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Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data
Yan, Weizheng1,2,3; Calhoun, Vince4; Song, Ming1,2,3; Cui, Yue1,2,3; Yan, Hao5,6; Liu, Shengfeng1,2,3; Fan, Lingzhong1,2,3; Zuo, Nianming1,2,3; Yang, Zhengyi1,2,3; Xu, Kaibin1,2,3; Yan, Jun5,6; Lv, Luxian7,8; Chen, Jun9; Chen, Yunchun10; Guo, Hua11; Li, Peng5,6; Lu, Lin5,6; Wan, Ping11; Wang, Huaning9,10; Wang, Huiling; Yang, Yongfeng7,8,12; Zhang, Hongxing7,13; Zhang, Dai5,6,14; Jiang, Tianzi1,2,3,12,15,16; Sui, Jing1,2,3,16
Source PublicationEBIOMEDICINE
ISSN2352-3964
2019-09-01
Volume47Pages:543-552
Corresponding AuthorJiang, Tianzi(jiangtz@nlpr.ia.ac.cn) ; Sui, Jing(jing.sui@nlpr.ia.ac.cn)
AbstractBackground: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information. Methods: Motivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fMRI independent components (ICs) directly. To increase interpretability, we also propose a leave-one-IC-out looping strategy for estimating the top contributing ICs. Findings: Accuracies of 83.2% and 80.2% were obtained respectively for the multi-site pooling and leave-one-site-out transfer classification. Subsequently, dorsal striatum and cerebellum components contribute the top two group-discriminative time courses, which is true even when adopting different brain atlases to extract time series. Interpretation: This is the first attempt to apply a multi-scale RNN model directly on IMRI time courses for classification of mental disorders, and shows the potential for multi-scale RNN-based neuroimaging classifications. (C) 2019 Published by Elsevier B.V.
KeywordRecurrent neural network (RNN) Schizophrenia Multi-site classification fMRI Striatum Cerebellum Deep learning
DOI10.1016/j.ebiom.2019.08.023
WOS KeywordSTATE FUNCTIONAL CONNECTIVITY ; WHOLE-BRAIN ; CLASSIFICATION ; STRIATUM ; BIPOLAR ; FRAMEWORK ; MOTION ; ICA
Indexed BySCI
Language英语
Funding ProjectNatural Science Foundation of China[61773380] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32040100] ; Beijing Municipal Science and Technology Commission[Z181100001518005] ; National Institute of Health[1R56MH117107] ; National Institute of Health[R01EB005846] ; National Institute of Health[R01MH094524] ; National Institute of Health[P20GM103472] ; National Science Foundation[1539067] ; Natural Science Foundation of China[61773380] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB32040100] ; Beijing Municipal Science and Technology Commission[Z181100001518005] ; National Institute of Health[1R56MH117107] ; National Institute of Health[R01EB005846] ; National Institute of Health[R01MH094524] ; National Institute of Health[P20GM103472] ; National Science Foundation[1539067]
Funding OrganizationNatural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Beijing Municipal Science and Technology Commission ; National Institute of Health ; National Science Foundation
WOS Research AreaGeneral & Internal Medicine ; Research & Experimental Medicine
WOS SubjectMedicine, General & Internal ; Medicine, Research & Experimental
WOS IDWOS:000486976200062
PublisherELSEVIER
Citation statistics
Cited Times:23[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27017
Collection脑网络组研究
Corresponding AuthorJiang, Tianzi; Sui, Jing
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Triinst Ctr Translat Res Neuroimaging & Data Sci, Atlanta, GA 30303 USA
5.Peking Univ, Hosp 6, Inst Mental Hlth, Beijing 100191, Peoples R China
6.Peking Univ, Key Lab Mental Hlth, Minist Hlth, Beijing 100191, Peoples R China
7.Xinxiang Med Univ, Affiliated Hosp 2, Henan Mental Hosp, Dept Psychiat, Xinxiang 453002, Henan, Peoples R China
8.Xinxiang Med Univ, Henan Key Lab Biol Psychiat, Xinxiang 453002, Henan, Peoples R China
9.Wuhan Univ, Renmin Hosp, Dept Radiol, Wuhan 430060, Hubei, Peoples R China
10.Fourth Mil Med Univ, Xijing Hosp, Dept Psychiat, Xian 710032, Shaanxi, Peoples R China
11.Zhumadian Psychiat Hosp, Zhumadian 463000, Henan, Peoples R China
12.Univ Elect Sci & Technol China, Sch Life Sci & Technol, Minist Educ, Key Lab NeuroInformat, Chengdu 610054, Sichuan, Peoples R China
13.Xinxiang Med Univ, Dept Psychol, Xinxiang 453002, Henan, Peoples R China
14.Peking Univ, McGovern Inst Brain Res, PKU IDG, Ctr Life Sci, Beijing 100871, Peoples R China
15.Univ Queensland, Queensland Brain Inst, Brisbane, Qld 4072, Australia
16.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yan, Weizheng,Calhoun, Vince,Song, Ming,et al. Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data[J]. EBIOMEDICINE,2019,47:543-552.
APA Yan, Weizheng.,Calhoun, Vince.,Song, Ming.,Cui, Yue.,Yan, Hao.,...&Sui, Jing.(2019).Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.EBIOMEDICINE,47,543-552.
MLA Yan, Weizheng,et al."Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data".EBIOMEDICINE 47(2019):543-552.
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