A Multi-Scale Multi-Hop Graph Convolution Network for Predicting Fluid Intelligence via Functional Connectivity

Predicting fluid intelligence via neuroimaging data is important to understand neural mechanisms underlying diverse complex cognitive tasks in human brain. Functional connectivity (FC) reflects interactions among brain regions providing rich information of brain organization, which has been widely used in various behavior predictions. With the success of deep neural networks, graph convolutional network (GCN) is regarded as a promising feature learning method in FC networks (FCNs). However, as a challenging task, the existing GCN models cannot achieve a satisfactory performance in fluid intelligence predication due to the insufficient information utilization of brain connectivity and the limitation of graph convolution layer. To tackle these problems, this paper developed a Multi-Scale Multi-Hop GCN (MS-MH-GCN) to estimate fluid intelligence score by using FC. In the proposed method, we considered the hierarchy of brain system and thus utilized FCs from multiple spatial scales as input for the subsequent feature learning to achieve a complete characterize of brain organization for each individual. We also designed a new multi-hop graph convolution layer that uses multi-hop neighbors instead of l-hop neighbor in traditional GCN to guide message passing of nodal feature at every step. The introduction of high-order graph information benefits to the model learning ability improvement. Additionally, it is also worth emphasizing that, during feature learning process, we added contrast constraint to multi-scale FCNs to improve the similarity of feature representations across different spatial scales within a subject. Experimental results showed that our proposed method performed much better than the other four art-of-the-state methods.