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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

Arabic

وتشير الدراسات الأخيرة إلى استخدام شبكات عصبية معيّنة لتخصيص التغيرات العصبيّة في حالات صحيّة متنوعة. أظهرت الفجوة العمرية في الدماغ كأحد العوامل البيولوجيّة المتاحة لتقييم صحة الدماغ من بيانات التغيرات العصبيّة باستخدام أدوات التعلم الآلي. أظهرت الدراسات الأخيرة استخدام شبكات عصبية معيّنة معيّنة (Variance Neural Networks (VNN)-driven pipelines based on the advances 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 who are at a

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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