Meta-Connectomic Analysis Reveals Commonly Disrupted Functional Architectures in Network Modules and Connectors across Brain Disorders. (9th November 2017)

• Record Type: Journal Article
• Authors: Sha, Zhiqiang; Xia, Mingrui; Lin, Qixiang; Cao, Miao; Tang, Yanqing; Xu, Ke; Song, Haiqing; Wang, Zhiqun; Wang, Fei; Fox, Peter T; Evans, Alan C; He, Yong
• Is Part Of: Cerebral cortex. Volume 28:Number 12(2018)
• Page Start: 4179
• Page End: 4194
• Publication Date: 2017-11-09
• Subjects: connectomics -- connector -- meta-analysis -- module -- resting-state fMRI; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bhx273
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract: Neuropsychiatric disorders are increasingly conceptualized as disconnection syndromes that are associated with abnormal network integrity in the brain. However, whether different neuropsychiatric disorders show commonly dysfunctional connectivity architectures in large-scale brain networks remains largely unknown. Here, we performed a meta-connectomic study to identify disorder-related functional modules and brain regions by combining meta-analyses of 182 published resting-state functional MRI studies in 11 neuropsychiatric disorders and graph-theoretical analyses of 3 independent resting-state functional MRI datasets with healthy and diseased populations (Alzheimer's disease and major depressive disorder [MDD]). Three major functional modules, the default mode, frontoparietal, and sensorimotor networks were commonly abnormal across disorders. Moreover, most of the disorders preferred to target the network connector nodes that were primarily involved in intermodule communications and multiple cognitive components. Apart from these common dysfunctions, different brain disorders were associated with specific alterations in network modules and connector regions. Finally, these meta-connectomic findings were confirmed by two empirical example cases of Alzheimer's disease and MDD. Collectively, our findings shed light on the shared biological mechanisms of network dysfunctions of diverse disorders and have implications for clinical diagnosis and treatment from a network perspective.