Communication efficiency and congestion of signal traffic in large-scale brain networks.

Bratislav Mišić,Anthony R Mcintosh,Olaf Sporns,Danielle S Bassett

doi:10.1371/journal.pcbi.1003427

Bratislav Mišić, Anthony R Mcintosh + Show 2 more

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https://doi.org/10.1371/journal.pcbi.1003427

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Abstract

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a “rich club” of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.

Highlights

Constrained by finite resources, such as metabolism and physical space, which place severe limits on the number and density of synaptic connections, brain networks are an example of how optimized topology may facilitate information flow
If information flow is introduced into the network, how does neural connectivity influence the efficacy and speed of communication? In other words, how does network topology enable and constrain the capacity of brain networks to globally integrate information? For instance, while certain areas may bridge distant communities and potentially function as hubs by virtue of their connectivity, other areas may be ill-suited as conduits for information transfer because of their position in the network
We find that neural network topology is configured in a way that prioritizes speed of information flow over reliability and total throughput

Summary

Introduction

Constrained by finite resources, such as metabolism and physical space, which place severe limits on the number and density of synaptic connections, brain networks are an example of how optimized topology may facilitate information flow. The structural topology of cortical networks can be represented and formally studied using the graph model [1,2,3], whereby the brain is spatially parcellated into a set of grey matter nodes interconnected by a set of white matter edges [4,5] This approach has revealed several aspects of network organization that theoretically confer an increased capacity for information processing, including small-world connectivity [6,7,8], the presence of hubs [9] and cores [10], cost-efficient spatial embedding [11,12] and the coexistence of local segregation and global integration [13]. Under conditions of elevated network traffic such regions could become bottlenecks, imposing limits on the relay of information [18]

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