Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis.

ABSTRACT: Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated "eigenconnectivity" patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF

SUBMITTER: Hirayama JI 

PROVIDER: S-EPMC5176286 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature