raamana/graynet
Subject-wise networks from structural MRI, both vertex- and voxel-wise features (thickness, GM density, curvature, gyrification)
This tool helps neuroscientists and medical researchers analyze structural MRI brain scans by converting individual subject's anatomical measurements into region-of-interest (ROI) networks. It takes raw brain imaging data (like cortical thickness or gray-matter density) and produces interpretable network edge weights and summary statistics for each subject. Researchers can use this for biomarker development, disease classification, or brain-behavior association studies.
Available on PyPI.
Use this if you need to transform structural neuroimaging features from individual subjects into quantitative brain networks or ROI summary statistics for downstream statistical analysis or machine learning.
Not ideal if your primary interest is functional connectivity or if you require backward compatibility with older graynet output formats without reprocessing your data.
Stars
39
Forks
9
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 22, 2026
Monthly downloads
384
Commits (30d)
0
Dependencies
7
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