Time-varying Analysis of Brain Networks Based on High-order Dynamic Functional Connections in Mild Cognitive Impairment

Existing research commonly uses functional connectivity (FC) combined with graph theory analysis to accomplish the auxiliary diagnosis of mild cognitive impairment (MCI). Traditional FC analysis methods usually target low-order FC networks, while high-order FC networks can reveal higher-level interactions in brain networks. However, there are few studies involving graph theory in high-order FC networks, and traditional graph theory indicators have limitations in high-order FC networks. This paper constructs a high-order FC network through high-order dynamic functional connections, combines graph theory to analyze the brain network status of MCI and normal cognition (NC), and defines two new graph theory indicators, blocking coefficient and average transition time, to characterize temporal variability in brain networks. The results show that the application of graph theory in high-order FC network can effectively extract the differential information between MCI group and NC group. The proposed blocking coefficient and average conversion time index can both show significant differences, providing a new analysis method for the study of high-order brain network.