GCN-based Group Level Analysis of Brain Functional Connectivity in fMRI

Brain functional network describes the functional connectivity (FC) between brain regions, and hence provides a crucial way for analyzing brain diseases. In order to explore neural mechanism of a brain disease, statistical test method is usually used to obtain the FC differences between normal group and abnormal group. However, it is difficult for statistical test method to utilize features from brain region nodes and brain connection edges simultaneously. In this study, we develop a method based on graph convolution network (GCN) for brain functional connectivity analysis in functional magnetic resonance imaging (fMRI). Graph convolution is used to extract the features from brain region nodes and brain connection edges simultaneously, and the interpretability of GCN is applied to obtain the FC differences between different groups. The proposed method is able to analyze the brain functional connectivity more comprehensively and can be a supplement to traditional statistical test method. A task-state public fMRI data set including healthy group, severe traumatic brain injury (TBI) patient group was used for training and testing of the models. And a statistical test method was used as baseline in the performance evaluation. The results showed that the proposed GCN-based method outperformed the statistical baseline method. This method has potential to find more useful FC when we analyzing the neural mechanisms of brain diseases.