Abstract

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion-weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior-beyond the conventional network efficiency metric-for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.

Highlights

  • The principle of compensation states that “to spend on one side, nature is forced to economise on the other side” West-Eberhard (2003)

  • We validate a model of communication dynamics atop the macroscale human structural connectome, finding that structural networks support dynamics that strike a balance between information transmission fidelity and lossy compression

  • == DRAFT == / Title: Efficient Coding in the Economics of Human Brain Connectomics along metabolically efficient direct anatomical pathways, our results suggest that connectome architecture and behavioral demands yield communication dynamics that accord to neurobiological and information theoretical principles of efficient coding and lossy compression

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Summary

Introduction

The principle of compensation states that “to spend on one side, nature is forced to economise on the other side” West-Eberhard (2003). In the economics of brain connectomics, natural selection optimizes network architecture for versatility, resilience, and efficiency under constraints of metabolism, materials, space, and time Bullmore and Sporns (2012); Laughlin (2001); West-Eberhard (2003). Efficient coding at the macroscale offers a parsimonious principle of compression characterizing the dimensionality of neural representations Mack, Preston, and Love (2020); Shine et al (2019); Stringer, Pachitariu, Steinmetz, Carandini, and Harris (2019); Tang et al (2019), as well as a parsimonious principle of transmission characterizing a spectrum of network communication mechanisms Avena-Koenigsberger et al (2015, 2017); Avena-Koenigsberger, Misic, and Sporns (2018); Bullmore and Sporns (2012); Goni et al (2013); Goni et al (2014); Misicet

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