title: Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer's Disease.
abstract: 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.
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Zhengjia
authors.initials: Z
authors.lastname: Dai
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA.
authors.forename: Chaogan
authors.initials: C
authors.lastname: Yan
authors.affiliation: Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China.
authors.forename: Kuncheng
authors.initials: K
authors.lastname: Li
authors.affiliation: Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China.
authors.forename: Zhiqun
authors.initials: Z
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research.
authors.forename: Jinhui
authors.initials: J
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Miao
authors.initials: M
authors.lastname: Cao
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Qixiang
authors.initials: Q
authors.lastname: Lin
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Ni
authors.initials: N
authors.lastname: Shu
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Mingrui
authors.initials: M
authors.lastname: Xia
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Yanchao
authors.initials: Y
authors.lastname: Bi
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000473:pathology / Q000503:physiopathology*; D001284:Atrophy; D001921:Brain / Q000473:pathology / Q000503:physiopathology*; D063132:Connectome / Q000379:methods*; D018450:Disease Progression; D005260:Female; D066128:Gray Matter / Q000473:pathology / Q000503:physiopathology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D009415:Nerve Net / Q000473:pathology / Q000503:physiopathology; D009483:Neuropsychological Tests; D015203:Reproducibility of Results
keywords: PCC/PCu; connectome; functional connectivity; graph theory; module