Individualized single-subject networks from T1 mri features such as cortical thickness and gray matter density.
- Individualized single-subject networks from T1-weighted magnetic resonance imaging (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.
- 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).
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size graynet-0.4.6-py3-none-any.whl (26.1 MB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size graynet-0.4.6.tar.gz (26.1 MB)||File type Source||Python version None||Upload date||Hashes View|