Brain-age prediction from MRI-derived biomarkers provides a compact measure of
biological brain aging and may support early characterization of neurodegenerative risk. The brain
age gap (BAG), defined as predicted brain age minus chronological age, offers an interpretable devi-
ation from normative aging. Prior work has highlighted neuroimaging-based brain-age biomarkers
as an innovative marker of brain aging [2]. Here, we benchmark multiple machine learning regres-
sors for brain-age estimation from ADNI MRI summary measures and provide an interpretable
baseline for downstream BAG-based neurodegenerative risk modeling.