The study of brain age has emerged over the past decade, aiming to estimate a person's
age based on brain imaging scans. Ideally, predicted brain age should match chronological …

Covariance-based data processing is widespread across signal processing and machine
learning applications due to its ability to model data interconnectivities and dependencies …

Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting
neurological conditions and neurodegenerative disorders. One of the most predominant …

Graph convolutional networks (GCN) leverage topology-driven graph convolutional
operations to combine information across the graph for inference tasks. In our recent work …

Foundation models pretrained on large-scale datasets via self-supervised learning
demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard …

Brain age is the estimate of biological age derived from neuroimaging datasets using
machine learning algorithms. Increasing\textit {brain age gap} characterized by an elevated …

Brain age prediction using fMRI images and graph neural networks is an advanced
approach in the field of neuroimaging data analysis. In this method, fMRI images are initially …