Biomechanics Of Traumatic Brain Injury Research Articles

Measurement of Brain Strains in a Goat Head Under Impact Loading

Abstract Measurements of brain deformations under dynamic loading are required to understand the biomechanics of traumatic brain injury (TBI). In this work, we have experimentally measured two-dimensional (2D) brain deformations in a sacrificed goat brain under injurious impact loading. To facilitate imaging, the goat head was dissected along the longitudinal midline. Goat head response was studied for sagittal plane rotation. Full-field, 2D deformations in the midsagittal plane of a goat brain were obtained at spatiotemporal resolutions of ∼1 mm and 0.4 ms, respectively. Results elucidate the dynamic strain evolution. The goat brain underwent large deformation. The strain pattern was heterogeneous. Peak strains in various brain regions were established within ∼20 ms, followed by negligible strain development due to the considerable viscous dissipation. The Cerebellum region experienced the highest strain, followed by cortical and subcortical regions. Strain concentration in the goat brain near the stiff membrane of the tentorium was observed. The strains in a brain simulant of a head surrogate (obtained previously) were also compared against the goat brain response. A response in the brain simulant was comparable to the goat brain in terms of strain pattern, peak strains in various substructures, and strain concentration near the membrane. However, the brain simulant was less dissipative than the goat brain. These results enhance the current understanding of the biomechanics of the brain under dynamic loading.

Rate- and Region-Dependent Mechanical Properties of Göttingen Minipig Brain Tissue in Simple Shear and Unconfined Compression.

Traumatic brain injury (TBI), particularly from explosive blasts, is a major cause of casualties in modern military conflicts. Computational models are an important tool in understanding the underlying biomechanics of TBI but are highly dependent on the mechanical properties of soft tissue to produce accurate results. Reported material properties of brain tissue can vary by several orders of magnitude between studies, and no published set of material parameters exists for porcine brain tissue at strain rates relevant to blast. In this work, brain tissue from the brainstem, cerebellum, and cerebrum of freshly euthanized adolescent male Göttingen minipigs was tested in simple shear and unconfined compression at strain rates ranging from quasi-static (QS) to 300 s-1. Brain tissue showed significant strain rate stiffening in both shear and compression. Minimal differences were seen between different regions of the brain. Both hyperelastic and hyper-viscoelastic constitutive models were fit to experimental stress, considering data from either a single loading mode (unidirectional) or two loading modes together (bidirectional). The unidirectional hyper-viscoelastic models with an Ogden hyperelastic representation and a one-term Prony series best captured the response of brain tissue in all regions and rates. The bidirectional models were generally able to capture the response of the tissue in high-rate shear and all compression modes, but not the QS shear. Our constitutive models describe the first set of material parameters for porcine brain tissue relevant to loading modes and rates seen in blast injury.

Subject-Specific Head Model Generation by Mesh Morphing: A Personalization Framework and Its Applications.

Finite element (FE) head models have become powerful tools in many fields within neuroscience, especially for studying the biomechanics of traumatic brain injury (TBI). Subject-specific head models accounting for geometric variations among subjects are needed for more reliable predictions. However, the generation of such models suitable for studying TBIs remains a significant challenge and has been a bottleneck hindering personalized simulations. This study presents a personalization framework for generating subject-specific models across the lifespan and for pathological brains with significant anatomical changes by morphing a baseline model. The framework consists of hierarchical multiple feature and multimodality imaging registrations, mesh morphing, and mesh grouping, which is shown to be efficient with a heterogeneous dataset including a newborn, 1-year-old (1Y), 2Y, adult, 92Y, and a hydrocephalus brain. The generated models of the six subjects show competitive personalization accuracy, demonstrating the capacity of the framework for generating subject-specific models with significant anatomical differences. The family of the generated head models allows studying age-dependent and groupwise brain injury mechanisms. The framework for efficient generation of subject-specific FE head models helps to facilitate personalized simulations in many fields of neuroscience.

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion.

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach.

Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20–40 rad/s), durations (30–60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

Towards animal surrogates for characterising large strain dynamic mechanical properties of human brain tissue

The regional dynamic mechanical properties of mouse, rat, pig, and human brain tissue were compared directly in this first-of-its-kind study. Our results suggest the use of pig or mouse brain tissue as suitable surrogates to characterise human brain tissue. The importance of this work is highlighted by the extensive use of constitutive data from animal brain tissue in traumatic brain injury research in the absence of human brain tissue data without any direct evidence supporting their use. Indentation force-relaxation experiments were performed on mouse, rat, pig, and human brains at 10/s strain rate up to 35% strain to determine the dynamic mechanical properties of brain tissue. Finite element models have become useful tools to investigate the biomechanics of traumatic brain injury - a global leading cause of death and disability and a risk factor for developing neurodegenerative diseases. However, these models require accurate constitutive data for brain tissue to produce reliable results. The results presented here provide validation for the use of pig and mouse brain tissue data in such models. Statement of SignificanceThe significance of this work is underscored by the extensive use of animal brain tissue as a surrogate for human brain tissue without any direct evidence supporting the validity of their use. For the first time ever, we demonstrate that porcine and murine brain tissue can be used as surrogates for human brain tissue under dynamic loading conditions. These findings will allow researchers to select appropriate animal surrogates for human brain tissue under dynamic loading conditions. Furthermore, our findings support the use of animal surrogate data to improve the fidelity of computational models of the human brain, and provide experimental data to develop constitutive models of brain tissue.

Multiscale Modeling of the Damage Biomechanics of Traumatic Brain Injury

Multiscale Modeling of the Damage Biomechanics of Traumatic Brain Injury

Regional mechanical and biochemical properties of the porcine cortical meninges

The meninges are pivotal in protecting the brain against traumatic brain injury (TBI), an ongoing issue in most mainstream sports. Improved understanding of TBI biomechanics and pathophysiology is desirable to improve preventative measures, such as protective helmets, and advance our TBI diagnostic/prognostic capabilities. This study mechanically characterised the porcine meninges by performing uniaxial tensile testing on the dura mater (DM) tissue adjacent to the frontal, parietal, temporal, and occipital lobes of the cerebellum and superior sagittal sinus region of the DM. Mechanical characterisation revealed a significantly higher elastic modulus for the superior sagittal sinus region when compared to other regions in the DM. The superior sagittal sinus and parietal regions of the DM also displayed local mechanical anisotropy. Further, fatigue was noted in the DM following ten preconditioning cycles, which could have important implications in the context of repetitive TBI. To further understand differences in regional mechanical properties, regional variations in protein content (collagen I, collagen III, fibronectin and elastin) were examined by immunoblot analysis. The superior sagittal sinus was found to have significantly higher collagen I, elastin, and fibronectin content. The frontal region was also identified to have significantly higher collagen I and fibronectin content while the temporal region had increased elastin and fibronectin content. Regional differences in the mechanical and biochemical properties along with regional tissue thickness differences within the DM reveal that the tissue is a non-homogeneous structure. In particular, the potentially influential role of the superior sagittal sinus in TBI biomechanics warrants further investigation. Statement of significanceThis study addresses the lack of regional mechanical analysis of the cortical meninges, particularly the dura mater (DM), with accompanying biochemical analysis. To mechanically characterise the stiffness of the DM by region, uniaxial tensile testing was carried out on the DM tissue adjacent to the frontal, parietal, temporal and occipital lobes along with the DM tissue associated with the superior sagittal sinus. To the best of the authors’ knowledge, the work presented here identifies, for the first time, the heterogeneous nature of the DM’s mechanical stiffness by region. In particular, this study identifies the significant difference in the stiffness of the DM tissue associated with the superior sagittal sinus when compared to the other DM regions. Constitutive modelling was carried out on the regional mechanical testing data for implementation in Finite Element models with improved biofidelity. This work also presents the first biochemical analysis of the collagen I and III, elastin, and fibronectin content within DM tissue by region, providing useful insights into the accompanying macro-scale biomechanical data.

Application of the time-strain superposition - Part I: Prediction of the nonlinear constant shear rate response of brain tissue.

show good agreement between the predicted CSR response and the previously published CSR measurements. We may justifiably speculate that the proposed approach serves well for prediction of the nonlinear CSR behaviour of the porcine brain tissue. Since the methodology is strongly supported by the physical background, it exhibits the potential to be utilized for prediction of nonlinear behaviour in other loading modes, as well as of other tissues or viscoelastic materials.

Semiquantitation of Axonal Injury in Traumatically Damaged Brains Using Color Deconvolution.

In traumatic brain injury biomechanics, macroscale biomechanical events need to be correlated with microscale neuropathologic changes and improved quantitation of microscopic axonal injury is an essential component of lesion evaluation. To develop a novel technique for automatically identifying injured amyloid precursor protein immunopositive axons and aggregating these observations over a macroscopic brain dissection. A color deconvolution method was adapted into Matlab to identify clusters of pixels with colors typical of amyloid precursor protein positive tissue from large-scale brain dissection. The methodology is demonstrated in the brain of a sheep subjected to a controlled cortical indentation. The technique will be of interest to pathologists and bioengineers seeking to quantitate brain injury over macroscales.

頭を強打すると脳に何が起こるのか ―脳震とうでも発症する高次脳機能障害の怖さ―

頭を強打すると脳に何が起こるのか ―脳震とうでも発症する高次脳機能障害の怖さ―

The mechanics of traumatic brain injury: a review of what we know and what we need to know for reducing its societal burden.

Traumatic brain injury (TBI) is a significant public health problem, on pace to become the third leading cause of death worldwide by 2020. Moreover, emerging evidence linking repeated mild traumatic brain injury to long-term neurodegenerative disorders points out that TBI can be both an acute disorder and a chronic disease. We are at an important transition point in our understanding of TBI, as past work has generated significant advances in better protecting us against some forms of moderate and severe TBI. However, we still lack a clear understanding of how to study milder forms of injury, such as concussion, or new forms of TBI that can occur from primary blast loading. In this review, we highlight the major advances made in understanding the biomechanical basis of TBI. We point out opportunities to generate significant new advances in our understanding of TBI biomechanics, especially as it appears across the molecular, cellular, and whole organ scale.

Monitoring Hippocampus Electrical Activity In Vitro on an Elastically Deformable Microelectrode Array

Interfacing electronics and recording electrophysiological activity in mechanically active biological tissues is challenging. This challenge extends to recording neural function of brain tissue in the setting of traumatic brain injury (TBI), which is caused by rapid (within hundreds of milliseconds) and large (greater than 5% strain) brain deformation. Interfacing electrodes must be biocompatible on multiple levels and should deform with the tissue to prevent additional mechanical damage. We describe an elastically stretchable microelectrode array (SMEA) that is capable of undergoing large, biaxial, 2-D stretch while remaining functional. The new SMEA consists of elastically stretchable thin metal films on a silicone membrane. It can stimulate and detect electrical activity from cultured brain tissue (hippocampal slices), before, during, and after large biaxial deformation. We have incorporated the SMEA into a well-characterized in vitro TBI research platform, which reproduces the biomechanics of TBI by stretching the SMEA and the adherent brain slice culture. Mechanical injury parameters, such as strain and strain rate, can be precisely controlled to generate specific levels of damage. The SMEA allowed for quantification of neuronal function both before and after injury, without breaking culture sterility or repositioning the electrodes for the injury event, thus enabling serial and long-term measurements. We report tests of the SMEA and an initial application to study the effect of mechanical stimuli on neuron function, which could be employed as a high-content, drug-screening platform for TBI.

Biomechanics of traumatic brain injury

A biomechanical model for traumatic brain injury and soft tissue damage is presented. A variational constitutive model for soft biological tissues is utilized to reproduce axonal damage and cavitation injury through inelastic deformation. The material response is split into elastoplastic and viscoelastic components, including rate effects, shear and porous plasticity, and finite viscoelasticity. Mechanical damage of brain tissue is classified as volumetric (compression/tension) and shear-type. Finite element simulations of brain injuries are presented, examining frontal and oblique head impacts with external objects. Localization, extension, intensity and reversibility/irreversibility of tissue damage are predicted. Future directions of this work, relating mechanical damage and physiological brain dysfunction, and application to relevant medical and engineering problems are discussed.

Biomechanics of Traumatic Brain Injury: Influences of the Morphologic Heterogeneities of the Cerebral Cortex

Traumatic brain injury (TBI) can be caused by accidents and often leads to permanent health issues or even death. Brain injury criteria are used for assessing the probability of TBI, if a certain mechanical load is applied. The currently used injury criteria in the automotive industry are based on global head kinematics. New methods, based on finite element modeling, use brain injury criteria at lower scale levels, e.g., tissue-based injury criteria. However, most current computational head models lack the anatomical details of the cerebrum. To investigate the influence of the morphologic heterogeneities of the cerebral cortex, a numerical model of a representative part of the cerebral cortex with a detailed geometry has been developed. Several different geometries containing gyri and sulci have been developed for this model. Also, a homogeneous geometry has been made to analyze the relative importance of the heterogeneities. The loading conditions are based on a computational head model simulation. The results of this model indicate that the heterogeneities have an influence on the equivalent stress. The maximum equivalent stress in the heterogeneous models is increased by a factor of about 1.3–1.9 with respect to the homogeneous model, whereas the mean equivalent stress is increased by at most 10%. This implies that tissue-based injury criteria may not be accurately applied to most computational head models used nowadays, which do not account for sulci and gyri.

The stereotactical phenomena in traumatic brain injury biomechanics—what long-term consequences?

The stereotactical phenomena in traumatic brain injury biomechanics—what long-term consequences?