Journal
EBIOMEDICINE
Volume 47, Issue -, Pages 543-552Publisher
ELSEVIER
DOI: 10.1016/j.ebiom.2019.08.023
Keywords
Recurrent neural network (RNN); Schizophrenia; Multi-site classification; fMRI; Striatum; Cerebellum; Deep learning
Funding
- 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, R01EB005846, R01MH094524, P20GM103472]
- National Science Foundation [1539067]
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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.
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