Jamie Lars Hanson

The paper examines how socioeconomic status affects subdivisions of the amygdala and hippocampus in children and adolescents, finding SES-related volume differences but no significant age or sex interactions.

The paper investigates how neuroanatomical variability relates to impulsivity in youth using data from the ABCD Study, finding sex-specific and dynamic associations across multiple brain features.

The paper uses machine learning to link socioeconomic status, scarcity, and neighborhood threat to cortical thickness in youth, finding consistent neural signatures in memory and visual processing regions.

This study shows that a brain network for sustained attention at age 14 predicts increased cannabis and cigarette use by age 23 in a longitudinal cohort.

The paper assesses the numerical and spatial reliability of hippocampal subfields and amygdala nuclei segmentation in FreeSurfer 7 using repeated scans from 923 participants.

This study compares three brain age algorithms to assess their reliability, demographic correlations, and predictive power for cognitive disorders.

The paper curates and provides analysis-ready diffusion MRI data for pediatric brain white-matter research, including automated quality control using a trained neural network.

The paper assesses how structural MRI quality affects brain anatomy measurements using Freesurfer and finds that image quality significantly impacts cortical and subcortical volume estimates.

The paper links lower childhood family income and adolescent depressive symptoms to reduced neural value signaling in the prefrontal cortex during reward learning tasks.

This study compares structural brain connectivity models using diffusion tensor imaging features in normal controls and maltreated children.