An early and accurate diagnosis of psychiatric disorders is critical for patients’ quality of life and deep understanding of the disorders. For this reason, many studies have proposed machine learning-based diagnostic procedures for functional magnetic resonance imaging (fMRI) data. Especially, these procedures often employed temporal models due to the time-varying nature of the brain activities and probabilistic generative models for understanding the underlying mechanism of the disorders. For leveraging the recent advantage of deep learning, we proposed a state-space model of fMRI images based on deep learning. The proposed deep state-space model is more flexible than conventional models and less likely to suffer from overfitting than a straightforward deep learning-based classifier. The proposed model estimates the subjects’ conditions more accurately than existing diagnostic procedures. Also, the proposed model potentially identifies brain regions related to the disorders.
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