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      Internal Jugular Vein Cross-Sectional Area and Cerebrospinal Fluid Pulsatility in the Aqueduct of Sylvius: A Comparative Study between Healthy Subjects and Multiple Sclerosis Patients

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          Abstract

          Objectives

          Constricted cerebral venous outflow has been linked with increased cerebrospinal fluid (CSF) pulsatility in the aqueduct of Sylvius in multiple sclerosis (MS) patients and healthy individuals. This study investigates the relationship between CSF pulsatility and internal jugular vein (IJV) cross-sectional area (CSA) in these two groups, something previously unknown.

          Methods

          65 relapsing-remitting MS patients (50.8% female; mean age = 43.8 years) and 74 healthy controls (HCs) (54.1% female; mean age = 43.9 years) were investigated. CSF flow quantification was performed on cine phase-contrast MRI, while IJV-CSA was calculated using magnetic resonance venography. Statistical analysis involved correlation, and partial least squares correlation analysis (PLSCA).

          Results

          PLSCA revealed a significant difference (p<0.001; effect size = 1.072) between MS patients and HCs in the positive relationship between CSF pulsatility and IJV-CSA at C5-T1, something not detected at C2-C4. Controlling for age and cardiovascular risk factors, statistical trends were identified in HCs between: increased net positive CSF flow (NPF) and increased IJV-CSA at C5-C6 (left: r = 0.374, p = 0.016; right: r = 0.364, p = 0.019) and C4 (left: r = 0.361, p = 0.020); and increased net negative CSF flow and increased left IJV-CSA at C5-C6 (r = -0.348, p = 0.026) and C4 (r = -0.324, p = 0.039), whereas in MS patients a trend was only identified between increased NPF and increased left IJV-CSA at C5-C6 (r = 0.351, p = 0.021). Overall, correlations were weaker in MS patients (p = 0.015).

          Conclusions

          In healthy adults, increased CSF pulsatility is associated with increased IJV-CSA in the lower cervix (independent of age and cardiovascular risk factors), suggesting a biomechanical link between the two. This relationship is altered in MS patients.

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          Most cited references35

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          Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review.

          Partial Least Squares (PLS) methods are particularly suited to the analysis of relationships between measures of brain activity and of behavior or experimental design. In neuroimaging, PLS refers to two related methods: (1) symmetric PLS or Partial Least Squares Correlation (PLSC), and (2) asymmetric PLS or Partial Least Squares Regression (PLSR). The most popular (by far) version of PLS for neuroimaging is PLSC. It exists in several varieties based on the type of data that are related to brain activity: behavior PLSC analyzes the relationship between brain activity and behavioral data, task PLSC analyzes how brain activity relates to pre-defined categories or experimental design, seed PLSC analyzes the pattern of connectivity between brain regions, and multi-block or multi-table PLSC integrates one or more of these varieties in a common analysis. PLSR, in contrast to PLSC, is a predictive technique which, typically, predicts behavior (or design) from brain activity. For both PLS methods, statistical inferences are implemented using cross-validation techniques to identify significant patterns of voxel activation. This paper presents both PLS methods and illustrates them with small numerical examples and typical applications in neuroimaging. Copyright © 2010 Elsevier Inc. All rights reserved.
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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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              Partial least squares methods: partial least squares correlation and partial least square regression.

              Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table. When the goal is to find the shared information between two tables, the approach is equivalent to a correlation problem and the technique is then called partial least square correlation (PLSC) (also sometimes called PLS-SVD). In this case there are two sets of latent variables (one set per table), and these latent variables are required to have maximal covariance. When the goal is to predict one data table the other one, the technique is then called partial least square regression. In this case there is one set of latent variables (derived from the predictor table) and these latent variables are required to give the best possible prediction. In this paper we present and illustrate PLSC and PLSR and show how these descriptive multivariate analysis techniques can be extended to deal with inferential questions by using cross-validation techniques such as the bootstrap and permutation tests.
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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, CA USA )
                1932-6203
                2 May 2016
                2016
                : 11
                : 5
                : e0153960
                Affiliations
                [1 ]Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
                [2 ]MRI Clinical Translational Research Center, School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America
                [3 ]Research Institute for Sport, Physical Activity and Leisure, Carnegie Faculty, Leeds Beckett University, Leeds, West Yorkshire, United Kingdom
                Instituto Cajal-CSIC, SPAIN
                Author notes

                Competing Interests: Clive B. Beggs, Christopher Magnano, Pavel Belov, Jacqueline Krawiecki, Deepa P. Ramasamy, and Jesper Hagemeier have nothing to disclose. Robert Zivadinov received personal compensation from Teva Pharmaceuticals, EMD Serono, Novartis and Genzyme for speaking and consultant fees. Dr. Zivadinov received financial support for research activities from, Teva Pharmaceuticals, Genzyme, Claret Medical and Novartis. However, the authors confirm that these competing interests do not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: RZ CM CBB. Performed the experiments: CM PB JK DPR JH. Analyzed the data: CBB PB JH RZ. Wrote the paper: CBB CM RZ.

                Article
                PONE-D-15-44490
                10.1371/journal.pone.0153960
                4852898
                27135831
                a092eae8-9ce9-4f1a-9643-76407f491dcf
                © 2016 Beggs et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 October 2015
                : 6 April 2016
                Page count
                Figures: 2, Tables: 5, Pages: 13
                Funding
                Funded by: Annette Funicello Research Fund for Neurological Diseases
                Clive Beggs received a travel grant from the Annette Funicello Research Fund for Neurological Diseases (no grant number) [ http://www.annetteconnection.com/About-the-Research-Fund.php]. This work was also supported by the Jacquemin Family Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Clinical Medicine
                Clinical Immunology
                Autoimmune Diseases
                Multiple Sclerosis
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                Immunology
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                Autoimmune Diseases
                Multiple Sclerosis
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