ANTs 2.2.0),
309
+ using brain-extracted versions of both T1w reference and the T1w template.
310
+ The following template was selected for spatial normalization:
311
+ *ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],
312
+
313
+ Functional data preprocessing
314
+
315
+ : For each of the 1 BOLD runs found per subject (across all
316
+ tasks and sessions), the following preprocessing was performed.
317
+ First, a reference volume and its skull-stripped version were generated
318
+ using a custom methodology of *fMRIPrep*.
319
+ Susceptibility distortion correction (SDC) was omitted.
320
+ The BOLD reference was then co-registered to the T1w reference using
321
+ `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
322
+ cost-function.
323
+ Co-registration was configured with nine degrees of freedom to account
324
+ for distortions remaining in the BOLD reference.
325
+ Head-motion parameters with respect to the BOLD reference
326
+ (transformation matrices, and six corresponding rotation and translation
327
+ parameters) are estimated before any spatiotemporal filtering using
328
+ `mcflirt` [FSL 5.0.9, @mcflirt].
329
+ The BOLD time-series (including slice-timing correction when applied)
330
+ were resampled onto their original, native space by applying
331
+ the transforms to correct for head-motion.
332
+ These resampled BOLD time-series will be referred to as *preprocessed
333
+ BOLD in original space*, or just *preprocessed BOLD*.
334
+ The BOLD time-series were resampled into standard space,
335
+ generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
336
+ First, a reference volume and its skull-stripped version were generated
337
+ using a custom methodology of *fMRIPrep*.
338
+ Several confounding time-series were calculated based on the
339
+ *preprocessed BOLD*: framewise displacement (FD), DVARS and
340
+ three region-wise global signals.
341
+ FD and DVARS are calculated for each functional run, both using their
342
+ implementations in *Nipype* [following the definitions by @power_fd_dvars].
343
+ The three global signals are extracted within the CSF, the WM, and
344
+ the whole-brain masks.
345
+ Additionally, a set of physiological regressors were extracted to
346
+ allow for component-based noise correction [*CompCor*, @compcor].
347
+ Principal components are estimated after high-pass filtering the
348
+ *preprocessed BOLD* time-series (using a discrete cosine filter with
349
+ 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
350
+ and anatomical (aCompCor).
351
+ tCompCor components are then calculated from the top 5% variable
352
+ voxels within a mask covering the subcortical regions.
353
+ This subcortical mask is obtained by heavily eroding the brain mask,
354
+ which ensures it does not include cortical GM regions.
355
+ For aCompCor, components are calculated within the intersection of
356
+ the aforementioned mask and the union of CSF and WM masks calculated
357
+ in T1w space, after their projection to the native space of each
358
+ functional run (using the inverse BOLD-to-T1w transformation). Components
359
+ are also calculated separately within the WM and CSF masks.
360
+ For each CompCor decomposition, the *k* components with the largest singular
361
+ values are retained, such that the retained components' time series are
362
+ sufficient to explain 50 percent of variance across the nuisance mask (CSF,
363
+ WM, combined, or temporal). The remaining components are dropped from
364
+ consideration.
365
+ The head-motion estimates calculated in the correction step were also
366
+ placed within the corresponding confounds file.
367
+ The confound time series derived from head motion estimates and global
368
+ signals were expanded with the inclusion of temporal derivatives and
369
+ quadratic terms for each [@confounds_satterthwaite_2013].
370
+ Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
371
+ were annotated as motion outliers.
372
+ All resamplings can be performed with *a single interpolation
373
+ step* by composing all the pertinent transformations (i.e. head-motion
374
+ transform matrices, susceptibility distortion correction when available,
375
+ and co-registrations to anatomical and output spaces).
376
+ Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
377
+ configured with Lanczos interpolation to minimize the smoothing
378
+ effects of other kernels [@lanczos].
379
+ Non-gridded (surface) resamplings were performed using `mri_vol2surf`
380
+ (FreeSurfer).
381
+
382
+
383
+ Many internal operations of *fMRIPrep* use
384
+ *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
385
+ mostly within the functional processing workflow.
386
+ For more details of the pipeline, see [the section corresponding
387
+ to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
388
+
389
+
390
+ ### Copyright Waiver
391
+
392
+ The above boilerplate text was automatically generated by fMRIPrep
393
+ with the express intention that users should copy and paste this
394
+ text into their manuscripts *unchanged*.
395
+ It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
396
+
397
+ ### References
398
+
399
+ </pre>
400
+ </div>
401
+ <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
402
+ using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
403
+ RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
404
+ (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
405
+
406
+ \begin{description}
407
+ \item[Anatomical data preprocessing]
408
+ The T1-weighted (T1w) image was corrected for intensity non-uniformity
409
+ (INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
410
+ ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
411
+ throughout the workflow. The T1w-reference was then skull-stripped with
412
+ a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
413
+ workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
414
+ segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
415
+ gray-matter (GM) was performed on the brain-extracted T1w using
416
+ \texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
417
+ Volume-based spatial normalization to one standard space
418
+ (MNI152NLin2009cAsym) was performed through nonlinear registration with
419
+ \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
420
+ of both T1w reference and the T1w template. The following template was
421
+ selected for spatial normalization: \emph{ICBM 152 Nonlinear
422
+ Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
423
+ RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
424
+ \item[Functional data preprocessing]
425
+ For each of the 1 BOLD runs found per subject (across all tasks and
426
+ sessions), the following preprocessing was performed. First, a reference
427
+ volume and its skull-stripped version were generated using a custom
428
+ methodology of \emph{fMRIPrep}. Susceptibility distortion correction
429
+ (SDC) was omitted. The BOLD reference was then co-registered to the T1w
430
+ reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
431
+ boundary-based registration \citep{bbr} cost-function. Co-registration
432
+ was configured with nine degrees of freedom to account for distortions
433
+ remaining in the BOLD reference. Head-motion parameters with respect to
434
+ the BOLD reference (transformation matrices, and six corresponding
435
+ rotation and translation parameters) are estimated before any
436
+ spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
437
+ 5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
438
+ correction when applied) were resampled onto their original, native
439
+ space by applying the transforms to correct for head-motion. These
440
+ resampled BOLD time-series will be referred to as \emph{preprocessed
441
+ BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
442
+ time-series were resampled into standard space, generating a
443
+ \emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
444
+ reference volume and its skull-stripped version were generated using a
445
+ custom methodology of \emph{fMRIPrep}. Several confounding time-series
446
+ were calculated based on the \emph{preprocessed BOLD}: framewise
447
+ displacement (FD), DVARS and three region-wise global signals. FD and
448
+ DVARS are calculated for each functional run, both using their
449
+ implementations in \emph{Nipype} \citep[following the definitions
450
+ by][]{power_fd_dvars}. The three global signals are extracted within the
451
+ CSF, the WM, and the whole-brain masks. Additionally, a set of
452
+ physiological regressors were extracted to allow for component-based
453
+ noise correction \citep[\emph{CompCor},][]{compcor}. Principal
454
+ components are estimated after high-pass filtering the
455
+ \emph{preprocessed BOLD} time-series (using a discrete cosine filter
456
+ with 128s cut-off) for the two \emph{CompCor} variants: temporal
457
+ (tCompCor) and anatomical (aCompCor). tCompCor components are then
458
+ calculated from the top 5\% variable voxels within a mask covering the
459
+ subcortical regions. This subcortical mask is obtained by heavily
460
+ eroding the brain mask, which ensures it does not include cortical GM
461
+ regions. For aCompCor, components are calculated within the intersection
462
+ of the aforementioned mask and the union of CSF and WM masks calculated
463
+ in T1w space, after their projection to the native space of each
464
+ functional run (using the inverse BOLD-to-T1w transformation).
465
+ Components are also calculated separately within the WM and CSF masks.
466
+ For each CompCor decomposition, the \emph{k} components with the largest
467
+ singular values are retained, such that the retained components' time
468
+ series are sufficient to explain 50 percent of variance across the
469
+ nuisance mask (CSF, WM, combined, or temporal). The remaining components
470
+ are dropped from consideration. The head-motion estimates calculated in
471
+ the correction step were also placed within the corresponding confounds
472
+ file. The confound time series derived from head motion estimates and
473
+ global signals were expanded with the inclusion of temporal derivatives
474
+ and quadratic terms for each \citep{confounds_satterthwaite_2013}.
475
+ Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
476
+ were annotated as motion outliers. All resamplings can be performed with
477
+ \emph{a single interpolation step} by composing all the pertinent
478
+ transformations (i.e.~head-motion transform matrices, susceptibility
479
+ distortion correction when available, and co-registrations to anatomical
480
+ and output spaces). Gridded (volumetric) resamplings were performed
481
+ using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
482
+ interpolation to minimize the smoothing effects of other kernels
483
+ \citep{lanczos}. Non-gridded (surface) resamplings were performed using
484
+ \texttt{mri\_vol2surf} (FreeSurfer).
485
+ \end{description}
486
+
487
+ Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
488
+ \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
489
+ processing workflow. For more details of the pipeline, see
490
+ \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
491
+ section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
492
+
493
+ \hypertarget{copyright-waiver}{%
494
+ \subsubsection{Copyright Waiver}\label{copyright-waiver}}
495
+
496
+ The above boilerplate text was automatically generated by fMRIPrep with
497
+ the express intention that users should copy and paste this text into
498
+ their manuscripts \emph{unchanged}. It is released under the
499
+ \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
500
+
501
+ \hypertarget{references}{%
502
+ \subsubsection{References}\label{references}}
503
+
504
+ \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
505
+ <h3>Bibliography</h3>
506
+ <pre>@article{fmriprep1,
507
+ 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},
508
+ title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
509
+ year = {2018},
510
+ doi = {10.1038/s41592-018-0235-4},
511
+ journal = {Nature Methods}
512
+ }
513
+
514
+ @article{fmriprep2,
515
+ 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.},
516
+ title = {fMRIPrep},
517
+ year = 2018,
518
+ doi = {10.5281/zenodo.852659},
519
+ publisher = {Zenodo},
520
+ journal = {Software}
521
+ }
522
+
523
+ @article{nipype1,
524
+ author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
525
+ doi = {10.3389/fninf.2011.00013},
526
+ journal = {Frontiers in Neuroinformatics},
527
+ pages = 13,
528
+ shorttitle = {Nipype},
529
+ title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
530
+ volume = 5,
531
+ year = 2011
532
+ }
533
+
534
+ @article{nipype2,
535
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+ }
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+ language = {eng},
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+ }
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+ }
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+ shorttitle = {ICA-AROMA},
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+ }
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+
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+ @article{power_fd_dvars,
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+ number = {Supplement C},
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+ pages = {320-341},
743
+ title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
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+ }
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+
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757
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758
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+ }
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765
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766
+ issn = {1662-5196},
767
+ journal = {Frontiers in Neuroinformatics},
768
+ language = {English},
769
+ title = {Machine learning for neuroimaging with scikit-learn},
770
+ url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
771
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+ }
774
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776
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778
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780
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784
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+ }
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795
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797
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+ }
800
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805
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806
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808
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+ }
813
+
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816
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817
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823
+ }
824
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+ @article{afni,
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828
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829
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830
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831
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832
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834
+ }
835
+
836
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837
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838
+ doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
839
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840
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841
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842
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843
+ volume = 42,
844
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845
+ }
846
+ </pre>
847
+ </div>
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+ </div>
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+ </div>
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+
851
+ <div id="errors">
852
+ <h1 class="sub-report-title">Errors</h1>
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+ <p>No errors to report!</p>
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