Connectivity cluster analysis for discovering discriminative subnetworks in schizophrenia

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 connections are further tested for association with the disease. CoCA is applied in the context of patients with schizophrenia, a disorder characterized as a disconnectivity syndrome. Our results suggest that CoCA can find reproducible sets of functional connections that behave cohesively. Applying this technique, we found that the connectivity clusters joining thalamus to parietal, temporal, and visuoparietal regions are highly discriminative of schizophrenia patients as well as reproducible using retest data and replicable in an independent confirmatory sample.