Title: Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis,
Abstract: Accurate diagnosis of psychiatric disorders plays a critical role in
improving 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: An end-to-end
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. Diagnosis accuracy was
improved by a large margin over competitive approaches, namely a support vector
machine, logistic regression, and multilayer perceptron with or without
unsupervised feature-extractors in addition to a Gaussian mixture model. The
proposed model visualizes brain regions largely related to the disorders, thus
motivating further biological investigation.