Manifold Learning Uncovers Nonlinear Interactions Between the Adolescent Brain and Environment That Predict Emotional and Behavioral Problems.

Abstract

Background: To progress adolescent mental health research beyond our present achievements-a complex account of brain and environmental risk factors without understanding neurobiological embedding in the environment-we need methods to uncover relationships between the developing brain and real-world environmental experiences.

Methods: We investigated associations between brain function, environments, and emotional and behavioral problems using participants from the Adolescent Brain Cognitive Development (ABCD) Study (n = 2401 female). We applied manifold learning, a promising technique for uncovering latent structure from high-dimensional biomedical data such as functional magnetic resonance imaging. Specifically, we developed exogenous PHATE (potential of heat-diffusion for affinity-based trajectory embedding) (E-PHATE) to model brain-environment interactions. We used E-PHATE embeddings of participants' brain activation during emotional and cognitive processing tasks to predict individual differences in cognition and emotional and behavioral problems both cross-sectionally and longitudinally.

Results: E-PHATE embeddings of participants' brain activation and environments at baseline showed moderate-to-large associations with total, externalizing, and internalizing problems at baseline, across several subcortical regions and large-scale cortical networks, compared with the zero-to-small effects achieved by voxelwise data or common low-dimensional embedding methods. E-PHATE embeddings of the brain and environment at baseline were also related to emotional and behavioral problems 2 years later. These longitudinal predictions showed a consistent moderate effect in the frontoparietal and attention networks.

Conclusions: The embedding of the adolescent brain in the environment yields enriched insight into emotional and behavioral problems. Using E-PHATE, we demonstrated how the harmonization of cutting-edge computational methods with longstanding developmental theories advances the detection and prediction of adolescent emotional and behavioral problems.