Structured deep generative model of fMRI signals for mental disorder diagnosis

Abstract

Machine learning-based accurate diagnosis of psychiatric disorders is expected to find their biomarkers and to evaluate the treatments. For this purpose, neuroimaging datasets have required special procedures including feature-selections and dimensional-reductions since they are still composed of a limited number of high-dimensional samples. Recent studies reported a certain success by applying generative models to fMRI data. Generative models can classify small datasets more accurately than discriminative models as long as their assumptions are appropriate. Leveraging our prior knowledge of fMRI signal and the flexibility of deep neural networks, we propose a structured deep generative model, which takes into account fMRI images, disorder, and individual variability. The proposed model estimates the subjects’ conditions more accurately than existing diagnostic procedures, general discriminative models, and recently-proposed generative models. Also, it identifies brain regions related to the disorders.