PyNets: Automated network analysis for rsfMRI-dMRI

[Remi-Gau]

Derek P., Aki, Charles, Andrew, Dan
https://github.com/dpisner453/PyNets

PyNets automates functional and diffusion-weighted MRI network analysis in python using the Networkx package.

Problem: Network analysis packages for neuroimaging are not implemented in python, preventing them from using the power of nipype.

Solution: In PyNets, we harness the power of nipype, nilearn, and networkx to automatically generate a range of graph theory metrics on a subject-by-subject basis.

More specifically: PyNets utilizes nilearn and networkx tools in a nipype workflow to automatically generate rsfMRI networks (whole-brain, or RSN's like the DMN) based on a variety of atlas-defined parcellation schemes, and then automatically plot associated adjacency matrices, connectome visualizations, and extract the following graph theoretical measures from those networks (both binary and weighted undirected versions, with a user-defined thresholding): global efficiency, local efficiency, transitivity, degree assortativity coefficient, average clustering coefficient, average shortest path length, betweenness centrality, degree pearson correlation coefficient, number of cliques