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

Nature Reviews Neuroscience volume 12, pages 43–56 (2011)Cite this article

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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.

Abstract

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.

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Figure 1: The evolution of the default mode network in the neuroscience literature.
Figure 2: Resting-state dynamics reflect interactions of the local dynamics through anatomical connections.
Figure 3: Resting-state networks emerge from a dynamic framework of noise, anatomical connectivity and time delays.
Figure 4: Ultra slowly varying resting-state networks resulting from noise-driven transitions between multistable states.

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Acknowledgements

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

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Authors and Affiliations

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

    Gustavo Deco

  2. Theoretical Neuroscience Group, Institut des Sciences du Mouvement UMR6233 CNRS, Marseille, France

    Viktor K. Jirsa

  3. Physics Department, Center for Complex Systems and Brain Sciences, Florida Atlantic University, USA

    Viktor K. Jirsa

  4. Rotman Research Institute of Baycrest Centre, University of Toronto, 3560 Bathurst Street, Toronto, M6A 2E1, Ontario, Canada

    Anthony R. McIntosh

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  1. Gustavo Deco
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Deco, G., Jirsa, V. & McIntosh, A. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 12, 43–56 (2011). https://doi.org/10.1038/nrn2961

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