Manifold learning uncovers nonlinear interactions between the adolescent brain and environment that predict emotional and behavioral problems.
Biol Psychiatry Cogn Neurosci Neuroimaging
; 2024 Jul 13.
Article
em En
| MEDLINE
| ID: mdl-39009136
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 unveil relationships between the developing brain and real-world environmental experiences.METHODS:
We investigated associations among brain function, environments, and emotional and behavioral problems using participants from the Adolescent Brain and Cognitive Development Study (N=2,401 female). We applied manifold learning, a promising technique for uncovering latent structure from high-dimensional biomedical data like functional magnetic resonance imaging (fMRI). Specifically, we developed exogenous PHATE (E-PHATE) to model brain-environment interactions. We used E-PHATE embeddings of participants' brain activation during emotional and cognitive processing 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 show moderate-to-large associations with total, externalizing, and internalizing problems at baseline, across several subcortical regions and large-scale cortical networks, relative to the zero-to-small effects achieved by voxel or PHATE methods. E-PHATE embeddings of the brain and environment at baseline also relate to emotional and behavioral problems two years later. These longitudinal predictions show a consistent, moderate effect in the frontoparietal and attention networks.CONCLUSIONS:
Adolescent brain's embedding in the environment yields enriched insight into emotional and behavioral problems. Using E-PHATE, we demonstrate how the harmonization of cutting-edge computational methods with longstanding developmental theories advances detection and prediction of adolescent emotional and behavioral problems.
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Base de dados:
MEDLINE
Idioma:
En
Revista:
Biol Psychiatry Cogn Neurosci Neuroimaging
Ano de publicação:
2024
Tipo de documento:
Article