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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

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Neuroimagen es una modalidad ampliamente utilizada para caracterizar la neurodegeneración en diversas condiciones de salud. La brecha de edad del cerebro ha surgido como un biomarcador prometedor para monitorear la salud del cerebro a partir de datos de neuroimagen utilizando algoritmos de aprendizaje automático. Estudios recientes han aprovechado las redes neuronales de coVariance (VNN) basadas en los avances en el procesamiento de señales de gráficos para la construcción relativamente transparente de la brecha de edad del cerebro a partir de datos de neuroimagen. En este artículo, nos centramos en un estudio longitudinal de la brecha de edad del cerebro basada en VNN en conexión con el Punto de Progresión de Alzheimer (APS) en individuos que están en un mayor riesgo de neurodegeneración debido a la historia familiar

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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