The brain connectome supports the information flow underlying human cognitions and should reflect the individual variability in human cognitive behaviors. Various studies have utilized the brain connectome to predict individual differences in human behaviors. However, traditional studies viewed the brain connectome feature as a vector of one dimension, a method which neglects topological structures of the brain connectome. To utilize topological properties of the brain connectome, we proposed that graph neural network which combines graph theory and neural network can be adopted. Different from previous node-driven graph neural networks that parameterize on the node feature transformation, we designed an edge-driven graph neural network named graph propagation network that parameterizes on the information propagation within the brain connectome. We compared various models in predicting the individual total cognition based on the resting-state functional connectome. The edge-driven graph propagation network showed the highest prediction accuracy and outperformed the node-driven graph neural network and traditional partial least square regression. The graph propagation network also revealed a directed network topology encoding the information flow, indicating that the high-level association cortices are responsible for the information integration underlying the total cognition. These results suggest that the edge-driven graph propagation network can better explore the topological structure of the brain connectome and can serve as a new method to associate the brain connectome and human behaviors.
bioRxiv Subject Collection: Neuroscience