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4.6 KiB
Markdown
97 lines
No EOL
4.6 KiB
Markdown
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# CAT12.8.1 derivatives for AOMIC-PIOP2
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## Dataset specific information
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The AOMIC-PIOP2 contains derivatives of 226 subjects.
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### Subjects
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N = 226 subjects
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Distribution of sexes: {'F': 0.57, 'M': 0.43}
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Mean age: 21.96 years
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Age range: (18.25, 25.75) years
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### Additional Dataset Metadata
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**References and Links**:
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['https://nilab-uva.github.io/AOMIC.github.io/', 'https://github.com/NILAB-UvA/PIOP2']
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**Dataset DOI**:
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10.18112/openneuro.ds002790.v2.0.0
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----------------------------
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## General information on the CAT 12.8.1 derivatives
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***This dataset provides a multitude of computational anatomy derivatives
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computed with CAT12, ready for statistical analysis or for model building.***
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### CAT12 Toolbox
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The **Computational Anatomy Toolbox CAT12** (https://neuro-jena.github.io/cat)
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is an extension to SPM12 (www.fil.ion.ucl.ac.uk/spm) in Matlab/Octave,
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as compiled standalone version (https://neuro-jena.github.io/enigma-cat12/#standalone)
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or as Singularity container (https://github.com/inm7-sysmed/ENIGMA-cat12-container).
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CAT12 covers diverse morphometric analysis methods such as Voxel-based morphometry
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(VBM), surface-based morphometry (SBM), deformation-based morphometry (DBM),
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and label- or region-based morphometry (RBM).
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***Brief description***: https://neuro-jena.github.io/cat12-help/#basic_vbm)
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***More details***: https://neuro-jena.github.io/cat12-help/#process_details.
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### Types of derivatives
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- **RBM: Region Based Morphometry** estimates the mean tissue volumes (and additional surface parameters such as cortical thickness) for different volume and surface-based atlas maps. All of these results are estimated in the native space before any spatial normalization and the mean value inside the ROI is estimated.
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https://neuro-jena.github.io/cat12-help/#roi
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- **VBM: Voxel-Based Morphometry** is based on the identification of specific brain tissue types – commonly gray and white matter (GM, WM) as well as CerebroSpinal Fluid (CSF) via segmentation. Every voxel contains the probability or volume fraction of GM, WM and CSF are transformed to match a standard template. The local amount of deformation in every voxel (=Jacobian determinant) is utilized to modulate tissue probability/fraction to quantify local tissue volume. https://neuro-jena.github.io/cat12-help/#vox_proc
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- **SBM: Surface-Based Morphometry** uses brain surface meshes for spatial registration, which may increase the accuracy of brain registration compared to volume-based registration. This permits new forms of analyses, such as gyrification which measure surface complexity in 3D or cortical thickness. In addition, inflation or spherical mapping of the cortical surface mesh raises the buried sulci to the surface so that mapped functional activity in these regions can be made easily visible.
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https://neuro-jena.github.io/cat12-help/#sbm
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### General dataset structure
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- All inputs (i.e. building blocks from other sources) are located in a datalad subdataset:
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`inputs/AOMIC-PIOP2`.
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- All custom code (e.g. code used for preprocessing the data) is located in `code/`.
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- The Singularity container to reproduce the preprocessing is in a datalad subdataset: `code/pipeline`.
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### Derivatives structure
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Generally each subject has processed data in the following folder structure:
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```
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── sub-*
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── (ses-*) (if applicable)
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├── label
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├── mri
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├── report
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└── surf
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```
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(***Note:*** The session structure only applies to certain datasets and is not present
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if this information does not apply to the dataset (see the dataset specific information above for details).)
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- **label** - contains region of interest (ROI) mean values of all volume and surface atlases included in CAT12
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- **mri** - contains modulated tissue propability estimates for vbm analysis
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> **Atlas parcellations** in subject space:
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cat_, cobra_, ..., julichbrain_, thalamus_
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**Image space prefix**: m = modulated;
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w = warped (spatially normalized using Shooting)
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**Image data prefix**:
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p = partial volume (PV) segmentation; 0 = PV label;
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1 = GM;
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2 = WM;
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3 = CSF;
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**Segmented Images**: mwp[0123]*.nii
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**Bias, noise and intensity corrected T1 image**: [w]m*.nii
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**Linear transforms** rigid body and affine: t = linear / it = inverse linear transformation matrix
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- **report** - contains full processing catlog, jpg sheet for initial quality assessment and processing parameters and quality metrics in xml
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- **surf** - contains surface measures, thickness estimates and possibly additional resampled and smoothed surface parameters like sulcus depth and gyrification
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