Grant: CIHR Fellowship

Although depression affects approximately 350 million people worldwide, a substantial percentage of patients do not respond to treatment even after trying several different therapies. Understanding inter-individual differences in brain function and structure will help provide new knowledge regarding profiles among individuals that might relate to treatment response, or treatment resistance. Applying novel machine learning methods to large-scale brain imaging data, will help stratify different "biotypes" of depression, and the neural mechanism that underlie them, which in turn will allow more personalized treatment strategy for any one individual. As part of this project, we will make use of population level data to identify depression "biotypes" and then assess these in a separate, large clinical sample, following the changes in brain connectivity over an 18-month period. This project aims to enhance our ability to identify subtypes of depression informed by neurobiological classification, and further our understanding of the brain mechanisms underlying treatment response vs. treatment-resistance.