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Alteration of brain structural connectivity in progression of Parkinson's disease: A connectome-wide network analysis.

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机构: [a]Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China [b]Peng Cheng Laboratory, Shenzhen, China [c]MindsGo Shenzhen Life Science Co. Ltd, Shenzhen, China [d]Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China [e]National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China [f]Department of Radiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China [g]Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Disease, Beijing, China
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关键词: DTI Parkinson disease Idiopathic rapid eye movement sleep behaviour disorder Brain structural network Multivariate distance matrix regression

摘要:
Pinpointing the brain dysconnectivity in idiopathic rapid eye movement sleep behaviour disorder (iRBD) can facilitate preventing the conversion of Parkinson's disease (PD) from prodromal phase. Recent neuroimage investigations reported disruptive brain white matter connectivity in both iRBD and PD, respectively. However, the intrinsic process of the human brain structural network evolving from iRBD to PD still remains largely unknown. To address this issue, 151 participants including iRBD, PD and age-matched normal controls were recruited to receive diffusion MRI scans and neuropsychological examinations. The connectome-wide association analysis was performed to detect reorganization of brain structural network along with PD progression. Eight brain seed regions in both cortical and subcortical areas demonstrated significant structural pattern changes along with the progression of PD. Applying machine learning on the key connectivity related to these seed regions demonstrated better classification accuracy compared to conventional network-based statistic. Our study shows that connectome-wide association analysis reveals the underlying structural connectivity patterns related to the progression of PD, and provide a promising distinct capability to predict prodromal PD patients.Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

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出版当年[2020]版:
大类 | 2 区 医学
小类 | 2 区 神经成像
最新[2023]版:
大类 | 2 区 医学
小类 | 2 区 神经成像
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出版当年[2019]版:
Q2 NEUROIMAGING
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Q2 NEUROIMAGING

影响因子: 最新[2023版] 最新五年平均 出版当年[2019版] 出版当年五年平均 出版前一年[2018版] 出版后一年[2020版]

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第一作者机构: [a]Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China
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通讯机构: [a]Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, China [b]Peng Cheng Laboratory, Shenzhen, China [d]Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China [e]National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China [g]Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Disease, Beijing, China [*1]Department of Electronics and Information, Harbin Institute of Technology at Shenzhen, Rm 1206, Information Building, HIT Campus, Shenzhen University Town, Nanshan District, Shenzhen, Guangdong Province 518055, China
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