Manifold learning uncovers nonlinear interactions between the adolescent brain and environment that predict emotional and behavioral problems

Abstract

Advanced statistical methods that capture the complex interplay between adolescents and their social environments are essential for improving our understanding of how differences in brain function contribute to mental health problems. To move the study of adolescent mental health beyond what we have achieved so far- a complex account of brain and environmental risk factors without understanding the neurobiological embedding of the social environment- we need to find ways of studying the complex, nonlinear relationships between brain function and adolescents' experiences in the real-world. Manifold learning techniques can discover and highlight latent structure from high-dimensional, complex biomedical data, such as fMRI. Here, we develop a novel manifold learning technique, exogenous PHATE (EPHATE), to capture the interplay between brain function and adolescents' social environments. By applying EPHATE, we demonstrate that harmonizing cutting-edge computational methods with longstanding developmental theory can advance efforts to detect and predict mental health problems during the transition to adolescence.