机构:[1]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research,[2]Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China,[3]Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA [4]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
Alzheimer's disease (AD) is associated not only with regional gray matter damages, but also with abnormalities in functional integration between brain regions. Here, we employed resting-state functional magnetic resonance imaging data and voxel-based graph-theory analysis to systematically investigate intrinsic functional connectivity patterns of whole-brain networks in 32 AD patients and 38 healthy controls (HCs). We found that AD selectively targeted highly connected hub regions (in terms of nodal functional connectivity strength) of brain networks, involving the medial and lateral prefrontal and parietal cortices, insula, and thalamus. This impairment was connectivity distance-dependent (Euclidean), with the most prominent disruptions appearing in the long-range connections (e.g., 100-130 mm). Moreover, AD also disrupted functional connections within the default-mode, salience and executive-control modules, and connections between the salience and executive-control modules. These disruptions of hub connectivity and modular integrity significantly correlated with the patients' cognitive performance. Finally, the nodal connectivity strength in the posteromedial cortex exhibited a highly discriminative power in distinguishing individuals with AD from HCs. Taken together, our results emphasize AD-related degeneration of specific brain hubs, thus providing novel insights into the pathophysiological mechanisms of connectivity dysfunction in AD and suggesting the potential of using network hub connectivity as a diagnostic biomarker.
基金:
the National Key Basic Research Program of China (grant nos 2013CB837300 and 2014CB846102),
the Natural Science Foundation of China (grant nos 81030028,81225012, 31221003, 81370037, and 81401479)
the Beijing Natural Science Foundation (grant no. Z111107067311036),
the Beijing Funding for Training Talents (grant no. 2012D009012000003),
the Major Project of National Social Science Foundation (grant no. 11&ZD186).
第一作者机构:[1]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research,[2]Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China,
通讯作者:
通讯机构:[1]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research,[2]Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China,
推荐引用方式(GB/T 7714):
Zhengjia Dai,Chaogan Yan,Kuncheng Li,et al.Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer's Disease[J].CEREBRAL CORTEX.2015,25(10):3723-3742.doi:10.1093/cercor/bhu246.
APA:
Zhengjia Dai,Chaogan Yan,Kuncheng Li,Zhiqun Wang,Jinhui Wang...&Yong He.(2015).Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer's Disease.CEREBRAL CORTEX,25,(10)
MLA:
Zhengjia Dai,et al."Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer's Disease".CEREBRAL CORTEX 25..10(2015):3723-3742