While recent methodological advancements have significantly enhanced our grasp of the
brain's spatial and temporal dynamics, challenges persist in advancing fMRI-based spatio …

We propose a geometric deep-learning-based framework, TractGeoNet, for performing
regression using diffusion magnetic resonance imaging (dMRI) tractography and associated …

Individual brains vary greatly in morphology, connectivity and organization. The applicability
of group-level parcellations is limited by the rapid development of precision medicine today …

Predicting individual behavior is a crucial area of research in neuroscience. Graph Neural
Networks (GNNs), as powerful tools for extracting graph-structured features, are increasingly …

Predicting fluid cognition via neuroimaging data is essential for understanding the neural
mechanisms underlying various complex cognitions in the human brain. Both brain …

Predicting individual behavior from functional connectivity (FC) using machine learning is a
critical research topic in neuroscience. While various models have been proposed, they …

Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive
neuroimaging modality that quantifies the changes in blood flow and oxygenation in the …