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      Progression of regional grey matter atrophy in multiple sclerosis

      research-article
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      Brain
      Oxford University Press
      multiple sclerosis, atrophy, grey matter, probabilistic modelling, disability accumulation

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          Abstract

          See Stankoff and Louapre (doi: [Related article:]10.1093/brain/awy114) for a scientific commentary on this article.

          Grey matter atrophy in multiple sclerosis affects certain areas preferentially. Eshaghi et al. use a data-driven computational model to predict the order in which regions atrophy, and use this sequence to stage patients. Atrophy begins in deep grey matter nuclei and posterior cortical regions, before spreading to other cortical areas.

          Abstract

          See Stankoff and Louapre (doi: [Related article:]10.1093/brain/awy114) for a scientific commentary on this article.

          Grey matter atrophy is present from the earliest stages of multiple sclerosis, but its temporal ordering is poorly understood. We aimed to determine the sequence in which grey matter regions become atrophic in multiple sclerosis and its association with disability accumulation. In this longitudinal study, we included 1417 subjects: 253 with clinically isolated syndrome, 708 with relapsing-remitting multiple sclerosis, 128 with secondary-progressive multiple sclerosis, 125 with primary-progressive multiple sclerosis, and 203 healthy control subjects from seven European centres. Subjects underwent repeated MRI (total number of scans 3604); the mean follow-up for patients was 2.41 years (standard deviation = 1.97). Disability was scored using the Expanded Disability Status Scale. We calculated the volume of brain grey matter regions and brainstem using an unbiased within-subject template and used an established data-driven event-based model to determine the sequence of occurrence of atrophy and its uncertainty. We assigned each subject to a specific event-based model stage, based on the number of their atrophic regions. Linear mixed-effects models were used to explore associations between the rate of increase in event-based model stages, and T 2 lesion load, disease-modifying treatments, comorbidity, disease duration and disability accumulation. The first regions to become atrophic in patients with clinically isolated syndrome and relapse-onset multiple sclerosis were the posterior cingulate cortex and precuneus, followed by the middle cingulate cortex, brainstem and thalamus. A similar sequence of atrophy was detected in primary-progressive multiple sclerosis with the involvement of the thalamus, cuneus, precuneus, and pallidum, followed by the brainstem and posterior cingulate cortex. The cerebellum, caudate and putamen showed early atrophy in relapse-onset multiple sclerosis and late atrophy in primary-progressive multiple sclerosis. Patients with secondary-progressive multiple sclerosis showed the highest event-based model stage (the highest number of atrophic regions, P < 0.001) at the study entry. All multiple sclerosis phenotypes, but clinically isolated syndrome, showed a faster rate of increase in the event-based model stage than healthy controls. T 2 lesion load and disease duration in all patients were associated with increased event-based model stage, but no effects of disease-modifying treatments and comorbidity on event-based model stage were observed. The annualized rate of event-based model stage was associated with the disability accumulation in relapsing-remitting multiple sclerosis, independent of disease duration ( P < 0.0001). The data-driven staging of atrophy progression in a large multiple sclerosis sample demonstrates that grey matter atrophy spreads to involve more regions over time. The sequence in which regions become atrophic is reasonably consistent across multiple sclerosis phenotypes. The spread of atrophy was associated with disease duration and with disability accumulation over time in relapsing-remitting multiple sclerosis.

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          Most cited references20

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          Virtual hypoxia and chronic necrosis of demyelinated axons in multiple sclerosis.

          Multiple sclerosis (MS), an inflammatory demyelinating disease, is a major cause of neurological disability in young adults in the developed world. Although the progressive neurological disability that most patients with MS eventually experience results from axonal degeneration, little is known about the mechanisms of axonal injury in MS. Accumulating evidence suggests that the increased energy demand of impulse conduction along excitable demyelinated axons and reduced axonal ATP production induce a chronic state of virtual hypoxia in chronically demyelinated axons. In response to such a state, key alterations that contribute to chronic necrosis of axons might include mitochondrial dysfunction (due to defective oxidative phosphorylation or nitric oxide production), Na+ influx through voltage-gated Na+ channels and axonal AMPA receptors, release of toxic Ca2+ from the axoplasmic reticulum, overactivation of ionotropic and metabotropic axonal glutamate receptors, and activation of voltage-gated Ca2+ channels, ultimately leading to excessive stimulation of Ca2+-dependent degradative pathways. The development of neuroprotective therapies that target these mechanisms might constitute effective adjuncts to currently used immune-modifying agents.
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            Avoiding asymmetry-induced bias in longitudinal image processing.

            Longitudinal image processing procedures frequently transfer or pool information across time within subject, with the dual goals of reducing the variability and increasing the accuracy of the derived measures. In this note, we discuss common difficulties in longitudinal image processing, focusing on the introduction of bias, and describe the approaches we have taken to avoid them in the FreeSurfer longitudinal processing stream. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Gray matter atrophy in multiple sclerosis: a longitudinal study.

              To determine gray matter (GM) atrophy rates in multiple sclerosis (MS) patients at all stages of disease, and to identify predictors and clinical correlates of GM atrophy. MS patients and healthy control subjects were observed over 4 years with standardized magnetic resonance imaging (MRI) and neurological examinations. Whole-brain, GM, and white matter atrophy rates were calculated. Subjects were categorized by disease status and disability progression to determine the clinical significance of atrophy. MRI predictors of atrophy were determined through multiple regression. Subjects included 17 healthy control subjects, 7 patients with clinically isolated syndromes, 36 patients with relapsing-remitting MS (RRMS), and 27 patients with secondary progressive MS (SPMS). Expressed as fold increase from control subjects, GM atrophy rate increased with disease stage, from 3.4-fold normal in clinically isolated syndromes patients converting to RRMS to 14-fold normal in SPMS. In contrast, white matter atrophy rates were constant across all MS disease stages at approximately 3-fold normal. GM atrophy correlated with disability. MRI measures of focal and diffuse tissue damage accounted for 62% of the variance in GM atrophy in RRMS, but there were no significant predictors of GM atrophy in SPMS. Gray matter tissue damage dominates the pathological process as MS progresses, and underlies neurological disabillity. Imaging correlates of gray matter atrophy indicate that mechanisms differ in RRMS and SPMS. These findings demonstrate the clinical relevance of gray matter atrophy in MS, and underscore the need to understand its causes.
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                Author and article information

                Journal
                Brain
                Brain
                brainj
                Brain
                Oxford University Press
                0006-8950
                1460-2156
                June 2018
                08 May 2018
                08 May 2018
                : 141
                : 6
                : 1665-1677
                Affiliations
                [1 ]Queen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
                [2 ]Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, UK
                [3 ]Translational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
                [4 ]National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
                [5 ]Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
                [6 ]Department of Neurosciences, S Camillo Forlanini Hospital, Rome, Italy
                [7 ]Department of Neurology and Psychiatry, University of Rome Sapienza, Rome, Italy
                [8 ]Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
                [9 ]MR Unit and Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
                [10 ]Department of Neurology/Neuroimmunology, Multiple Sclerosis Centre of Catalonia (CEMCAT), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
                [11 ]Department of Anatomy and Neurosciences, VUmc MS Center, Neuroscience Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
                [12 ]Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam, The Netherlands
                [13 ]Department of Neurology, MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
                [14 ]Department of Neurology, Medical University of Graz, Graz, Austria
                [15 ]Division of Neuroradiology, Department of Radiology, Medical University of Graz, Graz, Austria
                [16 ]Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
                [17 ]Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
                Author notes
                Correspondence to: Dr Arman Eshaghi Russell Square House, 10–12 Russell Square Queen Square Multiple Sclerosis Centre UCL Institute of Neurology, University College London, London, UK E-mail: arman.eshaghi.14@ 123456ucl.ac.uk

                Appendix 1.

                See Stankoff and Louapre (doi: [Related article:]10.1093/brain/awy114) for a scientific commentary on this article.

                Author information
                http://orcid.org/0000-0002-6652-3512
                http://orcid.org/0000-0002-7872-0142
                Article
                awy088
                10.1093/brain/awy088
                5995197
                29741648
                2adbd34e-9e27-4d31-8fe7-c85798fb0fbf
                © The Author(s) (2018). Published by Oxford University Press on behalf of the Guarantors of Brain.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 September 2017
                : 24 December 2017
                : 9 February 2018
                Page count
                Pages: 13
                Funding
                Funded by: EPSRC 10.13039/501100000266
                Award ID: M020533, M006093, J020990
                Categories
                Original Articles

                Neurosciences
                multiple sclerosis,atrophy,grey matter,probabilistic modelling,disability accumulation

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