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Exploring the network dynamics underlying brain activity during rest
Open accessAbstract
Since the mid 1990s, the intriguing dynamics of the brain at rest has been attracting a growing body of research in neuroscience. Neuroimaging studies have revealed distinct functional networks that slowly activate and deactivate, pointing to the existence of an underlying network dynamics emerging spontaneously during rest, with specific spatial, temporal and spectral characteristics. Several theoretical scenarios have been proposed and tested with the use of large-scale computational models of coupled brain areas. However, a mechanistic explanation that encompasses all the phenomena observed in the brain during rest is still to come. In this review, we provide an overview of the key findings of resting-state activity covering a range of neuroimaging modalities including fMRI, EEG and MEG. We describe how to best define and analyze anatomical and functional brain networks and how unbalancing these networks may lead to problems with mental health. Finally, we review existing large-scale models of resting-state dynamics in health and disease. An important common feature of resting-state models is that the emergence of resting-state functional networks is obtained when the model parameters are such that the system operates at the edge of a bifurcation. At this critical working point, the global network dynamics reveals correlation patterns that are spatially shaped by the underlying anatomical structure, leading to an optimal fit with the empirical BOLD functional connectivity. However, new insights coming from recent studies, including faster oscillatory dynamics and non-stationary functional connectivity, must be taken into account in future models to fully understand the network mechanisms leading to the resting-state activity.
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“Like other studies focused on measures of functional connectivity, our conclusions are tempered by questions about the origins and significance of correlated fluctuations in the blood-oxygen-level dependent (BOLD) fMRI signal61-63.”
From Evolutionarily conserved prefrontal-amygdalar dysfunction in early-life anxiety (2014) · cites this paper as [62]
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“this conceptualization takes into account our increasing understanding of these whole brain networks as dynamic entities, with patterns of activity and connectivity (both within-network and with other networks) shifting as required by ever-changing situational demands (Cabral, Kringelbach, & Deco, 2014;Shine et al., 2016).”
From Mental Time Travel? A Neurocognitive Model of Event Simulation. (2020) · cites this paper as Cabral, Kringelbach, & Deco, 2014
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“METHOD—SPATIOTEMPORAL”
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Brain dynamics: the temporal variability of connectivity, and differences in schizophrenia and ADHD
Rolls, E. T., Cheng, W., & Feng, J. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01197-x · Cited by 65
Luppi, A. I., Mediano, P. A. M., Rosas, F. E., Allanson, J. E., Pickard, J. D., Williams, G. B., Craig, M. M., Finoia, P., Peattie, A. R. D., Coppola, P., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-03330-y · Cited by 79
Putting the “dynamic” back into dynamic functional connectivity
Heitmann, S., & Breakspear, M. J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00041 · Cited by 65
Mapping Epileptic Activity: Sources or Networks for the Clinicians?
Pittau, F., Mégevand, P., Sheybani, L., Abela, E., Grouiller, F., Spinelli, L., Michel, C. M., Seeck, M., & Vulliémoz, S. (2014) · Frontiers in Neurology · doi:10.3389/fneur.2014.00218 · Cited by 65
“FC analysis of EEG/MEG considers the time-course of electromagnetic signals and looks at correlations of oscillating networks (42).”
BackgroundIt’s about time: Linking dynamical systems with human neuroimaging to understand the brain
John, Y. J., Sawyer, K. S., Srinivasan, K., Müller, E. J., Munn, B., & Shine, J. M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00230 · Cited by 71
“Advances in computational power have rendered DST much more tractable as a tool for neuroimaging (Breakspear, 2017; Cabral et al., 2014; Deco et al., 2009, 2011, 2013a, 2013b, 2015, 2021; Deco & Jirsa, 2012; Ghosh et al., 2008; Gollo et al., 2015; Hlinka & Coombes, 2012; Pillai & Jirsa, 2017; Sanz…”
MethodMetastability demystified — the foundational past, the pragmatic present and the promising future
Hancock, F., Rosas, F. E., Luppi, A. I., Zhang, M., Mediano, P. A. M., Cabral, J., Deco, G., Kringelbach, M. L., Breakspear, M. J., Kelso, J. A. S., et al. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00883-1 · Cited by 92
“…that this local signature of turbulence is a signature of local vorticity, perhaps analogous to brain spiralling 136 , and is in essence complementary with the concept of global metastability 135 but focusing on the local dynamics rather than the whole system described here 50,56,57,137–139 .”
Self-citationMikolas, P., Melicher, T., Skoch, A., Matejka, M., Slovakova, A., Bakstein, E., Hajek, T., & Spaniel, F. (2016) · Psychological Medicine · doi:10.1017/s0033291716000878 · Cited by 58
“As patients with established schizophrenia as well as those with FES show reduced asymmetry of rsFC, we used ROIs from both sides as seeds in each model (Damoiseaux et al. 2006; Swanson et al. 2011; Cabral et al. 2014; Guo et al. 2014).”
MethodThe pleasure of food: Underlying brain mechanisms of eating and other pleasures
Kringelbach, M. L. (2015) · Flavour · doi:10.1186/s13411-014-0029-2 · Cited by 62
“We are beginning to understand the metastable nature as well as the topological and functional features of these networks using advances in network science and graph theory together with advanced whole-brain computational models [38,39].”
BackgroundSelf-citationA new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain
Falcon, M. I., Jirsa, V., & Solodkin, A. (2016) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000344 · Cited by 59
Deco, G., Tagliazucchi, E., Laufs, H., Sanjuán, A., & Kringelbach, M. L. (2017) · eNeuro · doi:10.1523/eneuro.0106-17.2017 · Cited by 59
“tional modeling (Cabral et al., 2014), this raises the pos-”
BackgroundSelf-citationAtasoy, S., Vohryzek, J., Deco, G., Carhart-Harris, R. L., & Kringelbach, M. L. (2018) · Progress in brain research · doi:10.1016/bs.pbr.2018.08.009 · Cited by 59
Self-citationLarger GPU-accelerated brain simulations with procedural connectivity
Knight, J. C., & Nowotny, T. (2021) · Nature Computational Science · doi:10.1038/s43588-020-00022-7 · Cited by 59
Liu, X., Lauer, K. K., Ward, B. D., Roberts, C. J., Liu, S., Gollapudy, S., Rohloff, R., Gross, W. L., Chen, G., Xu, Z., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.12.043 · Cited by 60
“…evidence indicates that the low-frequency (< 0.1 Hz) fluctuations (LFFs) of blood oxygen-dependent (BOLD) fMRI signals in the resting state are correlated with spontaneous neuronal activity synchronized locally or over long distances in the brain (Cabral et al. 2014; Foster et al. 2016).”
BackgroundInfluentialNiu, H., Zhu, Z., Wang, M., Li, X., Yuan, Z., Sun, Y., & Han, Y. (2019) · Neurophotonics · doi:10.1117/1.nph.6.2.025010 · Cited by 56
“The FC state 1 represented the stationary connectivity-like pattern and emerged most frequently in dynamic brain activity.(58,59) The state 1 accounted for ∼70% in the dynamic connectivity and the other connectivity states account for much less frequency (ranging between 7% and 15%).”
BackgroundIs schizophrenia a spatiotemporal disorder of the brain's resting state?
Northoff, G. (2015) · World Psychiatry · doi:10.1002/wps.20177 · Cited by 54
“These different frequency bands are coupled with each other (5), constituting a complex temporal structure (6).”
BackgroundParbat, D., & Chakraborty, M. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102277 · Cited by 53
Functional connectivity arises from a slow rhythmic mechanism
Li, J. M., Bentley, W. J., & Snyder, L. H. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419837112 · Cited by 53
Spontaneous electrical low-frequency oscillations: a possible role in Hydra and all living systems
Hanson, A. J. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0763 · Cited by 55
Deco, G., van Hartevelt, T. J., Fernandes, H. M., Stevner, A. B. A., & Kringelbach, M. L. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.047 · Cited by 47
“…models typically use various mesoscopic top-down approximations of brain complexity with dynamical networks of local brain area attractor networks (Cabral et al., 2014a), where the more advanced models use a dynamic mean-field model derived from a proper reduction of a detailed spiking neuron…”
BackgroundMethodResultSelf-citationHomeostatic plasticity and emergence of functional networks in a whole-brain model at criticality
Rocha, R. P., Koçillari, L., Suweis, S., Corbetta, M., & Maritan, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-33923-9 · Cited by 50
Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms
Alderson, T. H., Bokde, A. L. W., Kelso, J. A. S., Maguire, L., & Coyle, D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25009 · Cited by 56
“…suggest that intrinsic neural architecture is organised to support a range of functional states which can be a posteriori selected via exogenous input (Cabral et al., 2014; Hansen et al., 2014; Kringelbach et al., 2015; Ponce-Alvarez et al., 2015; Deco and Kringelbach, 2016; Deco et al., 2017).”
BackgroundRemote synchronization of amplitudes across an experimental ring of non-linear oscillators
Minati, L. (2015) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.4936791 · Cited by 49
Moeller, S. J., London, E. D., & Northoff, G. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.11.010 · Cited by 50
Mancini, M., Brignani, D., Conforto, S., Mauri, P., Miniussi, C., & Pellicciari, M. C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.003 · Cited by 48
“Considering that the resting state patterns of brain connectivity are the result of robust and specific intrinsic neural activity (Cabral et al., 2014; Deligianni et al., 2014), our results highlight that tDCS is able to affect selectively the spontaneous neuronal fluctuations , modulating the…”
Altered dynamics of brain segregation and integration in poststroke aphasia
Guo, J., Biswal, B. B., Han, S., Li, J., Yang, S., Yang, M., & Chen, H. (2019) · Human Brain Mapping · doi:10.1002/hbm.24605 · Cited by 45
Dynamic functioning of transient resting‐state coactivation networks in the Human Connectome Project
Janes, A. C., Peechatka, A. L., Frederick, B. B., & Kaiser, R. H. (2019) · Human Brain Mapping · doi:10.1002/hbm.24808 · Cited by 44
“…network state); network persistence, (the average duration of time spent in a particular network [both used in prior studies using dynamic methods; Cabral et al., 2014; Calhoun, Miller, Pearlson, & Adali, 2014; Ma et al., 2014]); transitions into each network, (the frequency of moving into a…”
MethodRidley, B., Wirsich, J., Bettus, G., Rodionov, R., Murta, T., Chaudhary, U. J., Carmichael, D. W., Thornton, R., Vulliémoz, S., McEvoy, A. W., et al. (2017) · Brain Topography · doi:10.1007/s10548-017-0551-5 · Cited by 50
Alderson, T. H., Bokde, A. L. W., Kelso, J. A. S., Maguire, L., & Coyle, D. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.033 · Cited by 46
Claeys, E. H. I., Mantingh, T., Morrens, M., Yalın, N., & Stokes, P. R. A. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110465 · Cited by 48
Chen, T., Su, H., Zhong, N., Tan, H., Li, X., Meng, Y., Duan, C., Zhang, C., Bao, J., Xu, D., et al. (2020) · BMC Psychiatry · doi:10.1186/s12888-020-02743-5 · Cited by 46
Predicting network dynamics without requiring the knowledge of the interaction graph
Prasse, B., & Mieghem, P. V. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2205517119 · Cited by 49
Won, G. H., Kim, J. W., Choi, T. Y., Lee, Y. S., Min, K. J., & Seol, K. H. (2017) · Psychiatry Research · doi:10.1016/j.psychres.2017.12.021 · Cited by 42
“In addition, a task-evoked EEG potential contains a component of spontaneous EEG activities (Cabral et al., 2014), and task-induced activation patterns were incorporated into the functional connectivity patterns of resting-state fMRI signals (Smith et al., 2009).”
BackgroundZhang, D., Li, H., Wu, Z., Zhao, Q., Song, Y., Liu, L., Qian, Q., Wang, Y., Roodenrys, S. J., Johnstone, S. J., et al. (2017) · Journal of Attention Disorders · doi:10.1177/1087054717718263 · Cited by 51
“The interest stems from the perspective that brain resting activity has consistent networks across individuals and the networks are varied when relevant tasks are presented (Cabral et al., 2014; Raichle & Snyder, 2007).”
Influentialde Miras, J. R., Toscano, F. S., Iglesias‐Parro, S., Ibáñez-Molina, A. J., Casali, A. G., Laureys, S., Massimini, M., Esteban, F. J., Navas, J., & Langa, J. A. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.04.017 · Cited by 44
“An increasing literature in Theoretical Neuroscience is developing global modelling for whole brain dynamics (see, among others, [26,27,58].”
BackgroundNeuroscience of Reward, Motivation, and Drive
Kringelbach, M. L., & Berridge, K. C. (2016) · Advances in motivation and achievement · doi:10.1108/s0749-742320160000019020 · Cited by 42
Self-citationKlingner, C., Volk, G., Brodoehl, S., Witte, O., & Guntinas-Lichius, O. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.08.011 · Cited by 40
“One approach for examining connectivity between brain regions within a network involves quantifying temporal correlation in the blood oxygen level-dependent (BOLD) signal while the brain is in a resting state (Cabral et al., 2014).”
Influential
Metadata
- Authors
- Cabral, J., Kringelbach, M. L., & Deco, G.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Aarhus University (DK)
- Institució Catalana de Recerca i Estudis Avançats (ES)
- Corresponding author
- Cabral, Joana
- Venue
- Progress in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 114
- Pages
- 102-131
- ISSN
- 0301-0082 (ISSN-L)
- OpenAlex
- W1973272857
- Cited by
- 351
- References
- 254
- Citations by year
- 2026: 14 · 2025: 24 · 2024: 35 · 2023: 25 · 2022: 29 · 2021: 28 · 2020: 28 · 2019: 25 · 2018: 33 · 2017: 34 · 2016: 27 · 2015: 35 · 2014: 14
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Progress in Neurobiology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, other-oa)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc-nd)
- Funders
- ERC Advanced Grant DYSTRUCTURE (295129)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- resting-state brain activity, network dynamics, functional brain networks, bifurcation, criticality, EEG, MEG, functional MRI, oscillatory dynamics, mental health
- MeSH terms
- Animals, Brain, Humans, Neural Pathways, Rest, Nonlinear Dynamics, Neuroimaging
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