Dream Team Projects
Multidimensional Predictors of Major Depressive Disorder and Suicidal Behaviors In Adolescents
The goal of this longitudinal study is to leverage a well-characterized sample of healthy adolescents who experienced early life stress to integrate multi-system neurobiological and digital phenotypes with machine learning algorithms to identify risk factors and mechanistic targets involved in the onset of depression and engagement in suicidal behaviors. This project will facilitate the development of more timely and precise approaches to the prevention of these debilitating conditions and their devastating consequences.
Our approach is three-pronged:
- Integrate existing and new assessments of neurobiologically (functional and structural MRI, cortisol, immune function) and digitally (sleep, mobility, affective states) derived phenotypes measured prior to the onset of disorder to identify adolescents who are at risk for developing clinically significant depression and engaging in suicidal behaviors
- Use machine learning algorithms suited for multidimensional prediction to create a risk calculator that can be scaled and incorporated into existing health assessments
- Refine the algorithms by applying the risk calculator to data from larger national and international samples of children and young adolescents, allowing us to quantify risk before the first onset of depression and engagement in suicidal ideation
Because we are studying adolescents, we are uniquely positioned to characterize the early emergence of depression and suicidal behaviors that, if undetected, often cascade into lifelong difficulties. Our goal is perfectly aligned with the mission of PHIND to fundamentally revolutionize health care, and specifically, precision mental health.