Complexity of resting brain dynamics shaped by multiscale structural constraints

Abstract

The brain is a complex system exhibiting ever-evolving activity patterns without any external inputs or tasks. Such intrinsic dynamics (or lack thereof) are thought to play crucial roles in typical as well as atypical cognitive functioning. Linking the ever-changing intrinsic dynamics to the rather static anatomy is a challenging endeavor. Dynamical systems models are important tools for understanding how structure and function are linked in the brain. Here, we provide a novel modeling framework to examine such structure-function relations. Our deterministic approach complements previous modeling frameworks, which typically focus on noise-driven (or stochastic) dynamics near a single attractor. We examine the overall organizations of and coordination between all putative attractors. Using our approach, we first provide evidence that examining cross-attractor coordination between brain regions could better predict human functional connectivity than examining noise-driven near-attractor dynamics. Further, we observed that structural connections across scales modulate the energy costs of such cross-attractor coordination. Overall, our work provides a systematic framework for characterizing intrinsic brain dynamics as a web of cross-attractor transitions and associated energy costs. The framework may be used to predict transitions and energy costs associated with experimental or clinical interventions.