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Emerging concepts for the dynamical organization of resting-state activity in the brain

Nature Reviews Neuroscience volume 12, pages 4356 (2011) | Download Citation


A broad body of experimental work has demonstrated that apparently spontaneous brain activity is not random. At the level of large-scale neural systems, as measured with functional MRI (fMRI), this ongoing activity reflects the organization of a series of highly coherent functional networks. These so-called resting-state networks (RSNs) closely relate to the underlying anatomical connectivity but cannot be understood in those terms alone. Here we review three large-scale neural system models of primate neocortex that emphasize the key contributions of local dynamics, signal transmission delays and noise to the emerging RSNs. We propose that the formation and dissolution of resting-state patterns reflects the exploration of possible functional network configurations around a stable anatomical skeleton.

Key points

  • Spontaneous ongoing global activity of the brain at rest is highly structured in spatiotemporal patterns called resting-state networks.

  • Resting-state networks are related to the underlying neuroanatomical structure, but they emerge as a result of the interplay between dynamics and structure.

  • Resting-state networks therefore reflect the intrinsic properties of the brain network. These include neuroanatomical structure, local neuronal dynamics, signal transmission delays and genuine noise.

  • The formation and dissolution of resting-state networks reflects the exploration of possible functional network configurations around a stable anatomical framework.

  • Brain networks generally possess a small-world construction, in which there are multiple ways for different network elements to interact. This anatomical architecture provides the landscape within which different possible network configurations occur. In the absence of external stimulation, noise drives the network dynamics such that the system will visit these network configurations spontaneously.

  • Resting-state networks were originally characterized by indirect and slow measures of neuronal activity (for example, by blood oxygen level-dependent (BOLD) functional MRI (fMRI)). However, it is now clear that large-scale resting-state networks correlate with neuronal rhythms at faster frequencies.

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  1. 1.

    On noise in the nervous system. Psychol. Rev. 73, 242–247 (1966). An important paper in the archives of science that provides a compelling rationale for considering intrinsic activity as a vital part of brain function.

  2. 2.

    et al. Remembering the past: two facets of episodic memory explored with positron emission tomography. Am. J. Psychiatry 152, 1576–1585 (1995).

  3. 3.

    et al. Network analysis of positron emission tomography regional cerebral blood flow data: ensemble inhibition during episodic memory retrieval. J. Neurosci. 16, 3753–3759 (1996).

  4. 4.

    et al. Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. J. Cogn. Neurosci. 9, 648–663 (1997).

  5. 5.

    et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98, 676–682 (2001). The first complete articulation of the idea of a default mode of brain function based on both positron emission tomography (PET) blood flow studies and fMRI. This paper provided the framework for the study of intrinsic activity in neuroimaging.

  6. 6.

    et al. The maturing architecture of the brain's default network. Proc. Natl Acad. Sci. USA 105, 4028–4032 (2008).

  7. 7.

    Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis. Hum. Brain Mapp. 26, 15–29 (2005).

  8. 8.

    , , , & Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proc. Natl Acad. Sci. USA 106, 8719–8724 (2009).

  9. 9.

    et al. Rostrolateral prefrontal cortex involvement in relational integration during reasoning. Neuroimage 14, 1136–1149 (2001).

  10. 10.

    et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447, 83–86 (2007). This paper showed that resting-state networks correspond to functional networks that are activated under specific task or stimulation conditions, and suggested a strong relationship between the underlying neuroanatomical connectivity and the resting-state patterns.

  11. 11.

    et al. Large-amplitude, spatially correlated fluctuations in BOLD fMRI signals during extended rest and early sleep stages. Magn. Reson. Imaging 24, 979–992 (2006).

  12. 12.

    et al. Low frequency BOLD fluctuations during resting wakefulness and light sleep: a simultaneous EEG–fMRI study. Hum. Brain Mapp. 29, 671–682 (2008).

  13. 13.

    et al. fMRI differences between early and late stage-1 sleep. Neurosci. Lett. 441, 81–85 (2008).

  14. 14.

    , , & Human prefrontal cortex is not specific for working memory: a functional MRI study. Neuroimage 8, 274–282 (1998).

  15. 15.

    Can. cognitive processes be inferred from neuroimaging data? Trends Cogn. Sci. 10, 59–63 (2006).

  16. 16.

    Towards a network theory of cognition. Neural Netw. 13, 861–876 (2000).

  17. 17.

    Contexts and catalysts: a resolution of the localization and integration of function in the brain. Neuroinformatics 2, 175–182 (2004).

  18. 18.

    , , & Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995). This paper demonstrated that brain regions that activate jointly seem to maintain a high correlation of BOLD signal uctuations at rest, identifying a 'resting-state network' of 'functionally connected' regions. The paper's method of analysis is now known as functional connectivity-by-MRI (fcMRI) or resting-state fMRI.

  19. 19.

    , , & Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl Acad. Sci. USA 100, 253–258 (2003). This paper studied for the first time the resting-state connectivity analysis of the default mode and provided clear evidence for the existence of a cohesive default mode network. It also investigated how the default mode network is modulated by task demands and what functions it might serve.

  20. 20.

    , & Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. Neuroimage 7, 119–132 (1998).

  21. 21.

    , , & Assessing functional connectivity in the human brain by fMRI. Magn. Reson. Imaging 25, 1347–1357 (2007).

  22. 22.

    et al. Consistent resting-state networks across healthy subjects. Proc. Natl Acad. Sci. USA 103, 13848–13853 (2006).

  23. 23.

    et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl Acad. Sci. USA 102, 9673–9678 (2005). This work was a compelling demonstration of at least two intrinsically coherent networks that could be captured using resting-state fMRI. The work led to the characterization as a task-positive and task-negative network, the latter corresponding to the default mode network

  24. 24.

    , , & The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101, 3270–3283 (2009). This paper investigated the anticorrelation between different resting-state networks, showing how the level of anticorrelation is affected by global signal removal. However, the authors demonstrated that several characteristics of anticorrelated networks are not attributable to global signal removal, suggesting that they have a biological basis.

  25. 25.

    , , , & The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44, 893–905 (2009).

  26. 26.

    et al. Reduced resting-state brain activity in the “default network” in normal aging. Cereb. Cortex 18, 1856–1864 (2007).

  27. 27.

    et al. Aberrant 'default mode' functional connectivity in schizophrenia. Am. J. Psychiatry 164, 450–457 (2007).

  28. 28.

    Resting-state functional connectivity in neuropsychiatric disorders. Curr. Opin. Neurol. 21, 424–430 (2008). This paper discusses how resting-state networks are disrupted in disorders in which cognition is also affected.

  29. 29.

    et al. Model-free group analysis shows altered BOLD FMRI networks in dementia. Hum. Brain Mapp. 30, 256–266 (2009).

  30. 30.

    et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159 (2008).

  31. 31.

    & Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Rev. Neurosci. 10, 186–198 (2009). This is a comprehensive review of the principles of graph theory as they relate to brain networks. The empirical demonstrations are helpful in translating the measures to concrete applications.

  32. 32.

    & The small world of the cerebral cortex. Neuroinformatics 2, 145–162 (2004).

  33. 33.

    , , & Resting-state functional connectivity reflects structural connectivity in the default mode network. Cereb. Cortex 19, 72–78 (2009).

  34. 34.

    et al. Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl Acad. Sci. USA 106, 2035–2040 (2009). This paper used computational methods to compare anatomical and fMRI connectivity in humans. The authors concluded that the large-scale anatomical structure of the human cerebral cortex may constrain, but does not entirely account for, the observed global functional connectivity.

  35. 35.

    , & An investigation of functional and anatomical connectivity using magnetic resonance imaging. Neuroimage 16, 241–250 (2002).

  36. 36.

    , , & Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl Acad. Sci. USA 104, 10240–10245 (2007).

  37. 37.

    , , , & Noise during rest enables the exploration of the brain's dynamic repertoire. PLoS Comput. Biol. 4, e1000196 (2008). This paper proposed that the space–time structure of coupling and time delays in the presence of noise dene a dynamic framework for the emergence of activity uctuations in the resting brain. It showed that fluctuations destabilize the ground state, producing excursion in the dynamical repertoire of the global brain network that results in oscillations structured in the experimentally observed resting-state subnetworks.

  38. 38.

    , , , & Key role of coupling, delay, and noise in resting brain fluctuations. Proc. Natl Acad. Sci. USA 106, 10302–10307 (2009). This paper considered that resting-state ultraslow oscillations, and in particular the emergence of anticorrelated subnetworks, result from uctuation-driven transitions between multistable states. Here, multistable cluster synchronization states appear in coupled oscillator systems owing to the delay of transmission times, highlighting the importance of the space–time structure of couplings in networks.

  39. 39.

    et al. Model driven EEG/fMRI fusion of brain oscillations. Hum. Brain Mapp. 30, 2701–2721 (2009).

  40. 40.

    , , & Connecting mean field models of neural activity to EEG and fMRI data. Brain Topogr. 23, 139–149 (2010).

  41. 41.

    & Large-scale model of mammalian thalamocortical systems. Proc. Natl Acad. Sci. USA 105, 3593–3598 (2008).

  42. 42.

    & Depolarization induced suppression of excitation and the emergence of ultraslow rhythms in neural networks. Phys. Rev. Lett. 104, 068101 (2010).

  43. 43.

    & (eds) Modeling Phase Transitions in the Brain (Springer, 2010).

  44. 44.

    Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. Neuroinformatics 2, 127–144 (2004).

  45. 45.

    et al. Advanced database methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac). Phil. Trans. R. Soc. Lond. B 356, 1159–1186 (2001).

  46. 46.

    & Mapping brains without coordinates. Phil. Trans. R. Soc. Lond. B 360, 751–766 (2005).

  47. 47.

    , , & Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. Neuroimage 12, 466–477 (2000).

  48. 48.

    et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002).

  49. 49.

    , , & Very slow EEG fluctuations predict the dynamics of stimulus detection and oscillation amplitudes in humans. J. Neurosci. 28, 8268–8272 (2008).

  50. 50.

    et al. Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nature Neurosci. 11, 1100–1108 (2008).

  51. 51.

    & Theory of stochastic resonance. Phys. Rev. A 39, 4854–4869 (1989).

  52. 52.

    , & Relating macroscopic measures of brain activity to fast, dynamic neuronal interactions. Neural Comput. 12, 2805–2821 (2000).

  53. 53.

    , & The relationship between synchronization among neuronal populations and their mean activity levels. Neural Comput. 11, 1389–1411 (1999).

  54. 54.

    , & Structural and functional clusters of complex brain networks. Physica D 224, 202–212 (2006).

  55. 55.

    , , , & Hierarchical organization unveiled by functional connectivity in complex brain networks. Phys. Rev. Lett. 97, 238103 (2006).

  56. 56.

    et al. Functional dysconnectivity of the dorsolateral prefrontal cortex in first-episode schizophrenia using resting-state fMRI. Neurosci. Lett. 417, 297–302 (2007).

  57. 57.

    , , , & The Kuramoto model: a simple paradigm for synchronization phenomena. Rev. Mod. Phys. 77, 137–185 (2005).

  58. 58.

    in Lecture Notes in Physics (ed. Araki, H.) 420 (Springer, New York, 1975).

  59. 59.

    , , , & Spontaneously emerging cortical representations of visual attributes. Nature 425, 954–956 (2003).

  60. 60.

    , , & Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273, 1868–1871 (1996). This study used optical imaging in the cat visual cortex to characterize the dynamics of spontaneous activity at rest. It showed that the variability of stimulus-evoked activity is largely accounted for by variation in spontaneous activity.

  61. 61.

    , & Neural network model of the primary visual cortex: from functional architecture to lateral connectivity and back. J. Comput. Neurosci. 20, 219–241 (2006).

  62. 62.

    , & Organization of excitable dynamics in hierarchical biological networks. PLoS Comput. Biol. 4, e1000190 (2008).

  63. 63.

    , & Differential maturation of brain signal complexity in the human auditory and visual system. Front. Hum. Neurosci. 3, 48 (2009).

  64. 64.

    , & Increased brain signal variability accompanies lower behavioral variability in development. PLoS Comput. Biol. 4, e1000106 (2008).

  65. 65.

    , , & Blood oxygen level-dependent signal variability is more than just noise. J. Neurosci. 30, 4914–4921 (2010).

  66. 66.

    , , & Coherent spontaneous activity accounts for trial-to-trial variability in human evoked brain responses. Nature Neurosci. 9, 23–25 (2006). This paper demonstrated that coherent spontaneous fluctuations in human brain activity account for a substantial fraction of the variability in measured event-related BOLD responses, and that spontaneous and task-related activity are linearly superimposed in the human brain.

  67. 67.

    et al. Coherent spontaneous activity identifies a hippocampal-parietal memory network. J. Neurophysiol. 96, 3517–3531 (2006).

  68. 68.

    , & Noise in the nervous system. Nature Rev. Neurosci. 9, 292–303 (2008).

  69. 69.

    & The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27, 712–719 (2004).

  70. 70.

    , & Inference and computation with population codes. Annu. Rev. Neurosci. 26, 381–410 (2003).

  71. 71.

    , , , & Electrophysiological signatures of resting state networks in the human brain. Proc. Natl Acad. Sci. USA 104, 13170–13175 (2007).

  72. 72.

    et al. Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects. Proc. Natl Acad. Sci. USA 106, 6790–6795 (2009).

  73. 73.

    et al. A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. Cereb. Cortex 20, 1432–1447 (2009).

  74. 74.

    & Field theory of electromagnetic brain activity. Phys. Rev. Lett. 77, 960–963 (1996).

  75. 75.

    & A derivation of a macroscopic field theory of the brain from the quasi-microscopic neural dynamics. Physica D 99, 503–526 (1997).

  76. 76.

    , & Propagation and stability of waves of electrical activity in the cerebral cortex. Phys. Rev. E 56, 826–840 (1997).

  77. 77.

    et al. A unifying explanation of primary generalized seizures through nonlinear brain modeling and bifurcation analysis. Cereb. Cortex 16, 1296–1313 (2006).

  78. 78.

    The brain wave equation: a model for the EEG. Math. Biosci. 21, 279–297 (1974).

  79. 79.

    , & On the genesis of spike–wave oscillations in a mean-field model of human corticothalamic dynamics. Phys. Lett. A 355, 352–357 (2006).

  80. 80.

    & in Handbook on Brain Connectivity (eds Jirsa, V. K. & McIntosh, A. R.) 275–302 (Springer, New York, 2007).

  81. 81.

    Introduction to Theoretical Neurobiology (Cambridge Univ. Press, 1988).

  82. 82.

    , & The approach of a neuron population firing rate to a new equilibrium: an exact theoretical result. Neural Comput. 12, 1045–1055 (2000).

  83. 83.

    & Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12, 1–24 (1972).

  84. 84.

    , & Synchrony and clustering in heterogeneous networks with global coupling and parameter dispersion. Phys. Rev. Lett. 94, 018106 (2005).

  85. 85.

    & A low dimensional description of globally coupled heterogeneous neural networks of excitatory and inhibitory neurons. PLoS Comput. Biol. 4, e1000219 (2008).

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We are grateful for funding received from the James S. McDonnell Foundation (grant Brain NRG [JSMF22002082]).

Author information


  1. Institució Catalana de Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Computational Neuroscience, Plaça de la Mercè, 10–12, 08002 Barcelona, Spain.

    • Gustavo Deco
  2. Theoretical Neuroscience Group, Institut des Sciences du Mouvement UMR6233 CNRS, Marseille, France, and Center for Complex Systems and Brain Sciences, Physics Department, Florida Atlantic University, USA.

    • Viktor K. Jirsa
  3. Rotman Research Institute of Baycrest Centre, University of Toronto, 3560 Bathurst Street, Toronto, Ontario M6A 2E1 Canada.

    • Anthony R. McIntosh


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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Gustavo Deco.


Oxygen extraction

A quantitative medical imaging method to determine the regional consumption of oxygen in brain tissue.


Blood oxygen level-dependent (BOLD) functional MRI (fMRI) is a relatively new neuroimaging tool to map activity in the brain in vivo. Signal changes are dependent on the relative changes in blood oxygen levels in the brain capillary beds.

Functional connectivity

The relationship between activity in two or more neural sources. This usually refers to the temporal correlation between sources but has been extended to include correlations across trials or different experimental subjects. Functional connectivity methods include estimation of correlation coefficients and coherence. The estimation cannot be used to infer the direction of the relation between sources.

Diffusion spectrum imaging

An MRI technique that is similar to DTI, but with the added capability of resolving multiple directions of diffusion in each voxel of white matter. This allows multiple groups of fibres at each location, including intersecting fibre pathways, to be mapped.

Graph theory

A branch of mathematics that deals with the formal description and analysis of graphs. A graph is defined as a set of nodes (vertices) linked by connections (edges). The connections may be directed or undirected. A graph provides an abstract representation of the system's elements and their interaction.

Small-world architecture

A term derived from graph theory estimation referring to a network that combines high levels of local clustering among nodes (to form families or cliques) and short paths that globally link all nodes of the network.

Diffusion tensor imaging

(Often abbreviated to DTI.) An MRI technique that takes advantage of the restricted diffusion of water through myelinated nerve fibres in the brain to infer the anatomical connectivity between brain areas.

Neural field

Values of neural activity that are defined over a continuous physical space, typically the two- or three-dimensional cortical sheet.


In dynamical systems this refers to time-dependent states evolving towards an attractor.

Balloon–Windkessel haemodynamic model

A dynamical model in which neural activity (the BOLD signal) is transduced into brain perfusion changes. In this model, the BOLD signal is taken to be a static nonlinear function of normalized total deoxyhaemoglobin voxel content, normalized venous volume, resting net oxygen extraction fraction by the capillary bed and resting blood volume fraction.


In dynamical systems refers to a state that, if it is perturbed, will return after a time to the same configuration.


In dynamical systems refers to a state that, if it is perturbed, will escape away from the original configuration.

Carrier wave

A wave of a higher frequency that is modulated with another signal of a lower frequency, typically for the purpose of conveying information.

Hopf bifurcation

In nonlinear dynamics, a local bifurcation in which an initially stable fixed point of a dynamical system loses its stability in an oscillatory fashion.


In dynamical systems refers to a set towards which a system evolves. Geometrically, this set may be a point, a curve or a manifold.


In dynamical systems refers to a state that falls outside the natural equilibrium state of the system, but that persists for an extended period of time.

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