Exploring the Interplay between BOLD Signal Variability, Complexity, Static and Dynamic Functional Brain Network Features During Movie Viewing

Abstract Exploring the dynamics and complexity of brain signal is critical to advancing our understanding of brain function. Recent fMRI studies have revealed links between BOLD signal variability or complexity with static/dynamics features of functional brain networks (FBN). However, the association between variability/complexity and regional centrality is still understudied. Here we investigate the association between variability/complexity and static/dynamic nodal features of FBN using graph theory analysis with fMRI BOLD data acquired during naturalistic movie watching. We found that variability positively correlated with fine-scale complexity but negatively correlated with coarse-scale complexity. Specifically, regions with high centrality and clustering coefficient were related to less variable but more complex signal. Similar relationships persisted for dynamic FBN, but the associations with certain aspects (e.g., eigenvector centrality) of regional centrality dynamics became insignificant. Our findings demonstrate that the relationship between BOLD signal variability and static/dynamic FBN with BOLD signal complexity depends on the temporal scale of signal complexity and that time-varying features of FBN reflect the complexities of how BOLD signal variability/complexity coevolve with dynamic FBN.