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      Deep Grey Matter Volume is Reduced in Amateur Boxers as Compared to Healthy Age-matched Controls

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          Abstract

          Purpose

          Mild traumatic brain injuries (mTBI) sustained during contact sports like amateur boxing are found to have long-term sequelae, being linked to an increased risk of developing neurological conditions like Parkinson’s disease. The aim of this study was to assess differences in volume of anatomical brain structures between amateur boxers and control subjects with a special interest in the affection of deep grey matter structures.

          Methods

          A total of 19 amateur boxers and 19 healthy controls (HC), matched for age and intelligence quotient (IQ), underwent 3T magnetic resonance imaging (MRI) as well as neuropsychological testing. Body mass index (BMI) was evaluated for every subject and data about years of boxing training and number of fights were collected for each boxer. The acquired 3D high resolution T1 weighted MR images were analyzed to measure the volumes of cortical grey matter (GM), white matter (WM), cerebrospinal fluid (CSF) and deep grey matter structures. Multivariate analysis was applied to reveal differences between groups referencing deep grey matter structures to normalized brain volume (NBV) to adjust for differences in head size and brain volume as well as adding BMI as cofactor.

          Results

          Total intracranial volume (TIV), comprising GM, WM and CSF, was lower in boxers compared to controls (by 7.1%, P = 0.009). Accordingly, GM (by 5.5%, P = 0.038) and WM (by 8.4%, P = 0.009) were reduced in boxers. Deep grey matter showed statistically lower volumes of the thalamus (by 8.1%, P = 0.006), caudate nucleus (by 11.1%, P = 0.004), putamen (by 8.1%, P = 0.011), globus pallidus (by 9.6%, P = 0.017) and nucleus accumbens (by 13.9%, P = 0.007) but not the amygdala (by 5.5%, P = 0.221), in boxers compared to HC.

          Conclusion

          Several deep grey matter structures were reduced in volume in the amateur boxer group. Furthermore, longitudinal studies are needed to determine the damage pattern affecting deep grey matter structures and its neuropsychological relevance.

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          Most cited references52

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          N4ITK: improved N3 bias correction.

          A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.
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            A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
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              Adaptive non-local means denoising of MR images with spatially varying noise levels.

              To adapt the so-called nonlocal means filter to deal with magnetic resonance (MR) images with spatially varying noise levels (for both Gaussian and Rician distributed noise). Most filtering techniques assume an equal noise distribution across the image. When this assumption is not met, the resulting filtering becomes suboptimal. This is the case of MR images with spatially varying noise levels, such as those obtained by parallel imaging (sensitivity-encoded), intensity inhomogeneity-corrected images, or surface coil-based acquisitions. We propose a new method where information regarding the local image noise level is used to adjust the amount of denoising strength of the filter. Such information is automatically obtained from the images using a new local noise estimation method. The proposed method was validated and compared with the standard nonlocal means filter on simulated and real MRI data showing an improved performance in all cases. The new noise-adaptive method was demonstrated to outperform the standard filter when spatially varying noise is present in the images. (c) 2009 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                mousa.zidan@med.uni-heidelberg.de
                Journal
                Clin Neuroradiol
                Clin Neuroradiol
                Clinical Neuroradiology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1869-1439
                1869-1447
                16 December 2022
                16 December 2022
                2023
                : 33
                : 2
                : 475-482
                Affiliations
                [1 ]GRID grid.5253.1, ISNI 0000 0001 0328 4908, Department of Neuroradiology, , Heidelberg University Hospital, ; Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
                [2 ]National Training Center for Boxing, Heidelberg, Germany
                [3 ]GRID grid.5253.1, ISNI 0000 0001 0328 4908, Division of Experimental Radiology, Department of Neuroradiology, , Heidelberg University Hospital, ; Heidelberg, Germany
                [4 ]GRID grid.468184.7, ISNI 0000 0004 0490 7056, Department of Neurology, , Krankenhaus Nordwest, ; Frankfurt, Germany
                Author information
                http://orcid.org/0000-0002-5236-3992
                Article
                1233
                10.1007/s00062-022-01233-3
                10220131
                36525030
                12c604f0-d5c0-424e-9cb6-55065b4b2e0d
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 June 2022
                : 14 October 2022
                Funding
                Funded by: Medizinische Fakultät Heidelberg der Universität Heidelberg (9149)
                Categories
                Original Article
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2023

                Radiology & Imaging
                mild traumatic brain injury,dementia,parkinson’s disease,diffuse axonal injury,concussion

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