Getting started¶
graynet
currently offers the following streams of processing:
Cortical graynet : using vertex-wise ROIs defined on the cortex. This is useful to analyze network-level features based on cortical thickness, curvature, sulcal depth and gyrification. Base-level features can easily obtained from running Freesurfer
Volumetric graynet stream : using voxel-wise ROIs defined over the whole brain relying on a volumetric atlas. This is useful to analyze network-level features based on gray matter density dervied from voxel-based morphometry (VBM) or similar approaches. Base-level features can easily obtained from CAT12 toolbox within the SPM ecosystem.
We plan to offer the following soon:
support for other VBM tools such as SPM, ANTs, FSL, Freesurfer, or another suitable package, within the Volumetric graynet stream, .
Support for additional input formats for the Cortical graynet stream: CIVET , ANTs etc.
If you are interested in contributing, please take a look at the Contributing document and reach out to me. Thanks!
In both streams, in addition to the computation of pair-wise network-level features, graynet
will help you compute ROI-wise statistics (individual, not pair-wise) for visualization (median thickness in PCG, or variance in GM density within amygdala), as well as to serve as a baseline for network-level features.
The package offers both Command line interface and an API, to better integrate with your workflow. However, the CLI is the recommended/most-tested gateway.
For uniform processing across subjects, graynet
needs:
an atlas with pre-defined ROIs
each subject be registered to that atlas (vertex- or voxel-wise), so ROIs correspond across all subjects
the extracted features are in a format readable by
nibabel
(Freesurfer formats and Nifti are strongly recommended)
The following steps should help you get started and going quickly:
Install graynet using this command in a terminal:
pip install -U graynet
Refer to Cortical graynet and Volumetric graynet pages for more details on the individual streams of processing and examples.
If you run into any issues, or have a feature wish or suggestions, please let me know here by opening an issue.
Thanks for trying out graynet. I’d appreciate if you can cite it using the details in Citation.