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      Fractured columnar small-world functional network organization in volumes of L2/3 of mouse auditory cortex

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          Abstract

          The sensory cortices of the brain exhibit large-scale functional topographic organization, such as the tonotopic organization of the primary auditory cortex (A1) according to sound frequency. However, at the level of individual neurons, layer 2/3 (L2/3) A1 appears functionally heterogeneous. To identify if there exists a higher-order functional organization of meso-scale neuronal networks within L2/3 that bridges order and disorder, we used in vivo two-photon calcium imaging of pyramidal neurons to identify networks in three-dimensional volumes of L2/3 A1 in awake mice. Using tonal stimuli, we found diverse receptive fields with measurable colocalization of similarly tuned neurons across depth but less so across L2/3 sublayers. These results indicate a fractured microcolumnar organization with a column radius of ∼50 µm, with a more random organization of the receptive field over larger radii. We further characterized the functional networks formed within L2/3 by analyzing the spatial distribution of signal correlations (SCs). Networks show evidence of Rentian scaling in physical space, suggesting effective spatial embedding of subnetworks. Indeed, functional networks have characteristics of small-world topology, implying that there are clusters of functionally similar neurons with sparse connections between differently tuned neurons. These results indicate that underlying the regularity of the tonotopic map on large scales in L2/3 is significant tuning diversity arranged in a hybrid organization with microcolumnar structures and efficient network topologies.

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          Collective dynamics of 'small-world' networks.

          Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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            Complex network measures of brain connectivity: uses and interpretations.

            Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.
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              Complex brain networks: graph theoretical analysis of structural and functional systems.

              Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PNAS Nexus
                PNAS Nexus
                pnasnexus
                PNAS Nexus
                Oxford University Press (US )
                2752-6542
                February 2024
                12 February 2024
                12 February 2024
                : 3
                : 2
                : pgae074
                Affiliations
                Institute for Physical Science and Technology, University of Maryland , College Park, MD 20742, USA
                Fraunhofer USA Center Mid-Atlantic , Riverdale, MD 20737, USA
                Department of Biology, University of Maryland , College Park, MD 20742, USA
                Department of Anatomy and Neurobiology, University of Maryland School of Medicine , Baltimore, MD 21230, USA
                Institute for Physical Science and Technology, University of Maryland , College Park, MD 20742, USA
                Department of Biology, University of Maryland , College Park, MD 20742, USA
                Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD 20215, USA
                Kavli Neuroscience Discovery Institute, Johns Hopkins University , Baltimore, MD 20215, USA
                Author notes
                To whom correspondence should be addressed: Email: pkanold@ 123456jhu.edu

                Competing Interest: The authors declare no competing interest.

                Author information
                https://orcid.org/0000-0002-9046-7565
                https://orcid.org/0000-0002-1792-7860
                https://orcid.org/0000-0002-7529-5435
                Article
                pgae074
                10.1093/pnasnexus/pgae074
                10898513
                38415223
                5fb8a6bb-61ec-47c2-9f8d-0f87772ed987
                © The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence ( https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 03 November 2023
                : 06 February 2024
                : 27 February 2024
                Page count
                Pages: 12
                Funding
                Funded by: NIH, DOI 10.13039/100000002;
                Award ID: U19 NS107464
                Award ID: RO1DC017785
                Categories
                Biological, Health, and Medical Sciences
                AcademicSubjects/MED00010
                AcademicSubjects/SCI00010
                AcademicSubjects/SOC00010
                PNAS_Nexus/neuro

                primary auditory cortex,network,rentian,correlation,small-world

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