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Connectome-wide network analysis of white matter connectivity in Alzheimer's disease.

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收录情况： ◇ SCIE ◇ SSCI

作者：
Chenfei Ye[a,b] * ; Susumu Mori[c,d] ; Piu Chan[e,f,g,h] ; Ting Ma[a,b,e,i,*1] ;
机构： [a]Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China [b]Peng Cheng Laboratory, Shenzhen, Guangdong, China [c]The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA [d]F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA [e]National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China [f]Department of Neurobiology, Neurology and Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing Institute of Geriatrics, Beijing, China [g]Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, China [h]Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, China [i]Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
出处：
DOI： 10.1016/j.nicl.2019.101690
ISSN：

关键词： Diffusion tensor imaging Alzheimer's disease Mild cognitive impairment Connectome Multivariate regression Tractography

摘要：
A multivariate analytical strategy may pinpoint the structural connectivity patterns associated with Alzheimer's disease (AD) pathology in connectome-wide association studies. Diffusion magnetic resonance imaging data from 161 participants including subjects with healthy controls, AD, stable and converting mild cognitive impairment, were selected for group-wise comparisons. A multivariate distance matrix regression (MDMR) analysis was performed to detect abnormality in brain structural network along with disease progression. Based on the seed regions returned by the MDMR analysis, supervised learning was applied to evaluate the disease predictive performance. Nine brain regions, including the left orbital part of superior and middle frontal gyrus, the bilateral supplementary motor area, the bilateral insula, the left hippocampus, the left putamen, and the left thalamus demonstrated extremely significant structural pattern changes along with the progression of AD. The disease classification was more efficient when based on the key connectivity related to these seed regions than when based on whole-brain structural connectivity. MDMR analysis reveals brain network reorganization caused by AD pathology. The key structural connectivity detected in this study exhibits promising distinguishing capability to predict prodromal AD patients. Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

基金：
National Key Research and Development Program of China [2018YFC1312000]; Basic Research Foundation Key Project Track of Shenzhen Science and Technology Program [JCYJ20160509162237418, JCYJ20170413-110656460]; Basic Research Foundation of Shenzhen Science and Technology Program [JCYJ20150403161923510]; Beijing Municipal Administration of Hospitals' Mission Plan [SML20150803]; Beijing Municipal Science & Technology CommissionBeijing Municipal Science & Technology Commission [Z161100000216140, Z171100000117013]; Beijing Municipal Commission of Health and Family Planning [PXM2017_026283_000002]
语种：
外文
被引次数：
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PubmedID：
中科院(CAS)分区：
出版当年[2018]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
JCR分区：
出版当年[2017]版：
Q1 NEUROIMAGING
最新[2023]版：
Q2 NEUROIMAGING

影响因子： 最新[2023版] 最新五年平均 出版当年[2017版] 出版当年五年平均 出版前一年[2016版] 出版后一年[2018版]

第一作者：
第一作者机构： [a]Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China [b]Peng Cheng Laboratory, Shenzhen, Guangdong, China
通讯作者：
通讯机构： [a]Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong Province, China [*1]Department of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Rm 205C, C Building, HIT Campus, Shenzhen University Town, Nanshan District, 518055 Shenzhen, Guangdong Province, China. [b]Peng Cheng Laboratory, Shenzhen, Guangdong, China [e]National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China [i]Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
推荐引用方式(GB/T 7714)：
Chenfei Ye,Susumu Mori,Piu Chan,et al.Connectome-wide network analysis of white matter connectivity in Alzheimer's disease.[J].NEUROIMAGE-CLINICAL.2019,22:101690.doi:10.1016/j.nicl.2019.101690.
APA：
Chenfei Ye,Susumu Mori,Piu Chan&Ting Ma.(2019).Connectome-wide network analysis of white matter connectivity in Alzheimer's disease..NEUROIMAGE-CLINICAL,22,
MLA：
Chenfei Ye,et al."Connectome-wide network analysis of white matter connectivity in Alzheimer's disease.".NEUROIMAGE-CLINICAL 22.(2019):101690

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