PSYCH OpenIR  > 中国科学院行为科学重点实验室
Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome
Sun, Yu1; Bi, Qiuhui2,3,4,5; Wang, Xiaoni1; Hu, Xiaochen6; Li, Huijie7,8; Li, Xiaobo9; Ma, Ting10; Lu, Jie11; Chan, Piu1,12,13; Shu, Ni2,3,4,5; Han, Ying1,12,13,14
First AuthorSun, Yu
2019-01-10
Source PublicationFRONTIERS IN NEUROLOGY
ISSN1664-2295
Volume9Pages:15
Contribution Rank8
Abstract

Background: Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention of Alzheimer's disease (AD). Previous brain network studies have suggested topological disruptions of the brain connectome in aMCI patients. However, whether brain connectome markers at baseline can predict longitudinal conversion from aMCI to AD remains largely unknown. Methods: In this study, 52 patients with aMCI and 26 demographically matched healthy controls from a longitudinal cohort were evaluated. During 2 years of follow-up, 26 patients with aMCI were retrospectively classified as aMCI converters and 26 patients remained stable as aMCI non-converters based on whether they were subsequently diagnosed with AD. For each participant, diffusion tensor imaging at baseline and deterministic tractography were used to map the whole-brain white matter structural connectome. Graph theoretical analysis was applied to investigate the convergent and divergent connectivity patterns of structural connectome between aMCI converters and non-converters. Results: Disrupted topological organization of the brain structural connectome were identified in both aMCI converters and non-converters. More severe disruptions of structural connectivity in aMCI converters compared with non-converters were found, especially in the default-mode network regions and connections. Finally, a support vector machine-based classification demonstrated the good discriminative ability of structural connectivity in differentiating aMCI patients from controls with an accuracy of 98%, and in discriminating converters from non-converters with an accuracy of 81%. Conclusion: Our study provides potential structural connectome/connectivity-based biomarkers for predicting disease progression in aMCI, which is important for the early diagnosis of AD.

KeywordBrain Network Conversion Diffusion Tensor Imaging Graph Theory Mild Cognitive Impairment Machine Learning
DOI10.3389/fneur.2018.01178
Indexed BySCI
Language英语
Funding ProjectFundamental Research Funds for the Central Universities[2017XTCX04] ; Basic Research Foundation Key Project Track of Shenzhen Science and Technology Program[JCYJ20170413110656460] ; Basic Research Foundation Key Project Track of Shenzhen Science and Technology Program[JCYJ20160509162237418] ; Youth Innovation Promotion Association CAS[2016084] ; Beijing Nature Science Foundation[7132147] ; Beijing Nature Science Foundation[7161009] ; Beijing Municipal Commission of Health and Family Planning[PXM2018_026283_000002] ; National Basic Research Program (973 Program)[2015CB351702] ; National Natural Science Foundation of China[81871425] ; National Natural Science Foundation of China[81671761] ; National Natural Science Foundation of China[81471732] ; National Natural Science Foundation of China[31371007] ; National Natural Science Foundation of China[81471731] ; National Natural Science Foundation of China[81430037] ; National Natural Science Foundation of China[61633018] ; National Key Research and Development Program of China[2016YFC0103000] ; National Key Research and Development Program of China[2016YFC1306300] ; National Natural Science Foundation of China[81522021] ; National Natural Science Foundation of China[81522021] ; National Key Research and Development Program of China[2016YFC1306300] ; National Key Research and Development Program of China[2016YFC0103000] ; National Natural Science Foundation of China[61633018] ; National Natural Science Foundation of China[81430037] ; National Natural Science Foundation of China[81471731] ; National Natural Science Foundation of China[31371007] ; National Natural Science Foundation of China[81471732] ; National Natural Science Foundation of China[81671761] ; National Natural Science Foundation of China[81871425] ; National Basic Research Program (973 Program)[2015CB351702] ; Beijing Municipal Commission of Health and Family Planning[PXM2018_026283_000002] ; Beijing Nature Science Foundation[7161009] ; Beijing Nature Science Foundation[7132147] ; Youth Innovation Promotion Association CAS[2016084] ; Basic Research Foundation Key Project Track of Shenzhen Science and Technology Program[JCYJ20160509162237418] ; Basic Research Foundation Key Project Track of Shenzhen Science and Technology Program[JCYJ20170413110656460] ; Fundamental Research Funds for the Central Universities[2017XTCX04]
WOS Research AreaNeurosciences & Neurology
WOS SubjectClinical Neurology ; Neurosciences
WOS IDWOS:000455402100001
PublisherFRONTIERS MEDIA SA
WOS KeywordPositron-emission-tomography ; Temporal-lobe Atrophy ; White-matter ; Functional Connectivity ; Association Workgroups ; Diagnostic Guidelines ; National Institute ; Topological Organization ; Network Topology ; Dementia
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/28232
Collection中国科学院行为科学重点实验室
Corresponding AuthorShu, Ni; Han, Ying
Affiliation1.Capital Med Univ, Dept Neurol, XuanWu Hosp, Beijing, Peoples R China
2.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China
3.Beijing Normal Univ, IDG McGovern Inst Brain Res, Beijing, Peoples R China
4.Beijing Normal Univ, Ctr Collaborat & Innovat Brain & Learning Sci, Beijing, Peoples R China
5.Beijing Normal Univ, Beijing Key Lab Brain Imaging & Connect, Beijing, Peoples R China
6.Univ Cologne, Med Fac, Dept Psychiat & Psychotherapy, Cologne, Germany
7.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
8.Inst Psychol, CAS Key Lab Behav Sci, Beijing, Peoples R China
9.New Jersey Inst Technol, Dept Biomed Engn, Newark, NJ 07102 USA
10.Harbin Inst Technol, Dept Elect & Informat Engn, Shenzhen Grad Sch, Shenzhen, Peoples R China
11.Capital Med Univ, Dept Radiol, XuanWu Hosp, Beijing, Peoples R China
12.Capital Med Univ, Beijing Inst Geriatr, XuanWu Hosp, Beijing, Peoples R China
13.Natl Clin Res Ctr Geriatr Disorders, Beijing, Peoples R China
14.Beijing Inst Brain Disorders, Ctr Alzheimers Dis, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Sun, Yu,Bi, Qiuhui,Wang, Xiaoni,et al. Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome[J]. FRONTIERS IN NEUROLOGY,2019,9:15.
APA Sun, Yu.,Bi, Qiuhui.,Wang, Xiaoni.,Hu, Xiaochen.,Li, Huijie.,...&Han, Ying.(2019).Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome.FRONTIERS IN NEUROLOGY,9,15.
MLA Sun, Yu,et al."Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome".FRONTIERS IN NEUROLOGY 9(2019):15.
Files in This Item:
File Name/Size DocType Version Access License
PREDICTION OF CONVER(640KB)期刊论文出版稿限制开放CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sun, Yu]'s Articles
[Bi, Qiuhui]'s Articles
[Wang, Xiaoni]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sun, Yu]'s Articles
[Bi, Qiuhui]'s Articles
[Wang, Xiaoni]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sun, Yu]'s Articles
[Bi, Qiuhui]'s Articles
[Wang, Xiaoni]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: PREDICTION OF CONVERSION FROM AMNESTIC MILD COGNITIVE IMPAIRMENT TO ALZHEIMER’S .pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.