Subject-wise networks from structural MRI (thickness, gray matter density, curvature, gyrification)
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
docs
example_data/freesurfer
graynet
scripts
.gitattributes
.gitignore
.travis.yml
CODE_OF_CONDUCT.md
CONTRIBUTING.md
LICENSE
MANIFEST.in
Makefile
README.md
cmd2pkg
requirements.txt
setup.cfg
setup.py
versioneer.py

README.md

graynet

DOI Build Status Code Health Codacy Badge [Python versions]

Individualized single-subject networks from T1 mri features such as cortical thickness, gray matter density, subcortical morphometric features, gyrification and curvature.

Applicable for whenever network-level features are useful, among which common use cases are

  • biomarker development and
  • brain-behaviour relationships (e.g. for the diagnosis and prognosis of many brain disorders such as Alzheimer's, Parkinson's, Schizophrenia and the like).
  • aging (changes in network properties over age and their relations to other variables)

Docs: https://raamana.github.io/graynet/

Quick Illustration:

graynet_flyer

Installation

pip install -U graynet

Thanks.