Dysregulated Brain Dynamics in a Triple-Network Saliency Model of Schizophrenia and Its Relation to Psychosis.

Dynamic interactions between the salience network and two other large-scale brain networks—the central executive network and default mode network—are reduced, less persistent, and more variable in patients with schizophrenia compared with well-matched control subjects. These aberrant network dynamics distinguish patients from controls with 78% and 80% accuracy in two independent cohorts. Crucially, the degree of disruption correlates with positive psychotic symptoms but not with negative symptoms. The findings support an aberrant saliency model of psychosis, in which dysregulated time-varying engagement of the salience network contributes to the neurobiology of schizophrenia.

Study at a glance

Abstract

BACKGROUND Schizophrenia is a highly disabling psychiatric disorder characterized by a range of positive "psychosis" symptoms. However, the neurobiology of psychosis and associated systems-level disruptions in the brain remain poorly understood. Here, we test an aberrant saliency model of psychosis, which posits that dysregulated dynamic cross-network interactions among the salience network (SN), central executive network, and default mode network contribute to positive symptoms in patients with schizophrenia. METHODS Using task-free functional magnetic resonance imaging data from two independent cohorts, we examined 1) dynamic time-varying cross-network interactions among the SN, central executive network, and default mode network in 130 patients with schizophrenia versus well-matched control subjects; 2) accuracy of a saliency model-based classifier for distinguishing dynamic brain network interactions in patients versus control subjects; and 3) the relation between SN-centered network dynamics and clinical symptoms. RESULTS In both cohorts, we found that dynamic SN-centered cross-network interactions were significantly reduced, less persistent, and more variable in patients with schizophrenia compared with control subjects. Multivariate classification analysis identified dynamic SN-centered cross-network interaction patterns as factors that distinguish patients from control subjects, with accuracies of 78% and 80% in the two cohorts, respectively. Crucially, in both cohorts, dynamic time-varying measures of SN-centered cross-network interactions were correlated with positive, but not negative, symptoms. CONCLUSIONS Aberrations in time-varying engagement of the SN with the central executive network and default mode network is a clinically relevant neurobiological signature of psychosis in schizophrenia. Our findings provide strong evidence for dysregulated brain dynamics in a triple-network saliency model of schizophrenia and inform theoretically motivated systems neuroscience approaches for characterizing aberrant brain dynamics associated with psychosis.