Graham L. Baum

This paper introduces two empirical methods to correct B1+ transmit field biases in T1w/T2w myelin maps, improving their accuracy for neuroanatomical and statistical analyses.

The paper develops a theory of efficient coding in brain networks and shows that structural connectivity balances information fidelity and compression, predicting behavior and explaining biological organization.

The paper uses high-resolution myelin mapping to show graded variation in cortical myelination rates along the sensorimotor-association axis during adolescence.

The paper uses network control theory to show that structural brain networks mature during adolescence to reduce the energy cost of activating the frontoparietal system, supporting executive function development.

This study shows how functional topography of brain networks changes with age and relates to executive function and brain organization in youth.

This study examines how structure-function coupling in brain networks develops during youth, finding it aligns with cortical hierarchies and supports cognitive improvements.

This study evaluates DTI, NODDI, and MAPL in youth to assess their associations with age and motion, finding that multi-shell models offer advantages for neurodevelopmental studies despite motion sensitivity.

The paper uses machine learning to analyze brain states from fMRI data and shows that white matter connectivity influences their dynamics and development.

This paper evaluates how in-scanner head motion affects structural connectivity estimates from diffusion tractography in a developmental cohort, showing motion biases connectivity strength in a consistency and length-dependent manner.