The proposed research aims to characterize and validate MRI-based imaging patterns associated with healthy aging using large-scale, multimodal imaging data from the UK Biobank. The study focuses on understanding age-related variation in structural and tissue-related MRI features across multiple organ systems, including the brain, musculoskeletal system, and body composition, in adults without major diagnosed diseases. Aging-related imaging changes are closely linked to functional decline, chronic disease risk, and long-term health outcomes. However, robust population-scale analyses examining how such imaging features vary across adulthood, and how reliably they can be captured using modern analytical methods, remain limited. This project will investigate and evaluate machine learning approaches as analytical tools for identifying and summarizing age-related imaging patterns across organs. The findings are expected to support population health research by providing evidence on aging-related imaging variation and methodological considerations in large-scale MRI analysis.
