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. 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.

了解大规模脑网络中神经通信的机制仍然是神经科学的主要目标。我们研究了导航是否是连接组学的简约路由模型。导航网络涉及前进到距离所需目的地最近的下一个节点。我们开发了一种量化导航效率的方法，发现一系列哺乳动物（人类、小鼠和猕猴）中的连接组可以以接近最佳的效率（典型连接密度的最佳效率的 80% 以上）成功导航。重新布线网络拓扑或重新定位网络节点导致导航性能降低 45-60%。我们发现人类连接组不能逐步随机化或聚类以产生导航性能显着提高（> 5％）的拓扑，这表明规律性和随机性之间的拓扑平衡有利于有效导航。还发现导航到 (i ) 促进信息流量负载的资源高效分配，潜在地缓解通信瓶颈，以及 ( ii ) 解释功能连接的显着变化。与连接组学中通常研究的通信策略不同，导航不要求对网络拓扑的全局知识进行假设。我们得出结论，大脑网络的拓扑和几何结构有利于高效的分散式通信。