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Brain network communication: concepts, models and applications

Nature Reviews Neuroscience volume 24, pages 557–574 (2023)Cite this article

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Abstract

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

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Fig. 1: Core concepts in brain network communication models.
Fig. 2: A taxonomy of brain network communication models and measures.
Fig. 3: Current and emerging applications of communication matrices.
Fig. 4: Interpretable insight from network communication models: a brain stimulation case study.

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Acknowledgements

C.S. acknowledges support from the Australian Research Council (grant number DP170101815). O.S. acknowledges support from the National Institute of Health (R01 122957). A.Z. acknowledges support from the National Health and Medical Research Council of Australia (APP1118153).

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Authors and Affiliations

  1. Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia

    Caio Seguin & Andrew Zalesky

  2. Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA

    Caio Seguin & Olaf Sporns

  3. Program in Neuroscience, Indiana University, Bloomington, IN, USA

    Olaf Sporns

  4. Program in Cognitive Science, Indiana University, Bloomington, IN, USA

    Olaf Sporns

  5. Indiana University Network Science Institute, Indiana University, Bloomington, IN, USA

    Olaf Sporns

  6. Department of Biomedical Engineering, Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia

    Andrew Zalesky

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The authors all researched data for the article, provided substantial contributions to discussion of its content and reviewed and edited the manuscript before submission. C.S. wrote the manuscript.

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Nature Reviews Neuroscience thanks M. Cole; A. Kuceyeski; and J. Medaglia, who co-reviewed with H. Stoll, for their contribution to the peer review of this work.

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Glossary

Complex networks

Networks with non-trivial topology, with features such as modular structure, hub nodes or small-world architecture.

Connectomes

Networks of structural connections between neural elements. Connections may vary from single synapses to large-scale white matter tracts, depending on the spatial scale of neural elements.

Decentralized systems

Systems in which individual elements possess only local knowledge of network organization. They stand in contrast to centralized systems, in which elements or a global controller has access to a bird’s eye view of the network.

Delay cost

Efficiency of signal transmission through the network.

Diffusion processes

Network communication via broadcasting or random walks dynamics.

Dimensions of network communication cost

Putative evolutionary pressures that may have shaped connectome architecture and neural signalling mechanisms.

Energetic cost

Metabolic expenditure from signal transmission through the network.

Informational cost

Amount of knowledge about network topology required to communicate signals.

Network communication measure

A measure that quantifies specific properties of communication under a given model.

Network communication models

Signalling conceptualizations or propagation algorithms to guide communication between nodes.

Network topology

The organizational features of a network of interconnected elements.

Neural elements

A neural element could be a neuron, neuronal population or macroscale brain region and is represented by a node in a neural network.

Parametric models

Network communication via hybrid strategies that combine routing and diffusion.

Polysynaptic communication

A communication process mediated by one or more intermediate neural elements.

Routing protocols

Network communication via selective and efficient paths.

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Seguin, C., Sporns, O. & Zalesky, A. Brain network communication: concepts, models and applications. Nat. Rev. Neurosci. 24, 557–574 (2023). https://doi.org/10.1038/s41583-023-00718-5

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