A spectrum of routing strategies for brain networks

Abstract

Communication of signals among nodes in a complex network poses fundamental problems of efficiency and cost. Routing of messages along shortest paths requires global information about the topology, while spreading by diffusion, which operates according to local topological features, is informationally "cheap" but inefficient. We introduce a stochastic model for network communication that combines varying amounts of local and global information about the network topology. The model generates a continuous spectrum of dynamics that converge onto shortest-path and random-walk (diffusion) communication processes at the limiting extremes. We implement the model on two cohorts of human connectome networks and investigate the effects of varying amounts of local and global information on the network's communication cost. We identify routing strategies that approach a (highly efficient) shortest-path communication process with a relatively small amount of global information. Moreover, we show that the cost of routing messages from and to hub nodes varies as a function of the amount of global information driving the system's dynamics. Finally, we implement the model to identify individual subject differences from a communication dynamics point of view. The present framework departs from the classical shortest paths vs. diffusion dichotomy, suggesting instead that brain networks may exhibit different types of communication dynamics depending on varying functional demands and the availability of resources.

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Cited by in corpus (9)

• Network Geometry
• Models of communication and control for brain networks: distinctions, convergence, and future outlook
• Navigable maps of structural brain networks across species
• Enhancing transport properties in interconnected systems without altering their structure
• Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review
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• Network analysis of the human structural connectome including the brainstem
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• Effects of topological structure and destination selection strategies on agent dynamics in complex networks