Predicting Brain Regions Related to Alzheimer's Disease Based on Global Feature

doi:10.3389/fncom.2021.659838

CSpace > 应用数学研究所

	Predicting Brain Regions Related to Alzheimer's Disease Based on Global Feature
	Wang, Qi 1,2; Chen, Siwei 3; Wang, He 4; Chen, Luzeng 5; Sun, Yongan 3; Yan, Guiying1,2
	2021-05-21
发表期刊	FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
卷号	15 页码:9
摘要	Alzheimer's disease (AD) is a neurodegenerative disease that commonly affects the elderly; early diagnosis and timely treatment are very important to delay the course of the disease. In the past, most brain regions related to AD were identified based on imaging methods, and only some atrophic brain regions could be identified. In this work, the authors used mathematical models to identify the potential brain regions related to AD. In this study, 20 patients with AD and 13 healthy controls (non-AD) were recruited by the neurology outpatient department or the neurology ward of Peking University First Hospital from September 2017 to March 2019. First, diffusion tensor imaging (DTI) was used to construct the brain structural network. Next, the authors set a new local feature index 2hop-connectivity to measure the correlation between different regions. Compared with the traditional graph theory index, 2hop-connectivity exploits the higher-order information of the graph structure. And for this purpose, the authors proposed a novel algorithm called 2hopRWR to measure 2hop-connectivity. Then, a new index global feature score (GFS) based on a global feature was proposed by combing five local features, namely degree centrality, betweenness centrality, closeness centrality, the number of maximal cliques, and 2hop-connectivity, to judge which brain regions are related to AD. As a result, the top ten brain regions identified using the GFS scoring difference between the AD and the non-AD groups were associated to AD by literature verification. The results of the literature validation comparing GFS with the local features showed that GFS was superior to individual local features. Finally, the results of the canonical correlation analysis showed that the GFS was significantly correlated with the scores of the Mini-Mental State Examination (MMSE) scale and the Montreal Cognitive Assessment (MoCA) scale. Therefore, the authors believe the GFS can also be used as a new biomarker to assist in diagnosis and objective monitoring of disease progression. Besides, the method proposed in this paper can be used as a differential network analysis method for network analysis in other domains.
关键词	Alzheimer's disease diffusion tensor imaging brain structural network 2hop-connectivity global featurescore differential network analysis
DOI	10.3389/fncom.2021.659838
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[11631014]
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS记录号	WOS:000657649500001
出版者	FRONTIERS MEDIA SA
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/58747
专题	应用数学研究所
通讯作者	Sun, Yongan; Yan, Guiying
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China 3.Peking Univ First Hosp, Dept Neurol, Beijing, Peoples R China 4.Peking Univ First Hosp, Dept Med Imaging, Beijing, Peoples R China 5.Peking Univ First Hosp, Dept Ultrasound, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Qi,Chen, Siwei,Wang, He,et al. Predicting Brain Regions Related to Alzheimer's Disease Based on Global Feature[J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,2021,15:9.
APA	Wang, Qi,Chen, Siwei,Wang, He,Chen, Luzeng,Sun, Yongan,&Yan, Guiying.(2021).Predicting Brain Regions Related to Alzheimer's Disease Based on Global Feature.FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,15,9.
MLA	Wang, Qi,et al."Predicting Brain Regions Related to Alzheimer's Disease Based on Global Feature".FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 15(2021):9.