Efficient coding in the economics of human brain connectomics.

Autor:	Zhou D; Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Lynn CW; Initiative for the Theoretical Sciences, Graduate Center, City University of New York, New York, NY, USA.; Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ, USA., Cui Z; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Ciric R; Department of Bioengineering, Schools of Engineering and Medicine, Stanford University, Stanford, CA, USA., Baum GL; Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA., Moore TM; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA, USA., Roalf DR; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Detre JA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Gur RC; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA, USA., Gur RE; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA, USA., Satterthwaite TD; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Penn-Children's Hospital of Philadelphia Lifespan Brain Institute, Philadelphia, PA, USA., Bassett DS; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.; Department of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.; Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.; Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA.; Santa Fe Institute, Santa Fe, NM, USA.
Jazyk:	angličtina
Zdroj:	Network neuroscience (Cambridge, Mass.) [Netw Neurosci] 2022 Feb 01; Vol. 6 (1), pp. 234-274. Date of Electronic Publication: 2022 Feb 01 (Print Publication: 2022).
DOI:	10.1162/netn_a_00223
Abstrakt:	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. (© 2021 Massachusetts Institute of Technology.)
Databáze:	MEDLINE
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