Kristin A. Murtha

The paper identifies two neuroanatomical subtypes of callous-unemotional traits using machine learning in a community sample of youth, showing distinct clinical and environmental profiles.

The paper compares brain-behavior associations across different sampling models using ABCD Study data, finding increased effect sizes with tail-sampled groups but mixed results with propensity-matched models.

This study evaluates six compressed sensing diffusion spectrum imaging (CS-DSI) schemes, finding they provide accurate and reliable white matter structure measures with up to 80% reduced scan time compared to full DSI.

CuBIDS streamlines reproducible curation of large BIDS datasets by validating metadata, managing variability, and preparing data for preprocessing pipelines.

ModelArray is an R package for efficient statistical analysis of fixel data, supporting linear and nonlinear models with reduced memory usage.

This study links individual mobility patterns to mood, sleep, and brain connectivity by identifying unique mobility footprints.

The paper analyzes multi-scale functional brain networks in youths to identify developmental patterns of network re-organization and their cognitive implications.

This study examines how neighborhood socioeconomic status affects executive system activation during a working memory task in youth and identifies mediating brain activity patterns.

FlywheelTools provides software for reproducible data curation and manipulation on the Flywheel platform, addressing challenges in converting neuroimaging data to BIDS format.

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.