Marked changes occur in the brain during people’s lives, and individual rates of aging have revealed pronounced differences, giving rise to subject-specific brainprints that are the signature of the brain. These are shaped by a great variety of factors, both endogenous and exogenous. Accurate predictions of brain age (BA) can be derived from neuroimaging endophenotypes by using machine and deep learning (DL) techniques. Predictive models leading to accurate estimates while revealing which features contribute the most to final predictions are key to unveiling the mechanisms underlying the evolution of brain aging patterns. Explainable artificial intelligence (XAI) methods are emerging as enabling technology in different fields, and biomedicine is no exception. Within this framework, this article examines BA and presents a comprehensive review of recent advances in the exploitation of explainable machine learning (ML)/DL methods, highlighting the main open issues and providing hints for future directions.

中文翻译：

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磁共振成像老化脑图的可解释人工智能：基础和挑战

在人们的一生中，大脑中会发生显着的变化，个体的衰老速度已经显示出明显的差异，从而产生了作为大脑特征的特定主题的脑印。这些是由各种各样的因素塑造的，包括内生和外生的。通过使用机器和深度学习 (DL) 技术，可以从神经影像内表型中获得对脑年龄 (BA) 的准确预测。预测模型导致准确估计，同时揭示哪些特征对最终预测贡献最大，这是揭示大脑衰老模式演变机制的关键。可解释的人工智能 (XAI) 方法正在成为不同领域的使能技术，生物医学也不例外。在这个框架内，