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Cereb Cortex. 2018 Dec 1;28(12):4179-4194. doi: 10.1093/cercor/bhx273.

Meta-Connectomic Analysis Reveals Commonly Disrupted Functional Architectures in Network Modules and Connectors across Brain Disorders.

Sha Z1,2,3, Xia M1,2,3, Lin Q1,2,3, Cao M1,2,3, Tang Y4,5, Xu K6, Song H7, Wang Z8,9, Wang F4,5,6, Fox PT10,11,12,13, Evans AC14, He Y1,2,3.

Author information

1
National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
2
Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
3
IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
4
Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
5
Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
6
Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China.
7
Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
8
Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China.
9
Department of Radiology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.
10
Research Imaging Institute, University of Texas Health Science Center at San Antonio, TX, USA.
11
Department of Radiology, University of Texas Health Science Center at San Antonio, TX, USA.
12
South Texas Veterans Health Care System at San Antonio, TX, USA.
13
Shenzhen University School of Medicine, Shenzhen, China.
14
McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada.

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.

PMID:
29136110
DOI:
10.1093/cercor/bhx273

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