Transdiagnostic time-varying dysconnectivity across major psychiatric disorders

• Main Authors: Dong, M. (Author), Duan, J. (Author), Feng, R. (Author), Han, S. (Author), Jiang, X. (Author), Li, C. (Author), Tang, Y. (Author), Wang, F. (Author), Wei, Y. (Author), Womer, F.Y (Author), Xu, K. (Author), Yin, Y. (Author), Zhang, L. (Author), Zhang, X. (Author)
• Format: Article
• Language: English
• Published: John Wiley and Sons Inc 2021
• Subjects: adult; Adult; Article; bipolar disorder; Bipolar Disorder; brain cortex; Cerebral Cortex; connectome; Connectome; controlled study; Depressive Disorder, Major; diagnostic imaging; dynamic functional connectivity; false discovery rate; female; Female; frontoparietal network; functional connectivity; functional magnetic resonance imaging; human; Humans; k means clustering; Magnetic Resonance Imaging; major clinical study; major depression; major depressive disorder; male; Male; nerve cell network; Nerve Net; nuclear magnetic resonance imaging; pathophysiology; post hoc analysis; priority journal; procedures; resting state network; salience network; schizophrenia; Schizophrenia; somatomotor network; statistical analysis; time series analysis; transdiagnostic study; visual network; young adult; Young Adult
• Online Access: View Fulltext in Publisher

 Dynamic functional connectivity (DFC) analysis can capture time-varying properties of connectivity. However, studies on large samples using DFC to investigate transdiagnostic dysconnectivity across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are rare. In this study, we used resting-state functional magnetic resonance imaging and a sliding-window method to study DFC in a total of 610 individuals (150 with SZ, 100 with BD, 150 with MDD, and 210 healthy controls [HC]) at a single site. Using k-means clustering, DFCs were clustered into three functional connectivity states: one was a more frequent state with moderate positive and negative connectivity (State 1), and the other two were less frequent states with stronger positive and negative connectivity (State 2 and State 3). Significant 4-group differences (SZ, BD, MDD, and HC groups; q <.05, false-discovery rate [FDR]-corrected) in DFC were nearly only in State 1. Post hoc analyses (q <.05, FDR-corrected) in State 1 showed that transdiagnostic dysconnectivity patterns among SZ, BD and MDD featured consistently decreased connectivity within most networks (the visual, somatomotor, salience and frontoparietal networks), which was most obvious in both range and extent for SZ. Our findings suggest that there is more common dysconnectivity across SZ, BD and MDD than we previously expected and that such dysconnectivity is state-dependent, which provides new insights into the pathophysiological mechanism of major psychiatric disorders. © 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.