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

文献类型：期刊论文

作者Guo, Hao2,3; Li, Yao3; Xu, Yong1; Jin, Yanyi3; Xiang, Jie3; Chen, Junjie3
刊名FRONTIERS IN NEUROINFORMATICS
出版日期2018-05-15
卷号12页码:18
关键词depression hyper-network elasticnet grouplasso classification
ISSN号1662-5196
DOI10.3389/fninf.2018.00025
通讯作者Chen, Junjie(feiyu_guo@sina.com)
英文摘要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.
WOS关键词MAJOR DEPRESSIVE DISORDER ; ORDER INTERACTIONS ; CONNECTIVITY ; FMRI ; REGULARIZATION ; REGRESSION ; MRI ; CLASSIFICATION ; SELECTION ; DISEASE
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]
WOS研究方向Mathematical & Computational Biology ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000432585100001
出版者FRONTIERS MEDIA SA
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi
源URL[http://ir.ia.ac.cn/handle/173211/28175]  
专题自动化研究所_智能制造技术与系统研究中心_智能机器人团队
通讯作者Chen, Junjie
作者单位1.Shanxi Med Univ, Dept Psychiat, Hosp 1, Taiyuan, Shanxi, Peoples R China
2.Chinese Acad Sci, Natl Lab Pattern Reconit, Inst Automat, Beijing, Peoples R China
3.Taiyuan Univ Technol, Dept Software Engn, Coll Informat & Comp, 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.

入库方式： OAI收割

来源：自动化研究所

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