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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, View ORCID ProfileDani S. Bassett
doi: https://doi.org/10.1101/2020.01.14.906842
Dale Zhou
1Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Christopher W. Lynn
2Initiative for the Theoretical Sciences, Graduate Center, City University of New York, New York, NY 10016, USA
3Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544, USA
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Zaixu Cui
4Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Rastko Ciric
5Department of Bioengineering, Schools of Engineering and Medicine, Stanford University, Stanford, CA 94305 USA
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Graham L. Baum
6Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
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Tyler M. Moore
4Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
7Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA 19104, USA
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David R. Roalf
4Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
7Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA 19104, USA
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John A. Detre
8Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Ruben C. Gur
4Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
7Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA 19104, USA
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Raquel E. Gur
4Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
7Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA 19104, USA
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Theodore D. Satterthwaite
4Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
7Penn-Children’s Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA 19104, USA
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Dani S. Bassett
4Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
8Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
9Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
10Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
11Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
12Santa Fe Institute, Santa Fe, NM 87501, USA
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  • ORCID record for Dani S. Bassett
  • 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, 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.

Author Summary Macroscale communication between interconnected brain regions underpins most aspects of brain function and incurs substantial metabolic cost. Understanding efficient and behaviorally meaningful information transmission dependent on structural connectivity has remained challenging. 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. Notably, this balance is predictive of behavior and explanatory of biology. In addition to challenging and reformulating the currently held view that communication occurs by routing dynamics 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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 02, 2021.
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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, Dani 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, Dani S. Bassett
bioRxiv 2020.01.14.906842; doi: https://doi.org/10.1101/2020.01.14.906842

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