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Brain Age Gap Progression in Individuals at Risk for Neurodegeneration

Jason Scheffel, Saurabh Sihag · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.18794 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

神经成像是一种广泛使用的模式,用于在各种健康状况下特征神经退化。大脑年龄差已经出现了作为从神经成像数据监测大脑健康的有前途的生物标志物使用机器学习算法。最近的研究利用了基于图形信号处理的进展,以便从神经成像数据中相对透明地构建大脑年龄差距。在这篇论文中,我们专注于对基于VNN的大脑年龄差距的长度研究,结合阿尔茨海默氏病进展率(APS)在因阿尔茨海默氏病家族历史而处于较高的神经退化风险的个人中。APS是疾病进展的多模式标志物。APS和VNN衍生的大脑年龄差距指标的长度分析揭示出,在基线

Machine translation (Meta M2M100); the English original follows.

Abstract

Neuroimaging is a widely used modality for characterizing neurodegeneration in various health conditions. Brain age gap has emerged as a promising biomarker for monitoring brain health from neuroimaging data using machine learning algorithms. Recent studies have leveraged coVariance neural networks (VNN)-driven pipelines based on the advancements in graph signal processing for relatively transparent construction of brain age gap from neuroimaging data. In this paper, we focus on a longitudinal study of VNN-based brain age gap in conjunction with Alzheimer's Progression Score (APS) in individuals that are at a higher risk of neurodegeneration due to family history of Alzheimer's disease. APS is a multimodal marker of disease progression. The longitudinal analysis of APS and VNN-derived brain age gap metrics revealed that a higher risk of disease progression at baseline predicted a larger rate of increase in brain age gap in follow-up visits. Thus, our findings revealed potential stratification in the brain age gap modulated by clinical/biological risks of AD in the at-risk population.

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Topics: Neuroimaging, Neurodegeneration, Neuroscience, Medicine, Disease, Biomarker

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