Luis Fernando Antón‐Toro

This study tracks how electrophysiological functional connectivity changes during adolescence following alcohol initiation, finding divergent trajectories based on drinking severity.

The paper uses unsupervised machine learning to cluster electrophysiological data from healthy teenagers and identifies patterns linked to future binge drinking behavior.

This study uses regression models to analyze MEG power changes across age and brain regions, revealing non-linear trajectories and associations with cognitive performance and gray matter volume.

The paper predicts adolescent alcohol consumption using neuroanatomical and functional connectivity variables from MEG data in a cohort of 14-16 year olds.