Connectome-based prediction of individual behaviors via convolutional graph propagation network

Humans possess huge individual differences in behaviors and understanding how the individual differences in brain give rise to the individual differences in behaviors is crucial for understanding the mechanism of brain function. Previous studies indicate that brain functional connectome is important for explaining the individual differences in behaviors and many researchers have already conducted connectome based prediction of individual behaviors. However, the ability of current connectome based prediction model is still limited. In this study, we proposed the convolutional graph propagation network (cGPN) which is a graph neural network that performs graph convolution on the brain connectome and propagates information between brain regions. We performed the individual predictions of many cognitive behaviors and demonstrated that cGPN outperforms various baseline models and achieves state of the art performance in connectome based prediction. The current results contribute to the understanding of behavioral individual differences and may also help to identify the mechanism of brain function.