Control theory illustrates the energy efficiency in the dynamic reconfiguration of functional connectivity.

Brain / diagnostic imaging Connectome Conservation of Energy Resources Humans Magnetic Resonance Imaging

Journal

Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179

Informations de publication

Date de publication:
01 04 2022
Historique:
received: 07 06 2021
accepted: 22 02 2022
entrez: 2 4 2022
pubmed: 3 4 2022
medline: 6 4 2022
Statut: epublish

Résumé

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.

Identifiants

DOI: 10.1038/s42003-022-03196-0 PMID: 35365757 PMC: PMC8975837
pubmed: 35365757
doi: 10.1038/s42003-022-03196-0
pii: 10.1038/s42003-022-03196-0
pmc: PMC8975837
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

295

Subventions

Organisme : NIMH NIH HHS
ID : R01 MH074457
Pays : United States

Informations de copyright

© 2022. The Author(s).

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Auteurs

Shikuang Deng (S)

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.

Jingwei Li (J)

Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany.
Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
ORCID: 0000-0002-6395-8801

B T Thomas Yeo (BT)

Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore.
Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore.
N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore.
Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.

Shi Gu (S)

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China. gus@uestc.edu.cn.
ORCID: 0000-0003-2303-6770

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questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Control theory illustrates the energy...
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