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      Classification and characterisation of brain network changes in chronic back pain: A multicenter study

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          Abstract

          Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood.

          Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain.

          Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state.

          Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

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          Most cited references63

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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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              Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

              Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data CurationRole: Formal AnalysisRole: InvestigationRole: MethodologyRole: Project AdministrationRole: VisualizationRole: Writing – Original Draft Preparation
                Role: ConceptualizationRole: Data CurationRole: Formal AnalysisRole: InvestigationRole: MethodologyRole: Writing – Original Draft Preparation
                Role: ConceptualizationRole: Formal AnalysisRole: InvestigationRole: SoftwareRole: VisualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Role: Formal AnalysisRole: InvestigationRole: MethodologyRole: Writing – Original Draft Preparation
                Role: Formal AnalysisRole: Writing – Original Draft Preparation
                Role: Data CurationRole: Funding Acquisition
                Role: Data CurationRole: Investigation
                Role: Data CurationRole: Investigation
                Role: Data CurationRole: InvestigationRole: MethodologyRole: Resources
                Role: ConceptualizationRole: Formal AnalysisRole: InvestigationRole: Project AdministrationRole: Writing – Original Draft Preparation
                Role: Data CurationRole: InvestigationRole: MethodologyRole: Writing – Review & Editing
                Role: Data CurationRole: Funding AcquisitionRole: Investigation
                Role: ConceptualizationRole: Funding AcquisitionRole: MethodologyRole: Project AdministrationRole: SupervisionRole: Writing – Review & Editing
                Role: ConceptualizationRole: Formal AnalysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: VisualizationRole: Writing – Original Draft Preparation
                Role: ConceptualizationRole: Funding AcquisitionRole: MethodologyRole: Project AdministrationRole: SupervisionRole: Writing – Original Draft PreparationRole: Writing – Review & Editing
                Journal
                Wellcome Open Res
                Wellcome Open Res
                Wellcome Open Res
                Wellcome Open Research
                F1000 Research Limited (London, UK )
                2398-502X
                10 October 2018
                2018
                : 3
                : 19
                Affiliations
                [1 ]Center for Information and Neural Networks, National Institute of Information and Communications Technology, Osaka, Japan
                [2 ]Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
                [3 ]Graduate School of System Informatics, Kobe University, Kobe, Japan
                [4 ]Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, UK
                [5 ]Osaka University School of Medicine, Osaka, Japan
                [6 ]Immunology Frontiers Research Center, Osaka University, Osaka, Japan
                [7 ]School of Clinical Medicine, University of Cambridge, Cambridge, UK
                [8 ]Advanced Telecommunications Research Center International, Kyoto, Japan
                [9 ]Max-Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
                [10 ]Department of Computer Science, University College London, London, UK
                [1 ]Department of Anesthesiology and Pain Clinic, Tokyo Medical and Dental University Hospital of Medicine, Tokyo, Japan
                Department of Engineering, University of Cambridge, UK
                [1 ]Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
                Department of Engineering, University of Cambridge, UK
                [1 ]Departments of Surgery and Anesthesia, Northwestern University, Chicago, IL, USA
                Department of Engineering, University of Cambridge, UK
                Author notes

                No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Author information
                https://orcid.org/0000-0002-3691-3675
                https://orcid.org/0000-0002-5838-2916
                https://orcid.org/0000-0003-1724-5832
                Article
                10.12688/wellcomeopenres.14069.2
                5930551
                29774244
                b04a01ba-e34e-4e1f-96dc-64bedcbd5c0b
                Copyright: © 2018 Mano H et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 5 October 2018
                Funding
                Funded by: Arthritis Research UK
                Award ID: 21537
                Funded by: National Institute for Information and Communications Technology
                Funded by: University of Cambridge
                Funded by: Ministry of Education, Culture, Sports, Science and Technology
                Funded by: Japan Society for the Promotion of Science
                Funded by: Wellcome Trust
                Award ID: 097490
                Funded by: Japan Agency for Medical Research and Development
                The study was supported by the Wellcome Trust (097490; BS); National Institute for Information and Communications Technology (HM, KL, BS); ‘Engineering in Clinical Practice Grant’ of the University of Cambridge (BS, ECP Oct 2012); Strategic Research Program for Brain Sciences by Ministry of Education, Culture, Sports, Science and Technology (MEXT) and Japan Agency for Medical Research and Development (WY, BS, MK); the Japanese Society for the Promotion of Science (JSPS S2604; HM, AN, WY, BS, TY), and Arthritis Research UK (21357, BS). OPP funding was provided by the National Institute of Neurological Disorders and Stroke (NINDS) and National Institute of Drug Abuse (NIDA). OPP data are disseminated by the Apkarian Lab, Physiology Department at the Northwestern University, Chicago.
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Articles

                chronic pain,nociception,connectomics,graph theory,deep learning,sensorimotor,multislice modularity,hub disruption,osteoarthritis,arthritis,rostral acc,endogenous modulation

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