Navigation of brain networks

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.

References in corpus (12)

• Efficient Behavior of Small-World Networks
• Specificity and stability in topology of protein networks
• Structural Properties of the Caenorhabditis elegans Neuronal Network
• Communicability in complex networks
• Navigability of Complex Networks
• Generative models of the human connectome
• Simulation of Robustness against Lesions of Cortical Networks
• Mapping how local perturbations influence systems-level brain dynamics
• Multi-scale community organization of the human structural connectome and its relationship with resting-state functional connectivity
• The hidden geometry of weighted complex networks
• Navigable Networks as Nash Equilibria of Navigation Games
• Navigable maps of structural brain networks across species

Cited by in corpus (12)

• Network Geometry
• Geometric renormalization unravels self-similarity of the multiscale human connectome
• Navigable maps of structural brain networks across species
• Navigability evaluation of complex networks by greedy routing efficiency
• The specificity and robustness of long-distance connections in weighted, interareal connectomes
• Ising model with variable spin/agent strengths
• Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review
• Can biological quantum networks solve NP-hard problems?
• Hubs-biased resistance distances on graphs and networks
• Optimal navigability of weighted human brain connectomes in physical space
• Ising model with varying spin strength on a scale-free network: scaling functions and critical amplitude ratios
• Towards an information theoretical description of communication in brain networks