Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network. Issue 7 (21st January 2019)

• Record Type: Journal Article
• Authors: Kottaram, Akhil; Johnston, Leigh A.; Cocchi, Luca; Ganella, Eleni P.; Everall, Ian; Pantelis, Christos; Kotagiri, Ramamohanarao; Zalesky, Andrew
• Is Part Of: Human brain mapping. Volume 40:Issue 7(2019)
• Page Start: 2212
• Page End: 2228
• Publication Date: 2019-01-21
• Subjects: default mode network -- hidden Markov model -- neural dynamics -- resting state fMRI -- resting state networks -- schizophrenia; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.24519
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting‐state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia ( n = 41) and healthy comparison individuals ( n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting‐state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8‐min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4–5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76–85%.