Project Overview
Depression is one of the most common mental health challenges faced by young people, yet choosing the right treatment remains a process of trial and error. While both therapy and medication can be effective, there are no reliable tests to predict which treatment will work best for each individual. Even more critically, some young people with depression are at risk of suicide, and doctors currently lack objective ways to assess this risk.
Our research aims to change this by using electroencephalography (EEG)—a safe and non-invasive way to measure brain activity—to better understand how young people process social experiences, like feeling included or rejected. We will study how the brain responds to social rejection and inclusion and whether these patterns predict how well someone will respond to therapy or medication.
This research is part of the Treatment Interrupts Depression Early (TIDE) trial, a large clinical study focused on youth experiencing their first episode of depression. We will analyze EEG data using machine learning to identify brain activity patterns linked to depression, suicide risk, and treatment response. Our goal is to create a tool that could help doctors make more informed treatment decisions, moving away from a one-size-fits-all approach to mental health care.
By identifying biological markers of depression and suicidality, this study could transform the way we diagnose and treat youth depression. In the future, simple EEG tests may help clinicians personalize treatment, reduce suicide risk, and give young people the best chance at recovery. Our work also takes diversity into account, ensuring findings apply to youth from different backgrounds and lived experiences. With earlier and more precise intervention, we can improve outcomes for young people struggling with depression—helping them regain hope, resilience, and a path forward.
