提出可精确定位脑区衰老的弱监督模型，助力疾病研究与个体化评估。
ReBA-Pred-Net: Weakly-Supervised Regional Brain Age Prediction on MRI
- 基于师生框架，利用功能相关脑区变化一致性约束提升预测精度。
- 在多骨干网络上验证，区域年龄差分布与健康对照组一致且与神经疾病相关。
- 适合关注脑区特异性衰老、神经疾病机制或精准医学的研究者。
脑龄已成为脑健康的重要生物标志物。然而，以往研究多聚焦全脑脑龄（WBA），该方法粗略，难以支持疾病表征或发育老化模式研究，因相关变化通常局限于特定脑区而非全脑范围。因此，建立稳健的区域脑龄（ReBA）估计模型至关重要，但尚无广泛通用的方法。本文提出区域脑龄预测网络（ReBA-Pred-Net），一种教师-学生框架，用于细粒度脑龄估计。教师生成软标签以指导学生输出可靠的区域脑龄估计，并引入临床先验一致性约束（功能相近脑区应类似变化）。为严格评估，提出两个间接指标：健康对照相似性（HCS），通过检验训练集与未见健康对照组中区域脑龄差（ReBA减去实际年龄）分布是否一致来评估统计一致性；神经疾病相关性（NDC），通过检查已确认患者在疾病关联脑区是否呈现更高的脑龄差来评估事实一致性。在多个骨干网络上的实验验证了方法的统计与事实有效性。
原文摘要 · Abstract (English)
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.
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