Brain Aging

Brain aging research aims to understand the biological processes underlying age-related changes in brain structure and function, ultimately seeking to identify biomarkers for neurodegenerative diseases and improve diagnostic accuracy. Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs), vision transformers (ViTs), and graph neural networks, applied to neuroimaging data (primarily MRI) to predict brain age and identify regions exhibiting accelerated aging. These advanced techniques, often incorporating voxel-wise analysis and uncertainty quantification, offer improved accuracy and interpretability compared to previous methods, providing valuable insights into regional variations in aging trajectories and their association with various neurological disorders. This work has significant implications for early disease detection, personalized medicine, and a deeper understanding of the complex interplay between brain aging and neurological conditions.