Studying depression using imaging and machine learning methods

• Past studies successfully studied depression using machine learning and imaging.
• Past studies have limitations in their methods.
• Methods for future studies can be improved.
• Future studies could yield more robust models to diagnosis and treat depression.

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.