Navigation of brain networks

Caio Seguin*, Martijn P. Van Den Heuvel, Andrew Zalesky

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


Understanding the mechanisms of neural communication in largescale brain networks remains a major goal in neuroscience. We investigated whether navigation is a parsimonious routing model for connectomics. Navigating a network involves progressing to the next node that is closest in distance to a desired destination. We developed a measure to quantify navigation efficiency and found that connectomes in a range of mammalian species (human, mouse, and macaque) can be successfully navigated with near-optimal efficiency (>80% of optimal efficiency for typical connection densities). Rewiring network topology or repositioning network nodes resulted in 45-60% reductions in navigation performance. We found that the human connectome cannot be progressively randomized or clusterized to result in topologies with substantially improved navigation performance (>5%), suggesting a topological balance between regularity and randomness that is conducive to efficient navigation. Navigation was also found to (i) promote a resource-efficient distribution of the information traffic load, potentially relieving communication bottlenecks, and (ii) explain significant variation in functional connectivity. Unlike commonly studied communication strategies in connectomics, navigation does not mandate assumptions about global knowledge of network topology. We conclude that the topology and geometry of brain networks are conducive to efficient decentralized communication.

Original languageEnglish
Pages (from-to)6297-6302
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number24
Early online date30 May 2018
Publication statusPublished - 12 Jun 2018


  • Complex networks
  • Connectome
  • Network navigation
  • Neural communication


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