| 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
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| 发表期刊 | EBIOMEDICINE
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| ISSN | 2352-3964 |
| 2019-09-01 | |
| 卷号 | 47页码:543-552 |
| 通讯作者 | Jiang, Tianzi(jiangtz@nlpr.ia.ac.cn) ; Sui, Jing(jing.sui@nlpr.ia.ac.cn) |
| 摘要 | Background: 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. |
| 关键词 | Recurrent neural network (RNN) Schizophrenia Multi-site classification fMRI Striatum Cerebellum Deep learning |
| DOI | 10.1016/j.ebiom.2019.08.023 |
| 关键词[WOS] | STATE FUNCTIONAL CONNECTIVITY ; WHOLE-BRAIN ; CLASSIFICATION ; STRIATUM ; BIPOLAR ; FRAMEWORK ; MOTION ; ICA |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | 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] ; 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] |
| 项目资助者 | Natural 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研究方向 | General & Internal Medicine ; Research & Experimental Medicine |
| WOS类目 | Medicine, General & Internal ; Medicine, Research & Experimental |
| WOS记录号 | WOS:000486976200062 |
| 出版者 | ELSEVIER |
| 七大方向——子方向分类 | 人工智能+医疗 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/27017 |
| 专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
| 通讯作者 | Jiang, Tianzi; Sui, Jing |
| 作者单位 | 1.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 |
| 第一作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
| 通讯作者单位 | 模式识别国家重点实验室; 中国科学院自动化研究所 |
| 推荐引用方式 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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