Brain Age

Brain age estimation aims to determine an individual's biological brain age from neuroimaging data, typically MRI scans, to identify deviations from chronological age that may indicate neurological disorders or accelerated aging. Current research heavily utilizes deep learning, employing various architectures such as convolutional neural networks (CNNs), vision transformers (ViTs), and graph neural networks (GNNs), often incorporating techniques like contrastive learning and normalizing flows to improve robustness and accuracy across diverse datasets. This field is significant because accurate brain age prediction can serve as a valuable biomarker for early disease detection, personalized medicine, and a deeper understanding of the aging process itself.