Deep Generative State-Space Modeling of FMRI Images for Psychiatric Disorder Diagnosis

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.