Material Properties Of Brain Tissue Research Articles

Revealing the role of material properties in impact-related injuries: Investigating the influence of brain and skull density variations on head injury severity

Traumatic brain injuries (TBI) resulting from head impacts are a major public health concern, which prompted our research to investigate the complex relationship between the material properties of brain tissue and the severity of TBI. The goal of this research is to investigate how variations in brain and skull density influence the vulnerability of brain tissue to traumatic injury, thereby enhancing our understanding of injury mechanism.To achieve this goal, we employed a well-validated finite element head model (FEHM). The current investigation was divided into two phases: in the first one, three distinct brain viscoelastic materials that had been utilized in prior studies were analyzed. The review of the properties of these three materials has been meticulous, encompassing both the spectrum of mechanical properties and the behaviors that are relevant to the way in which brain tissue reacts to traumatic loading conditions. In the second phase, the material properties of both the brain and skull tissue, alongside the impact conditions, were held constant. After this step, the focus was directed towards the variation of density in the brain and skull, which was consistent with the results obtained from previous experimental investigations, in order to determine the precise impact of these variations in density. This approach allowed a more profound comprehension of the impacts that density had on the simulation results.In the first phase, Material No. 2 exhibited the highest maximum first principal strain value in the frontal region (εmax=15.41%), indicating lower stiffness to instantaneous deformation. This characteristic suggests that Material No. 2 may deform more extensively upon impact, potentially increasing the risk of injury due to its viscoelastic behavior. In contrast, Material No. 1, with a lower maximum first principal strain in the frontal region (εmax=7.87%), displayed greater stiffness to instantaneous deformation, potentially reducing the risk of brain injury upon head impact. The second phase provided quantitative findings revealing a proportional relationship between brain tissue density and the pressures experienced by the brain. A 2 % increase in brain tissue density corresponded to approximately a 1 % increase in pressure on the brain tissue. Similarly, changes in skull density exhibited a similar quantitative relationship, with a 6 % increase in skull density leading to a 2.5 % increase in brain pressure. This preliminary approximate ratio of 2 to 1 between brain and skull density variations provides an initial quantitative framework for assessing the impact of density changes on brain vulnerability. These findings have several implications for the development of protective measures and injury prevention strategies, particularly in contexts where head trauma is a major issue.

Origins of brain tissue elasticity under multiple loading modes by analyzing the microstructure-based models.

Constitutive behaviors and material properties of brain tissue play an essential role in accurately modeling its mechanical responses. However, the measured mechanical behaviors of brain tissue exhibit a large variability, and the reported elastic modulus can differ by orders of magnitude. Here we develop the micromechanical models based on the actual microstructure of the longitudinally anisotropic plane of brain tissue to investigate the microstructural origins of the large variability. Specifically, axonal fiber bundles with the specified configurations are distributed in an equivalent matrix. All micromechanical models are subjected to multiple loading modes, such as tensile, compressive, and shear loading, under periodic boundary conditions. The predicted results agree well with the experimental results. Furthermore, we investigate how brain tissue elasticity varies with its microstructural features. It is revealed that the large variability in brain tissue elasticity stems from the volume fraction of axonal fiber, the aspect ratio of axonal fiber, and the distribution of axonal fiber orientation. The volume fraction has the greatest impact on the mechanical behaviors of brain tissue, followed by the distribution of axonal fiber orientation, then the aspect ratio. This study provides critical insights for understanding the microstructural origins of the large variability in brain tissue elasticity.

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.

Mind the gap: A mechanobiological hypothesis for the role of gap junctions in the mechanical properties of injured brain tissue

Despite more than half a century of work on the brain biomechanics, there are still significant unknowns about this tissue. Since the brain is highly susceptible to injury, damage biomechanics has been one of the main areas of interest to the researchers in the field of brain biomechanics. In many previous studies, mechanical properties of brain tissue under sub-injury and injury level loading conditions have been addressed; however, to the best of our knowledge, the role of cell-cell interactions in the mechanical behavior of brain tissue has not been well examined yet. This note introduces the hypothesis that gap junctions as the major type of cell-cell junctions in the brain tissue play a pivotal role in the mechanical properties of the tissue and their failure during injury leads to changes in brain's material properties. According to this hypothesis, during an injury, the gap junctions are damaged, leading to a decrease in tissue stiffness, whereas following the injury, new junction proteins are expressed, leading to an increase in tissue stiffness. We suggest that considering the mechanobiological effect of gap junctions in the material properties of brain tissue may help better understand the brain injury mechanism.

Tension Strain-Softening and Compression Strain-Stiffening Behavior of Brain White Matter.

Brain, the most important component of the central nervous system (CNS), is a soft tissue with a complex structure. Understanding the role of brain tissue microstructure in mechanical properties is essential to have a more profound knowledge of how brain development, disease, and injury occur. While many studies have investigated the mechanical behavior of brain tissue under various loading conditions, there has not been a clear explanation for variation reported for material properties of brain tissue. The current study compares the ex-vivo mechanical properties of brain tissue under two loading modes, namely compression and tension, and aims to explain the differences observed by closely examining the microstructure under loading. We tested bovine brain samples under uniaxial tension and compression loading conditions, and fitted hyperelastic material parameters. At 20% strain, we observed that the shear modulus of brain tissue in compression is about 6 times higher than in tension. In addition, we observed that brain tissue exhibited strain-stiffening in compression and strain-softening in tension. In order to investigate the effect of loading modes on the tissue microstructure, we fixed the samples using a novel method that enabled keeping the samples at the loaded stage during the fixation process. Based on the results of histology, we hypothesize that during compressive loading, the strain-stiffening behavior of the tissue could be attributed to glial cell bodies being pushed against surroundings, contacting each other and resisting compression, while during tension, cell connections are detached and the tissue displays softening behavior.

Influence of saline solution absorption and compressive rate on the material properties of brain tissue

Traumatic brain injuries (TBI) affect millions of people each year and can result in long-term difficulties in thinking or focusing. Due to the number of people affected by these injuries, significant research has been dedicated to determining the mechanical properties of the brain using postmortem tissue from animals harvested within 24 h. The postmortem brain tissue is often stored in a solution until a rheological experiment is ready to begin. However, the effect of storage duration on the mechanical behavior of brain tissue is not understood. In this paper, postmortem porcine brains were placed in normal saline solution (0.9% NaCl) and refrigerated between 30 min and 6.5 h to allow the brain to absorb the solution. Afterwards, samples from both soaked and freshly extracted brains were subjected to unconfined compression tests at compressive rates of 5, 50, and 500 mm/min. The fractional Zener viscoelastic model was applied to obtain the brain's mechanical properties. While the results did not show a significant relationship between absorption and the long-term stiffness (E∞), both the relaxation time (τ0) and fractional order (α) were statistically influenced by both the length of time in the solution and compressive rate. Further, the instantaneous stiffness (E0) was statistically influenced by the length of time in solution, though not the compressive rate.

Application of the time-strain superposition - Part II: Prediction of the frequency-dependent behaviour of brain tissue.

Knowing the real material properties of brain tissue is of great importance when it comes to the precise prediction of its mechanical response. The efficiency of these procedures depends on the adequacy of experimental data and the analytical and numerical tools utilized. In this study, we combine existing approaches within the theory of viscoelasticity in order to predict the frequency-dependent behaviour of the porcine brain from the known stress relaxation data. Time-strain superposition is applied to the brain shear relaxation segments for the construction of the long-term master curve in the linear viscoelastic range. A widely-used and well-established numerical procedure is then utilized for the prediction of the frequency-dependent modulus based on the constructed master curve. The demonstrated methodology is evaluated using the porcine brain experimental data available from the literature. The results show reasonably good agreement between the predicted and the previously measured and published storage modulus data in the whole frequency range investigated. On the other hand, prediction of the loss modulus is only possible within certain frequency ranges related to the time frame of experimentally known relaxation behaviour. Nevertheless, the outcomes of the paper speak in favour of the validity of the linear viscoelastic interconversion relations between the time- and frequency-dependent material functions of the porcine brain tissue exposed to strain up to the tissue's linear viscoelastic limit.

Long-term changes in the material properties of brain tissue at the implant–tissue interface

Objective. Brain tissue undergoes dramatic molecular and cellular remodeling at the implant–tissue interface that evolves over a period of weeks after implantation. The biomechanical impact of such remodeling on the interface remains unknown. In this study, we aim to assess the changes in the mechanical properties of the brain–electrode interface after chronic implantation of a microelectrode. Approach. Microelectrodes were implanted in the rodent cortex at a depth of 1 mm for different durations—1 day (n = 4), 10–14 days (n = 4), 4 weeks (n = 4) and 6–8 weeks (n = 7). After the initial duration of implantation, the microelectrodes were moved an additional 1 mm downward at a constant speed of 10 µm s−1. Forces experienced by the microelectrode were measured during movement and after termination of movement. The biomechanical properties of the interfacial brain tissue were assessed from measured force–displacement curves using two separate models—a two-parameter Mooney–Rivlin hyperelastic model and a viscoelastic model with a second-order Prony series. Main results. Estimated shear moduli using a second-order viscoelastic model increased from 0.5–2.6 kPa (day 1 of implantation) to 25.7–59.3 kPa (after 4 weeks of implantation) and subsequently decreased to 0.8–7.9 kPa after 6–8 weeks of implantation in 6 of the 7 animals. The estimated elastic modulus increased from 4.1–7.8 kPa on the day of implantation to 24–44.9 kPa after 4 weeks. The elastic modulus was estimated to be 6.8–33.3 kPa in 6 of the 7 animals after 6–8 weeks of implantation. The above estimates suggest that the brain tissue surrounding the microelectrode evolves from a stiff matrix with maximal shear and elastic modulus after 4 weeks of implantation into a composite of two different layers with different mechanical properties—a stiff compact inner layer surrounded by softer brain tissue that is biomechanically similar to brain tissue—during the first week of implantation. Tissue micromotion-induced stresses on the microelectrode constituted 12–55% of the steady-state stresses on the microelectrode on the day of implantation (n = 4), 2–21% of the steady-state stresses after 4 weeks of implantation (n = 4), and 4–10% of the steady-state stresses after 6–8 weeks of implantation (n = 7). Significance. Understanding biomechanical behavior at the brain–microelectrode interface is necessary for the long-term success of implantable neuroprosthetics and microelectrode arrays. Such quantitative physical characterization of the dynamic changes in the electrode–tissue interface will (a) drive the design and development of more mechanically optimal, chronic brain implants, and (b) lead to new insights into key cellular and molecular events such as neuronal adhesion, migration and function in the immediate vicinity of the brain implant.

Influence of Uncertainties in the Material Properties of Brain Tissue on the Probabilistic Volume of Tissue Activated

The aim of this study was to examine the influence of uncertainty of the material properties of brain tissue on the probabilistic voltage response and the probabilistic volume of tissue activated (VTA) in a volume conductor model of deep brain stimulation. To quantify the uncertainties of the desired quantities without changing the deterministic model, a nonintrusive projection method was used by approximating these quantities by a polynomial expansion on a multidimensional basis known as polynomial chaos. The coefficients of this expansion were computed with a multidimensional quadrature on sparse Smolyak grids. The deterministic model combines a finite element model based on a digital brain atlas and a multicompartmental model of mammalian nerve fibers. The material properties of brain tissue were modeled as uniform random parameters using data from several experimental studies. Different magnitudes of uncertainty in the material properties were computed to allow predictions on the resulting uncertainties in the desired quantities. The results showed a major contribution of the uncertainties in the electrical conductivity values of brain tissue on the voltage response as well as on the predicted VTA, while the influence of the uncertainties in the relative permittivity was negligible.

Volumetric intraoperative brain deformation compensation: model development and phantom validation.

During neurosurgery, nonrigid brain deformation may affect the reliability of tissue localization based on preoperative images. To provide accurate surgical guidance in these cases, preoperative images must be updated to reflect the intraoperative brain. This can be accomplished by warping these preoperative images using a biomechanical model. Due to the possible complexity of this deformation, intraoperative information is often required to guide the model solution. In this paper, a linear elastic model of the brain is developed to infer volumetric brain deformation associated with measured intraoperative cortical surface displacement. The developed model relies on known material properties of brain tissue, and does not require further knowledge about intraoperative conditions. To provide an initial estimation of volumetric model accuracy, as well as determine the model's sensitivity to the specified material parameters and surface displacements, a realistic brain phantom was developed. Phantom results indicate that the linear elastic model significantly reduced localization error due to brain shift, from > 16 mm to under 5 mm, on average. In addition, though in vivo quantitative validation is necessary, preliminary application of this approach to images acquired during neocortical epilepsy cases confirms the feasibility of applying the developed model to in vivo data.

Constitutive model for brain tissue under finite compression

While advances in computational models of mechanical phenomena have made it possible to simulate dynamically complex problems in biomechanics, accurate material models for soft tissues, particularly brain tissue, have proven to be very challenging. Most studies in the literature on material properties of brain tissue are performed in shear loading and very few tackle the behavior of brain in compression. In this study, a viscoelastic constitutive model of bovine brain tissue under finite step-and-hold uniaxial compression with 10s–1 ramp rate and 20s hold time has been developed. The assumption of quasi-linear viscoelasticity (QLV) was validated for strain levels of up to 35%. A generalized Rivlin model was used for the isochoric part of the deformation and it was shown that at least three terms (C10, C01 and C11) are needed to accurately capture the material behavior. Furthermore, for the volumetric deformation, a two parameter Ogden model was used and the extent of material incompressibility was studied. The hyperelastic material parameters were determined through extracting and fitting to two isochronous curves (0.06s and 14s) approximating the instantaneous and steady-state elastic responses. Viscoelastic relaxation was characterized at five decay rates (100, 10, 1, 0.1, 0s−1) and the results in compression and their extrapolation to tension were compared against previous models.

Age-dependent material properties of the porcine cerebrum: effect on pediatric inertial head injury criteria

During growth and development, the immature central nervous system undergoes rapid alterations in constituents and structure. We hypothesize that these alterations are accompanied by changes in the mechanical properties of brain tissue which, in turn, influence the response of the brain to traumatic inertial loads. Samples of frontal cerebrum from neonatal (2–3 days) and adult pigs were harvested and tested within 3 h post-mortem. The complex shear modulus of the samples was measured in a custom-designed oscillatory shear testing device at engineering shear strain amplitudes of 2.5% or 5% from 20–200 Hz, at 25°C and 100% humidity. In this range, the elastic and viscous components of the complex shear modulus increased significantly with the development of the cerebral region of the brain. Using an idealized model of the developing head, the age-dependent material properties of brain tissue were shown to affect the mechanical response of the brain to inertial loading. This study is a first step toward developing head injury tolerance criteria specifically for the pediatric population.