A5046858414- Functional Brain Connectivity Studies
- EEG and Brain-Computer Interfaces
- Health, Environment, Cognitive Aging
- Neural dynamics and brain function
- Advanced Neuroimaging Techniques and Applications
Georgia Institute of Technology
2023
Emory University
2022
Mapping the connectome of human brain using structural or functional connectivity has become one most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power modeling complex networked data. Despite superior performance in many fields, there not yet been a systematic study how design effective GNNs network To bridge this gap, we present BrainGB, benchmark analysis...
Recent neuroimaging studies have highlighted the importance of network-centric brain analysis, particularly with functional magnetic resonance imaging. The emergence Deep Neural Networks has fostered a substantial interest in predicting clinical outcomes and categorizing individuals based on networks. However, conventional approach involving static network analysis offers limited potential capturing dynamism function. Although recent attempted to harness dynamic networks, their high...
Mapping the connectome of human brain using structural or functional connectivity has become one most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power modeling complex networked data. Despite superior performance in many fields, there not yet been a systematic study how design effective GNNs network To bridge this gap, we present BrainGB, benchmark analysis...
Recent neuroimaging studies have highlighted the importance of network-centric brain analysis, particularly with functional magnetic resonance imaging. The emergence Deep Neural Networks has fostered a substantial interest in predicting clinical outcomes and categorizing individuals based on networks. However, conventional approach involving static network analysis offers limited potential capturing dynamism function. Although recent attempted to harness dynamic networks, their high...
Mapping the connectome of human brain using structural or functional connectivity has become one most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power modeling complex networked data. Despite superior performance in many fields, there not yet been a systematic study how design effective GNNs network To bridge this gap, we present BrainGB, benchmark analysis...