{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yan W"],"funding":["Strategic Priority Research Program of the Chinese Academy of Sciences","Natural Science Foundation of China","the National Science Foundation","NIMH NIH HHS","National Institute of Health","Beijing Municipal Science and Technology Commission"],"pagination":["543-552"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC6796503"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["47"],"pubmed_abstract":["<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 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.<h4>Findings</h4>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.<h4>Interpretation</h4>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."],"journal":["EBioMedicine"],"pubmed_title":["Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data."],"pmcid":["PMC6796503"],"funding_grant_id":["R01 MH117107","1R56MH117107","R01MH094524","R01EB005846","XDB32040100","61773380","Z181100001518005","1539067","P20GM103472"],"pubmed_authors":["Xu K","Calhoun V","Yan J","Wan P","Zhang D","Chen J","Zhang H","Zuo N","Li P","Liu S","Wang H","Jiang T","Yan W","Lu L","Yang Z","Lv L","Song M","Yang Y","Cui Y","Guo H","Fan L","Sui J","Chen Y","Yan H"],"additional_accession":[]},"is_claimable":false,"name":"Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.","description":"<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 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.<h4>Findings</h4>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.<h4>Interpretation</h4>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.","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Sep","modification":"2024-11-06T21:15:07.777Z","creation":"2019-11-05T08:07:08Z"},"accession":"S-EPMC6796503","cross_references":{"pubmed":["31420302"],"doi":["10.1016/j.ebiom.2019.08.023"]}}