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Communication dynamics in complex brain networks

Nature Reviews Neuroscience volume 19, pages 17–33 (2018)Cite this article

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Key Points

  • The topology of structural brain networks shapes patterns of interaction and signalling among neurons and brain regions, and the resulting communication dynamics is important for brain function.

  • Different aspects of network topology imply different communication mechanisms, from routing of information through shortest paths to alternative models that involve spreading, diffusion and broadcasting.

  • Different topological attributes promote different types of communication mechanisms.

  • Communication dynamics are subject to competing constraints and demands (trade-offs) among efficiency, cost, versatility and resilience. One aspect of cost is the amount of information needed to implement network communication. This cost is high for routing and low for diffusion, and is likely to be an important factor for determining the biological feasibility of a given communication model.

Abstract

Neuronal signalling and communication underpin virtually all aspects of brain activity and function. Network science approaches to modelling and analysing the dynamics of communication on networks have proved useful for simulating functional brain connectivity and predicting emergent network states. This Review surveys important aspects of communication dynamics in brain networks. We begin by sketching a conceptual framework that views communication dynamics as a necessary link between the empirical domains of structural and functional connectivity. We then consider how different local and global topological attributes of structural networks support potential patterns of network communication, and how the interactions between network topology and dynamic models can provide additional insights and constraints. We end by proposing that communication dynamics may act as potential generative models of effective connectivity and can offer insight into the mechanisms by which brain networks transform and process information.

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Figure 1: A conceptual framework for linking structural connectivity and functional connectivity.
Figure 2: Network topology and communication.
Figure 3: Interplay between architecture and communication dynamics.
Figure 4: Communication processes as a function of time.

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Acknowledgements

The authors gratefully acknowledge insightful discussions with R. F. Betzel and J. Goñi. O.S. acknowledges support from the Indiana Clinical Translational Sciences Institute (NIH UL1TR0011808), the James S. McDonnell Foundation (220020387), the National Science Foundation (1636892) and the US National Institutes of Health (R01-AT009036, R01-B022574 and P30-AG010133). B.M. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN #017-04265) and from the Fonds de recherche du Quebec - Santé.

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Authors and Affiliations

  1. Department of Psychological and Brain Sciences, Indiana University, Bloomington, 47405, Indiana, USA

    Andrea Avena-Koenigsberger & Olaf Sporns

  2. Montreal Neurological Institute, McGill University, Montreal, H3A 2B4, Quebec, Canada

    Bratislav Misic

  3. IU Network Science Institute, Indiana University, Bloomington, 47405, Indiana, USA

    Olaf Sporns

Authors
  1. Andrea Avena-Koenigsberger
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  2. Bratislav Misic
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  3. Olaf Sporns
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Contributions

The authors all researched data for the article, provided a substantial contribution to discussion of the content, wrote the article and reviewed and edited the manuscript before submission.

Corresponding author

Correspondence to Olaf Sporns.

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PowerPoint slides

Glossary

Random walk

A stochastic process that describes a succession of random steps taken on a network.

Network topology

The patterns of connectivity of a network.

Neural elements

Unit elements of a neural network. The unit is defined by the spatial scale. Neural elements can represent, for example, a single synapse, a neuron, a neuronal population or an entire brain region.

Adjacency matrix

A mathematical representation of a network as a matrix. Elements of the matrix indicate whether two nodes are connected or not.

Resilience

A network's ability to adapt and/or recover from structural failures.

Betweenness centrality

A nodal measure of influence determined by the proportion of shortest paths that traverse a node.

Routing

The process of sending a message or signal through a determined path.

Fibre tracts

A bundle of axons connecting two brain regions.

Search information

The amount of information needed to discover a path in a network.

Edge weight

A measure of the strength of the relationship between two connected nodes.

Connection density

The fraction of connections present in a network, or a subsystem of a network, with respect to the maximum number of possible connections.

Morphospace

A space in which possible, impossible and real-world network architectures can be mapped.

Hubs

Highly connected nodes.

Path transitivity

The frequency of detours comprising two edges (that is, of length 2) that are available along a path.

Core–periphery

A tendency for a network to contain a densely interconnected central component.

Modularity

Propensity for nodes to form internally densely connected clusters.

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Avena-Koenigsberger, A., Misic, B. & Sporns, O. Communication dynamics in complex brain networks. Nat Rev Neurosci 19, 17–33 (2018). https://doi.org/10.1038/nrn.2017.149

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