Journal
EBIOMEDICINE
Volume 30, Issue -, Pages 74-85Publisher
ELSEVIER
DOI: 10.1016/j.ebiom.2018.03.017
Keywords
Schizophrenia; Deep learning; Connectome; fMRI; Striatum; Cerebellum
Funding
- National Science Foundation of China [61722313, 61503397, 61420106001, 61773391, 31571149, 81571309]
- Fok Ying Tung Education Foundation [161057]
- National Clinical Research Center on Mental Disorders [2015BAI13B02]
- Key Research and Development Program of Shaanxi Province [2017ZDXM-SF-047]
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Background: A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning methods may be capable of learning subtle hidden patterns from high dimensional imaging data, overcome potential site-related variation, and achieve reproducible cross-site classification. However, deep learning-based cross-site transfer classification, despite less imaging site-specificity and more generalizability of diagnostic models, has not been investigated in schizophrenia. Methods: A large multi-site functional MRI sample (n = 734, including 357 schizophrenic patients from seven imaging resources) was collected, and a deep discriminant autoencoder network, aimed at learning imaging site-shared functional connectivity features, was developed to discriminate schizophrenic individuals from healthy controls. Findings: Accuracies of approximately 85.0% and 81.0% were obtained in multi-site pooling classification and leave-site-out transfer classification, respectively. The learned functional connectivity features revealed dysregulation of the cortical-striatal-cerebellar circuit in schizophrenia, and the most discriminating functional connections were primarily located within and across the default, salience, and control networks. Interpretation: The findings imply that dysfunctional integration of the cortical-striatal-cerebellar circuit across the default, salience, and control networks may play an important role in the disconnectivity model underlying the pathophysiology of schizophrenia. The proposed discriminant deep learning method may be capable of learning reliable connectome patterns and help in understanding the pathophysiology and achieving accurate prediction of schizophrenia across multiple independent imaging sites. (C) 2018 The Authors. Published by Elsevier B.V.
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