Efficient Coding in the Economics of Human Brain Connectomics
Efficient Coding in the Economics of Human Brain Connectomics
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.
Zhou Dale、Bassett Danielle S.、Detre John A.、Gur Ruben C.、Ciric Rastko、Satterthwaite Theodore D.、Roalf David R.、Moore Tyler M.、Gur Raquel E.、Cui Zaixu、Baum Graham L.、Lynn Christopher W.
Neuroscience Graduate Group, Perelman School of Medicine, University of PennsylvaniaDepartment of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania||Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania||Department of Neurology, Perelman School of Medicine, University of Pennsylvania||Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania||Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania||Santa Fe InstituteDepartment of Neurology, Perelman School of Medicine, University of PennsylvaniaDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania||Penn-Children?ˉs Hospital of Philadelphia Lifespan Brain InstituteDepartment of Bioengineering, Schools of Engineering and Medicine, Stanford UniversityDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania||Penn-Children?ˉs Hospital of Philadelphia Lifespan Brain InstituteDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania||Penn-Children?ˉs Hospital of Philadelphia Lifespan Brain InstituteDepartment of Psychiatry, Perelman School of Medicine, University of PennsylvaniaDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania||Penn-Children?ˉs Hospital of Philadelphia Lifespan Brain InstituteDepartment of Psychiatry, Perelman School of Medicine, University of PennsylvaniaDepartment of Psychology and Center for Brain Science, Harvard UniversityDepartment of Physics & Astronomy, College of Arts and Sciences, University of Pennsylvania
信息科学、信息技术系统科学、系统技术生物科学理论、生物科学方法
Zhou Dale,Bassett Danielle S.,Detre John A.,Gur Ruben C.,Ciric Rastko,Satterthwaite Theodore D.,Roalf David R.,Moore Tyler M.,Gur Raquel E.,Cui Zaixu,Baum Graham L.,Lynn Christopher W..Efficient Coding in the Economics of Human Brain Connectomics[EB/OL].(2025-03-28)[2025-05-22].https://www.biorxiv.org/content/10.1101/2020.01.14.906842.点此复制
评论