MT-ReBA: Anatomically Grounded Regional Brain Age Representation Learning via Weak Supervision

Brain age estimation provides a measure of brain aging, but most existing methods produce only global estimates, obscuring regional aging heterogeneity. Learning regional aging representations remains challenging because chronological age is defined at the subject level, while regional biological ages are neither directly observable nor supported by regional annotations. We introduce Multi-Task Regional Brain Age Estimation (MT-ReBA), a weakly supervised framework for learning anatomically grounded regional aging representations from T1-weighted MRI without regional labels. The key idea is to replace one atlas-defined parcel with its counterpart from another healthy subject, thereby coupling a localized image modification with a donor-age weak target and converting subject-level age labels into localized weak supervision. Specifically, an atlas-defined donor parcel is integrated into a recipient brain, and the donor's chronological age supervises the corresponding regional head as a weak proxy for regional aging. We evaluate MT-ReBA across six public cohorts. MT-ReBA achieves a global brain MAE of 3.10 years and a regional age-ranking accuracy of 69.63 ± 0.11%, shows agreement with constructed weak targets on mixed images, and exhibits anatomically specific responses and exploratory disease associations on unspliced scans.