Newest papers

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

French

Neuroimagerie est une modalité largement utilisée pour caractériser la neurodégénération dans diverses conditions de santé. L'écart d'âge du cerveau est apparu comme un biomarqueur prometteur pour la surveillance de la santé du cerveau à partir de données de neuroimagerie à l'aide d'algorithmes d'apprentissage automatique. Des études récentes ont tiré parti des réseaux neuronaux de coVariance (VNN) basés sur les avancées dans le traitement des signaux graphiques pour la construction relativement transparente de l'écart d'âge du cerveau à partir de données de neuroimagerie. Dans ce document, nous nous concentrons sur une étude longitudinale de l'écart d'âge du cerveau bas

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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