CAT12.8.1 derivatives for ADHD200

Dataset specific information

The ADHD200 contains derivatives of 961 subjects and data from 4 sessions. The dataset was recorded in 10 sites.

Sites

NYU, OHSU, NeuroIMAGE, Peking_3, Pittsburgh, Brown, WashU, Peking_1, KKI, Peking_2

Sessions

['1', '2', '3', '4']

Subjects

NYU:
N = 263 subjects
Distribution of sexes: {'Male': 0.65, 'Female': 0.35}
Mean age (years): 11.35
Age range (years): (7.17, 17.96)

OHSU:
N = 113 subjects
Distribution of sexes: {'Male': 0.54, 'Female': 0.46}
Mean age (years): 9.1
Age range (years): (7.17, 12.5)

NeuroIMAGE:
N = 73 subjects
Distribution of sexes: {'Male': 0.59, 'Female': 0.41}
Mean age (years): 17.64
Age range (years): (11.05, 26.31)

Peking_3:
N = 42 subjects
Distribution of sexes: {'Male': 1.0}
Mean age (years): 13.24
Age range (years): (11.0, 16.0)

Pittsburgh:
N = 98 subjects
Distribution of sexes: {'Male': 0.56, 'Female': 0.44}
Mean age (years): 15.06
Age range (years): (10.11, 20.45)

Brown:
N = 26 subjects
Distribution of sexes: {'Female': 0.65, 'Male': 0.35}
Mean age (years): 14.54
Age range (years): (8.5, 17.87)

WashU:
N = 60 subjects
Distribution of sexes: {'Male': 0.53, 'Female': 0.47}
Mean age (years): 11.52
Age range (years): (7.09, 21.83)

Peking_1:
N = 136 subjects
Distribution of sexes: no information available
Mean age (years): no information available
Age range (years): no information available

KKI:
N = 83 subjects
Distribution of sexes: {'Male': 0.55, 'Female': 0.45}
Mean age (years): 10.24
Age range (years): (8.02, 12.99)

Peking_2:
N = 67 subjects
Distribution of sexes: {'Male': 0.99, 'Female': 0.01}
Mean age (years): 12.12
Age range (years): (8.75, 15.83)

Additional Dataset Metadata

References and Links: The ADHD-200 Sample Attention Deficit Hyperactivity Disorder

Dataset DOI:
no information available

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General information on the CAT 12.8.1 derivatives

This dataset provides a multitude of computational anatomy derivatives computed with CAT12, ready for statistical analysis or for model building.

CAT12 Toolbox

The Computational Anatomy Toolbox CAT12 (https://neuro-jena.github.io/cat) is an extension to SPM12 (www.fil.ion.ucl.ac.uk/spm) in Matlab/Octave, as compiled standalone version (https://neuro-jena.github.io/enigma-cat12/#standalone) or as Singularity container (https://github.com/inm7-sysmed/ENIGMA-cat12-container).
CAT12 covers diverse morphometric analysis methods such as Voxel-based morphometry (VBM), surface-based morphometry (SBM), deformation-based morphometry (DBM), and label- or region-based morphometry (RBM).

Brief description: https://neuro-jena.github.io/cat12-help/#basic_vbm)
More details: https://neuro-jena.github.io/cat12-help/#process_details.

Types of derivatives

• 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. https://neuro-jena.github.io/cat12-help/#roi

• 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

• 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. https://neuro-jena.github.io/cat12-help/#sbm

General dataset structure

• All inputs (i.e. building blocks from other sources) are located in a datalad subdataset: inputs/ADHD200.

• All custom code (e.g. code used for preprocessing the data) is located in code/.

• The Singularity container to reproduce the preprocessing is in a datalad subdataset: code/pipeline.

Derivatives structure

Generally each subject has processed data in the following folder structure:

── sub-*
    ── (ses-*) (if applicable)
        ├── label
        ├── mri
        ├── report
        └── surf

(Note: The session structure only applies to certain datasets and is not present if this information does not apply to the dataset (see the dataset specific information above for details).)

• label - contains region of interest (ROI) mean values of all volume and surface atlases included in CAT12

• mri - contains modulated tissue propability estimates for vbm analysis

Atlas parcellations in subject space: cat_, cobra_, ..., julichbrain_, thalamus_
Image space prefix: m = modulated; w = warped (spatially normalized using Shooting)
Image data prefix: p = partial volume (PV) segmentation; 0 = PV label; 1 = GM; 2 = WM; 3 = CSF;
Segmented Images: mwp[0123].nii
Bias, noise and intensity corrected T1 image: [w]m.nii
Linear transforms rigid body and affine: t = linear / it = inverse linear transformation matrix

• report - contains full processing catlog, jpg sheet for initial quality assessment and processing parameters and quality metrics in xml

• surf - contains surface measures, thickness estimates and possibly additional resampled and smoothed surface parameters like sulcus depth and gyrification