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

被引:21
作者
Yang, Yanwu [1 ]
Ye, Chenfei [2 ]
Sun, Junyan [7 ]
Liang, Li [1 ]
Lv, Haiyan [3 ]
Gao, Linlin [7 ]
Fang, Jiliang [6 ]
Ma, Ting [1 ,2 ,4 ,5 ]
Wu, Tao [7 ]
机构
[1] Harbin Inst Technol Shenzhen, Dept Elect & Informat Engn, Rm 1206,Informat Bldg,HIT Campus, Shenzhen 518055, Guangdong, Peoples R China
[2] Peng Cheng Lab, Shenzhen, Peoples R China
[3] MindsGo Shenzhen Life Sci Co Ltd, Shenzhen, Peoples R China
[4] Capital Med Univ, Adv Innovat Ctr Human Brain Protect, Beijing, Peoples R China
[5] Capital Med Univ, Natl Clin Res Ctr Geriatr Disorders, Xuanwu Hosp, Beijing, Peoples R China
[6] China Acad Chinese Med Sci, Guanganmen Hosp, Dept Radiol, Beijing, Peoples R China
[7] Capital Med Univ, Xuanwu Hosp, Natl Clin Res Ctr Geriatr Dis, Dept Neurobiol Neurol & Geriatr, Beijing, Peoples R China
关键词
DTI; Parkinson disease; Brain structural network; Multivariate distance matrix regression; Idiopathic rapid eye movement sleep behaviour disorder; SLEEP BEHAVIOR DISORDER; FUNCTIONAL CONNECTIVITY; SPHERICAL-DECONVOLUTION; DELAYED EMERGENCE; DIFFUSION MRI; ASSOCIATION; VALIDATION; PATHOLOGY; CRITERIA; PATTERN;
D O I
10.1016/j.nicl.2021.102715
中图分类号
R445 [影像诊断学];
学科分类号
100207 ;
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.
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页数:11
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