In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" for an individual. Importantly, the discordance between brain age and chronological age (referred to as "brain age gap") can capture accelerated aging due to adverse health conditions and therefore, can reflect increased vulnerability towards neurological disease or cognitive impairments. However, widespread adoption of brain age for clinical decision support has been hindered due to lack of transparency and methodological justifications in most existing brain age prediction algorithms. In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable framework for brain age prediction using cortical thickness features. Specifically, our brain age prediction framework extends beyond the coarse metric of brain age gap in Alzheimer's disease (AD) and we make two important observations: (i) VNNs can assign anatomical interpretability to elevated brain age gap in AD by identifying contributing brain regions, (ii) the interpretability offered by VNNs is contingent on their ability to exploit specific eigenvectors of the anatomical covariance matrix. Together, these observations facilitate an explainable and anatomically interpretable perspective to the task of brain age prediction.

翻译：在计算神经科学领域，利用脑成像数据开发机器学习算法以预测个体"脑龄"的研究日益受到关注。重要的是，脑龄与实际年龄之间的差异（称为"脑龄差距"）能够捕捉因不良健康状况导致的加速衰老过程，从而反映神经系统疾病或认知障碍的易感性增加。然而，现有多数脑龄预测算法因缺乏透明性和方法论依据，阻碍了其在临床决策支持中的广泛应用。本文利用协方差神经网络（VNN），提出一种基于解释驱动且具有解剖学可解释性的脑龄预测框架，采用皮层厚度特征进行分析。具体而言，本研究的脑龄预测框架超越了传统阿尔茨海默病（AD）中脑龄差距的粗粒度度量，并得出两项重要发现：（i）VNN通过识别贡献性脑区，可为AD中升高的脑龄差距赋予解剖学可解释性；（ii）VNN所提供的可解释性取决于其利用解剖协方差矩阵特定特征向量的能力。综合而言，这些发现为脑龄预测任务提供了可解释且具有解剖学意义的视角。