CBICA: Identification of Sparse Connectivity Patterns in rsfMRI (SCPLearn)

This software is used to calculate Sparse Connectivity Patterns (SCPs) from resting state fMRI connectivity data. SCPs consist of those regions whose between-region connectivity co-varies across subjects. This algorithm was developed as a complementary approach to existing network identification methods.

SCPLearn has the following advantages:

Does not require thresholding of correlation matrices
Allows for both positive and negative correlations
Does not constrain the SCPs to have spatial/temporal orthogonality/independence
Provides group-common SCPs and subject-specific measures of average correlation within each SCP
Can be run within a hierarchical framework to get "primary" (large spatial extent) and "secondary" level (small spatial extent) SCPs

Subject-level coefficients can be used for subsequent group-level analysis.

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