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.

翻译：脑年龄已成为脑健康的重要生物标志物。然而，先前的研究大多聚焦于全脑年龄（WBA），这是一种较为粗略的范式，难以有效支持疾病表征以及发育与衰老模式研究等任务，因为相关的脑变化通常是区域选择性的，而非全脑性的。因此，稳健的区域脑年龄（ReBA）估计至关重要，但目前尚未建立起一个具有广泛泛化能力的模型。本文提出区域脑年龄预测网络（ReBA-Pred-Net），这是一种专为细粒度脑年龄估计设计的师生框架。教师网络生成软区域脑年龄以指导学生网络，在临床先验一致性约束（即功能相近的脑区应呈现相似的变化模式）下产生可靠的重估估计。为进行严谨评估，我们引入了两项间接评价指标：健康对照相似性（HCS），通过检验训练集与未见健康对照者的区域脑年龄差（ReBA减去实际年龄）分布是否一致来评估统计一致性；以及神经疾病相关性（NDC），通过验证临床确诊患者在疾病相关脑区是否表现出更高的脑年龄差来评估事实一致性。在多种骨干网络上的实验证明了我们方法在统计与事实层面的有效性。