Trevor K. M. Day

This paper uses supervised learning to classify brain hemispheres as left or right based on functional connectivity, achieving high accuracy in right-handed and left-handed participants.

This study examines language lateralization in older adults, finding it remains strongly left-lateralized and stable over time.

The paper develops an age-appropriate functional reference to estimate individualized brain networks in young children, revealing early network organization and stability.

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.

This study parcellates the cerebral cortex in toddlers using functional connectivity and shows high reproducibility across ages, with area boundaries more similar to adults than neonates.

This study identifies infant brain areas with adult-like functional network organization, showing less age-related variation and higher reliability, and suggests implications for using adult network models in infants.

The paper introduces Baby Open Brains (BOBs), an open-source repository of manually curated infant brain segmentations to improve reproducibility in neuroimaging research.