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      DotMotif: an open-source tool for connectome subgraph isomorphism search and graph queries

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          Abstract

          Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration.

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          Most cited references63

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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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            Statistical mechanics of complex networks

            Reviews of Modern Physics, 74(1), 47-97
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              The Structure and Function of Complex Networks

              M. Newman (2003)
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                Author and article information

                Contributors
                jordan.matelsky@jhuapl.edu
                william.gray.roncal@jhuapl.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                22 June 2021
                22 June 2021
                2021
                : 11
                : 13045
                Affiliations
                [1 ]GRID grid.474430.0, ISNI 0000 0004 0630 1170, The Johns Hopkins University Applied Physics Laboratory, ; Laurel, MD 20723 USA
                [2 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Bioengineering, School of Engineering and Applied Science, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [3 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Neuroscience Graduate Group, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [4 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Electrical and Systems Engineering, School of Engineering and Applied Science, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [5 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Neurology, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [6 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Physics and Astronomy, College of Arts and Sciences, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [7 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Psychiatry, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                [8 ]GRID grid.209665.e, ISNI 0000 0001 1941 1940, Santa Fe Institute, ; Santa Fe, NM 87501 USA
                [9 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Computer Science, , Johns Hopkins University, ; Baltimore, MD 21218 USA
                Article
                91025
                10.1038/s41598-021-91025-5
                8219732
                34158519
                f0ff0615-08b7-4e9d-bc85-229c309717bd
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 January 2021
                : 29 April 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R24MH114799
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                computational neuroscience,network topology,programming language,software,neural circuits,network models

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