A transversely isotropic viscoelastic constitutive equation for brainstem undergoing finite deformation

A novel in-situ dynamic mechanical analysis for human plantar soft tissue: The device design, definition of characteristics, test protocol, and preliminary results

The in-situ mechanical characterization of elastomers is not highly regarded due to the existence of a well-established set of sample-based standard tests for research and industry. However, there are certain situations or materials, like biological soft tissue, where an in-situ approach is necessary due to the impossibility of sampling from a living body. We have developed a dynamic mechanical analysis (DMA)-like device to approach in-vivo and in-situ multidimensional stress-strain properties of human plantar soft tissues. This work elucidates the operational mechanism of the novel measurement, with the definition of a new set of moduli, test standardization and protocol. Exploratory results of a volunteer's living plantar, silica rubber samples are presented with well preciseness and consistence as expected.

Viscoelastic response of gray matter and white matter brain tissues under creep and relaxation

Accurate measurement of the mechanical properties of brain tissue is of paramount importance for understanding its mechanics-biology relationship. Most published studies on brain viscoelasticity have been conducted using a single relaxation test, without validating the validity of linear viscoelasticity, which is insufficient to establish an accurate constitutive equation for brain tissue. We obtained the creep and relaxation profiles of fresh adult porcine white matter (N = 120) and gray matter (N = 56) under finite step-and-hold uniaxial compression, using a mechanical testing machine, with 16.67 mm/s loading rate and 80 s hold time. These curves were employed to determine viscoelastic properties and demonstrated an excellent fit with a concise power-law function. The average initial modulus for gray matter (GM) was 6.619 kPa, higher than that for white matter under transverse loading (WM-2D) at 5.579 kPa (p < 0.01), yet lower than that for white matter under axial loading (WM-1D) at 6.759 kPa (p = 0.0121). Notably, WM-2D exhibited the highest degree of fluidity (β = 0.216). Our findings reveal that gray matter behaves as a linear viscoelastic material with power-law creep compliance and relaxation modulus. Conversely, the creep and relaxation behavior of white matter deviates from the verification relationship derived from linear viscoelastic theory, indicating its nonlinearity. This fact underscores the inaccuracy of assuming a linear constitutive relationship to characterize the viscoelastic properties of white matter. By combining the power-law function with the experimentally obtained creep compliance and relaxation modulus, we offer a unique approach to determining the viscoelastic characteristics of brain tissue.

An atlas of the heterogeneous viscoelastic brain with local power-law attenuation synthesised using Prony-series

This review addresses the acute need to acknowledge the mechanical heterogeneity of brain matter and to accurately calibrate its local viscoelastic material properties accordingly. Specifically, it is important to compile the existing and disparate literature on attenuation power-laws and dispersion to make progress in wave physics of brain matter, a field of research that has the potential to explain the mechanisms at play in diffuse axonal injury and mild traumatic brain injury in general. Currently, viscous effects in the brain are modelled using Prony-series, i.e., a sum of decaying exponentials at different relaxation times. Here we collect and synthesise the Prony-series coefficients appearing in the literature for twelve regions: brainstem, basal ganglia, cerebellum, corona radiata, corpus callosum, cortex, dentate gyrus, hippocampus, thalamus, grey matter, white matter, homogeneous brain, and for eight different mammals: pig, rat, human, mouse, cow, sheep, monkey and dog. Using this data, we compute the fractional-exponent attenuation power-laws for different tissues of the brain, the corresponding dispersion laws resulting from causality, and the averaged Prony-series coefficients. STATEMENT OF SIGNIFICANCE: Traumatic brain injuries are considered a silent epidemic and finite element methods (FEMs) are used in modelling brain deformation, requiring access to viscoelastic properties of brain. To the best of our knowledge, this work presents 1) the first multi-frequency viscoelastic atlas of the heterogeneous brain, 2) the first review focusing on viscoelastic modelling in both FEMs and experimental works, 3) the first attempt to conglomerate the disparate existing literature on the viscoelastic modelling of the brain and 4) the largest collection of viscoelastic parameters for the brain (212 different Prony-series spanning 12 different tissues and 8 different animal surrogates). Furthermore, this work presents the first brain atlas of attenuation power-laws essential for modelling shear waves in brain.

Bidirectional hyperelastic characterization of brain white matter tissue

Biomechanical study of brain injuries originated from mechanical damages to white matter tissue requires detailed information on mechanical characteristics of its main components, the axonal fibers and extracellular matrix, which is very limited due to practical difficulties of direct measurement. In this paper, a new theoretical framework was established based on microstructural modeling of brain white matter tissue as a soft composite for bidirectional hyperelastic characterization of its main components. First the tissue was modeled as an Ogden hyperelastic material, and its principal Cauchy stresses were formulated in the axonal and transverse directions under uniaxial and equibiaxial tension using the theory of homogenization. Upon fitting these formulae to the corresponding experimental test data, direction-dependent hyperelastic constants of the tissue were obtained. These directional properties then were used to estimate the strain energy stored in the homogenized model under each loading scenario. A new microstructural composite model of the tissue was also established using principles of composites micromechanics, in which the axonal fibers and surrounding matrix are modeled as different Ogden hyperelastic materials with unknown constants. Upon balancing the strain energies stored in the homogenized and composite models under different loading scenarios, fully coupled nonlinear equations as functions of unknown hyperelastic constants were derived, and their optimum solutions were found in a multi-parametric multi-objective optimization procedure using the response surface methodology. Finally, these solutions were implemented, in a bottom-up approach, into a micromechanical finite element model to reproduce the tissue responses under the same loadings and predict the tissue responses under unseen non-equibiaxial loadings. Results demonstrated a very good agreement between the model predictions and experimental results in both directions under different loadings. Moreover, the axonal fibers with hyperelastic characteristics stiffer than the extracellular matrix were shown to play the dominant role in directional reinforcement of the tissue.

Morphological changes in glial cells arrangement under mechanical loading: A quantitative study

The mechanical properties and microstructure of brain tissue, as its two main physical parameters, could be affected by mechanical stimuli. In previous studies, microstructural alterations due to mechanical loading have received less attention than the mechanical properties of the tissue. Therefore, the current study aimed to investigate the effect of ex-vivo mechanical forces on the micro-architecture of brain tissue including axons and glial cells. A three-step loading protocol (i.e., loading-recovery-loading) including eight strain levels from 5% to 40% was applied to bovine brain samples with axons aligned in one preferred direction (each sample experienced only one level of strain). After either the first or secondary loading step, the samples were fixed, cut in planes parallel and perpendicular to the loading direction, and stained for histology. The histological images were analyzed to measure the end-to-end length of axons and glial cell-cell distances. The results showed that after both loading steps, as the strain increased, the changes in the cell nuclei arrangement in the direction parallel to axons were more significant compared to the other two perpendicular directions. Based on this evidence, we hypothesized that the spatial pattern of glial cells is highly affected by the orientation of axonal fibers. Moreover, the results revealed that in both loading steps, the maximum cell-cell distance occurred at 15% strain, and this distance decreased for higher strains. Since 15% strain is close to the previously reported brain injury threshold, this evidence could suggest that at higher strains, the axons start to rupture, causing a reduction in the displacement of glial cells. Accordingly, it was concluded that more attention to glial cells' architecture during mechanical loading may lead to introduce a new biomarker for brain injury.

Tissue-Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

Brain tissue is one of the most complex and softest tissues in the human body. Due to its ultrasoft and biphasic nature, it is difficult to control the deformation state during biomechanical testing and to quantify the highly nonlinear, time-dependent tissue response. In numerous experimental studies that have investigated the mechanical properties of brain tissue over the last decades, stiffness values have varied significantly. One reason for the observed discrepancies is the lack of standardized testing protocols and corresponding data analyses. The tissue properties have been tested on different length and time scales depending on the testing technique, and the corresponding data have been analyzed based on simplifying assumptions. In this review, we highlight the advantage of using nonlinear continuum mechanics based modeling and finite element simulations to carefully design experimental setups and protocols as well as to comprehensively analyze the corresponding experimental data. We review testing techniques and protocols that have been used to calibrate material model parameters and discuss artifacts that might falsify the measured properties. The aim of this work is to provide standardized procedures to reliably quantify the mechanical properties of brain tissue and to more accurately calibrate appropriate constitutive models for computational simulations of brain development, injury and disease. Computational models can not only be used to predictively understand brain tissue behavior, but can also serve as valuable tools to assist diagnosis and treatment of diseases or to plan neurosurgical procedures. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Development and Validation of a New Anisotropic Visco-Hyperelastic Human Head Finite Element Model Capable of Predicting Multiple Brain Injuries

This paper reports on the latest refinement of the Finite Element Global Human Body Models Consortium 50th percentile (GHBMC M50) adult male head model by the development and incorporation of a new material model into the white matter tissue of the brain. The white matter is represented by an anisotropic visco-hyperelastic material model capable of simulating direction-dependent response of the brain tissue to further improve the bio-fidelity and injury predictive capability of the model. The parameters representing the material were optimized by comparing model responses to seven experimentally reported strain responses of brains of postmortem human subjects (PMHS) subjected to head impact. The head model was subjected to rigorous validation against experimental data on force–deflection responses in the skull and face, intracranial pressure, and brain strain responses from over 34 PMHS head impact experiments. Crash-induced injury indices (CIIs) for facial bone fracture, skull fracture, cerebral contusion, acute subdural hematomas (ASDHs), and diffuse brain injury were developed by reconstructing 32 PMHS and real-world injury cases with the model. Model predicted maximum principal strain (MPS) and stress were determined as fracture CIIs for compact bone and spongy bones, respectively, in the skull and face. Brain responses in terms of MPS, MPS rates, and pressure distribution in injury producing experimental impacts were determined using the model and analyzed with logistic regression and survival analysis to develop CIIs for brain contusions, diffuse brain injuries, and ASDH. The statistical models using logistic regression and survival analysis showed high accuracy with area under the receiver operating curve greater than 0.8. Because of lack of sufficient moderate diffuse brain injury data, a statistical model was not created, but all indications are that the MPS rate is an essential brain response that discriminates between moderate and severe brain injuries. The authors stated that the current GHBMC M50 v.6.0 is an advanced tool for injury prediction and mitigation of injuries in automotive crashes, sports, recreational, and military environments.

Cortex tissue relaxation and slow to medium load rates dependency can be captured by a two-phase flow poroelastic model

This paper investigates the complex time-dependent behavior of cortex tissue, under adiabatic condition, using a two-phase flow poroelastic model. Motivated by experiments and Biot's consolidation theory, we tackle time-dependent uniaxial loading, confined and unconfined, with various geometries and loading rates from 1μm/s to 100μm/s. The cortex tissue is modeled as the porous solid saturated by two immiscible fluids, with dynamic viscosities separated by four orders, resulting in two different characteristic times. These are respectively associated to interstitial fluid and glial cells. The partial differential equations system is discretized in space by the finite element method and in time by Euler-implicit scheme. The solution is computed using a monolithic scheme within the open-source computational framework FEniCS. The parameters calibration is based on Sobol sensitivity analysis, which divides them into two groups: the tissue specific group, whose parameters represent general properties, and sample specific group, whose parameters have greater variations. Our results show that the experimental curves can be reproduced without the need to resort to viscous solid effects, by adding an additional fluid phase. Through this process, we aim to present multiphase poromechanics as a promising way to a unified brain tissue modeling framework in a variety of settings.

From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury

The relationship between biomechanical forces and neuropathology is key to understanding traumatic brain injury. White matter tracts are damaged by high shear forces during impact, resulting in axonal injury, a key determinant of long-term clinical outcomes. However, the relationship between biomechanical forces and patterns of white matter injuries, associated with persistent diffusion MRI abnormalities, is poorly understood. This limits the ability to predict the severity of head injuries and the design of appropriate protection. Our previously developed human finite element model of head injury predicted the location of post-traumatic neurodegeneration. A similar rat model now allows us to experimentally test whether strain patterns calculated by the model predicts in vivo MRI and histology changes. Using a controlled cortical impact, mild and moderate injuries (1 and 2 mm) were performed. Focal and axonal injuries were quantified with volumetric and diffusion 9.4 T MRI at 2 weeks post injury. Detailed analysis of the corpus callosum was conducted using multi-shell diffusion MRI and histopathology. Microglia and astrocyte density, including process parameters, along with white matter structural integrity and neurofilament expression were determined by quantitative immunohistochemistry. Linear mixed effects regression analyses for strain and strain rate with the employed outcome measures were used to ascertain how well immediate biomechanics could explain MRI and histology changes. The spatial pattern of mechanical strain and strain rate in the injured cortex shows good agreement with the probability maps of focal lesions derived from volumetric MRI. Diffusion metrics showed abnormalities in the corpus callosum, indicating white matter changes in the segments subjected to high strain, as predicted by the model. The same segments also exhibited a severity-dependent increase in glia cell density, white matter thinning and reduced neurofilament expression. Linear mixed effects regression analyses showed that mechanical strain and strain rate were significant predictors of in vivo MRI and histology changes. Specifically, strain and strain rate respectively explained 33% and 28% of the reduction in fractional anisotropy, 51% and 29% of the change in neurofilament expression and 51% and 30% of microglia density changes. The work provides evidence that strain and strain rate in the first milliseconds after injury are important factors in determining patterns of glial and axonal injury and serve as experimental validators of our computational model of traumatic brain injury. Our results provide support for the use of this model in understanding the relationship of biomechanics and neuropathology and can guide the development of head protection systems, such as airbags and helmets.

Correlating Tissue Mechanics and Spinal Cord Injury: Patient-Specific Finite Element Models of Unilateral Cervical Contusion Spinal Cord Injury in Non-Human Primates

Non-human primate (NHP) models are the closest approximation of human spinal cord injury (SCI) available for pre-clinical trials. The NHP models, however, include broader morphological variability that can confound experimental outcomes. We developed subject-specific finite element (FE) models to quantify the relationship between impact mechanics and SCI, including the correlations between FE outcomes and tissue damage. Subject-specific models of cervical unilateral contusion SCI were generated from pre-injury MRIs of six NHPs. Stress and strain outcomes were compared with lesion histology using logit analysis. A parallel generic model was constructed to compare the outcomes of subject-specific and generic models. The FE outcomes were correlated more strongly with gray matter damage (0.29 < R² < 0.76) than white matter (0.18 < R² < 0.58). Maximum/minimum principal strain, Von-Mises and Tresca stresses showed the strongest correlations (0.31 < R² < 0.76) with tissue damage in the gray matter while minimum principal strain, Von-Mises stress, and Tresca stress best predicted white matter damage (0.23 < R² < 0.58). Tissue damage thresholds varied for each subject. The generic FE model captured the impact biomechanics in two of the four models; however, the correlations between FE outcomes and tissue damage were weaker than the subject-specific models (gray matter [0.25 < R² < 0.69] and white matter [R² < 0.06] except for one subject [0.26 < R² < 0.48]). The FE mechanical outputs correlated with tissue damage in spinal cord white and gray matters, and the subject-specific models accurately mimicked the biomechanics of NHP cervical contusion impacts.

Dynamic mechanical characterization and viscoelastic modeling of bovine brain tissue

Brain tissue is vulnerable and sensitive, predisposed to potential damage under various conditions of mechanical loading. Although its material properties have been investigated extensively, the frequency-dependent viscoelastic characterization is currently limited. Computational models can provide a non-invasive method by which to analyze brain injuries and predict the mechanical response of the tissue. The brain injuries are expected to be induced by dynamic loading, mostly in compression and measurement of dynamic viscoelastic properties are essential to improve the accuracy and variety of finite element simulations on brain tissue. Thus, the aim of this study was to investigate the compressive frequency-dependent properties of brain tissue and present a mathematical model in the frequency domain to capture the tissue behavior based on experimental results. Bovine brain specimens, obtained from four locations of corona radiata, corpus callosum, basal ganglia and cortex, were tested under compression using dynamic mechanical analysis over a range of frequencies between 0.5 and 35 Hz to characterize the regional and directional response of the tissue. The compressive dynamic properties of bovine brain tissue were heterogenous for regions but not sensitive to orientation showing frequency dependent statistical results, with viscoelastic properties increasing with frequency. The mean storage and loss modulus were found to be 12.41 kPa and 5.54 kPa, respectively. The material parameters were obtained using the linear viscoelastic model in the frequency domain and the numeric simulation can capture the compressive mechanical behavior of bovine brain tissue across a range of frequencies. The frequency-dependent viscoelastic characterization of brain tissue will improve the fidelity of the computational models of the head and provide essential information to the prediction and analysis of brain injuries in clinical treatments.

Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury

With the growing rate of traumatic brain injury (TBI), there is an increasing interest in validated tools to predict and prevent brain injuries. Finite element models (FEM) are valuable tools to estimate tissue responses, predict probability of TBI, and guide the development of safety equipment. In this study, we developed and validated an anisotropic pig brain multi-scale FEM by explicitly embedding the axonal tract structures and utilized the model to simulate experimental TBI in piglets undergoing dynamic head rotations. Binary logistic regression, survival analysis with Weibull distribution, and receiver operating characteristic curve analysis, coupled with repeated k-fold cross-validation technique, were used to examine 12 FEM-derived metrics related to axonal/brain tissue strain and strain rate for predicting the presence or absence of traumatic axonal injury (TAI). All 12 metrics performed well in predicting of TAI with prediction accuracy rate of 73-90%. The axonal-based metrics outperformed their rival brain tissue-based metrics in predicting TAI. The best predictors of TAI were maximum axonal strain times strain rate (MASxSR) and its corresponding optimal fraction-based metric (AF-MASxSR7.5) that represents the fraction of axonal fibers exceeding MASxSR of 7.5 s-1. The thresholds compare favorably with tissue tolerances found in in-vitro/in-vivo measurements in the literature. In addition, the damaged volume fractions (DVF) predicted using the axonal-based metrics, especially MASxSR (DVF = 0.05-4.5%), were closer to the actual DVF obtained from histopathology (AIV = 0.02-1.65%) in comparison with the DVF predicted using the brain-related metrics (DVF = 0.11-41.2%). The methods and the results from this study can be used to improve model prediction of TBI in humans.

The Strain Rates in the Brain, Brainstem, Dura, and Skull under Dynamic Loadings

Knowing the precise material properties of intracranial head organs is crucial for studying the biomechanics of head injury. It has been shown that these biological tissues are significantly rate-dependent; hence, their material properties should be determined with respect to the range of deformation rate they experience. In this paper, a validated finite element human head model is used to investigate the biomechanics of the head in impact and blast, leading to traumatic brain injuries (TBI). We simulate the head under various directions and velocities of impacts, as well as helmeted and unhelmeted head under blast shock waves. It is demonstrated that the strain rates for the brain are in the range of 36 to 241 s−1, approximately 1.9 and 0.86 times the resulting head acceleration under impacts and blast scenarios, respectively. The skull was found to experience a rate in the range of 14 to 182 s−1, approximately 0.7 and 0.43 times the head acceleration corresponding to impact and blast cases. The results of these incident simulations indicate that the strain rates for brainstem and dura mater are respectively in the range of 15 to 338 and 8 to 149 s−1. These findings provide a good insight into characterizing the brain tissue, cranial bone, brainstem and dura mater, and also selecting material properties in advance for computational dynamical studies of the human head.

Displacement- and Strain-Based Discrimination of Head Injury Models across a Wide Range of Blunt Conditions

Successful validation of a head injury model is critical to ensure its biofidelity. However, there is an ongoing debate on what experimental data are suitable for model validation. Here, we report that CORrelation and Analysis (CORA) scores based on the commonly adopted relative brain-skull displacements or recent marker-based strains from cadaveric head impacts may not be effective in discriminating model-simulated whole-brain strains across a wide range of blunt conditions. We used three versions of the Worcester Head Injury Model (WHIM; isotropic and anisotropic WHIM V1.0, and anisotropic WHIM V1.5) to simulate 19 experiments, including eight high-rate cadaveric impacts, seven mid-rate cadaveric pure rotations simulating impacts in contact sports, and four in vivo head rotation/extension tests. All WHIMs achieved similar average CORA scores based on cadaveric displacement (~ 0.70; 0.47-0.88) and strain (V1.0: 0.86; 0.73-0.97 vs. V1.5: 0.78; 0.62-0.96), using the recommended settings. However, WHIM V1.5 produced ~ 1.17-2.69 times strain of the two V1.0 variants with substantial differences in strain distribution as well (Pearson correlation of ~ 0.57-0.92) when comparing their whole-brain strains across the range of blunt conditions. Importantly, their strain magnitude differences were similar to that in cadaveric marker-based strain (~ 1.32-3.79 times). This suggests that cadaveric strains are capable of discriminating head injury models for their simulated whole-brain strains (e.g., by using CORA magnitude sub-rating alone or peak strain magnitude ratio), although the aggregated CORA may not. This study may provide fresh insight into head injury model validation and the harmonization of simulation results from diverse head injury models. It may also facilitate future experimental designs to improve model validation.

Investigate the Variations of the Head and Brain Response in a Rodent Head Impact Acceleration Model by Finite Element Modeling

Diffuse axonal injury (DAI) is a severe form of traumatic brain injury and often induced by blunt trauma. The closed head impact acceleration (IA) model is the most widely used rodent DAI model. However, this model results in large variations of injury severity. Recently, the impact device/system was modified to improve the consistency of the impact energy, but variations of the head kinematics and subsequent brain injuries were still observed. This study was aimed to utilize a Finite Element (FE) model of a rat head/body and simulation to investigate the potential biomechanical factors influencing the impact energy transfer to the head. A detailed FE rat head model containing detailed skull and brain anatomy was developed based on the MRI, microCT and atlas data. The model consists of over 722,000 elements, of which 310,000 are in the brain. The white matter structures consisting of highly aligned axonal fibers were simulated with transversely isotropic material. The rat body was modeled to provide a realistic boundary at the spine-medulla junction. Rodent experiments including dynamic cortical deformation, brain-skull displacement, and IA kinematics were simulated to validate the FE model. The model was then applied to simulate the rat IA experiments. Parametric studies were conducted to investigate the effect of the helmet inclination angles (0°-5°) and skull stiffness (varied 20%) on the resulting head kinematics and maximum principal strain in the brain. The inclination angle of the helmet at 5° could vary head linear acceleration by 8-31%. The change in head rotational velocity was inversely related to the change in linear acceleration. Varying skull stiffness resulted in changes in head linear acceleration by 3% but with no effect on rotational velocity. The brain strain in the corpus callosum was only affected by head rotation while the strain in the brainstem was influenced by the combined head kinematics, local skull deformation, and head-neck position. Validated FE models of rat impact head injury can assist in exploring various biomechanical factors influencing the head impact response and internal brain response. Identification of these variables may help explain the variability of injury severity observed among experiments and across different labs.

A Three-Dimensional Statistical Volume Element for Histology Informed Micromechanical Modeling of Brain White Matter

This study presents a novel statistical volume element (SVE) for micromechanical modeling of the white matter structures, with histology-informed randomized distribution of axonal tracts within the extracellular matrix. The model was constructed based on the probability distribution functions obtained from the results of diffusion tensor imaging as well as the histological observations of scanning electron micrograph, at two structures of white matter susceptible to traumatic brain injury, i.e. corpus callosum and corona radiata. A simplistic representative volume element (RVE) with symmetrical arrangement of fully alligned axonal fibers was also created as a reference for comparison. A parametric study was conducted to find the optimum grid and edge size which ensured the periodicity and ergodicity of the SVE and RVE models. A multi-objective evolutionary optimization procedure was used to find the hyperelastic and viscoelastic material constants of the constituents, based on the experimentally reported responses of corpus callosum to axonal and transverse loadings. The optimal material properties were then used to predict the homogenized and localized responses of corpus callosum and corona radiata. The results indicated similar homogenized responses of the SVE and RVE under quasi-static extension, which were in good agreement with the experimental data. Under shear strain, however, the models exhibited different behaviors, with the SVE model showing much closer response to the experimental observations. Moreover, the SVE model displayed a significantly better agreement with the reports of the experiments at high strain rates. The results suggest that the randomized fiber architecture has a great influence on the validity of the micromechanical models of white matter, with a distinguished impact on the model's response to shear deformation and high strain rates. Moreover, it can provide a more detailed presentation of the localized responses of the tissue substructures, including the stress concentrations around the low caliber axonal tracts, which is critical for studying the axonal injury mechanisms.

Nonlinear mechanics of soft composites: hyperelastic characterization of white matter tissue components

This paper presents a bi-directional closed-form analytical solution, in the framework of nonlinear soft composites mechanics, for top-down hyperelastic characterization of brain white matter tissue components, based on the directional homogenized responses of the tissue in the axial and transverse directions. The white matter is considered as a transversely isotropic neo-Hookean composite made of unidirectional distribution of axonal fibers within the extracellular matrix. First, two homogenization formulations are derived for the homogenized axial and transverse shear moduli of the tissue, based on definition of the strain energy density function. Next, the rule of mixtures and Hashin-Shtrikman theories are used to derive two coupled nonlinear equations which correlates the tissue shear moduli to these of its components. Closed-form solutions for shear moduli of the components are then obtained by solving these equations simultaneously. In order to validate the hyperelastic characteristics of components obtained in previous step, they are used in a bottom-up approach in a micromechanical model of the tissue with the aim of predicting the directional homogenized responses of the tissue. Comparison of model predictions with the experimental test results reported for corona radiata and corpus callosum white matter structures reveals very good agreements with the experimental results in both directions. The model predictions are also in good agreement with the analytical solution obtained by the iterated homogenization technique. Results indicate that axonal fibers are almost ten times stiffer than the extracellular matrix under large deformations.

Magnetic resonance elastography of brain: Comparison between anisotropic and isotropic stiffness and its correlation to age

PURPOSE

Noninvasive measurement of mechanical properties of brain tissue using magnetic resonance elastography (MRE) has been a promising method for investigating neurologic disorders such as multiple sclerosis, hydrocephalus, and Alzheimer's. However, because of the regional and directional dependency of brain stiffness, estimating anisotropic stiffness is important. This study investigates isotropic and anisotropic stiffness as a function of age as well as the correlation between isotropic and anisotropic stiffness.

METHODS

MRE and diffusion tensor imaging (DTI) were performed on 28 healthy subjects with age ranges between 18-62 y. Isotropic and anisotropic stiffness was measured and compared with age for different regions of interest such as the thalamus, corpus callosum, gray matter, white matter, and whole brain.

RESULTS

Isotropic stiffness in gray matter (rs = -0.57; P = 0.001) showed a significant decrease with age. Anisotropic stiffness in gray matter showed a significant decrease with age in C11 through C66 and in the thalamus, only in C33 . Between anisotropic and isotropic stiffness, gray matter showed a significant positive correlation in C11 through C66 , C22 and C66 showed a significant negative correlation in the thalamus and whole brain, and C44 showed a negative correlation in the corpus callosum. No significant difference between genders was observed in any measurements.

CONCLUSION

This study demonstrated a change in isotropic and anisotropic stiffness with age in different regions of the brain along with a correlation of anisotropic stiffness to isotropic stiffness.

The Influence of Shear Anisotropy in mTBI: A White Matter Constitutive Model

We examine the influence of shear anisotropy of brain tissue on the potential for mild traumatic brain injury. First we develop a new constitutive description for the white matter in the brain that can capture the anisotropic behavior of the white matter in both tension and shear. The material parameters for the models are determined using a set of three experiments already published in the literature. The calibrated and parameterized model is then implemented in a computational (finite element) model of the head. This computational model is two-dimensional and is used to simulate a previously published injury-causing event in the National Hockey League, using axonal strain as criterion to assess the level of diffuse axonal injury. It is demonstrated that the inclusion of shear anisotropy affects both the nature and the extent of predicted injury. Further, the locations of the predicted injury are more consistent with observations in the literature.

Determining constitutive behavior of the brain tissue using digital image correlation and finite element modeling

Detailed knowledge about the mechanical properties of brain can improve numerical modeling of the brain under various loading conditions. The success of this modeling depends on constitutive model and reliable extraction of its material constants. The isotropy of the brain tissue is a key factor which affects the form of constitutive models. In this study, compression tests were performed on different parts of the sheep brain tissue. Also, the digital image correlation (DIC) method was utilized to investigate the direction dependency of brain parts considering their microstructures. To this aim, the DIC method was employed to measure the transverse strain of two lateral sides of the tissue samples. The results of DIC method revealed that the brain stem and corona radiata were isotropic, while the mixed white and gray matter showed an unrepeatable behavior depending on the extracted sample. To examine and validate DIC method, stress-strain diagrams were also used to investigate the isotropy. It could be concluded that axonal fibers had no reinforcing role in the brain tissue. Furthermore, the DIC method indicated incompressibility of the brain tissue. Then, the significance of using a correct method to extract the material constants of brain was discussed. In other words, the effect of the real boundary conditions in experiments, which was neglected in most previous studies, was taken into account here. Finally, the particle swarm optimization algorithm along with the finite element modeling was used to estimate the hyper-viscoelastic constants of different parts of the brain tissue.

A three-dimensional micromechanical model of brain white matter with histology-informed probabilistic distribution of axonal fibers

This paper presents a three-dimensional micromechanical model of brain white matter tissue as a transversely isotropic soft composite described by the generalized Ogden hyperelastic model. The embedded element technique, with corrected stiffness redundancy in large deformations, was used for the embedment of a histology-informed probabilistic distribution of the axonal fibers in the extracellular matrix. The model was linked to a multi-objective, multi-parametric optimization algorithm, using the response surface methodology, for characterization of material properties of the axonal fibers and extracellular matrix in an inverse finite element analysis. The optimum hyperelastic characteristics of the tissue constituents, obtained based on the axonal and transverse direction test results of the corona radiata tissue samples, indicated that the axonal fibers were almost thirteen times stiffer than the extracellular matrix under large deformations. Simulation of the same tissue under a different loading condition, as well as that of another white matter tissue, i.e., the corpus callosum, in the axonal and transverse directions, using the optimized hyperelastic characteristics revealed tissue responses very close to those of the experiments. The results of the model at the sub-tissue level indicated that the stress concentrations were considerably large around the small axons, which might contribute into the brain injury.

Fluid-structure interaction simulation of the brain-skull interface for acute subdural haematoma prediction

Traumatic brain injury is a leading cause of disability and mortality. Finite element-based head models are promising tools for enhanced head injury prediction, mitigation and prevention. The reliability of such models depends heavily on adequate representation of the brain-skull interaction. Nevertheless, the brain-skull interface has been largely simplified in previous three-dimensional head models without accounting for the fluid behaviour of the cerebrospinal fluid (CSF) and its mechanical interaction with the brain and skull. In this study, the brain-skull interface in a previously developed head model is modified as a fluid-structure interaction (FSI) approach, in which the CSF is treated on a moving mesh using an arbitrary Lagrangian-Eulerian multi-material formulation and the brain on a deformable mesh using a Lagrangian formulation. The modified model is validated against brain-skull relative displacement and intracranial pressure responses and subsequently imposed to an experimentally determined loading known to cause acute subdural haematoma (ASDH). Compared to the original model, the modified model achieves an improved validation performance in terms of brain-skull relative motion and is able to predict the occurrence of ASDH more accurately, indicating the superiority of the FSI approach for brain-skull interface modelling. The introduction of the FSI approach to represent the fluid behaviour of the CSF and its interaction with the brain and skull is crucial for more accurate head injury predictions.

White Matter Anisotropy for Impact Simulation and Response Sampling in Traumatic Brain Injury

Advanced neuroimaging provides new opportunities to enhance head injury models, including the incorporation of white matter (WM) structural anisotropy. Information from high-resolution neuroimaging, however, usually has to be "down-sampled" to match a typically coarse brain mesh. To understand how this mesh-image resolution mismatch affects impact simulation and subsequent response sampling, we compared three competing anisotropy implementations (using either voxels, tractography, or a multiscale submodeling) and two response sampling strategies (element-wise or tractography-based, using brain mesh or neuroimaging for region segmentation, respectively). Using the combination of high resolution options as a baseline, we studied how the choice in each individual category affected the resulting injury metrics. By simulating a recorded loss of consciousness head impact, we found that injury metrics including peak strain and injury susceptibility in the deep WM regions based on fiber strain, but not on maximum principal strain, were sensitive to the anisotropy implementation, response sampling, and region segmentation. Overall, it was recommended to use tractography for anisotropy implementation and response sampling, and to employ neuroimaging for region segmentation, because they led to more accurate injury metrics. Further refining mesh locally via submodeling was unnecessary. Brain strain responses were also parametrically found to be closer to that from minimum fiber reinforcement, consistent with the fact that the majority of WM had a rather high degree of fiber dispersion. Finally, the upgraded Worcester Head Injury Model incorporating WM anisotropy was successfully re-validated against cadaveric impacts and an in vivo head rotation ("good" to "excellent" validation with an average Correlation Analysis score of 0.437 and 0.509, respectively). These investigations may facilitate further continual development of head injury models to better study traumatic brain injury.

Compressive mechanical characterization of non-human primate spinal cord white matter

The goal of developing computational models of spinal cord injury (SCI) is to better understand the human injury condition. However, finite element models of human SCI have used rodent spinal cord tissue properties due to a lack of experimental data. Central nervous system tissues in non human primates (NHP) closely resemble that of humans and therefore, it is expected that material constitutive models obtained from NHPs will increase the fidelity and the accuracy of human SCI models. Human SCI most often results from compressive loading and spinal cord white matter properties affect FE predicted patterns of injury; therefore, the objectives of this study were to characterize the unconfined compressive response of NHP spinal cord white matter and present an experimentally derived, finite element tractable constitutive model for the tissue. Cervical spinal cords were harvested from nine male adult NHPs (Macaca mulatta). White matter biopsy samples (3 mm in diameter) were taken from both lateral columns of the spinal cord and were divided into four strain rate groups for unconfined dynamic compression and stress relaxation (post-mortem <1-hour). The NHP spinal cord white matter compressive response was sensitive to strain rate and showed substantial stress relaxation confirming the viscoelastic behavior of the material. An Ogden 1st order model best captured the non-linear behavior of NHP white matter in a quasi-linear viscoelastic material model with 4-term Prony series. This study is the first to characterize NHP spinal cord white matter at high (>10/sec) strain rates typical of traumatic injury. The finite element derived material constitutive model of this study will increase the fidelity of SCI computational models and provide important insights for transferring pre-clinical findings to clinical treatments.

STATEMENT OF SIGNIFICANCE

Spinal cord injury (SCI) finite element (FE) models provide an important tool to bridge the gap between animal studies and human injury, assess injury prevention technologies (e.g. helmets, seatbelts), and provide insight into the mechanisms of injury. Although, FE model outcomes depend on the assumed material constitutive model, there is limited experimental data for fresh spinal cords and all was obtained from rodent, porcine or bovine tissues. Central nervous system tissues in non human primates (NHP) more closely resemble humans. This study characterizes fresh NHP spinal cord material properties at high strains rates and large deformations typical of SCI for the first time. A constitutive model was defined that can be readily implemented in finite strain FE analysis of SCI.

Measurement and Finite Element Model Validation of Immature Porcine Brain-Skull Displacement during Rapid Sagittal Head Rotations

Computational models are valuable tools for studying tissue-level mechanisms of traumatic brain injury, but to produce more accurate estimates of tissue deformation, these models must be validated against experimental data. In this study, we present in situ measurements of brain-skull displacement in the neonatal piglet head (n = 3) at the sagittal midline during six rapid non-impact rotations (two rotations per specimen) with peak angular velocities averaging 51.7 ± 1.4 rad/s. Marks on the sagittally cut brain and skull/rigid potting surfaces were tracked, and peak values of relative brain-skull displacement were extracted and found to be significantly less than values extracted from a previous axial plane model. In a finite element model of the sagittally transected neonatal porcine head, the brain-skull boundary condition was matched to the measured physical experiment data. Despite smaller sagittal plane displacements at the brain-skull boundary, the corresponding finite element boundary condition optimized for sagittal plane rotations is far less stiff than its axial counterpart, likely due to the prominent role of the boundary geometry in restricting interface movement. Finally, bridging veins were included in the finite element model. Varying the bridging vein mechanical behavior over a previously reported range had no influence on the brain-skull boundary displacements. This direction-specific sagittal plane boundary condition can be employed in finite element models of rapid sagittal head rotations.

Material properties of the brain in injury-relevant conditions - Experiments and computational modeling

Material properties of the brain have been extensively studied but remain poorly characterized. The vast variations in constitutive models and material constants are well documented. However, no study exists to translate the variations into disparities in impact-induced brain strains most relevant to brain injury. Here, we reviewed a subset of injury-relevant brain material properties either characterized in experiments or adopted in recent head injury models. To highlight how variations in measured brain material properties manifested in simulated brain strains, we selected six experiments that have provided a complete set of brain material model and constants to implement a common head injury model. Responses resulting from two extreme events representing a high-rate cadaveric head impact and a low-rate in vivo head rotation, respectively, varied substantially. We hypothesized, and further confirmed, that the time-varying shear moduli at the appropriate time scales (e.g., ~5 ms and ~40 ms corresponding to the impulse durations of the major acceleration peaks for the two impacts, respectively), rather than the initial or long-term shear moduli, were the most indicative of impact-induced brain strains. These results underscored the need to implement measured brain material properties into an actual head injury model for evaluation. They may also provide guidelines to better characterize brain material properties in future experiments and head injury models. Finally, our finding provided a practical solution to satisfy head injury model validation requirements at both ends of the impact severity spectrum. This would improve the confidence in model simulation performance across a range of time scales relevant to concussion and sub-concussion in the real-world.

Micromechanics of brain white matter tissue: A fiber-reinforced hyperelastic model using embedded element technique

A transverse-plane hyperelastic micromechanical model of brain white matter tissue was developed using the embedded element technique (EET). The model consisted of a histology-informed probabilistic distribution of axonal fibers embedded within an extracellular matrix, both described using the generalized Ogden hyperelastic material model. A correcting method, based on the strain energy density function, was formulated to resolve the stiffness redundancy problem of the EET in large deformation regime. The model was then used to predict the homogenized tissue behavior and the associated localized responses of the axonal fibers under quasi-static, transverse, large deformations. Results indicated that with a sufficiently large representative volume element (RVE) and fine mesh, the statistically randomized microstructure implemented in the RVE exhibits directional independency in transverse plane, and the model predictions for the overall and local tissue responses, characterized by the normalized strain energy density and Cauchy and von Mises stresses, are independent from the modeling parameters. Comparison of the responses of the probabilistic model with that of a simple uniform RVE revealed that only the first one is capable of representing the localized behavior of the tissue constituents. The validity test of the model predictions for the corona radiata against experimental data from the literature indicated a very close agreement. In comparison with the conventional direct meshing method, the model provided almost the same results after correcting the stiffness redundancy, however, with much less computational cost and facilitated geometrical modeling, meshing, and boundary conditions imposing. It was concluded that the EET can be used effectively for detailed probabilistic micromechanical modeling of the white matter in order to provide more accurate predictions for the axonal responses, which are of great importance when simulating the brain trauma or tumor growth.

Effect of in vitro storage duration on measured mechanical properties of brain tissue

Accurate characterization of the mechanical properties of brain tissue is essential for understanding the mechanisms of traumatic brain injuries and developing protective gears or facilities. However, how storage conditions might affect the mechanical properties of brain tissue remains unclear. The objective of this study is to investigate the effect of in vitro storage duration on the mechanical performance of brain tissue since measurements are usually carried out in vitro. Differential Scanning Calorimetry (DSC) measurements and uniaxial compression mechanical experiments are carried out. The results indicate that, for brain tissue stored at 1 °C without any liquid medium, the bio-molecular interactions and the mechanical strength of both white and grey matter deteriorate with prolonged storage duration. Transmission Electron Microscopy (TEM) results reveal the degeneration of myelin sheaths and the vacuolization of cristae with prolonged storage duration, suggesting that the in vitro storage duration should be carefully controlled. The findings from this study might facilitate the development of guidelines and standards for the in vitro storage of brain tissue.

Measurement of anisotropic mechanical properties in porcine brain white matter ex vivo using magnetic resonance elastography

The mechanical properties of brain tissue, particularly those of white matter (WM), need to be characterized accurately for use in finite element (FE) models of brain biomechanics and traumatic brain injury (TBI). Magnetic resonance elastography (MRE) is a powerful tool for non-invasive estimation of the mechanical properties of soft tissues. While several studies involving direct mechanical tests of brain tissue have shown mechanical anisotropy, most MRE studies of brain tissue assume an isotropic model. In this study, an incompressible transversely isotropic (TI) material model parameterized by minimum shear modulus (μ₂), shear anisotropy parameter (ϕ), and tensile anisotropy parameter (ζ) is applied to analyze MRE measurements of ex vivo porcine white matter (WM) brain tissue. To characterize shear anisotropy, "slow" (pure transverse) shear waves were propagated at 100, 200 and 300Hz through sections of ex vivo brain tissue including both WM and gray matter (GM). Shear waves were found to propagate with elliptical fronts, consistent with TI material behavior. Shear wave fields were also analyzed within regions of interest (ROI) to find local shear wavelengths parallel and perpendicular to fiber orientation. FE simulations of a TI material with a range of plausible shear modulus (μ₂) and shear anisotropy parameters (ϕ) were run and the results were analyzed in the same fashion as the experimental case. Parameters of the FE simulations which most closely matched each experiment were taken to represent the mechanical properties of that particular sample. Using this approach, WM in the ex vivo porcine brain was found to be mildly anisotropic in shear with estimates of minimum shear modulus (actuation frequencies listed in parenthesis): μ₂= 1.04 ± 0.12 kPa (at 100Hz), μ₂= 1.94 ± 0.29 kPa (at 200Hz), and μ₂= 2.88 ± 0.34 kPa (at 300Hz) and corresponding shear anisotropy factors of ϕ= 0.27 ± 0.09 (at 100Hz), ϕ= 0.29 ± 0.14 (at 200Hz) and ϕ= 0.34 ± 0.13 (at 300Hz). Future MRE studies will focus on tensile anisotropy, which will require both slow and fast shear waves for accurate estimation.

Micromechanical analysis of brain's diffuse axonal injury

Computational models are important tools which help researchers understand traumatic brain injury (TBI). A mechanistic multi-scale numerical approach is introduced to quantify diffuse axonal injury (DAI), the most important mechanism of TBI, induced by a mechanical insult at micro-scale regions of the white matter or voxels where fiber orientations are the same. Using the mechanical properties of a single axon with a viscoelastic constitutive relation and its functional failure in terms of electrophysiological impairment, a numerical 2D micro-level lattice method is implemented to directly analyze the percentage of injured axons in a voxel containing a bundle of axons all with the same orientation under biaxial stretches. Reference micro-injury maps are then developed with the input parameters based on the principal strain or stretch values and their direction with respect to axons, which provide the percentage of injured axons in the voxel of interest as the output. The methodology is independent of any statistical analyses of the accident data and medical reports to derive probabilistic injury risk curves for DAI. Avoiding a structurally detailed full finite element head model, this study proposes a micro-mechanical approach which considers the anatomical structure of neural axons in the white matter together with their mechanical properties using a numerical lattice method to analyze the brain's diffuse axonal injury. This work has the potential to help develop safer prevention tools and more effective diagnosis methods for DAI.

Acoustic Radiation Force Impulse (ARFI)-Induced Peak Displacements Reflect Degree of Anisotropy in Transversely Isotropic Elastic Materials

In transversely isotropic (TI) materials, mechanical properties (Young's modulus, shear modulus, and Poisson's ratio) are different along versus across the axis of symmetry (AoS). In this work, the feasibility of interrogating such directional mechanical property differences using acoustic radiation force impulse (ARFI) imaging is investigated. We herein test the hypotheses that 1) ARFI-induced peak displacements (PDs) vary with TI material orientations when an asymmetrical ARFI excitation point spread function (PSF) is used, but not when a symmetrical ARFI PSF is employed; and 2) the ratio of PDs induced with the long axis of an asymmetrical ARFI PSF oriented along versus across the material's AoS is related to the degree of anisotropy of the material. These hypotheses were tested in silico using finite element method (FEM) models and Field II. ARFI excitations had F/1.5, 3, 4, or 5 focal configurations, with the F/1.5 and F/5 cases having the most asymmetrical and symmetrical PSFs at the focal depth, respectively. These excitations were implemented for ARFI imaging in 52 different simulated TI materials with varying degrees of anisotropy, and the ratio of ARFI-induced PDs was calculated. The change in the ratio of PDs with respect to the anisotropy of the materials was highest for the F/1.5, indicating that PD was most strongly impacted by the material orientation when the ARFI excitation was the most asymmetrical. On the contrary, the ratio of PDs did not depend on the anisotropy of the material for the F/5 ARFI excitation, suggesting that PD did not depend on material orientation when the ARFI excitation was symmetrical. Finally, the ratio of PDs achieved using asymmetrical ARFI PSF reflected the degree of anisotropy in TI materials. These results support that symmetrical ARFI focal configurations are desirable when the orientation of the ARFI excitation to the AoS is not specifically known and measurement standardization is important, such as for longitudinal or cross-sectional studies of anisotropic organs. However, asymmetrical focal configurations are useful for exploiting anisotropy, which may be diagnostically relevant. Feasibility for future experimental implementation is demonstrated by simulating ultrasonic displacement tracking and by varying the ARF duration.

A computational study of invariant I5 in a nearly incompressible transversely isotropic model for white matter

The aligned axonal fiber bundles in white matter make it suitable to be modeled as a transversely isotropic material. Recent experimental studies have shown that a minimal form, nearly incompressible transversely isotropic (MITI) material model, is capable of describing mechanical anisotropy of white matter. Here, we used a finite element (FE) computational approach to demonstrate the significance of the fifth invariant (I₅) when modeling the anisotropic behavior of white matter in the large-strain regime. We first implemented and validated the MITI model in an FE simulation framework for large deformations. Next, we applied the model to a plate-hole structural problem to highlight the significance of the invariant I₅ by comparing with the standard fiber reinforcement (SFR) model. We also compared the two models by fitting the experiment data of asymmetric indentation, shear test, and uniaxial stretch of white matter. Our results demonstrated the significance of I₅ in describing shear deformation/anisotropy, and illustrated the potential of the MITI model to characterize transversely isotropic white matter tissues in the large-strain regime.

Influence of high deformation rate, brain region, transverse compression, and specimen size on rat brain shear stress morphology and magnitude

An external mechanical insult to the brain, such as a blast, may create internal stress and deformation waves, which have shear and longitudinal components that can induce combined shear and compression of the brain tissue. To isolate the consequences of such interactions for the shear stress and to investigate the role of the extracellular fluid in the mechanical response, translational shear stretch at 10/s, 60/s, and 100/s translational shear rates under either 0% or 33% fixed transverse compression is applied without preconditioning to rat brain specimens. The specimens from the cerebrum, the cerebellum grey matter, and the brainstem white matter are nearly the full length of their respective regions. The translational shear stress response to translational shear deformation is characterized by the effect that each of four factors, high deformation rate, brain region, transverse compression, and specimen size, have on the shear stress magnitude averaged over ten specimens for each combination of factors. Increasing the deformation rate increases the magnitude of the shear stress at a given translational shear stretch, and as tested by ANOVAs so does applying transverse fixed compression of 33% of the thickness. The stress magnitude differs by the region that is the specimen source: cerebrum, cerebellum or brainstem. The magnitude of the shear stress response at a given deformation rate and stretch depends on the specimen length, called a specimen size effect. Surprisingly, under no compression a shorter length specimen requires more shear stress, but under 33% compression a shorter length specimen requires less shear stress, to meet a required shear deformation rate. The shear specimen size effect calls into question the applicability of the classical shear stress definition to hydrated soft biological tissue.

Characterizing white matter tissue in large strain via asymmetric indentation and inverse finite element modeling

Characterizing the mechanical properties of white matter is important to understand and model brain development and injury. With embedded aligned axonal fibers, white matter is typically modeled as a transversely isotropic material. However, most studies characterize the white matter tissue using models with a single anisotropic invariant or in a small-strain regime. In this study, we combined a single experimental procedure - asymmetric indentation - with inverse finite element (FE) modeling to estimate the nearly incompressible transversely isotropic material parameters of white matter. A minimal form comprising three parameters was employed to simulate indentation responses in the large-strain regime. The parameters were estimated using a global optimization procedure based on a genetic algorithm (GA). Experimental data from two indentation configurations of porcine white matter, parallel and perpendicular to the axonal fiber direction, were utilized to estimate model parameters. Results in this study confirmed a strong mechanical anisotropy of white matter in large strain. Further, our results suggested that both indentation configurations are needed to estimate the parameters with sufficient accuracy, and that the indenter-sample friction is important. Finally, we also showed that the estimated parameters were consistent with those previously obtained via a trial-and-error forward FE method in the small-strain regime. These findings are useful in modeling and parameterization of white matter, especially under large deformation, and demonstrate the potential of the proposed asymmetric indentation technique to characterize other soft biological tissues with transversely isotropic properties.

Numerical Simulation of Focused Shock Shear Waves in Soft Solids and a Two-Dimensional Nonlinear Homogeneous Model of the Brain

Shear waves that propagate in soft solids, such as the brain, are strongly nonlinear and can develop into shock waves in less than one wavelength. We hypothesize that these shear shock waves could be responsible for certain types of traumatic brain injuries (TBI) and that the spherical geometry of the skull bone could focus shear waves deep in the brain, generating diffuse axonal injuries. Theoretical models and numerical methods that describe nonlinear polarized shear waves in soft solids such as the brain are presented. They include the cubic nonlinearities that are characteristic of soft solids and the specific types of nonclassical attenuation and dispersion observed in soft tissues and the brain. The numerical methods are validated with analytical solutions, where possible, and with self-similar scaling laws where no known solutions exist. Initial conditions based on a human head X-ray microtomography (CT) were used to simulate focused shear shock waves in the brain. Three regimes are investigated with shock wave formation distances of 2.54 m, 0.018 m, and 0.0064 m. We demonstrate that under realistic loading scenarios, with nonlinear properties consistent with measurements in the brain, and when the shock wave propagation distance and focal distance coincide, nonlinear propagation can easily overcome attenuation to generate shear shocks deep inside the brain. Due to these effects, the accelerations in the focal are larger by a factor of 15 compared to acceleration at the skull surface. These results suggest that shock wave focusing could be responsible for diffuse axonal injuries.

The Transverse Isotropy of Spinal Cord White Matter Under Dynamic Load

The rostral-caudally aligned fiber-reinforced structure of spinal cord white matter (WM) gives rise to transverse isotropy in the material. Stress and strain patterns generated in the spinal cord parenchyma following spinal cord injury (SCI) are multidirectional and dependent on the mechanism of the injury. Our objective was to develop a WM constitutive model that captures the material transverse isotropy under dynamic loading. The WM mechanical behavior was extracted from the published tensile and compressive experiments. Combinations of isotropic and fiber-reinforcing models were examined in a conditional quasi-linear viscoelastic (QLV) formulation to capture the WM mechanical behavior. The effect of WM transverse isotropy on SCI model outcomes was evaluated by simulating a nonhuman primate (NHP) contusion injury experiment. A second-order reduced polynomial hyperelastic energy potential conditionally combined with a quadratic reinforcing function in a four-term Prony series QLV model best captured the WM mechanical behavior (0.89 < R2 < 0.99). WM isotropic and transversely isotropic material models combined with discrete modeling of the pia mater resulted in peak impact forces that matched the experimental outcomes. The transversely isotropic WM with discrete pia mater resulted in maximum principal strain (MPS) distributions which effectively captured the combination of ipsilateral peripheral WM sparing, ipsilateral injury and contralateral sparing, and the rostral/caudal spread of damage observed in in vivo injuries. The results suggest that the WM transverse isotropy could have an important role in correlating tissue damage with mechanical measures and explaining the directional sensitivity of the spinal cord to injury.

Brain injury tolerance limit based on computation of axonal strain

Traumatic brain injury (TBI) is the leading cause of death and permanent impairment over the last decades. In both the severe and mild TBIs, diffuse axonal injury (DAI) is the most common pathology and leads to axonal degeneration. Computation of axonal strain by using finite element head model in numerical simulation can enlighten the DAI mechanism and help to establish advanced head injury criteria. The main objective of this study is to develop a brain injury criterion based on computation of axonal strain. To achieve the objective a state-of-the-art finite element head model with enhanced brain and skull material laws, was used for numerical computation of real world head trauma. The implementation of new medical imaging data such as, fractional anisotropy and axonal fiber orientation from Diffusion Tensor Imaging (DTI) of 12 healthy patients into the finite element brain model was performed to improve the brain constitutive material law with more efficient heterogeneous anisotropic visco hyper-elastic material law. The brain behavior has been validated in terms of brain deformation against Hardy et al. (2001), Hardy et al. (2007), and in terms of brain pressure against Nahum et al. (1977) and Trosseille et al. (1992) experiments. Verification of model stability has been conducted as well. Further, 109 well-documented TBI cases were simulated and axonal strain computed to derive brain injury tolerance curve. Based on an in-depth statistical analysis of different intra-cerebral parameters (brain axonal strain rate, axonal strain, first principal strain, Von Mises strain, first principal stress, Von Mises stress, CSDM (0.10), CSDM (0.15) and CSDM (0.25)), it was shown that axonal strain was the most appropriate candidate parameter to predict DAI. The proposed brain injury tolerance limit for a 50% risk of DAI has been established at 14.65% of axonal strain. This study provides a key step for a realistic novel injury metric for DAI.

An anisotropic hyperelastic constitutive model of brain white matter in biaxial tension and structural-mechanical relationships

Computational models of the brain require accurate and robust constitutive models to characterize the mechanical behavior of brain tissue. The anisotropy of white matter has been previously demonstrated; however, there is a lack of data describing the effects of multi-axial loading, even though brain tissue experiences multi-axial stress states. Therefore, a biaxial tensile experiment was designed to more fully characterize the anisotropic behavior of white matter in a quasi-static loading state, and the mechanical data were modeled with an anisotropic hyperelastic continuum model. A probabilistic analysis was used to quantify the uncertainty in model predictions because the mechanical data of brain tissue can show a high degree of variability, and computational studies can benefit from reporting the probability distribution of model responses. The axonal structure in white matter can be heterogeneous and regionally dependent, which can affect computational model predictions. Therefore, corona radiata and corpus callosum regions were tested, and histology and transmission electron microscopy were performed on tested specimens to relate the distribution of axon orientations and the axon volume fraction to the mechanical behavior. These measured properties were implemented into a structural constitutive model. Results demonstrated a significant, but relatively low anisotropic behavior, yet there were no conclusive mechanical differences between the two regions tested. The inclusion of both biaxial and uniaxial tests in model fits improved the accuracy of model predictions. The mechanical anisotropy of individual specimens positively correlated with the measured axon volume fraction, and, accordingly, the structural model exhibited slightly decreased uncertainty in model predictions compared to the model without structural properties.

On the accuracy and fitting of transversely isotropic material models

Fiber reinforced structures are central to the form and function of biological tissues. Hyperelastic, transversely isotropic material models are used widely in the modeling and simulation of such tissues. Many of the most widely used models involve strain energy functions that include one or both pseudo-invariants (I4 or I5) to incorporate energy stored in the fibers. In a previous study we showed that both of these invariants must be included in the strain energy function if the material model is to reduce correctly to the well-known framework of transversely isotropic linear elasticity in the limit of small deformations. Even with such a model, fitting of parameters is a challenge. Here, by evaluating the relative roles of I4 and I5 in the responses to simple loadings, we identify loading scenarios in which previous models accounting for only one of these invariants can be expected to provide accurate estimation of material response, and identify mechanical tests that have special utility for fitting of transversely isotropic constitutive models. Results provide guidance for fitting of transversely isotropic constitutive models and for interpretation of the predictions of these models.

Mechanical Characterization of Immature Porcine Brainstem in Tension at Dynamic Strain Rates

Background

Many brain injury cases involve pediatric road traffic accidents, and among these, brainstem injury causes disastrous outcomes. A thorough understanding of the tensile characterization of immature brainstem tissue is crucial in modeling traumatic brain injury sustained by children, but limited experimental data in tension is available for the immature brain tissue at dynamic strain rates.

Material/Methods

We harvested brainstem tissue from immature pigs (about 4 weeks old, and at a developmental stage similar to that of human toddlers) as a byproduct from a local slaughter house and very carefully prepared the samples. Tensile tests were performed on specimens at dynamic strain rates of 2/s, 20/s, and 100/s using a biological material instrument. The constitutive models, Fung, Ogden, Gent, and exponential function, for immature brainstem tissue material property were developed for the recorded experimental data using OriginPro^® 8.0 software. The t test was performed for infinitesimal shear modules.

Results

The curves of stress-versus-stretch ratio were convex in shape, and inflection points were found in all the test groups at the strain of about 2.5%. The average Lagrange stress of the immature brainstem specimen at the 30% strain at the strain rates of 2, 20, and 100/s was 273±114, 515±107, and 1121±197 Pa, respectively. The adjusted R-Square (R²) of Fung, Ogden, Gent, and exponential model was 0.820≤R²≤0.933, 0.774≤R²≤0.940, 0.650≤R²≤0.922, and 0.852≤R²≤0.981, respectively. The infinitesimal shear modulus of the strain energy functions showed a significant association with the strain rate (p<0.01).

Conclusions

The immature brainstem is a rate-dependent material in dynamic tensile tests, and the tissue becomes stiffer with increased strain rate. The reported results may be useful in the study of brain injuries in children who sustain injuries in road traffic accidents. Further research in more detail should be performed in the future.

Nodal versus Total Axonal Strain and the Role of Cholesterol in Traumatic Brain Injury

Traumatic brain injury (TBI) is a health threat that affects every year millions of people involved in motor vehicle and sporting accidents, and thousands of soldiers in battlefields. Diffuse axonal injury (DAI) is one of the most frequent types of TBI leading to death. In DAI, the initial traumatic event is followed by a cascade of biochemical changes that take time to develop in full, so that symptoms may not become apparent until days or weeks after the original injury. Hence, DAI is a dynamic process, and the opportunity exists to prevent its progression provided the initial trauma can be predicted at the molecular level. Here, we present preliminary evidence from micro-finite element (FE) simulations that the mechanical response of central nervous system myelinated fibers is dependent on the axonal diameter, the ratio between axon diameter and fiber diameter (g-ratio), the microtubules density, and the cholesterol concentration in the axolemma and myelin. A key outcome of the simulations is that there is a significant difference between the overall level of strain in a given axonal segment and the level of local strain in the Ranvier nodes contained in that segment, with the nodal strain being much larger than the total strain. We suggest that the acquisition of this geometric and biochemical information by means of already available high resolution magnetic resonance imaging techniques, and its incorporation in current FE models of the brain will enhance the models capacity to predict the site and magnitude of primary axonal damage upon TBI.