Connectivity cluster analysis for discovering discriminative subnetworks in schizophrenia.

ABSTRACT: In this manuscript, we present connectivity cluster analysis (CoCA), a novel computational framework that takes advantage of structure of the brain networks to magnify reproducible signals and quash noise. Resting state functional Magnetic Resonance Imaging (fMRI) data that is used in estimating functional brain networks is often noisy, leading to reduced power and inconsistent findings across independent studies. There is a need for techniques that can unearth signals in noisy datasets, while addressing redundancy in the functional connections that are used for testing association. CoCA is a data driven approach that addresses the problems of redundancy and noise by first finding groups of region pairs that behave in a cohesive way across the subjects. These cohesive sets of functional co

SUBMITTER: Atluri G 

PROVIDER: S-EPMC6869283 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature