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      Revisiting Brain Atrophy and Its Relationship to Disability in Multiple Sclerosis

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          Abstract

          Background

          Brain atrophy is a well-accepted imaging biomarker of multiple sclerosis (MS) that partially correlates with both physical disability and cognitive impairment.

          Methodology/Principal Findings

          Based on MRI scans of 60 MS cases and 37 healthy volunteers, we measured the volumes of white matter (WM) lesions, cortical gray matter (GM), cerebral WM, caudate nucleus, putamen, thalamus, ventricles, and brainstem using a validated and completely automated segmentation method. We correlated these volumes with the Expanded Disability Status Scale (EDSS), MS Severity Scale (MSSS), MS Functional Composite (MSFC), and quantitative measures of ankle strength and toe sensation. Normalized volumes of both cortical and subcortical GM structures were abnormally low in the MS group, whereas no abnormality was found in the volume of the cerebral WM. High physical disability was associated with low cerebral WM, thalamus, and brainstem volumes (partial correlation coefficients ∼0.3–0.4) but not with low cortical GM volume. Thalamus volumes were inversely correlated with lesion load ( r = −0.36, p<0.005).

          Conclusion

          The GM is atrophic in MS. Although lower WM volume is associated with greater disability, as might be expected, WM volume was on average in the normal range. This paradoxical result might be explained by the presence of coexisting pathological processes, such as tissue damage and repair, that cause both atrophy and hypertrophy and that underlie the observed disability.

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          Most cited references 51

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          Accurate, robust, and automated longitudinal and cross-sectional brain change analysis.

          Quantitative measurement of brain size, shape, and temporal change (for example, in order to estimate atrophy) is increasingly important in biomedical image analysis applications. New methods of structural analysis attempt to improve robustness, accuracy, and extent of automation. A fully automated method of longitudinal (temporal change) analysis, SIENA, was presented previously. In this paper, improvements to this method are described, and also an extension of SIENA to a new method for cross-sectional (single time point) analysis. The methods are fully automated, robust, and accurate: 0.15% brain volume change error (longitudinal): 0.5-1% brain volume accuracy for single-time point (cross-sectional). A particular advantage is the relative insensitivity to differences in scanning parameters. The methods provide easy manual review of their output by the automatic production of summary images which show the results of the brain extraction, registration, tissue segmentation, and final atrophy estimation.
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            Development of a multiple sclerosis functional composite as a clinical trial outcome measure.

            The primary clinical outcome measure for evaluating multiple sclerosis in clinical trials has been Kurtzke's expanded disability status scale (EDSS). New therapies appear to favourably impact the course of multiple sclerosis and render continued use of placebo control groups more difficult. Consequently, future trials are likely to compare active treatment groups which will most probably require increased sample sizes in order to detect therapeutic efficacy. Because more responsive outcome measures will be needed for active arm comparison studies, the National Multiple Sclerosis Society's Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis appointed a Task Force that was charged with developing improved clinical outcome measures. This Task Force acquired contemporary clinical trial and historical multiple sclerosis data for meta-analyses of primary and secondary outcome assessments to provide a basis for recommending a new outcome measure. A composite measure encompassing the major clinical dimensions of arm, leg and cognitive function was identified and termed the multiple sclerosis functional composite (MSFC). The MSFC consists of three objective quantitative tests of neurological function which are easy to administer. Change in this MSFC over the first year of observation predicted subsequent change in the EDSS, suggesting that the MSFC is more sensitive to change than the EDSS. This paper provides details concerning the development and testing of the MSFC.
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              Multiple Sclerosis Severity Score: using disability and disease duration to rate disease severity.

              There is no consensus method for determining progression of disability in patients with multiple sclerosis (MS) when each patient has had only a single assessment in the course of the disease. Using data from two large longitudinal databases, the authors tested whether cross-sectional disability assessments are representative of disease severity as a whole. An algorithm, the Multiple Sclerosis Severity Score (MSSS), which relates scores on the Expanded Disability Status Scale (EDSS) to the distribution of disability in patients with comparable disease durations, was devised and then applied to a collection of 9,892 patients from 11 countries to create the Global MSSS. In order to compare different methods of detecting such effects the authors simulated the effects of a genetic factor on disability. Cross-sectional EDSS measurements made after the first year were representative of overall disease severity. The MSSS was more powerful than the other methods the authors tested for detecting different rates of disease progression. The Multiple Sclerosis Severity Score (MSSS) is a powerful method for comparing disease progression using single assessment data. The Global MSSS can be used as a reference table for future disability comparisons. While useful for comparing groups of patients, disease fluctuation precludes its use as a predictor of future disability in an individual.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                15 May 2012
                : 7
                : 5
                Affiliations
                [1 ]Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
                [2 ]Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland, United States of America
                [3 ]Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
                [4 ]Neuroradiology Division, Departments of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, United States of America
                [5 ]Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, Maryland, United States of America
                [6 ]Department of Neurology, Johns Hopkins University, Baltimore, Maryland, United States of America
                [7 ]Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
                [8 ]Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, United States of America
                [9 ]Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, United States of America
                Innsbruck Medical University, Austria
                Author notes

                Conceived and designed the experiments: KMZ PAC DSR. Performed the experiments: KMZ SKF DMH SDN JNR PAC DSR. Analyzed the data: NS BSC DSR. Contributed reagents/materials/analysis tools: NS PLB DLP. Wrote the paper: NS DSR.

                Article
                PONE-D-11-10705
                10.1371/journal.pone.0037049
                3352847
                22615886
                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
                Counts
                Pages: 9
                Categories
                Research Article
                Biology
                Anatomy and Physiology
                Neurological System
                Central Nervous System
                Medicine
                Clinical Immunology
                Autoimmune Diseases
                Multiple Sclerosis
                Neurology
                Demyelinating Disorders
                Multiple Sclerosis
                Radiology
                Diagnostic Radiology
                Magnetic Resonance Imaging

                Uncategorized

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