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      Functional and Structural Neuroimaging Correlates of Repetitive Low-Level Blast Exposure in Career Breachers

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          Abstract

          Combat military and civilian law enforcement personnel may be exposed to repetitive low-intensity blast events during training and operations. Persons who use explosives to gain entry (i.e., breach) into buildings are known as “breachers” or dynamic entry personnel. Breachers operate under the guidance of established safety protocols, but despite these precautions, breachers who are exposed to low-level blast throughout their careers frequently report performance deficits and symptoms to healthcare providers. Although little is known about the etiology linking blast exposure to clinical symptoms in humans, animal studies demonstrate network-level changes in brain function, alterations in brain morphology, vascular and inflammatory changes, hearing loss, and even alterations in gene expression after repeated blast exposure. To explore whether similar effects occur in humans, we collected a comprehensive data battery from 20 experienced breachers exposed to blast throughout their careers and 14 military and law enforcement controls. This battery included neuropsychological assessments, blood biomarkers, and magnetic resonance imaging measures, including cortical thickness, diffusion tensor imaging of white matter, functional connectivity, and perfusion. To better understand the relationship between repetitive low-level blast exposure and behavioral and imaging differences in humans, we analyzed the data using similarity-driven multi-view linear reconstruction (SiMLR). SiMLR is specifically designed for multiple modality statistical integration using dimensionality-reduction techniques for studies with high-dimensional, yet sparse, data (i.e., low number of subjects and many data per subject). We identify significant group effects in these data spanning brain structure, function, and blood biomarkers.

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          Most cited references80

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          Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

          Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. Copyright © 2011 Elsevier Inc. All rights reserved.
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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 reproducible evaluation of ANTs similarity metric performance in brain image registration.

              The United States National Institutes of Health (NIH) commit significant support to open-source data and software resources in order to foment reproducibility in the biomedical imaging sciences. Here, we report and evaluate a recent product of this commitment: Advanced Neuroimaging Tools (ANTs), which is approaching its 2.0 release. The ANTs open source software library consists of a suite of state-of-the-art image registration, segmentation and template building tools for quantitative morphometric analysis. In this work, we use ANTs to quantify, for the first time, the impact of similarity metrics on the affine and deformable components of a template-based normalization study. We detail the ANTs implementation of three similarity metrics: squared intensity difference, a new and faster cross-correlation, and voxel-wise mutual information. We then use two-fold cross-validation to compare their performance on openly available, manually labeled, T1-weighted MRI brain image data of 40 subjects (UCLA's LPBA40 dataset). We report evaluation results on cortical and whole brain labels for both the affine and deformable components of the registration. Results indicate that the best ANTs methods are competitive with existing brain extraction results (Jaccard=0.958) and cortical labeling approaches. Mutual information affine mapping combined with cross-correlation diffeomorphic mapping gave the best cortical labeling results (Jaccard=0.669±0.022). Furthermore, our two-fold cross-validation allows us to quantify the similarity of templates derived from different subgroups. Our open code, data and evaluation scripts set performance benchmark parameters for this state-of-the-art toolkit. This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling. Copyright © 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                J Neurotrauma
                J Neurotrauma
                neu
                Journal of Neurotrauma
                Mary Ann Liebert, Inc., publishers (140 Huguenot Street, 3rd FloorNew Rochelle, NY 10801USA )
                0897-7151
                1557-9042
                December 1, 2020
                06 November 2020
                06 November 2020
                : 37
                : 23
                : 2468-2481
                Affiliations
                [ 1 ]Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA.
                [ 2 ]Behavioral Neurology Unit, National Institute of Neurological Disorders and Stroke, National Institute of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA.
                [ 3 ]Tissue Injury Branch, National Institute of Nursing Research, National Institutes of Health, Bethesda, Maryland, USA.
                [ 4 ]Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA.
                [ 5 ]Department of Psychology, Pennsylvania State University, University Park, Pennsylvania, USA.
                [ 6 ]Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA.
                [ 7 ]Military Emergency Medicine, Uniformed Services University, Bethesda, Maryland, USA.
                [ 8 ]Center for Military Psychiatry and Neuroscience, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA.
                [ 9 ]Health Mission Initiative, DoD Joint Artificial Intelligence Center, Washington, DC, USA.
                [ 10 ]School of Psychology, University of Glasgow, Glasgow, United Kingdom.
                [ 11 ]Neurotrauma Department, Naval Medical Research Center, Silver Spring, Maryland, USA.
                [ 12 ]Behavioral Biology Branch, Walter Reed Army Institute of Research, Silver Spring, Maryland, USA.
                [ 13 ]Operational and Undersea Medicine Directorate, Naval Medical Research Center, Silver Spring, Maryland, USA.
                Author notes
                [*]Address correspondence to: James R. Stone, MD, PhD, Department of Radiology and Medical Imaging, University of Virginia, 480 Ray C Hunt Drive, Box 801339, Charlottesville, VA 22903, USA jrs7r@ 123456virginia.edu
                Article
                10.1089/neu.2020.7141
                10.1089/neu.2020.7141
                7703399
                32928028
                0c603b28-fe6b-408d-a9b7-a400763af84f
                © James R. Stone et al., 2020; Published by Mary Ann Liebert, Inc.

                This Open Access article is distributed under the terms of the Creative Commons Attribution Noncommercial License ( http://creativecommons.org/licenses/by-nc/4.0/) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

                History
                Page count
                Figures: 7, Tables: 2, References: 80, Pages: 14
                Categories
                Original Articles

                breachers,cortical thickness,diffusion tensor imaging,functional mri,perfusion imaging,simlr

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