Benjamin De Leener

The paper generates lifespan normative charts for cervical spinal cord morphometry using MRI data, revealing non-linear changes and sex differences across ages 0-100.

The paper creates an in-silico brain phantom to evaluate how susceptibility anisotropy affects susceptibility separation algorithms, finding increased error in negative susceptibility estimates with anisotropy.

This study maps age-related structural and functional changes in the spinal cord and brain, identifying shared aging patterns across the central nervous system.

The paper introduces EPISeg, a deep learning model for automated spinal cord segmentation on gradient-echo EPI data, showing improved performance over existing methods.

This study shows body size parameters significantly correlate with brain and spinal cord MRI metrics and should be included in neuroimaging studies.

This study uses resting-state fMRI to reveal a somatotopic gradient in cerebro-spinal functional connectivity, showing coordinated sensorimotor organization between the brain and spinal cord.

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.