Major depression is a debilitating disorder with significant personal and societal consequences. Neuroimaging investigations of depression have held promise but have, so far, failed to identify reliably the neural underpinnings of this disorder. A given neuroimaging study of depression is most often small, comprising 20 depressed and 20 healthy subjects. This makes individual studies potentially insensitive to detecting subtle effects in the imaging data. In the current project, we propose to apply cutting-edge machine learning approaches to the significant neuroimaging data resources of the UK Biobank in order to identify a neural signature of depression that will expedite the diagnosis, prediction, and treatment of this disease. We expect the proposed investigation to take three years.
