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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]
In the citing paper
“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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MethodEfficacy of functional connectome fingerprinting using tangent-space brain networks
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“…representation of functional connectivity, have been used to study group-averaged properties of the brain in the resting state, during tasks, and in the context of disease (Allen et al., 2022; Cabral, Kringelbach, & Deco, 2014; Du, Fu, & Calhoun, 2018; Faryadras, Burles, Iaria, & Davidsen, 2024).”
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A statistical mechanical problem?
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“The results show, for all models, similar predictions and good agreement between the experimental and simulated correlations (Cabral et al., 2013).”
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“Note that, these dynamics are mostly route-based or diffusion-based (Avena-Koenigsberger et al. 2018)—although several biophysically grounded extensions exist (Cabral et al. 2014; Deco et al. 2017).”
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ResultAetiology and Risk Factors of Schizophrenia
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“…computational methodologies have helped to understand the underlying mechanisms of age-related changes in sleep architecture and brain connectivity, highlighting changes in neural network dynamics, synaptic activity patterns, and regional brain communication (Cabral et al., 2014; Stevner, 2019).”
Discrete Attractor Pattern Recognition During Resting State in EEG Signal
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“The popular features that have been extensively studied are graph-theoretical measures [3][4] and subgraph patterns [5] in the functional brain networks.”
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The impact of functional architecture dynamics in bipolar disorder staging
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“Such phenomena are not unique to BD, but they have been documented both in computational models and across various clinical populations (Cabral et al., 2014; Fornito et al., 2015; Li et al., 2024).”
Peripheral and Central Mechanisms in Irritable Bowel Syndrome : in search of links
Bednarska, O. (2019) · Linköping University medical dissertations · doi:10.3384/diss.diva-156669 · Cited by 1
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Self-citationFishers’ well-being as affected by anthropogenic stressors in Surigao del Sur, Philippines
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A brief review of the neuroimaging modalities in schizophrenia and their scope
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Exploring dynamical whole-brain models in high-dimensional parameter spaces
Wischnewski, K. J., Jarre, F., Eickhoff, S. B., & Popovych, O. V. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322983 · Cited by 1
“We worked with the Kuramoto model of coupled phase oscillators, which is one of the simplest models suited for simulating the features of resting-state brain activity [83].”
Computing and optimizing over all fixed-points of discrete systems on large networks
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“Using dynamical systems models at such scales offers the potential for greater explanatory power relative to purely statistical descriptions [35,36].”
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Self-citationOn brain modeling in resting-state as a network of coupled oscillators
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“Recent studies have then discovered that this kind of organization and activation among the nodes is not static, but gives rise to a temporal evolution of resting-state activity: correlations both within and between brain sub-networks evolve during time [4], [6].”
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“…seeks to apply computational modeling to connectomics data in order to understand brain network dynamics as they manifest in neuropsychiatric disorders (see Cabral et al. 2014; Deco and Kringelbach 2014; Fornito et al. 2015; Xia et al. 2016, for excellent renderings of this topic).”
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On Mathematical Modeling and Analysis of Brain Network
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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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