Dataset Information

Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.

ABSTRACT: 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 fMRI time courses for classification of mental disorders, and shows the potential for multi-scale RNN-based neuroimaging classifications. FUND: Natural Science Foundation of China, the Strategic Priority Research Program of the Chinese Academy of Sciences, National Institutes of Health Grants, National Science Foundation.

SUBMITTER: Yan W

PROVIDER: S-EPMC6796503 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.

Yan Weizheng W Calhoun Vince V Song Ming M Cui Yue Y Yan Hao H Liu Shengfeng S Fan Lingzhong L Zuo Nianming N Yang Zhengyi Z Xu Kaibin K Yan Jun J Lv Luxian L Chen Jun J Chen Yunchun Y Guo Hua H Li Peng P Lu Lin L Wan Ping P Wang Huaning H Wang Huiling H Yang Yongfeng Y Zhang Hongxing H Zhang Dai D Jiang Tianzi T Sui Jing J

EBioMedicine 20190813

<h4>Background</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 31420302

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Dataset Information

Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.

Publications

Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.

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