Depression Detection Using Combination of sMRI and fMRI Image Features

Abstract

Automatic detection of Major Depression Disorder (MDD) from brain MRI images with machine learning has been an active area of study. In this paper several methods are explored for MDD detection by combining features from structural and functional brain MRI images, and combining Atlas-based and spatial cube-based features. Experiments demonstrate good classification performance on an imbalanced dataset. The paper also presents a visualization that captures the spatial overlapping between the top discriminating spatial cube pairs and the regions of interests in the Harvard Atlas.