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      Brain and retinal atrophy in African-Americans versus Caucasian-Americans with multiple sclerosis: a longitudinal study

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          Abstract

          <p class="first" id="d171217e421">See Aly and Korn (doi: <span class="generated">[Related article:]</span>10.1093/brain/awy269) for a scientific commentary on this article. </p><p id="d171217e426">African Americans with multiple sclerosis demonstrate higher inflammatory disease activity and poorer prognosis than Caucasian Americans. Gonzalez Caldito <i>et al.</i> report that African Americans patients exhibit faster rates of brain and retinal tissue loss, emphasizing the need for future studies involving this group to identify individual differences in treatment responses. </p><p id="d171217e434">On average, African Americans with multiple sclerosis demonstrate higher inflammatory disease activity, faster disability accumulation, greater visual dysfunction, more pronounced brain tissue damage and higher lesion volume loads compared to Caucasian Americans with multiple sclerosis. Neurodegeneration is an important component of multiple sclerosis, which in part accounts for the clinical heterogeneity of the disease. Brain atrophy appears to be widespread, although it is becoming increasingly recognized that regional substructure atrophy may be of greater clinical relevance. Patient race (within the limitations of self-identified ancestry) is regarded as an important contributing factor. However, there is a paucity of studies examining differences in neurodegeneration and brain substructure volumes over time in African Americans relative to Caucasian American patients. Optical coherence tomography is a non-invasive and reliable tool for measuring structural retinal changes. Recent studies support its utility for tracking neurodegeneration and disease progression <i>in vivo</i> in multiple sclerosis. Relative to Caucasian Americans, African American patients have been found to have greater retinal structural injury in the inner retinal layers. Increased thickness of the inner nuclear layer and the presence of microcystoid macular pathology at baseline predict clinical and radiological inflammatory activity, although whether race plays a role in these changes has not been investigated. Similarly, assessment of outer retinal changes according to race in multiple sclerosis remains incompletely characterized. Twenty-two African Americans and 60 matched Caucasian Americans with multiple sclerosis were evaluated with brain MRI, and 116 African Americans and 116 matched Caucasian Americans with multiple sclerosis were monitored with optical coherence tomography over a mean duration of 4.5 years. Mixed-effects linear regression models were used in statistical analyses. Grey matter (−0.9%/year versus −0.5%: <i>P</i> =0.02), white matter (−0.7%/year versus −0.3%: <i>P</i> =0.04) and nuclear thalamic (−1.5%/year versus −0.7%/year: <i>P</i> =0.02) atrophy rates were approximately twice as fast in African Americans. African Americans also exhibited higher proportions of microcystoid macular pathology (12.1% versus 0.9%, <i>P</i> =0.001). Retinal nerve fibre layer (−1.1% versus −0.8%: <i>P</i> =0.02) and ganglion cell+ inner plexiform layer (−0.7%/year versus −0.4%/year: <i>P</i> =0.01) atrophy rates were faster in African versus Caucasian Americans. African Americans on average exhibited more rapid neurodegeneration than Caucasian Americans and had significantly faster brain and retinal tissue loss. These results corroborate the more rapid clinical progression reported to occur, in general, in African Americans with multiple sclerosis and support the need for future studies involving African Americans in order to identify individual differences in treatment responses in multiple sclerosis. </p>

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          Most cited references59

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          Multi-atlas segmentation of biomedical images: A survey.

          Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.
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            Racial and Ethnic Disparities in the Quality of Health Care.

            The annual National Healthcare Quality and Disparities Reports document widespread and persistent racial and ethnic disparities. These disparities result from complex interactions between patient factors related to social disadvantage, clinicians, and organizational and health care system factors. Separate and unequal systems of health care between states, between health care systems, and between clinicians constrain the resources that are available to meet the needs of disadvantaged groups, contribute to unequal outcomes, and reinforce implicit bias. Recent data suggest slow progress in many areas but have documented a few notable successes in eliminating these disparities. To eliminate these disparities, continued progress will require a collective national will to ensure health care equity through expanded health insurance coverage, support for primary care, and public accountability based on progress toward defined, time-limited objectives using evidence-based, sufficiently resourced, multilevel quality improvement strategies that engage patients, clinicians, health care organizations, and communities.
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              The measurement and clinical relevance of brain atrophy in multiple sclerosis.

              Brain atrophy has emerged as a clinically relevant component of disease progression in multiple sclerosis. Progressive loss of brain tissue bulk can be detected in vivo in a sensitive and reproducible manner by MRI. Clinical studies have shown that brain atrophy begins early in the disease course. The increasing amount of data linking brain atrophy to clinical impairments suggest that irreversible tissue destruction is an important determinant of disease progression to a greater extent than can be explained by conventional lesion assessments. In this review, we will summarise the proposed mechanisms contributing to brain atrophy in patients with multiple sclerosis. We will critically discuss the wide range of MRI-based methods used to quantify regional and whole-brain-volume loss. Based on a review of current information, we will summarise the rate of atrophy among phenotypes for multiple sclerosis, the clinical relevance of brain atrophy, and the effect of disease-modifying treatments on its progression.
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                Author and article information

                Journal
                Brain
                Oxford University Press (OUP)
                0006-8950
                1460-2156
                November 2018
                November 01 2018
                October 11 2018
                November 2018
                November 01 2018
                October 11 2018
                : 141
                : 11
                : 3115-3129
                Affiliations
                [1 ]Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                [2 ]F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
                [3 ]Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
                [4 ]Massachusetts General Hospital, Boston, MA, USA
                [5 ]Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA
                [6 ]Harvard Medical School, Boston, MA, USA
                [7 ]Department of Neurology, University of Texas Austin Dell Medical School, Austin TX, USA
                [8 ]Department of Neurology, New York University Langone Medical Center, New York, NY, USA
                [9 ]Department of Biostatistics, Johns Hopkins University, Baltimore MD, USA
                [10 ]Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD, USA
                [11 ]Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, USA
                [12 ]Division of Neurology, St. Michael’s Hospital, University of Toronto, 30 Bond Street, Toronto, Ontario, Canada
                [13 ]Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
                Article
                10.1093/brain/awy245
                6202573
                30312381
                b9e2dbfe-c170-4fd3-b819-920de9a1c8fe
                © 2018

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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