Age is one of the major known risk factors for Alzheimer's Disease (AD). Detecting AD early is crucial for effective treatment and preventing irreversible brain damage. Brain age, a measure derived from brain imaging reflecting structural changes due to aging, may have the potential to identify AD onset, assess disease risk, and plan targeted interventions. Deep learning-based regression techniques to predict brain age from magnetic resonance imaging (MRI) scans have shown great accuracy recently. However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects. This weakens the reliability of predicted brain age as a valid biomarker for downstream clinical applications. Here, we reformulate the brain age prediction task from regression to classification to address the issue of systematic bias. Recognizing the importance of preserving ordinal information from ages to understand aging trajectory and monitor aging longitudinally, we propose a novel ORdinal Distance Encoded Regularization (ORDER) loss that incorporates the order of age labels, enhancing the model's ability to capture age-related patterns. Extensive experiments and ablation studies demonstrate that this framework reduces systematic bias, outperforms state-of-art methods by statistically significant margins, and can better capture subtle differences between clinical groups in an independent AD dataset. Our implementation is publicly available at https://github.com/jaygshah/Robust-Brain-Age-Prediction.

翻译：年龄是阿尔茨海默病已知的主要风险因素之一。早期检测AD对于有效治疗和预防不可逆脑损伤至关重要。脑龄是一种从脑成像中推导出的反映衰老相关结构变化的指标，可能具有识别AD发病、评估疾病风险及规划靶向干预的潜力。基于深度学习的回归技术从磁共振成像扫描中预测脑龄，近年来已展现出极高的准确性。然而，这些方法存在固有的均值回归效应，导致系统性偏差：对年轻受试者的脑龄高估，对老年受试者脑龄低估。这削弱了预测脑龄作为下游临床应用有效生物标志物的可靠性。为应对系统性偏差问题，本文将从回归范式重构为分类任务。鉴于保留年龄序数信息对理解衰老轨迹和纵向监测衰老进程的重要性，我们提出了一种新颖的序数距离编码正则化损失函数，该损失函数整合了年龄标签的序数关系，增强了模型捕获年龄相关模式的能力。大量实验和消融研究表明，该框架可减少系统性偏差，以统计显著性优势超越现有最优方法，并在独立AD数据集中更好地捕捉临床组间的细微差异。我们的实现在https://github.com/jaygshah/Robust-Brain-Age-Prediction公开获取。