Alfredo Lucas

The paper identifies shared dimensions of variation in diffusion MRI statistics and maps spatial patterns of microstructural similarity across the brain.

This study evaluates low-field MRI processing strategies to improve cortical thickness correspondence with high-field MRI in young people, finding that recon-all-clinical on multi-orientation T2w images provides the best results.

This study shows white matter signals during seizures reflect communication between brain regions and differentiate epilepsy pathophysiologies, challenging past assumptions about their limited value.

This study shows that neuronal hyperactivity and seizures increase tau spread in an Alzheimer's disease mouse model and correlate with memory deficits.

DeepCombat removes batch effects from neuroimaging data using a deep learning approach, outperforming existing methods in preserving biological variation.

This paper develops a deep learning model to accurately segment the nucleus basalis of Meynert on 3T MRI, showing improved performance over standard atlases in both healthy subjects and TLE patients.

The paper develops deep learning algorithms to quantify seizure spread beyond onset zones and finds associations with structural connectivity and surgical outcomes.