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Efficient Coding in the Economics of Human Brain Connectomics

By Dale Zhou, Christopher W Lynn, Zaixu Cui, Rastko Ciric, Graham L Baum, Tyler M. Moore, David R. Roalf, John A. Detre, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite, Danielle S. Bassett

Posted 15 Jan 2020
bioRxiv DOI: 10.1101/2020.01.14.906842

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, metabolic and information transfer efficiency across structural networks are not understood. In a large cohort of youth, we find metabolic costs associated with structural path strengths supporting information diffusion. Metabolism is balanced with the coupling of structures supporting diffusion and network modularity. To understand efficient network communication, we develop a theory specifying minimum rates of message diffusion that brain regions should transmit for an expected fidelity, and we test five predictions from the theory. We introduce compression efficiency, which quantifies differing trade-offs between lossy compression and communication fidelity in structural networks. Compression efficiency evolves with development, heightens when metabolic gradients guide diffusion, constrains network complexity, explains how rich-club hubs integrate information, and correlates with cortical areal scaling, myelination, and speed-accuracy trade-offs. Our findings elucidate how network structures and metabolic resources support efficient neural communication.

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