4 天前 - … learning framework for multiscale brain network analysis, which effectively delineates
… -stage self-supervised learning framework for multiscale brain network modeling, which …

4 天前 - … for estimating brain age. However, the over-squashing impedes interactions between
… -based GNNs to learn the topological structure of brain networks. Graph rewiring methods …

4 天前 - … based on neural networks. Deep learning is a subset of machine learning that
employs algorithms inspired by the structure and function of the brain’s neural networks to learn …

4 天前 - … of anatomical brain networks underlying language … seizure activities involve
extensive brain networks, not limited to a … the whole brain as a large distributed network called a “…

4 天前 - … networks as a weighted directed graph, incorporating the ideas of message passing
in graph neural networks … Reinforcement Learning and the challenges in learning to solve …

5 天前 - … We conducted a series of experiments to learn the most optimal graph, which we
define by its ability to act as a predictor feature within a classification probe in our network. The …

6 天前 - … The model was trained and tested in an inductive learning context to … brain activity
synchronization, offer novel insights into the operational dynamics of aberrant brain networks …

6 天前 - … constructed a brain network modeling the functional connectivity of a subject's brain
and … We adopt mutual learning between different brain atlases in a pair of parallel AM-GCNs…

6 天前 - … ANNs, we study how a brain-inspired graph-based approach can be … learning
strategies inspired by the biological mechanisms and modeled with ANNs. We show that graph …

7 天前 - … Electroencephalography (EEG) has demonstrated significant value in diagnosing
brain diseases. In particular, brain networks have gained prominence as they offer additional …