A Novel Spatio-Temporal Hub Identification Method for Dynamic Functional Networks

Functional connectivity (FC) has been widely investigated to understand the cognition and behavior that emerge from human brain. Recently, there is overwhelming evidence showing that quantifying temporal changes in FC may provide greater insight into fundamental properties of brain network. However, scant attentions has been given to characterize the functional dynamics of network organization. To address this challenge, we propose a novel spatio-temporal hub identification method for functional brain networks by simultaneously identifying hub nodes in each static sliding window and maintaining the reasonable dynamics across the sliding windows, which allows us to further characterize the full-spectrum evolution of hub nodes along with the subject-specific functional dynamics. We have evaluated our spatio-temporal hub identification method on resting-state functional resonance imaging (fMRI) data from an obsessive-compulsive disease (OCD) study, where our new functional hub detection method outperforms current methods (without considering functional dynamics) in terms of accuracy and consistency.