  Accurate diagnosis of psychiatric disorders plays a critical role in
improving the quality of life for patients and potentially supports the
development of new treatments. Many studies have been conducted on machine
learning techniques that seek brain imaging data for specific biomarkers of
disorders. These studies have encountered the following dilemma: A direct
classification overfits to a small number of high-dimensional samples but
unsupervised feature-extraction has the risk of extracting a signal of no
interest. In addition, such studies often provided only diagnoses for patients
without presenting the reasons for these diagnoses. This study proposed a deep
neural generative model of resting-state functional magnetic resonance imaging
(fMRI) data. The proposed model is conditioned by the assumption of the
subject's state and estimates the posterior probability of the subject's state
given the imaging data, using Bayes' rule. This study applied the proposed
model to diagnose schizophrenia and bipolar disorders. Diagnostic accuracy was
improved by a large margin over competitive approaches, namely classifications
of functional connectivity, discriminative/generative models of region-wise
signals, and those with unsupervised feature-extractors. The proposed model
visualizes brain regions largely related to the disorders, thus motivating
further biological investigation.
