Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods

doi:10.3389/fninf.2018.00025

	Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods
	Guo, Hao1,2 ; Li, Yao 1; Xu, Yong 3; Jin, Yanyi 1; Xiang, Jie 1; Chen, Junjie 1
发表期刊	FRONTIERS IN NEUROINFORMATICS
ISSN	1662-5196
	2018-05-15
卷号	12 页码:18
通讯作者	Chen, Junjie(feiyu_guo@sina.com)
摘要	Brain network analysis has been widely applied in neuroimaging studies. A hyper-network construction method was previously proposed to characterize the high-order relationships among multiple brain regions, where every edge is connected to more than two brain regions and can be represented by a hyper-graph. A brain functional hyper-network is constructed by a sparse linear regression model using resting-state functional magnetic resonance imaging (fMRI) time series, which in previous studies has been solved by the lasso method. Despite its successful application in many studies, the lasso method has some limitations, including an inability to explain the grouping effect. That is, using the lasso method may cause relevant brain regions be missed in selecting related regions. Ideally, a hyper-edge construction method should be able to select interacting brain regions as accurately as possible. To solve this problem, we took into account the grouping effect among brain regions and proposed two methods to improve the construction of the hyper-network: the elastic net and the group lasso. The three methods were applied to the construction of functional hyper-networks in depressed patients and normal controls. The results showed structural differences among the hyper-networks constructed by the three methods. The hyper-network structure obtained by the lasso was similar to that obtained by the elastic net method but very different from that obtained by the group lasso. The classification results indicated that the elastic net method achieved better classification results than the lasso method with the two proposed methods of hyper-network construction. The elastic net method can effectively solve the grouping effect and achieve better classification performance.
关键词	depression hyper-network elasticnet grouplasso classification
DOI	10.3389/fninf.2018.00025
关键词[WOS]	MAJOR DEPRESSIVE DISORDER ; ORDER INTERACTIONS ; CONNECTIVITY ; FMRI ; REGULARIZATION ; REGRESSION ; MRI ; CLASSIFICATION ; SELECTION ; DISEASE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61373101] ; National Natural Science Foundation of China[61472270] ; National Natural Science Foundation of China[61402318] ; National Natural Science Foundation of China[61672374] ; National Natural Science Foundation of China[61741212] ; Natural Science Foundation of Shanxi Province[201601D021073] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2016139] ; National Natural Science Foundation of China[61373101] ; National Natural Science Foundation of China[61472270] ; National Natural Science Foundation of China[61402318] ; National Natural Science Foundation of China[61672374] ; National Natural Science Foundation of China[61741212] ; Natural Science Foundation of Shanxi Province[201601D021073] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2016139]
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi
WOS研究方向	Mathematical & Computational Biology ; Neurosciences & Neurology
WOS类目	Mathematical & Computational Biology ; Neurosciences
WOS记录号	WOS:000432585100001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/28176
专题	智能制造技术与系统研究中心_智能机器人
通讯作者	Chen, Junjie
作者单位	1.Taiyuan Univ Technol, Dept Software Engn, Coll Informat & Comp, Taiyuan, Shanxi, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Reconit, Inst Automat, Beijing, Peoples R China 3.Shanxi Med Univ, Dept Psychiat, Hosp 1, Taiyuan, Shanxi, Peoples R China
推荐引用方式 GB/T 7714	Guo, Hao,Li, Yao,Xu, Yong,et al. Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods[J]. FRONTIERS IN NEUROINFORMATICS,2018,12:18.
APA	Guo, Hao,Li, Yao,Xu, Yong,Jin, Yanyi,Xiang, Jie,&Chen, Junjie.(2018).Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods.FRONTIERS IN NEUROINFORMATICS,12,18.
MLA	Guo, Hao,et al."Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods".FRONTIERS IN NEUROINFORMATICS 12(2018):18.