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 …

Significant changes in brain morphology occur during the third trimester of gestation. The
capability of deep learning in leveraging these morphological features has enhanced the …

Modeling spatiotemporal interactions in multivariate time series is key to their effective
processing, but challenging because of their irregular and often unknown structure …

Brain age is the estimate of biological age derived from neuroimaging datasets using
machine learning algorithms. Increasing brain age with respect to chronological age can …

Covariance Neural Networks (VNNs) perform graph convolutions on the covariance matrix
of tabular data and achieve success in a variety of applications. However, the empirical …

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

Brain age gap can be estimated from brain images, serving as a valuable biomarker for
aging-associated diseases, using deep neural networks. Traditional brain age prediction …

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 …

Background Impaired recovery of blood pressure (BP) in response to standing up is a
prevalent condition in older individuals. We evaluated the relationship between the early …