Navigation of brain networks

TL;DR

This paper demonstrates that brain networks across multiple species can be efficiently navigated using a decentralized routing model based on local spatial information, highlighting the importance of spatial embedding in neural communication.

Contribution

The study introduces a measure of navigation efficiency in connectomes and shows that brain networks are optimized for near-global efficiency through local, spatially-informed routing strategies.

Findings

Brain connectomes can be navigated with >80% of optimal efficiency.

Rewiring or repositioning nodes reduces navigation performance by 45-60%.

Navigation explains significant variation in functional connectivity.

Abstract

Understanding the mechanisms of neural communication in large-scale 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. Specifically, we found that brain networks cannot be progressively rewired (randomized or clusterized) to result in topologies with significantly improved navigation performance. 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 prevalently studied communication strategies in connectomics, navigation does not mandate biologically unrealistic assumptions about global knowledge of network topology. We conclude that the wiring and spatial embedding of brain networks is conducive to effective decentralized communication. Graph-theoretic studies of the connectome should consider measures of network efficiency and centrality that are consistent with decentralized models of neural communication.