Functional Network Disruptions in Schizophrenia

Abstract

It has been long recognized that schizophrenia, unlike certain other mental disorders, appears to be delocalized, i.e., difficult to attribute to a dysfunction of a few specific brain areas, and may be better understood as a disruption of brain’s emergent network properties. In this chapter, we focus on topological properties of functional brain networks obtained from fMRI data, and demonstrate that some of those properties can be used as discriminative features of schizophrenia in multivariate predictive setting. While the prior work on schizophrenia networks has been primarily focused on discovering statistically significant differences in network properties, this work extends the prior art by exploring the generalization (prediction) ability of network models for schizophrenia, which is not necessarily captured by such significance tests. Moreover, we show that significant disruption of the topological and spatial structure of functional MRI networks in schizophrenia (a) cannot be explained by a disruption to area-based task-dependent responses, i.e., indeed relates to the emergent properties, (b) is global in nature, affecting most dramatically long-distance correlations, and (c) can be leveraged to achieve high classification accuracy (93%) when discriminating between schizophrenic vs. control subjects based just on a single fMRI experiment using a simple auditory task.