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      Gray matter atrophy patterns in multiple sclerosis: A 10-year source-based morphometry study

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          Abstract

          Objectives

          To investigate spatial patterns of gray matter (GM) atrophy and their association with disability progression in patients with early relapsing-remitting multiple sclerosis (MS) in a longitudinal setting.

          Methods

          Brain MRI and clinical neurological assessments were obtained in 152 MS patients at baseline and after 10 years of follow-up. Patients were classified into those with confirmed disability progression (CDP) ( n = 85) and those without CDP ( n = 67) at the end of the study. An optimized, longitudinal source-based morphometry (SBM) pipeline, which utilizes independent component analysis, was used to identify eight spatial patterns of common GM volume co-variation in a data-driven manner. GM volume at baseline and rates of change were compared between patients with CDP and those without CDP.

          Results

          The identified patterns generally included structurally or functionally related GM regions. No significant differences were detected at baseline GM volume between the sub-groups. Over the follow-up, patients with CDP experienced a significantly greater rate of GM atrophy within two of the eight patterns, after correction for multiple comparisons (corrected p-values of 0.001 and 0.007). The patterns of GM atrophy associated with the development of CDP included areas involved in motor functioning and cognitive domains such as learning and memory.

          Conclusion

          SBM analysis offers a novel way to study the temporal evolution of regional GM atrophy. Over 10 years of follow-up, disability progression in MS is related to GM atrophy in areas associated with motor and cognitive functioning.

          Highlights

          • We present a longitudinal source-based morphometry (SBM) pipeline for detecting gray matter atrophy in multiple sclerosis.

          • We report a significantly greater rate of atrophy in patients developing disability progression over 10 years of follow-up.

          • We show that longitudinal SBM is potentially more sensitive than voxel-based morphometry in detecting gray matter atrophy.

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          Most cited references26

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          Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

          All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.
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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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              Source-based morphometry: the use of independent component analysis to identify gray matter differences with application to schizophrenia.

              We present a multivariate alternative to the voxel-based morphometry (VBM) approach called source-based morphometry (SBM), to study gray matter differences between patients and healthy controls. The SBM approach begins with the same preprocessing procedures as VBM. Next, independent component analysis is used to identify naturally grouping, maximally independent sources. Finally, statistical analyses are used to determine the significant sources and their relationship to other variables. The identified "source networks," groups of spatially distinct regions with common covariation among subjects, provide information about localization of gray matter changes and their variation among individuals. In this study, we first compared VBM and SBM via a simulation and then applied both methods to real data obtained from 120 chronic schizophrenia patients and 120 healthy controls. SBM identified five gray matter sources as significantly associated with schizophrenia. These included sources in the bilateral temporal lobes, thalamus, basal ganglia, parietal lobe, and frontotemporal regions. None of these showed an effect of sex. Two sources in the bilateral temporal and parietal lobes showed age-related reductions. The most significant source of schizophrenia-related gray matter changes identified by SBM occurred in the bilateral temporal lobe, while the most significant change found by VBM occurred in the thalamus. The SBM approach found changes not identified by VBM in basal ganglia, parietal, and occipital lobe. These findings show that SBM is a multivariate alternative to VBM, with wide applicability to studying changes in brain structure.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                05 November 2017
                2018
                05 November 2017
                : 17
                : 444-451
                Affiliations
                [a ]Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA
                [b ]Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
                [c ]Department of Radiology, 1st Faculty of Medicine and General University Hospital, Charles University, Prague, Czech Republic
                [d ]Translational Imaging Center at Clinical Translational Research Center, University at Buffalo, State University of New York, Buffalo, NY, USA
                Author notes
                [* ]Corresponding author at: Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York, Buffalo, NY, 100 High St., Buffalo, NY 14203, USA.Buffalo Neuroimaging Analysis CenterDepartment of NeurologyJacobs School of Medicine and Biomedical SciencesState University of New York, Buffalo, NY100 High St.BuffaloNY14203USA npbergsland@ 123456bnac.net
                Article
                S2213-1582(17)30282-6
                10.1016/j.nicl.2017.11.002
                5684496
                29159057
                22866b99-6189-4e8f-acca-efee364c002e
                © 2017 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 18 September 2017
                : 30 October 2017
                : 2 November 2017
                Categories
                Regular Article

                multiple sclerosis,disability,mri,atrophy,gray matter
                multiple sclerosis, disability, mri, atrophy, gray matter

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