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Manifold learning uncovers nonlinear interactions between the adolescent brain and social environment that predict psychopathology.

Erica L BuschMay I ConleyArielle R Baskin-Sommers
Published in: bioRxiv : the preprint server for biology (2024)
Background : Advanced statistical methods to model the interplay between adolescents and their social environments are essential for understanding how differences in brain function contribute to psychopathology. To progress adolescent mental health research beyond our present achievements - a complex account of brain and environmental risk factors without understanding the neurobiological embedding of the social environment - we need methods to unveil relationships between the developing brain and real-world environmental experiences. Methods : Here, we investigated associations among psychopathology, social environments, and brain function using participants from the Adolescent Brain and Cognitive Development Study (N=5,235; 2,672 female). Manifold learning is a promising technique for uncovering latent structure from high-dimensional biomedical data like functional magnetic resonance imaging (fMRI). To model brain-social environment interactions and psychopathology, we developed a manifold learning technique called exogenous PHATE (E-PHATE). We used E-PHATE embeddings of participants' brain activation during emotional and cognitive processing to predict measures of cognition and psychopathology both cross-sectionally and longitudinally. Results : Manifold embeddings of brain activation highlight individual differences in cognition and in psychopathology symptoms which are obscured in high-dimensional (voxel-wise) activity. Specifically, E-PHATE embeddings of participants' brain activation and social environments at baseline relate to overall psychopathology, externalizing, and internalizing behaviors at both the baseline and at a 2-year follow-up. Conclusions : Our findings indicate that the adolescent brain's embedding in the social environment yields enriched insight into psychopathology. Using E-PHATE, we demonstrate how the harmonization of cutting-edge computational methods with longstanding developmental theories advances detection and prediction of adolescent psychopathology.
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