Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This longitudinal study analyzes the progression of brain age gap, derived via covariance neural networks (VNNs) and graph signal processing, in individuals at high risk for neurodegeneration. The research correlates VNN-based brain age gap metrics with the Alzheimer's Progression Score (APS), a multimodal disease marker. Findings indicate that a higher baseline risk of disease progression predicts a significantly greater rate of increase in brain age gap over time, suggesting clinical/biological risk factors modulate brain age gap trajectory in at-risk populations.
Baseline Alzheimer's risk predicts accelerated brain aging, revealing a critical link between genetic predisposition and neurodegenerative trajectory.
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.