Neurodegeneration, characterized by the progressive loss of neuronal structure or function, is commonly assessed in clinical practice through reductions in cortical thickness or brain volume, as visualized by structural MRI. While informative, these conventional approaches lack the statistical sophistication required to fully capture the spatially correlated and heterogeneous nature of neurodegeneration, which manifests both in healthy aging and in neurological disorders. To address these limitations, brain age gap has emerged as a promising data-driven biomarker of brain health. The brain age gap prediction (BAGP) models estimate the difference between a person's predicted brain age from neuroimaging data and their chronological age. The resulting brain age gap serves as a compact biomarker of brain health, with recent studies demonstrating its predictive utility for disease progression and severity. However, practical adoption of BAGP models is hindered by their methodological obscurities and limited generalizability across diverse clinical populations. This tutorial article provides an overview of BAGP and introduces a principled framework for this application based on recent advancements in graph signal processing (GSP). In particular, we focus on graph neural networks (GNNs) and introduce the coVariance neural network (VNN), which leverages the anatomical covariance matrices derived from structural MRI. VNNs offer strong theoretical grounding and operational interpretability, enabling robust estimation of brain age gap predictions. By integrating perspectives from GSP, machine learning, and network neuroscience, this work clarifies the path forward for reliable and interpretable BAGP models and outlines future research directions in personalized medicine.

翻译：神经退行性变以神经元结构或功能的进行性丧失为特征，临床实践中通常通过结构MRI显示的大脑皮层厚度或脑容量减少进行评估。尽管这些传统方法具有参考价值，但其统计复杂度不足以完整捕捉神经退行性变的空间相关性与异质性特征——这些特征既存在于健康衰老过程，也显现在神经系统疾病中。为突破这些局限，脑龄差已成为一种具有前景的脑健康数据驱动生物标志物。脑龄差预测模型通过神经影像数据估算个体的预测脑龄与实际年龄之间的差值，所得脑龄差可作为脑健康的紧凑型生物标志物，近期研究已证实其对疾病进展与严重程度的预测效用。然而，脑龄差预测模型在实际应用中面临方法学模糊性与跨临床人群泛化能力有限的挑战。本综述文章系统阐述脑龄差预测方法，并基于图信号处理领域的最新进展，为此类应用提出原理性框架。我们特别聚焦图神经网络，并引入协方差神经网络——该模型利用结构MRI衍生的解剖协方差矩阵，具备坚实的理论基础与操作可解释性，能够实现脑龄差预测的稳健估计。通过整合图信号处理、机器学习与网络神经科学的多元视角，本研究为构建可靠且可解释的脑龄差预测模型厘清发展路径，并为个性化医疗的未来研究方向提供框架指引。