Transdiagnostic time‐varying dysconnectivity across major psychiatric disorders

• Published in: Human brain mapping Vol. 42; no. 4; pp. 1182 - 1196
• Main Authors: Li, Chao, Dong, Mengshi, Womer, Fay Y., Han, Shaoqiang, Yin, Yi, Jiang, Xiaowei, Wei, Yange, Duan, Jia, Feng, Ruiqi, Zhang, Luheng, Zhang, Xizhe, Wang, Fei, Tang, Yanqing, Xu, Ke
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.03.2021
• Subjects: Adult; Bipolar disorder; Bipolar Disorder - diagnostic imaging; Bipolar Disorder - physiopathology; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - physiopathology; Clustering; Connectome - methods; Depressive Disorder, Major - diagnostic imaging; Depressive Disorder, Major - physiopathology; dynamic functional connectivity; Female; Functional magnetic resonance imaging; Humans; Magnetic Resonance Imaging; major depressive disorder; Male; Mental depression; Mental disorders; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Schizophrenia; Schizophrenia - diagnostic imaging; Schizophrenia - physiopathology; transdiagnostic study; Young Adult; dynamic functional connectivity; transdiagnostic study; bipolar disorder; schizophrenia; major depressive disorder
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.25285

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. We identified three functional connectivity states across multiple psychiatric disorders and healthy controls: one was a more frequent state with weak connectivity, and the other two were less frequent states with strong connectivity. Dysconnectivity in the psychiatric patients was found nearly only in the weak connectivity state. Psychiatric patients spent less time in the weak connectivity state than HC patients.