Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations
Open accessAbstract
Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.
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Present BNMs only allow for a very limited number of parameters to be estimated, e.g., a single global scaling factor of connection strength.
“Present BNMs only allow for a very limited number of parameters to be estimated, e.g., a single global scaling factor of connection strength (Deco et al., 2013).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Deco et al., 2013
The Figure 3 caption describes fitting empirical functional connectivity by varying a single global coupling parameter, G. But the ‘Exhaustive parameter exploration’ section also compares fits across values of both G and local excitatory recurrence, w. This paper therefore illustrates limited-parameter fitting, but does not establish that BNMs generally allow only a very limited number of parameters to be estimated. (LLM, conf=0.85)
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“Present BNMs only allow for a very limited number of parameters to be estimated, e.g., a single global scaling factor of Frontiers in Human Neuroscience | www.frontiersin.org 19 November 2016 | Volume 10 | Article 550 connection strength (Deco et al., 2013).”
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Self-citationResting state functional connectivity in the human spinal cord
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A generative model of whole-brain effective connectivity
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Diversity of meso-scale architecture in human and non-human connectomes
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Sensory-motor cortices shape functional connectivity dynamics in the human brain
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Spectral mapping of brain functional connectivity from diffusion imaging
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Task-evoked activity quenches neural correlations and variability across cortical areas
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Rare long-range cortical connections enhance human information processing
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Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain
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Disruption of structural and functional networks in long-standing multiple sclerosis
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Dynamical Mechanisms of Interictal Resting-State Functional Connectivity in Epilepsy
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Clinical Concepts Emerging from fMRI Functional Connectomics
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- Authors
- Deco, G., Ponce-Alvarez, A., Mantini, D., Romani, G. L., Hagmann, P., & Corbetta, M.
- Affiliations
- Institució Catalana de Recerca i Estudis Avançats (ES)
- ETH Zurich (CH)
- University of Chieti-Pescara (IT)
- Centre Hospitalier Universitaire Vaudois (CH)
- Washington University in St. Louis (US)
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2013-07-03
- Volume
- 33
- Issue
- 27
- Pages
- 11239-11252
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3718368
- OpenAlex
- W2127186958
- Cited by
- 576
- References
- 48
- Citations by year
- 2026: 43 · 2025: 65 · 2024: 41 · 2023: 36 · 2022: 58 · 2021: 46 · 2020: 46 · 2019: 34 · 2018: 51 · 2017: 40 · 2016: 31 · 2015: 43 · 2014: 34 · 2013: 8
- FWCI
- 21.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, other-oa)
- SERVAL (Université de Lausanne) (publishedVersion, other-oa)
- IRIS (submittedVersion)
- Digital Commons@Becker (Washington University School of Medicine) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc-sa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by-nc-sa)
- Padua Research Archive (University of Padova) (submittedVersion)
- ETH Zürich Research Collection
- Funders
- James S. McDonnell Foundation
- Fondation Leenaards
- National Institutes of Health
- National Institute of Mental Health (R01MH096482)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD061117)
- FP7 Ideas: European Research Council (295129)
- FP7 Information and Communication Technologies
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- resting-state functional connectivity, structure-function relationship, noise propagation, statistical moments, neural dynamics, brain network dynamics
- MeSH terms
- Action Potentials, Adult, Brain Mapping, Cerebral Cortex, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Rest, Linear Models, Young Adult
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