机构:[1]National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China,[2]Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China,[3]IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China,[4]Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, 110001, China,[5]Brain Function Research Section, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China,[6]Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning 110001, China,[7]Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China,神经内科首都医科大学宣武医院[8]Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China,放射科首都医科大学宣武医院[9]Department of Radiology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China,[10]Research Imaging Institute, University of Texas Health Science Center at San Antonio, TX 78229, USA,[11]Department of Radiology, University of Texas Health Science Center at San Antonio, TX 78229, USA,[12]South Texas Veterans Health Care System at San Antonio, TX 78229, USA,[13]Shenzhen University School of Medicine, Shenzhen 518061, China[14]McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada H3A2B4
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.
基金:
Natural Science Foundation of China [91432115, 81620108016, 81671767, 81401479, 31521063, 81571648]; Changjiang Scholar Professorship Award [T2015027]; Beijing Municipal Science & Technology Commission [Z161100000216152, Z151100003915082]; Beijing Brain Project [Z161100000216125]; Fundamental Research Funds for the Central Universities [2017XTCX04, 2015KJJCA13]
第一作者机构:[1]National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China,[2]Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China,[3]IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China,
共同第一作者:
通讯作者:
通讯机构:[*]National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Key Laboratory of Brain Imaging and Connectomics, IDG/McGovern Institute for Brain Research, Beijing Normal University, China.
推荐引用方式(GB/T 7714):
Zhiqiang Sha,Mingrui Xia,Qixiang Lin,et al.Meta-Connectomic Analysis Reveals Commonly Disrupted Functional Architectures in Network Modules and Connectors across Brain Disorders[J].CEREBRAL CORTEX.2018,28(12):4179-4194.doi:10.1093/cercor/bhx273.
APA:
Zhiqiang Sha,Mingrui Xia,Qixiang Lin,Miao Cao,Yanqing Tang...&Yong He.(2018).Meta-Connectomic Analysis Reveals Commonly Disrupted Functional Architectures in Network Modules and Connectors across Brain Disorders.CEREBRAL CORTEX,28,(12)
MLA:
Zhiqiang Sha,et al."Meta-Connectomic Analysis Reveals Commonly Disrupted Functional Architectures in Network Modules and Connectors across Brain Disorders".CEREBRAL CORTEX 28..12(2018):4179-4194