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      Pain-Evoked Reorganization in Functional Brain Networks

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          Abstract

          Recent studies indicate that a significant reorganization of cerebral networks may occur in patients with chronic pain, but how immediate pain experience influences the organization of large-scale functional networks is not yet well characterized. To investigate this question, we used functional magnetic resonance imaging in 106 participants experiencing both noxious and innocuous heat. Painful stimulation caused network-level reorganization of cerebral connectivity that differed substantially from organization during innocuous stimulation and standard resting-state networks. Noxious stimuli increased somatosensory network connectivity with (a) frontoparietal networks involved in context representation, (b) “ventral attention network” regions involved in motivated action selection, and (c) basal ganglia and brainstem regions. This resulted in reduced “small-worldness,” modularity (fewer networks), and global network efficiency and in the emergence of an integrated “pain supersystem” (PS) whose activity predicted individual differences in pain sensitivity across 5 participant cohorts. Network hubs were reorganized (“hub disruption”) so that more hubs were localized in PS, and there was a shift from “connector” hubs linking disparate networks to “provincial” hubs connecting regions within PS. Our findings suggest that pain reorganizes the network structure of large-scale brain systems. These changes may prioritize responses to painful events and provide nociceptive systems privileged access to central control of cognition and action during pain.

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          Most cited references154

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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

                Journal
                Cerebral Cortex
                Oxford University Press (OUP)
                1047-3211
                1460-2199
                May 2020
                May 14 2020
                December 09 2019
                May 2020
                May 14 2020
                December 09 2019
                : 30
                : 5
                : 2804-2822
                Affiliations
                [1 ]School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, P. R. China
                [2 ]Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, P. R. China
                [3 ]Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Republic of Korea
                [4 ]Department of Biomedical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
                [5 ]Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309, USA
                [6 ]Institute of Cognitive Science, University of Colorado, Boulder, CO 80309, USA
                [7 ]The School of Public Health, University of Haifa, Haifa, 3498838, Israel
                [8 ]National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD 20892, USA
                [9 ]National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
                [10 ]National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA
                [11 ]Department of Psychology, McGill University, Montréal, Quebec H3A 0G4, Canada
                [12 ]Control-Interoception-Attention (CIA) team, Institut du Cerveau et de la Moelle épinière (ICM), Sorbonne University / CNRS / INSERM, 75013 Paris, France
                [13 ]Department of Psychology, Brooklyn College of the City University of New York, Brooklyn, NY 11210, USA
                [14 ]Department of Psychology, University of Amsterdam, Amsterdam, 1018 WS, The Netherlands
                [15 ]Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
                Article
                10.1093/cercor/bhz276
                31813959
                d4ee04ab-29c7-42f3-a656-842f381bbc35
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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