ReBA-Pred-Net: Weakly-Supervised Regional Brain Age Prediction on MRI
This work addresses the need for robust regional brain age estimation to support disease research and aging studies, representing an incremental advancement with a novel method for a known bottleneck.
The paper tackled the problem of regional brain age prediction from MRI, which is more informative than whole-brain age for tasks like disease characterization, by proposing ReBA-Pred-Net, a weakly-supervised Teacher-Student framework that achieved statistical and factual validity in experiments across multiple backbones.
Brain age has become a prominent biomarker of brain health. Yet most prior work targets whole brain age (WBA), a coarse paradigm that struggles to support tasks such as disease characterization and research on development and aging patterns, because relevant changes are typically region-selective rather than brain-wide. Therefore, robust regional brain age (ReBA) estimation is critical, yet a widely generalizable model has yet to be established. In this paper, we propose the Regional Brain Age Prediction Network (ReBA-Pred-Net), a Teacher-Student framework designed for fine-grained brain age estimation. The Teacher produces soft ReBA to guide the Student to yield reliable ReBA estimates with a clinical-prior consistency constraint (regions within the same function should change similarly). For rigorous evaluation, we introduce two indirect metrics: Healthy Control Similarity (HCS), which assesses statistical consistency by testing whether regional brain-age-gap (ReBA minus chronological age) distributions align between training and unseen HC; and Neuro Disease Correlation (NDC), which assesses factual consistency by checking whether clinically confirmed patients show elevated brain-age-gap in disease-associated regions. Experiments across multiple backbones demonstrate the statistical and factual validity of our method.