ggseg 0.1

Python module for ggseg-like visualizations

python-ggseg

Python module for ggseg-like visualizations.

Dependencies

Requires matplotlib>=3.4 and numpy>=1.21.

Install

pip install ggseg

Usage

In order to work with python-ggseg, the data should be prepared as a dictionary where each item is one region of a given atlas assigned with some numeric value. The current version includes three atlases: the Desikan-Killiany (DK) atlas , the Johns Hopkins University (JHU) white-matter atlas and the FreeSurfer aseg atlas.

Cortical ROIs (Desikan-Killiany)

Cortical ROI data such as using the DK atlas may be structured as follows:

{'bankssts_left': 1.1,
 'caudalanteriorcingulate_left': 1.0,
 'caudalmiddlefrontal_left': 2.6,
 'cuneus_left': 2.6,
 'entorhinal_left': 0.6,
 ...}

Then be passed to the ggseg.plot_dk function:

import ggseg
ggseg.plot_dk(data, cmap='Spectral', figsize=(15,15),
              background='k', edgecolor='w', bordercolor='gray',
              ylabel='Cortical thickness (mm)', title='Title of the figure')

The comprehensive list of applicable regions can be found in this folder.

Subcortical regions (FreeSurfer aseg atlas)

data = {'Left-Lateral-Ventricle': 12289.6,
        'Left-Thalamus': 8158.3,
        'Left-Caudate': 3463.3,
        'Left-Putamen': 4265.3,
        'Left-Pallidum': 1620.9,
        '3rd-Ventricle': 1635.6,
        '4th-Ventricle': 1115.6,
        ...}

ggseg.plot_aseg(data, cmap='Spectral',
                background='k', edgecolor='w', bordercolor='gray',
                ylabel='Volume (mm3)', title='Title of the figure')

The comprehensive list of applicable regions can be found in this folder.

White-matter fiber tracts (Johns Hopkins University)

data = {'Anterior thalamic radiation L': 0.3004812598228455,
        'Anterior thalamic radiation R': 0.2909256815910339,
        'Corticospinal tract L': 0.3517134189605713,
        'Corticospinal tract R': 0.3606771230697632,
        'Cingulum (cingulate gyrus) L': 0.3149917721748352,
        'Cingulum (cingulate gyrus) R': 0.3126821517944336,
        ...}

ggseg.plot_jhu(data_jhu, background='k', edgecolor='w', cmap='Spectral',
               bordercolor='gray', ylabel='Mean Fractional Anisotropy',
               title='Title of the figure')

The comprehensive list of applicable regions can be found in this folder.

Tests

The current development version of python-ggseg has a coverage rate close to 100%. The corresponding tests can be found in this folder.

Examples

A Jupyter Notebook with examples can be found there.