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      Increased functional sensorimotor network efficiency relates to disability in multiple sclerosis

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          Abstract

          Background:

          Network abnormalities could help explain physical disability in multiple sclerosis (MS), which remains poorly understood.

          Objective:

          This study investigates functional network efficiency changes in the sensorimotor system.

          Methods:

          We included 222 MS patients, divided into low disability (LD, Expanded Disability Status Scale (EDSS) ⩽3.5, n = 185) and high disability (HD, EDSS ⩾6, n = 37), and 82 healthy controls (HC). Functional connectivity was assessed between 23 sensorimotor regions. Measures of efficiency were computed and compared between groups using general linear models corrected for age and sex. Binary logistic regression models related disability status to local functional network efficiency (LE), brain volumes and demographics. Functional connectivity patterns of regions important for disability were explored.

          Results:

          HD patients demonstrated significantly higher LE of the left primary somatosensory cortex (S1) and right pallidum compared to LD and HC, and left premotor cortex compared to HC only. The logistic regression model for disability ( R 2 = 0.38) included age, deep grey matter volume and left S1 LE. S1 functional connectivity was increased with prefrontal and secondary sensory areas in HD patients, compared to LD and HC.

          Conclusion:

          Clinical disability in MS associates with functional sensorimotor increases in efficiency and connectivity, centred around S1, independent of structural damage.

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          Most cited references35

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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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            Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS).

            J. Kurtzke (1983)
            One method of evaluating the degree of neurologic impairment in MS has been the combination of grades (0 = normal to 5 or 6 = maximal impairment) within 8 Functional Systems (FS) and an overall Disability Status Scale (DSS) that had steps from 0 (normal) to 10 (death due to MS). A new Expanded Disability Status Scale (EDSS) is presented, with each of the former steps (1,2,3 . . . 9) now divided into two (1.0, 1.5, 2.0 . . . 9.5). The lower portion is obligatorily defined by Functional System grades. The FS are Pyramidal, Cerebellar, Brain Stem, Sensory, Bowel & Bladder, Visual, Cerebral, and Other; the Sensory and Bowel & Bladder Systems have been revised. Patterns of FS and relations of FS by type and grade to the DSS are demonstrated.
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              Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria

              New evidence and consensus has led to further revision of the McDonald Criteria for diagnosis of multiple sclerosis. The use of imaging for demonstration of dissemination of central nervous system lesions in space and time has been simplified, and in some circumstances dissemination in space and time can be established by a single scan. These revisions simplify the Criteria, preserve their diagnostic sensitivity and specificity, address their applicability across populations, and may allow earlier diagnosis and more uniform and widespread use. Ann Neurol 2011
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                Author and article information

                Contributors
                Journal
                Mult Scler
                Mult Scler
                MSJ
                spmsj
                Multiple Sclerosis (Houndmills, Basingstoke, England)
                SAGE Publications (Sage UK: London, England )
                1352-4585
                1477-0970
                26 October 2020
                August 2021
                : 27
                : 9
                : 1364-1373
                Affiliations
                [1-1352458520966292]Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands/Department of Radiology and Medicine, The University of Melbourne, Melbourne, VIC, Australia
                [2-1352458520966292]NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK/National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
                [3-1352458520966292]Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands/Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
                [4-1352458520966292]Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
                [5-1352458520966292]Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
                [6-1352458520966292]NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK/Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Health, University of Genoa, Genoa, Italy/Ospedale Policlinico San Martino-IRCCS, Genoa, Italy
                [7-1352458520966292]Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
                [8-1352458520966292]Department of Radiology and Medicine, The University of Melbourne, Melbourne, VIC, Australia/Department of Neurosciences, Central Clinical School, Monash University, Melbourne, VIC, Australia
                [9-1352458520966292]Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
                [10-1352458520966292]Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
                Author notes
                [*]M Strik Department of Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, PO Box 7057, 1007 MB, Amsterdam, The Netherlands. m.strik@ 123456amsterdamumc.nl
                Author information
                https://orcid.org/0000-0001-8995-9899
                https://orcid.org/0000-0003-3076-2682
                https://orcid.org/0000-0003-4685-1380
                https://orcid.org/0000-0002-2504-6959
                Article
                10.1177_1352458520966292
                10.1177/1352458520966292
                8358536
                33104448
                cb5f1642-ff78-438f-9f5a-8d915cf31e73
                © The Author(s), 2020

                This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 8 April 2020
                : 23 September 2020
                : 24 September 2020
                Funding
                Funded by: University of Melbourne, FundRef https://doi.org/10.13039/501100001782;
                Award ID: Melbourne Research Scholarship
                Funded by: Stichting MS Research, FundRef https://doi.org/10.13039/501100003000;
                Award ID: 08-650, 13-820, 14-358e
                Categories
                Original Research Papers
                Custom metadata
                ts1

                Immunology
                multiple sclerosis,resting-state,functional mri,disability,network,efficiency
                Immunology
                multiple sclerosis, resting-state, functional mri, disability, network, efficiency

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