Center for Biomedical Image Computing and Analytics SBIA License Yes University of Pennsylvania NITRC CBICA: Identification of Sparse Connectivity Patterns in rsfMRI (SCPLearn) Ashish Singh 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. 2018-7-30 1.0.0 CBICA: Identification of Sparse Connectivity Patterns in rsfMRI (SCPLearn) Clinical Neuroinformatics, MR, Computational Neuroscience, SBIA License