Alice M. Graham

BIBSNet is a deep learning model for infant brain segmentation in MRI scans that outperforms JLF in accuracy and speed, with compatible outputs for further analysis.

The paper introduces SHAMAN, a method to assess motion impact on brain-behavior associations in fMRI studies, finding significant motion overestimation and underestimation in various traits.

The paper measures striatal iron deposition in neonates using MRI and finds age-related changes in specific subregions but not others.

This study characterizes cortical thickness development in Japanese macaques using latent growth models and identifies how maternal diet and inflammation influence regional cortical maturation patterns.

A curated collection of processed ABCD neuroimaging derivatives plus utilities for access, sharing and analysis, letting researchers exchange derivatives they cannot share through the central archive.

Brain-behaviour correlations in ABCD are tiny (median |r|=0.01), so samples of typical size (N=25) give inflated, irreproducible results; estimates only stabilise around N⪆2,000.