Erica L. Busch

Developmental tuning of functional manifold dimensionality across the human brain

The paper estimates intrinsic dimensionality in fMRI data across development, finding that task-relevant brain regions have higher-dimensional activity in adults, while infants show no such modulation.

Accelerated learning of a noninvasive human brain-computer interface via manifold geometry

This study shows that leveraging the intrinsic manifold geometry of brain activity can accelerate learning of a noninvasive BCI by aligning brain activity within the manifold during task performance.

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

The paper uses manifold learning to uncover brain-environment interactions predicting emotional and behavioral problems in adolescents.

Dissociation of reliability, heritability, and predictivity in coarse- and fine-scale functional connectomes during development

This study examines reliability, heritability, and behavioral relevance of resting-state functional connectivity at different spatial scales in children, finding that fine-scale connectivity is more reliable but less heritable than coarse-scale connectivity.

Multi-view manifold learning of human brain statetrajectories

This paper introduces T-PHATE, a nonlinear manifold learning algorithm for time-series data like fMRI, showing improvements in visualization, classification, and segmentation.

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

This paper introduces hybrid hyperalignment, a method that aligns both neural response and functional connectivity data across brains using a single model, showing improved alignment for both types of data compared to previous methods.