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

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.

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

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).

Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development.

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition.

Representational Dissimilarity of Faces and Places during a Working Memory Task is Associated with Subsequent Recognition Memory during Development.

Nearly 50 years of research has focused on faces as a special visual category, especially during development. Yet it remains unclear how spatial patterns of neural similarity of faces and places relate to how information processing supports subsequent recognition of items from these categories. The current study uses representational similarity analysis and functional imaging data from 9- and 10-year-old youth during an emotional n-back task from the Adolescent Brain and Cognitive Development Study 3.

Multi-view manifold learning of human brain-state trajectories.

The complexity of the human brain gives the illusion that brain activity is intrinsically high-dimensional. Nonlinear dimensionality-reduction methods such as uniform manifold approximation and t-distributed stochastic neighbor embedding have been used for high-throughput biomedical data. However, they have not been used extensively for brain activity data such as those from functional magnetic resonance imaging (fMRI), primarily due to their inability to maintain dynamic structure.

Hybrid hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity.

Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals' brain data into a common model space while maintaining the geometric relationships between distinct patterns of activity or connectivity. The dimensions of this common model capture functional profiles that are shared across individuals such as cortical response profiles collected during a common time-locked stimulus presentation (e.