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README.md

@@ -1,7 +1,24 @@
-# Description
+---
+license: odc-by
+language:
+  - en
+task_categories:
+  - image-classification
+tags:
+  - medical
+  - brain-data
+  - mri
+pretty_name: 3D Brain Structure MRI Scans
+---
+# Dataset Description
+**Homepage:** [Radiata](https://radiata.ai/)
+**Contact:** [Jesse Brown](https://huggingface.co/jesse-radiata)
+
+
+## Dataset Summary
 3794 3D structural MRI brain scans (T1-weighted MPRAGE NIfTI files) from 2607 individuals included in five publicly available datasets: [IXI](https://brain-development.org/ixi-dataset/), [DLBS](https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html), [NKI-RS](https://fcon_1000.projects.nitrc.org/indi/enhanced/sharing_neuro.html), [OASIS-1](https://sites.wustl.edu/oasisbrains/home/oasis-1/), and [OASIS-2](https://sites.wustl.edu/oasisbrains/home/oasis-2/). Subjects have a mean age of 45 ± 24. 3773 scans come from cognitively normal individuals and 261 scans from individuals with an Alzheimer's disease clinical diagnosis. Scans dimensions are 113x137x113, 1.5mm^3 resolution, aligned to MNI152 space (see methods).
 
-Scans are processed and no protected health information (PHI) is included - only the skull-stripped scan, integer age, biological sex, and clinical diagnosis. [Radiata](https://radiata.ai/) compiles and processes publicly available neuroimaging datasets to create this open, unified, and harmonized dataset. For more information see https://radiata.ai/public-studies. Example uses including developing foundation-like models or tailored models for brain age prediction and disease classification.
+Scans have been processed and all protected health information (PHI) is excluded. Only the skull-stripped scan, integer age, biological sex, clinical diagnosis, and scan metadata are included. [Radiata](https://radiata.ai/) compiles and processes publicly available neuroimaging datasets to create this open, unified, and harmonized dataset. For more information see https://radiata.ai/public-studies. Example uses including developing foundation-like models or tailored models for brain age prediction and disease classification.
 
 
 # License
@@ -69,7 +86,7 @@ brain-structure/
 ```
 
 
-# Example usage:
+# Example usage
 ```
 # install Hugging Face Datasets library
 pip install datasets
@@ -159,7 +176,7 @@ Citation: OASIS-2: Longitudinal: https://doi.org/10.1162/jocn.2009.21407
 # Methods
 ## Image processing 
 T1-weighted structural MRI scans were processed with [CAT12](https://neuro-jena.github.io/cat12-help/) ([Gaser et al, 2024](https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae049/7727520)). The image processing steps were:  
-- correct for bias, noise and intensity
+- correct for bias, noise, and intensity
 - mask to brain-only (gray matter + white matter + CSF)
 - register to ICBM 2009c Nonlinear Asymmetric space (MNI152NLin2009cAsym 1.5mm^3) using linear affine registration with 12 degrees of freedom in [FSL FLIRT](https://fsl.fmrib.ox.ac.uk/fsl/docs/#/registration/flirt/index) ('flirt -in t1.nii.gz -ref mni_icbm152_t1_tal_nlin_asym_09c_brain_1_5_mm.nii.gz -dof 12 -noresampblur').  
 
@@ -177,8 +194,8 @@ Scans were partitioned into train/validation/test datasets with a 80%/10%/10% sp
   author    = {Jesse Brown and Clayton Young},
   title     = {Brain-Structure: A Collection of Processed Structural MRI Scans},
   year      = {2025},
-  url          = { https://huggingface.co/datasets/radiata-ai/brain-structure },
+  url          = {https://huggingface.co/datasets/radiata-ai/brain-structure},
   note      = {Version 1.0},
-  publisher    = { Hugging Face }
+  publisher    = {Hugging Face}
 }
 ```