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Efficient Coding in the Economics of Human Brain Connectomics

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
doi: https://doi.org/10.1101/2020.01.14.906842
Dale Zhou
1Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Christopher W. Lynn
2Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania
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Zaixu Cui
3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
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Rastko Ciric
4Department of Bioengineering, Schools of Engineering and Medicine, Stanford University, Stanford, CA 94305
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Graham L. Baum
5Department of Psychology and Center for Brain Science, Harvard University, Cambridge MA USA
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Tyler M. Moore
3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
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David R. Roalf
3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
6Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute
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John A. Detre
7Department of Neurology, Perelman School of Medicine, University of Pennsylvania
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Ruben C. Gur
3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
6Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute
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Raquel E. Gur
3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
6Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute
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Theodore D. Satterthwaite
3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
6Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute
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Danielle S. Bassett
2Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania
3Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
7Department of Neurology, Perelman School of Medicine, University of Pennsylvania
8Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania
9Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania
10Santa Fe Institute, Santa Fe, NM 87501 USA
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  • For correspondence: dsb@seas.upenn.edu
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Abstract

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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Posted January 16, 2020.
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Efficient Coding in the Economics of Human Brain Connectomics
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
bioRxiv 2020.01.14.906842; doi: https://doi.org/10.1101/2020.01.14.906842
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Efficient Coding in the Economics of Human Brain Connectomics
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
bioRxiv 2020.01.14.906842; doi: https://doi.org/10.1101/2020.01.14.906842

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