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      Every hit matters: White matter diffusivity changes in high school football athletes are correlated with repetitive head acceleration event exposure

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          Abstract

          Recent evidence of short-term alterations in brain physiology associated with repeated exposure to moderate intensity subconcussive head acceleration events (HAEs), prompts the question whether these alterations represent an underlying neural injury. A retrospective analysis combining counts of experienced HAEs and longitudinal diffusion-weighted imaging explored whether greater exposure to incident mechanical forces was associated with traditional diffusion-based measures of neural injury—reduced fractional anisotropy (FA) and increased mean diffusivity (MD). Brains of high school athletes ( N = 61) participating in American football exhibited greater spatial extents (or volumes) experiencing substantial changes (increases and decreases) in both FA and MD than brains of peers who do not participate in collision-based sports ( N = 15). Further, the spatial extents of the football athlete brain exhibiting traditional diffusion-based markers of neural injury were found to be significantly correlated with the cumulative exposure to HAEs having peak translational acceleration exceeding 20 g. This finding demonstrates that subconcussive HAEs induce low-level neurotrauma, with prolonged exposure producing greater accumulation of neural damage. The duration and extent of recovery associated with periods in which athletes do not experience subconcussive HAEs now represents a priority for future study, such that appropriate participation and training schedules may be developed to minimize the risk of long-term neurological dysfunction.

          Highlights

          • Brain volumes evidencing injury are larger in football athletes than controls.

          • Spatial extent of decreased FA correlates with head acceleration event exposure.

          • Spatial extent of increased MD correlates with head acceleration event exposure.

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          Chronic traumatic encephalopathy in athletes: progressive tauopathy after repetitive head injury.

          Since the 1920s, it has been known that the repetitive brain trauma associated with boxing may produce a progressive neurological deterioration, originally termed dementia pugilistica, and more recently, chronic traumatic encephalopathy (CTE). We review 48 cases of neuropathologically verified CTE recorded in the literature and document the detailed findings of CTE in 3 profession althletes, 1 football player and 2 boxers. Clinically, CTE is associated with memory disturbances, behavioral and personality changes, parkinsonism, and speech and gait abnormalities. Neuropathologically, CTE is characterized by atrophy of the cerebral hemispheres, medial temporal lobe, thalamus, mammillary bodies, and brainstem, with ventricular dilatation and a fenestrated cavum septum pellucidum. Microscopically, there are extensive tau-immunoreactive neurofibrillary tangles, astrocytic tangles, and spindle-shaped and threadlike neurites throughout the brain. The neurofibrillary degeneration of CTE is distinguished from other tauopathies by preferential involvement of the superficial cortical layers, irregular patchy distribution in the frontal and temporal cortices, propensity for sulcal depths, prominent perivascular, periventricular, and subpial distribution, and marked accumulation of tau-immunoreactive astrocytes. Deposition of beta-amyloid, most commonly as diffuse plaques, occurs in fewer than half the cases. Chronic traumatic encephalopathy is a neuropathologically distinct slowly progressive tauopathy with a clear environmental etiology.
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            Longitudinal development of human brain wiring continues from childhood into adulthood.

            Healthy human brain development is a complex process that continues during childhood and adolescence, as demonstrated by many cross-sectional and several longitudinal studies. However, whether these changes end in adolescence is not clear. We examined longitudinal white matter maturation using diffusion tensor tractography in 103 healthy subjects aged 5-32 years; each volunteer was scanned at least twice, with 221 total scans. Fractional anisotropy (FA) and mean diffusivity (MD), parameters indicative of factors including myelination and axon density, were assessed in 10 major white matter tracts. All tracts showed significant nonlinear development trajectories for FA and MD. Significant within-subject changes occurred in the vast majority of children and early adolescents, and these changes were mostly complete by late adolescence for projection and commissural tracts. However, association tracts demonstrated postadolescent within-subject maturation of both FA and MD. Diffusion parameter changes were due primarily to decreasing perpendicular diffusivity, although increasing parallel diffusivity contributed to the prolonged increases of FA in association tracts. Volume increased significantly with age for most tracts, and longitudinal measures also demonstrated postadolescent volume increases in several association tracts. As volume increases were not directly associated with either elevated FA or reduced MD between scans, the observed diffusion parameter changes likely reflect microstructural maturation of brain white matter tracts rather than just gross anatomy.
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              Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images

              Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion. Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurements. This is caused by bulk (subject or physiological) motion during the diffusion encoding part of the imaging sequence. We suggest a method to detect slices affected by signal loss and replace them by a non-parametric prediction, in order to minimise their impact on subsequent analysis. The outlier detection and replacement, as well as correction of other dMRI distortions (susceptibility-induced distortions, eddy currents (EC) and subject motion) are performed within a single framework, allowing the use of an integrated approach for distortion correction. Highly realistic simulations have been used to evaluate the method with respect to its ability to detect outliers (types 1 and 2 errors), the impact of outliers on retrospective correction of movement and distortion and the impact on estimation of commonly used diffusion tensor metrics, such as fractional anisotropy (FA) and mean diffusivity (MD). Data from a large imaging project studying older adults (the Whitehall Imaging sub-study) was used to demonstrate the utility of the method when applied to datasets with severe subject movement. The results indicate high sensitivity and specificity for detecting outliers and that their deleterious effects on FA and MD can be almost completely corrected.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                16 July 2019
                2019
                16 July 2019
                : 24
                : 101930
                Affiliations
                [a ]School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States of America
                [b ]Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, United States of America
                [c ]Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States of America
                [d ]College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States of America
                [e ]School of Mechanical Engineering, Purdue University, West Lafayette, IN, United States of America
                [f ]School of Health Sciences, Purdue University, West Lafayette, IN, United States of America
                [g ]Department of Health and Kinesiology, Purdue University, West Lafayette, IN, United States of America
                [h ]Department of Basic Medical Sciences, Purdue University, West Lafayette, IN, United States of America
                Author notes
                [* ]Corresponding author at: School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Ave, West Lafayette, IN 47907-2035, United States of America. jibikbam@ 123456gmail.com
                Article
                S2213-1582(19)30280-3 101930
                10.1016/j.nicl.2019.101930
                6807364
                31630026
                f5e6200e-cb03-4c4a-b737-fac520bdbdad
                © 2019 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 22 December 2018
                : 29 June 2019
                : 9 July 2019
                Categories
                Regular Article

                subconcussive injury,traumatic brain injury,magnetic resonance imaging,diffusion-weighted imaging,diffusion tensor imaging

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