Multimodal brain age prediction reveals dissociable signatures of health, cognition and disease risk in 24,648 UK Biobank participants

Brain aging proceeds through multiple tissue compartments -- grey matter, white matter, iron-rich subcortical nuclei and the cerebrovascular tree -- yet most deep-learning brain-age models rely on a single MRI contrast, leaving compartment-specific contributions unresolved. Here we train 3D DenseNet121 models to predict chronological age from five MRI modalities -- T1, T2 FLAIR, T1+T2 early fusion, diffusion MRI (dMRI) and susceptibility-weighted imaging (SWI) -- in up to 24,648 UK Biobank participants, with external validation in the Parkinsons Progression Markers Initiative (PPMI). T1+T2 fusion achieved the lowest within-cohort error (mean absolute error 2.19 yr; Pearson r = 0.934), yet do