Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated by geometric deep learning have attracted broad interest due to their established power for modeling complex networked data.
Healthcare knowledge graphs (HKGs) are valuable tools for organizing biomedical concepts and their relationships with interpretable structures. The recent advent of large language models (LLMs) has paved the way for building more comprehensive and accurate HKGs.
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated by geometric deep learning have attracted broad interest due to their established power for modeling complex networked data.