Kaustubh Supekar1, Weidong Cai2, Rajeev Krishnadas3, Lena Palaniyappan4, Vinod Menon5. 1. Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California. Electronic address: ksupekar@stanford.edu. 2. Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California. 3. Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Sackler Institute of Psychobiological Research, University of Glasgow, United Kingdom. 4. Department of Psychiatry and Robarts Research Institute, University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada. 5. Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, California; Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, California; Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, California. Electronic address: menon@stanford.edu.
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.
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.
Authors: Robert A McCutcheon; Toby Pillinger; Maria Rogdaki; Juan Bustillo; Oliver D Howes Journal: Transl Psychiatry Date: 2021-05-27 Impact factor: 6.222