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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

Indonesian

Neuroimaging adalah modal yang banyak digunakan untuk menggambarkan neurodegenerasi dalam berbagai kondisi kesehatan. Jarak usia otak telah muncul sebagai biomarker yang menjanjikan untuk memantau kesehatan otak dari data neuroimaging menggunakan algoritma pembelajaran mesin. Studi terbaru telah memanfaatkan jaringan neural coVariance (VNN)-driven pipelines berdasarkan kemajuan dalam pemrosesan sinyal grafis untuk konstruksi yang relatif transparan dari gap usia otak dari data neuroimaging. Dalam makalah ini, kami berfokus pada studi longitudinal dari gap usia otak berbasis VNN dalam kaitannya dengan Alzheimer's Progression Score (APS) pada individu yang berada pada risiko neurodegenerasi yang lebih tinggi karena riwayat keluarga penyakit Alzheimer. APS adalah penanda multi-modal perkembangan penyakit. Analisis longitudinal dari APS dan metrik gap usia otak yang berasal dari VNN

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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