ArticleCommunications biology2022
Control theory illustrates the energy efficiency in the dynamic reconfiguration of functional connectivity.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Network-specific metabolic cost of functional connectivity in the human brain.bioRxiv : the preprint server for biology · 2026Article
- Thalamic organization differentially contributes to clinical conditions in epilepsy.Communications medicine · 2026Article
- Quantifying State-Dependent Control Properties of Brain Dynamics from Perturbation Responses.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Controllability of morphometric network colocalize with underlying neurobiology in major depression.Psychological medicine · 2026Article
- Dynamic functional connectivity following cochlear implantation: Predicting speech outcomes and exploring brain network dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Thalamic Network Controllability Predicts Cognitive Impairment in Multiple Sclerosis.Human brain mapping · 2025Article
- Network control theory applied to the human connectome: A study on variability and discriminability of fMRI connectomic features under normal and defective sensorineural conditions.Network neuroscience (Cambridge, Mass.) · 2025Article
- Task-Related Controllability of Functional Connectome During a Working Memory Task in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder.Research (Washington, D.C.) · 2025Article
- Functional Connectome Controllability in Patients with Mild Cognitive Impairment after Repetitive Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex.Journal of clinical medicine · 2024Article
- Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder.EBioMedicine · 2024Article
- The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2024Article
- Higher general intelligence is associated with stable, efficient, and typical dynamic functional brain connectivity patterns.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Edge-centric network control on the human brain structural network.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Functional brain controllability in Parkinson's disease and its association with motor outcomes after deep brain stimulation.Frontiers in neuroscience · 2024Article
- Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia.Schizophrenia bulletin · 2023Article
- Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity.Network neuroscience (Cambridge, Mass.) · 2023Article
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4 authors.
Funding
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 (Combination vs. Control: t = 9.41, p = 1.64e-13; Combination vs. Graph: t = 4.92, p = 3.81e-6). Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks.
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