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      Objectively Measured Physical Activity Is Associated with Brain Volumetric Measurements in Multiple Sclerosis

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          Abstract

          Background. Little is known about physical activity and its association with volumes of whole brain gray matter and white matter and deep gray matter structures in persons with multiple sclerosis (MS). Purpose. This study examined the association between levels of physical activity and brain volumetric measures from magnetic resonance imaging (MRI) in MS. Method. 39 persons with MS wore an accelerometer for a 7-day period and underwent a brain MRI. Normalized GM volume (NGMV), normalized WM volume (NWMV), and deep GM structures were calculated from 3D T1-weighted structural brain images. We conducted partial correlations ( pr) controlling for demographic and clinical variables. Results. Moderate-to-vigorous physical activity (MVPA) was significantly associated with NGMV ( pr = 0.370, p < 0.05), NWMV ( pr = 0.433, p < 0.01), hippocampus ( pr = 0.499, p < 0.01), thalamus ( pr = 0.380, p < 0.05), caudate ( pr = 0.539, p < 0.01), putamen ( pr = 0.369, p < 0.05), and pallidum ( pr = 0.498, p < 0.01) volumes, when controlling for sex, age, clinical course of MS, and Expanded Disability Status Scale score. There were no associations between sedentary and light physical activity with MRI outcomes. Conclusion. Our results provide the first evidence that MVPA is associated with volumes of whole brain GM and WM and deep GM structures that are involved in motor and cognitive functions in MS.

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          Most cited references 32

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          Fast robust automated brain extraction.

           Teri S Krebs (2002)
          An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods. Copyright 2002 Wiley-Liss, Inc.
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            Improved optimization for the robust and accurate linear registration and motion correction of brain images.

            Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.
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              Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm.

               Y. Zhang,  M. Brady,  S. Smith (2001)
              The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation--no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. We show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.
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                Author and article information

                Journal
                Behav Neurol
                Behav Neurol
                BN
                Behavioural Neurology
                Hindawi Publishing Corporation
                0953-4180
                1875-8584
                2015
                4 June 2015
                : 2015
                Affiliations
                1Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
                2Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
                Author notes

                Academic Editor: Olivier Piguet

                Article
                10.1155/2015/482536
                4471328
                Copyright © 2015 Rachel E. Klaren et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Research Article

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