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      The Shrinking Brain: Cerebral Atrophy Following Traumatic Brain Injury

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          Abstract

          Cerebral atrophy in response to traumatic brain injury is a well-documented phenomenon in both primary investigations and review articles. Recent atrophy studies focus on exploring the region-specific patterns of cerebral atrophy; yet, there is no study that analyzes and synthesizes the emerging atrophy patterns in a single comprehensive review. Here we attempt to fill this gap in our current knowledge by integrating the current literature into a cohesive theory of preferential brain tissue loss and by identifying common risk factors for accelerated atrophy progression. Our review reveals that observations for mild traumatic brain injury remain inconclusive, whereas observations for moderate-to-severe traumatic brain injury converge towards robust patterns: brain tissue loss is on the order of 5% per year, and occurs in the form of generalized atrophy, across the entire brain, or focal atrophy, in specific brain regions. The most common regions of focal atrophy are the thalamus, hippocampus, and cerebellum in gray matter and the corpus callosum, corona radiata, and brainstem in white matter. We illustrate the differences of generalized and focal gray and white matter atrophy on emerging deformation and stress profiles across the whole brain using computational simulation. The characteristic features of our atrophy simulations—a widening of the cortical sulci, a gradual enlargement of the ventricles, and a pronounced cortical thinning—agree well with clinical observations. Understanding region-specific atrophy patterns in response to traumatic brain injury has significant implications in modeling, simulating, and predicting injury outcomes. Computational modeling of brain atrophy could open new strategies for physicians to make informed decisions for whom, how, and when to administer pharmaceutical treatment to manage the chronic loss of brain structure and function.

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          Cerebral atrophy in Parkinson's disease with and without dementia: a comparison with Alzheimer's disease, dementia with Lewy bodies and controls.

          Parkinson's disease is a common neurodegenerative disorder primarily characterized by rigidity, tremor and bradykinesia. Cognitive impairment and neuropsychiatric symptoms are frequent in Parkinson's disease, with a 70% cumulative incidence of dementia. The aim of this cross-sectional study was to establish the pattern of cerebral atrophy on MRI in Parkinson's disease patients with dementia. We used voxel-based morphometry (VBM) to provide an unbiased means of investigating brain volume loss. Whole brain structural T1-weighted MRI scans from Parkinson's disease patients with dementia (PDD, n = 26), Parkinson's disease patients without dementia (n = 31), Alzheimer's disease patients (n = 28), patients with dementia with Lewy bodies (DLB, n = 17) and control subjects (n = 36) were acquired. Images were analysed using SPM99 and the optimized method of VBM. Reduced grey matter volume in PDD patients compared with controls was observed bilaterally in the temporal lobe, including the hippocampus and parahippocampal gyrus, and in the occipital lobe, the right frontal lobe and the left parietal lobe, as well as some subcortical regions. Parkinson's disease patients without dementia showed reduced grey matter volume in the frontal lobe compared with control subjects. There was significant grey matter atrophy bilaterally in the occipital lobe of PDD patients compared with Parkinson's disease patients. In addition, significant temporal lobe atrophy, including the hippocampus and parahippocampal gyrus was detected in Alzheimer's disease relative to PDD. No significant volumetric differences were observed in PDD compared with DLB. Thus, Parkinson's disease involves grey matter loss in frontal areas. In PDD, this extends to temporal, occipital and subcortical areas, with occipital atrophy in PDD being the only difference between the two groups. This provides important information about the pattern of cerebral atrophy in Parkinson's disease and PDD.
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            Concussion, microvascular injury, and early tauopathy in young athletes after impact head injury and an impact concussion mouse model

            The mechanisms underpinning concussion, traumatic brain injury (TBI) and chronic traumatic encephalopathy (CTE) are poorly understood. Using neuropathological analyses of brains from teenage athletes, a new mouse model of concussive impact injury, and computational simulations, Tagge et al. show that head injuries can induce TBI and early CTE pathologies independent of concussion.
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              Mechanical characterization of human brain tissue.

              Mechanics are increasingly recognized to play an important role in modulating brain form and function. Computational simulations are a powerful tool to predict the mechanical behavior of the human brain in health and disease. The success of these simulations depends critically on the underlying constitutive model and on the reliable identification of its material parameters. Thus, there is an urgent need to thoroughly characterize the mechanical behavior of brain tissue and to identify mathematical models that capture the tissue response under arbitrary loading conditions. However, most constitutive models have only been calibrated for a single loading mode. Here, we perform a sequence of multiple loading modes on the same human brain specimen - simple shear in two orthogonal directions, compression, and tension - and characterize the loading-mode specific regional and directional behavior. We complement these three individual tests by combined multiaxial compression/tension-shear tests and discuss effects of conditioning and hysteresis. To explore to which extent the macrostructural response is a result of the underlying microstructural architecture, we supplement our biomechanical tests with diffusion tensor imaging and histology. We show that the heterogeneous microstructure leads to a regional but not directional dependence of the mechanical properties. Our experiments confirm that human brain tissue is nonlinear and viscoelastic, with a pronounced compression-tension asymmetry. Using our measurements, we compare the performance of five common constitutive models, neo-Hookean, Mooney-Rivlin, Demiray, Gent, and Ogden, and show that only the isotropic modified one-term Ogden model is capable of representing the hyperelastic behavior under combined shear, compression, and tension loadings: with a shear modulus of 0.4-1.4kPa and a negative nonlinearity parameter it captures the compression-tension asymmetry and the increase in shear stress under superimposed compression but not tension. Our results demonstrate that material parameters identified for a single loading mode fail to predict the response under arbitrary loading conditions. Our systematic characterization of human brain tissue will lead to more accurate computational simulations, which will allow us to determine criteria for injury, to develop smart protection systems, and to predict brain development and disease progression.
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                Author and article information

                Contributors
                ekuhl@stanford.edu
                Journal
                Ann Biomed Eng
                Ann Biomed Eng
                Annals of Biomedical Engineering
                Springer US (New York )
                0090-6964
                1573-9686
                17 October 2018
                17 October 2018
                2019
                : 47
                : 9
                : 1941-1959
                Affiliations
                GRID grid.168010.e, ISNI 0000000419368956, Stanford University, ; Stanford, CA USA
                Author notes

                Associate Editor Mark Horstemeyer oversaw the review of this article.

                Author information
                http://orcid.org/0000-0002-6283-935X
                Article
                2148
                10.1007/s10439-018-02148-2
                6757025
                30341741
                90bca3b3-d9c9-47ca-b1c7-de4645ae6cb3
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 21 June 2018
                : 1 October 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000147, Division of Civil, Mechanical and Manufacturing Innovation;
                Award ID: CMMI 1727268
                Funded by: Stanford BioX
                Award ID: Bio-X IIP Molecular Mechanisms of Chronic Traumatic Encephalopathy
                Funded by: Stanford Graduate Fellowship
                Categories
                State-of-the-Art Modeling and Simulation of the Brain’s Response to Mechanical Loads
                Custom metadata
                © Biomedical Engineering Society 2019

                Biomedical engineering
                traumatic brain injury,cerebral atrophy,neurodegeneration,computational simulation,finite element modeling

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