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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 (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
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Cites
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Self-citationSpontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Friston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Self-citationFunctional and Effective Connectivity: A Review
Friston, K. J. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0008 · Cited by 3,415
Predicting human resting-state functional connectivity from structural connectivity
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0811168106 · Cited by 3,307
Self-citationElectrophysiological signatures of resting state networks in the human brain
Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700668104 · Cited by 1,938
Self-citationEmerging concepts for the dynamical organization of resting-state activity in the brain
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn2961 · Cited by 1,798
Self-citationIntrinsic functional architecture in the anaesthetized monkey brain
Vincent, J. L., Patel, G. H., Fox, M. D., Snyder, A. Z., Baker, J. T., Van Essen, D. C., Zempel, J. M., Snyder, L. H., Corbetta, M., & Raichle, M. E. (2007) · Nature · doi:10.1038/nature05758 · Cited by 1,786
Self-citationNetwork structure of cerebral cortex shapes functional connectivity on multiple time scales
Honey, C. J., Kötter, R., Breakspear, M. J., & Sporns, O. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701519104 · Cited by 1,727
Fransson, P. (2005) · Human Brain Mapping · doi:10.1002/hbm.20113 · Cited by 1,388
Brovelli, A., Ding, M., Ledberg, A., Chen, Y., Nakamura, R., & Bressler, S. L. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308538101 · Cited by 1,073
Dynamics of neuronal firing correlation: modulation of "effective connectivity"
Aertsen, A. M. H. J., Gerstein, G. L., Habib, M., & Palm, G. (1989) · Journal of Neurophysiology · doi:10.1152/jn.1989.61.5.900 · Cited by 1,073
A Recurrent Network Mechanism of Time Integration in Perceptual Decisions
Wong, K.-F., & Wang, X.-J. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.3733-05.2006 · Cited by 1,029
Key role of coupling, delay, and noise in resting brain fluctuations
Deco, G., Jirsa, V., McIntosh, A. R., Sporns, O., & Kötter, R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0901831106 · Cited by 777
Self-citationHutchison, R. M., Womelsdorf, T., Gati, J. S., Everling, S., & Menon, R. S. (2012) · Human Brain Mapping · doi:10.1002/hbm.22058 · Cited by 732
Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors
Deco, G., & Jirsa, V. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.2523-11.2012 · Cited by 704
Self-citationBrunel, N., & Wang, X.-J. (2001) · Journal of Computational Neuroscience · doi:10.1023/a:1011204814320 · Cited by 620
Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire
Ghosh, A., Rho, Y.-A., McIntosh, A. R., Kötter, R., & Jirsa, V. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000196 · Cited by 576
Brunel, N., & Wang, X. (2014) · Journal of Computational Neuroscience · doi:10.1007/s10827-014-0506-8 · Cited by 538
Role of local network oscillations in resting-state functional connectivity
Cabral, J., Hugues, E., Sporns, O., & Deco, G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.04.010 · Cited by 531
Self-citationPropagation and stability of waves of electrical activity in the cerebral cortex
Robinson, P. A., RENNIE, C. J., & Wright, J. J. (1997) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.56.826 · Cited by 486
A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain
de Pasquale, F., Penna, S. D., Snyder, A. Z., Marzetti, L., Pizzella, V., Romani, G. L., & Corbetta, M. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.031 · Cited by 422
Self-citationGating multiple signals through detailed balance of excitation and inhibition in spiking networks
Vogels, T. P., & Abbott, L. F. (2009) · Nature Neuroscience · doi:10.1038/nn.2276 · Cited by 375
Prediction of electroencephalographic spectra from neurophysiology
Robinson, P. A., RENNIE, C. J., Wright, J. J., Bahramali, H., Gordon, E., & ROWE, D. L. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.63.021903 · Cited by 366
Top-down modulation of early sensory cortex
Shulman, G. L. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.3.193 · Cited by 328
The Dynamical Balance of the Brain at Rest
Deco, G., & Corbetta, M. (2010) · The Neuroscientist · doi:10.1177/1073858409354384 · Cited by 323
Self-citationWilson, F. A. W., ÓScalaidhe, S. P., & Goldman‐Rakic, P. S. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.9.4009 · Cited by 303
Role of Delays in Shaping Spatiotemporal Dynamics of Neuronal Activity in Large Networks
Roxin, A., Brunel, N., & Hansel, D. (2005) · Physical Review Letters · doi:10.1103/physrevlett.94.238103 · Cited by 290
Biophysical Mechanisms of Multistability in Resting-State Cortical Rhythms
Freyer, F., Roberts, J. A., Becker, R., Robinson, P. A., Ritter, P., & Breakspear, M. J. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6693-10.2011 · Cited by 289
How Structure Determines Correlations in Neuronal Networks
Pernice, V., Staude, B., Cardanobile, S., & Rotter, S. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002059 · Cited by 280
A brief history of the resting state: The Washington University perspective
Snyder, A. Z., & Raichle, M. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.044 · Cited by 257
Functional connectivity in resting-state fMRI: Is linear correlation sufficient?
Hlinka, J., Paluš, M., Vejmelka, M., Mantini, D., & Corbetta, M. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.08.042 · Cited by 217
Self-citationUnit activity in monkey parietal cortex related to haptic perception and temporary memory
Koch, K. W., & Fuster, J. M. (1989) · Experimental Brain Research · doi:10.1007/bf00247889 · Cited by 191
The spontaneous activity of neurones in the cat’s cerebral cortex
Burns, F. R. S. B. D., & Webb, A. C. (1976) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1976.0074 · Cited by 157
Drivers and modulators from push-pull and balanced synaptic input
Abbott, L. F., & Chance, F. S. (2005) · Progress in brain research · doi:10.1016/s0079-6123(05)49011-1 · Cited by 153
Tractography-based priors for dynamic causal models
Stephan, K. E., Tittgemeyer, M., Knösche, T. R., Moran, R., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.096 · Cited by 151
Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Adalı, T., Pearlson, G. D., & Calhoun, V. D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.026 · Cited by 117
Population dynamics under the Laplace assumption
Marreiros, A. C., Kiebel, S. J., Jean Daunizeau, A., Harrison, L. M., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.008 · Cited by 85
Robinson, P. A. (2012) · Physical Review E · doi:10.1103/physreve.85.011912 · Cited by 83
Rodriguez, R., & Tuckwell, H. C. (1996) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.54.5585 · Cited by 80
BOLD responses to stimuli: Dependence on frequency, stimulus form, amplitude, and repetition rate
Robinson, P. A., Drysdale, P. M., van der Merwe, H., Kyriakou, E., Rigozzi, M. K., Germanoska, B., & RENNIE, C. J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.12.026 · Cited by 54
Critical Fluctuations in Cortical Models Near Instability
Aburn, M. J., Holmes, C. A., Roberts, J. A., Boonstra, T. W., & Breakspear, M. J. (2012) · Frontiers in Physiology · doi:10.3389/fphys.2012.00331 · Cited by 47
Eckhoff, P. A., Wong‐Lin, K., & Holmes, P. J. (2011) · SIAM Journal on Applied Dynamical Systems · doi:10.1137/090770096 · Cited by 28
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Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx179 · Cited by 3,657
Damaraju, E., Allen, E. A., Belger, A., Ford, J. M., McEwen, S. C., Mathalon, D. H., Mueller, B. A., Pearlson, G. D., Potkin, S. G., Preda, A., et al. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.07.003 · Cited by 1,088
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Hindriks, R., Adhikari, M. H., Murayama, Y., Ganzetti, M., Mantini, D., Logothetis, N. K., & Deco, G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.11.055 · Cited by 632
Self-citationZuo, X., & Xing, X.-X. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.05.009 · Cited by 678
Rethinking segregation and integration: contributions of whole-brain modelling
Deco, G., Tononi, G., Boly, M., & Kringelbach, M. L. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3963 · Cited by 658
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Metadata
- 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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