Dynamic Characteristics of Local Functional Connectivity Revealed by Dynamic Regional Phase Synchrony - Jie Zhou; Hong Zhu; Jijun Wang; Junfeng Sun (IEEE, 2023)
Studies on functional connectivity (FC) between remote brain regions or in local brain region have revealed ample statistical associations between the brain activities of corresponding brain units and deepened our understanding of brain. However, the dynamics of local FC were largely unexplored. In this study, we employed the dynamic regional phase synchrony (DRePS) method to investigate local dynamic FC based on multiple sessions resting state functional magnetic resonance imaging (rs-fMRI) data. We observed consistent spatial distribution of voxels with high or low temporal averaged DRePS in some specific brain regions across subjects. To quantify the dynamic change of local FC patterns, we calculated the average regional similarity of local FC patterns across all volume pairs under different volume interval and observed that the average regional similarity decreased quickly as volume interval increased, and would reach different steady ranges with only small fluctuations. Four metrics, i.e., the local minimal similarity, the turning interval, the mean of steady similarity, and the variance of steady similarity, were proposed to characterize the change of average regional similarity. We found that both the local minimal similarity and the mean of steady similarity had high test-retest reliability, and had negative correlation with the regional temporal variability of global FC in some functional subnetworks, which indicates the existence of local-to-global FC correlation. Finally, we demonstrated that the feature vectors constructed with the local minimal similarity may serve as brain “fingerprint” and gained good performance in individual identification. Together, our findings offer a new perspective for exploring the local spatial-temporal functional organization of brain.