Control Theory Illustrates the Energy Efficiency in the Dynamic Reconfiguration of Functional Connectivity

TL;DR

This paper uses control theory and energetic analysis to show that the brain's dynamic functional connectivity is more energy-efficient than static connectivity, and that combining these measures improves behavioral predictions.

Contribution

It introduces an energy-based control framework to analyze brain network reconfiguration, revealing energy efficiency benefits of dynamic connectivity and enhancing behavioral prediction.

Findings

Dynamic connectivity reduces energy cost by 60% compared to static connectivity.

Energy-based control measures complement graph metrics for behavioral prediction.

Dynamic reconfiguration supports energy-efficient brain function during rest.

Abstract

The brain's functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores. Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks.