2022/01/07 by Shikuang Deng, Jingwei Li, Deng, Shikuang +5 · 1 citation
Neuroscience · #FOS: Biological sciences #Functional Brain Connectivity Studies #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Photoreceptor and optogenetics research #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2201.02340
openalex publication_date 2022/01/07 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
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.