Wu Yutong, Sun Shen, Zhang Chen, Ma Xiangge, Zhu Xinyu, Li Yanxue, Lin Lan, Fu Zhenrong
Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China.
Bioengineering (Basel). 2025 Jun 3;12(6):607. doi: 10.3390/bioengineering12060607.
This study proposes a novel brain-region-level aging assessment paradigm based on Shapley value interpretation, aiming to overcome the interpretability limitations of traditional brain age prediction models. Although deep-learning-based brain age prediction models using neuroimaging data have become crucial tools for evaluating abnormal brain aging, their unidimensional brain age-chronological age discrepancy metric fails to characterize the regional heterogeneity of brain aging. Meanwhile, despite Shapley additive explanations having demonstrated potential for revealing regional heterogeneity, their application in complex deep learning algorithms has been hindered by prohibitive computational complexity. To address this, we innovatively developed a computational framework featuring efficient Shapley value approximation through a novel multi-stage computational strategy that significantly reduces complexity, thereby enabling an interpretable analysis of deep learning models. By establishing a reference system based on standard Shapley values from healthy populations, we constructed an anatomically specific Regional Brain Aging Deviation Index (RBADI) that maintains age-related validity. Experimental validation using UK Biobank data demonstrated that our framework successfully identified the thalamus (THA) and hippocampus (HIP) as core contributors to brain age prediction model decisions, highlighting their close associations with physiological aging. Notably, it revealed significant correlations between the insula (INS) and alcohol consumption, as well as between the inferior frontal gyrus opercular part (IFGoperc) and smoking history. Crucially, the RBADI exhibited superior performance in the tri-class classification of prodromal neurodegenerative diseases (HCs vs. MCI vs. AD: AUC = 0.92; HCs vs. pPD vs. PD: AUC = 0.86). This framework not only enables the practical implementation of Shapley additive explanations in brain age prediction deep learning models but also establishes anatomically interpretable biomarkers. These advancements provide a novel spatial analytical dimension for investigating brain aging mechanisms and demonstrate significant clinical translational value for early neurodegenerative disease screening, ultimately offering a new methodological tool for deciphering the neural mechanisms of aging.
本研究提出了一种基于沙普利值解释的新型脑区水平衰老评估范式，旨在克服传统脑年龄预测模型的可解释性局限。尽管使用神经影像数据的基于深度学习的脑年龄预测模型已成为评估异常脑衰老的关键工具，但其单维的脑年龄与实际年龄差异度量无法表征脑衰老的区域异质性。同时，尽管沙普利加性解释已显示出揭示区域异质性的潜力，但其在复杂深度学习算法中的应用却因过高的计算复杂度而受阻。为解决这一问题，我们创新性地开发了一个计算框架，通过一种新颖的多阶段计算策略实现高效的沙普利值近似，该策略显著降低了复杂度，从而能够对深度学习模型进行可解释分析。通过基于健康人群的标准沙普利值建立一个参考系统，我们构建了一个具有解剖学特异性的区域脑衰老偏差指数（RBADI），该指数保持了与年龄相关的有效性。使用英国生物银行数据进行的实验验证表明，我们的框架成功地将丘脑（THA）和海马体（HIP）识别为脑年龄预测模型决策的核心贡献者，突出了它们与生理衰老的密切关联。值得注意的是，它揭示了脑岛（INS）与酒精消费之间以及额下回岛盖部（IFGoperc）与吸烟史之间存在显著相关性。至关重要的是，RBADI在前驱神经退行性疾病的三类分类中表现出卓越性能（健康对照 vs. 轻度认知障碍 vs. 阿尔茨海默病：AUC = 0.92；健康对照 vs. 帕金森病前驱期 vs. 帕金森病：AUC = 0.86）。该框架不仅使沙普利加性解释在脑年龄预测深度学习模型中得以实际应用，还建立了解剖学上可解释的生物标志物。这些进展为研究脑衰老机制提供了一个新的空间分析维度，并在早期神经退行性疾病筛查中显示出显著的临床转化价值，最终为解读衰老的神经机制提供了一种新的方法工具。
