Abstract
The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here we investigate how this intrinsic hierarchical organization of the brain shapes the transmission of information among regions. The hierarchical positioning of individual regions was quantified by applying diffusion map embedding to resting-state functional MRI networks. Structural networks were reconstructed from diffusion spectrum imaging and topological shortest paths among all brain regions were computed. Sequences of nodes encountered along a path were then labeled by their hierarchical position, tracing out path motifs. We find that the cortical hierarchy guides communication in the network. Specifically, nodes are more likely to forward signals to nodes closer in the hierarchy and cover a range of unimodal and transmodal regions, potentially enriching or diversifying signals en route. We also find evidence of systematic detours, particularly in attention networks, where communication is rerouted. Altogether, the present work highlights how the cortical hierarchy shapes signal exchange and imparts behaviorally relevant communication patterns in brain networks.
Highlights
Understanding the mechanisms of neural communication in largescale brain networks remains a major goal in neuroscience
Using a measure of navigation efficiency, we evaluated the navigability of a range of mammalian connectomes
We found multiple lines of evidence suggesting that the topology and spatial embedding of nervous systems is conducive to efficient navigation
Summary
Understanding the mechanisms of neural communication in largescale brain networks remains a major goal in neuroscience. N ervous systems are networks and one of the key functions of a network is to facilitate communication Complex topological properties such as small worldness [1, 2], modularity [3], and a core of highly interconnected hubs [4] are universally found across the brain networks of advanced and simple species, including mouse [5, 6], macaque [7, 8], and human connectomes [9]. This requirement for centralized knowledge has been challenged on the basis that nervous systems are decentralized, motivating alternative models of large-scale neural communication, such as spreading dynamics [11], path ensembles [16], communicability [17, 18], and diffusion models [19,20,21] These studies indicate that brain networks may support efficient communication without the need for centralized knowledge. Random walkers can be biased to travel via efficient routes [22] and shortest paths help facilitate fast spreading of local stimuli [11]
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