Multiscale Community Detection in Functional Brain Networks Constructed Using Dynamic Time Warping

Previous studies have focused on the detection of community structures of brain networks constructed with resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation is often used to describe the connections between nodes in the construction of functional brain networks, which typically ignores the inherent timing and validity of fMRI time series. To solve this problem, this study applied the Dynamic Time Warp (DTW) algorithm to determine the correlation between two brain regions by comparing the synchronization and asynchrony of the time series. In addition, to determine the best community structure for each subject, we further divided the brain network into different scales, and then detected the different communities in these brain networks by using Modularity, Variation of Information (VI) and Normalized Mutual Information (NMI) as structural monitoring variables. Finally, we affirmed each subject's best community structure based on them. The experiments showed that through the method proposed in this paper, we not only accurately discovered important components of seven basic functional subnetworks, but also found that the putamen and Heschl's gyrus have a relationship with the inferior parietal network. Most importantly, this method can also determine each subject's functional brain network density, thus confirming the findings of studies testing real brain networks.