Mechanical Response Of Brain Tissue Research Articles

Data-driven modeling on anisotropic mechanical behavior of brain tissue with internal pressure

Brain tissue is one of the softest parts of the human body, composed of white matter and grey matter. The mechanical behavior of the brain tissue plays an essential role in regulating brain morphology and brain function. Besides, traumatic brain injury (TBI) and various brain diseases are also greatly influenced by the brain's mechanical properties. Whether white matter or grey matter, brain tissue contains multiscale structures composed of neurons, glial cells, fibers, blood vessels, etc., each with different mechanical properties. As such, brain tissue exhibits complex mechanical behavior, usually with strong nonlinearity, heterogeneity, and directional dependence. Building a constitutive law for multiscale brain tissue using traditional function-based approaches can be very challenging. Instead, this paper proposes a data-driven approach to establish the desired mechanical model of brain tissue. We focus on blood vessels with internal pressure embedded in a white or grey matter matrix material to demonstrate our approach. The matrix is described by an isotropic or anisotropic nonlinear elastic model. A representative unit cell (RUC) with blood vessels is built, which is used to generate the stress-strain data under different internal blood pressure and various proportional displacement loading paths. The generated stress-strain data is then used to train a mechanical law using artificial neural networks to predict the macroscopic mechanical response of brain tissue under different internal pressures. Finally, the trained material model is implemented into finite element software to predict the mechanical behavior of a whole brain under intracranial pressure and distributed body forces. Compared with a direct numerical simulation that employs a reference material model, our proposed approach greatly reduces the computational cost and improves modeling efficiency. The predictions made by our trained model demonstrate sufficient accuracy. Specifically, we find that the level of internal blood pressure can greatly influence stress distribution and determine the possible related damage behaviors.

The brain as a structure: A model of how fluid–structure interactions stiffen brain tissue after injury

The brain contains a tree of vasculature that contributes not only oxygen and nutrients but also structural integrity to the brain parenchyma. The structure formed by this vasculature affects the mechanical response of brain tissue both before and after injury, and maybe a determinant of an individual’s injury susceptibility. To investigate structural changes of brain tissue after injury, we constructed an idealized representative volume element model of fluid–structure interactions within brain parenchyma and applied it to study how vessel size, cerebral spinal fluid (CSF) flow velocity, and CSF flow networks relate to previous experimental observations of healthy and injured brain tissue. We hypothesized that injury-associated structural changes to the neurovasculature may alter the mechanical responses of brain tissue and predispose an individual to subsequent injury. Parameters representing the mechanics of uninjured and injured brain tissue were verified against experimental characterizations of these tissue in uniaxial compression and shear. Results supported our hypothesis, and highlight the importance of considering cerebral vasculature as a structure in predicting and analyzing the brain’s response to mechanical loading.

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

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

Poro-Viscoelastic Effects During Biomechanical Testing of Human Brain Tissue

Brain tissue is one of the softest tissues in the human body and the quantification of its mechanical properties has challenged scientists over the past decades. Associated experimental results in the literature have been contradictory as characterizing the mechanical response of brain tissue not only requires well-designed experimental setups that can record the ultrasoft response, but also appropriate approaches to analyze the corresponding data. Due to the extreme complexity of brain tissue behavior, nonlinear continuum mechanics has proven an expedient tool to analyze testing data and predict the mechanical response using a combination of hyper-, visco-, or poro-elastic models. Such models can not only allow for personalized predictions through finite element simulations, but also help to comprehensively understand the physical mechanisms underlying the tissue response. Here, we use a nonlinear poro-viscoelastic computational model to evaluate the effect of different intrinsic material properties (permeability, shear moduli, nonlinearity, viscosity) on the tissue response during different quasi-static biomechanical measurements, i.e., large-strain compression and tension as well as indentation experiments. We show that not only the permeability but also the properties of the viscoelastic solid largely control the fluid flow within and out of the sample. This reveals the close coupling between viscous and porous effects in brain tissue behavior. Strikingly, our simulations can explain why indentation experiments yield that white matter tissue in the human brain is stiffer than gray matter, while large-strain compression experiments show the opposite trend. These observations can be attributed to different experimental loading and boundary conditions as well as assumptions made during data analysis. The present study provides an important step to better understand experimental data previously published in the literature and can help to improve experimental setups and data analysis for biomechanical testing of brain tissue in the future.

Brain-mimicking phantom for biomechanical validation of motion sensitive MR imaging techniques

Motion sensitive MR imaging techniques allow for the non-invasive evaluation of biological tissues by using different excitation schemes, including physiological/intrinsic motions caused by cardiac pulsation or respiration, and vibrations caused by an external actuator. The mechanical biomarkers extracted through these imaging techniques have been shown to hold diagnostic value for various neurological disorders and conditions. Amplified MRI (aMRI), a cardiac gated imaging technique, can help track and quantify low frequency intrinsic motion of the brain. As for high frequency actuation, the mechanical response of brain tissue can be measured by applying external high frequency actuation in combination with a motion sensitive MR imaging sequence called Magnetic Resonance Elastography (MRE). Due to the frequency-dependent behavior of brain mechanics, there is a need to develop brain phantom models that can mimic the broadband mechanical response of the brain in order to validate motion-sensitive MR imaging techniques. Here, we have designed a novel phantom test setup that enables both the low and high frequency responses of a brain-mimicking phantom to be captured, allowing for both aMRI and MRE imaging techniques to be applied on the same phantom model. This setup combines two different vibration sources: a pneumatic actuator, for low frequency/intrinsic motion (1 Hz) for use in aMRI, and a piezoelectric actuator for high frequency actuation (30–60 Hz) for use in MRE. Our results show that in MRE experiments performed from 30 Hz through 60 Hz, propagating shear waves attenuate faster at higher driving frequencies, consistent with results in the literature. Furthermore, actuator coupling has a substantial effect on wave amplitude, with weaker coupling causing lower amplitude wave field images, specifically shown in the top-surface shear loading configuration. For intrinsic actuation, our results indicate that aMRI linearly amplifies motion up to at least an amplification factor of 9 for instances of both visible and sub-voxel motion, validated by varying power levels of pneumatic actuation (40%–80% power) under MR, and through video analysis outside the MRI scanner room. While this investigation used a homogeneous brain-mimicking phantom, our setup can be used to study the mechanics of non-homogeneous phantom configurations with bio-interfaces in the future.

A computational investigation of strain concentration in the brain in response to a rapid temperature rise

Following the mysterious health attacks on U.S. diplomats in Cuba in 2016, the cause of concussion-like symptoms concurrent with strange noises heard by the diplomats remains undetermined. A wide range of possible causes of the sensations have been proposed: pulsed microwave exposure, infrasound acoustic devices, pesticides/neurotoxins, and even mass hysteria (psychogenic illness). Here, we numerically examine the pulsed microwave exposure hypothesis and the simulated mechanical response of brain tissue. A computational model is used to examine the influence of various spatially varying temperature gradients and pulse durations on the mechanical response of brain tissue. We show that a stress-focusing effect due to a rapid temperature increase may result in brain tissue strains larger than the initially applied thermal strains.

Simulation of Brain Response to Noncontact Impacts Using Coupled Eulerian-Lagrangian Method.

Finite element (FE) method has been widely used for gaining insights into the mechanical response of brain tissue during impacts. In this study, a coupled Eulerian-Lagrangian (CEL) formulation is implemented in impact simulations of a head system to overcome the mesh distortion difficulties due to large deformation in the cerebrospinal fluid (CSF) region and provide a biofidelic model of the interaction between the brain and skull. The head system used in our FE model is constructed from the transverse section of the human brain, with CSF modeled by Eulerian elements. Spring connectors are applied to represent the pia-arachnoid connection between the brain and skull. Validations of the CEL formulation and the FE model are performed using the experimental results. The dynamic response of brain tissue under noncontact impacts and the brain regions susceptible to injury are evaluated based on the intracranial pressure (ICP), maximum principal strain (MPS), and von Mises stress. While tracking the critical MPS location on the brain, higher likelihood of contrecoup injury than coup injury is found when sudden brain-skull motion takes place. The accumulation effect of CSF in the ventricle system, under large relative brain-skull motion, is also identified. The FE results show that adding relative angular velocities, to the translational impact model, not only causes a diffuse high strain area, but also cause the temporal lobes to be susceptible to cerebral contusions since the protecting CSF is prone to be squeezed away at the temporal sites due to the head rotations.

Materials Characterization of Cranial Simulants for Blast-Induced Traumatic Brain Injury

The mechanical response of brain tissue to high-speed forces in the blast and blunt traumatic brain injury is poorly understood. Object-to-object variation and interspecies differences are current limitations in animal and cadaver studies conducted to study damage mechanisms. Biofidelic and transparent tissue simulants allow the use of high-speed optical diagnostics during a blast event, making it possible to observe deformations and damage patterns for comparison to observed injuries seen post-mortem in traumatic brain injury victims. Material properties of several tissue simulants were quantified using standard mechanical characterization techniques, that is, shear rheometric, tensile, and compressive testing. Polyacrylamide simulants exhibited the best optical and mechanical property matching with the fewest trade-offs in the design of a cranial test object. Polyacrylamide gels yielded densities of ~1.04g/cc and shear moduli ranging 1.3-14.55kPa, allowing gray and white matter simulant tuning to a 30-35% difference in shear for biofidelity. These materials are intended for use as layered cranial phantoms in a shock tube and open field blasts, with focus on observing phenomena occurring at the interfaces of adjacent tissue simulant types or material-fluid boundaries. Mechanistic findings from these studies may be used to inform the design of protective gear to mitigate blast injuries.

Rheological Experimentation to Investigate History Dependent Viscoelastic Properties of ex-vivo Ovine Brain Tissue

Much controversy exists around the issue of repetitive Traumatic Brain Injuries (TBIs) and long-term brain health. Little is known about the mechanical response of brain tissue to traumatic impacts and head accelerations. While history dependent characteristics of other biological tissues have been investigated experimentally, no methodology currently exists for investigating the mechanical response of brain tissue to cyclic loading and its fatigue properties. This investigation presents sample preparation, conditioning and rheological methodology for undertaking repetitive loading of ex-vivo ovine brain tissue. Rheological amplitude sweep tests undertaken at 3 Hz on ø10 mm by 5 mm ovine brain tissue samples, employing a periodic moisturisation and a normal force of 50 mN, yielded results agreeing with those reported in literature for low number of cycle loading and showed signs of history dependence at higher number of cycles. Understanding brain tissue response to repeated loading, fatigue properties and associated trauma mechanisms can be advances by undertaking high cycle rheological testing with the methodology presented in this article.

Establishment of an Experimental Intracerebral Haemorrhage Model for Mass Effect Research using a Thermo-sensitive Hydrogel

The mechanical response of brain tissue closely relates to cerebral blood flow and brain diseases. During intracerebral haemorrhage (ICH), a mass effect occurs during the initial bleeding and results in significant tissue deformation. However, fewer studies have focused on the brain damage mechanisms and treatment approaches associated with mass effects compared to the secondary brain injuries after ICH, which may be a result of the absence of acceptable animal models mimicking a mass effect. Thus, a thermo-sensitive poly (N-isopropylacrylamide) (PNIPAM) hydrogel was synthesized and injected into the rat brain to establish an ICH model for mass effect research. The PNIPAM hydrogel or autologous blood was injected to establish an ICH animal model, and the space-occupying volumes, brain tissue elasticity, brain oedema, neuronal cell death, iron deposition and behavioural recovery were evaluated. The lower critical solution temperature of PNIPAM hydrogel was 32 °C, and the PNIPAM hydrogel had a rough surface with similar topography and pore structure to a blood clot. Furthermore, the ICH model animals who received an injection of PNIPAM and blood produced similar lesion volumes, elasticity changes and mechanically activated ion channel piezo-2 upregulation in brain tissue. Meanwhile, slight iron deposition, neuronal cell death and brain oedema were observed in the PNIPAM hydrogel model compared to the blood model. In addition, the PNIPAM hydrogel showed good biocompatibility and stability in vivo via subcutaneous implantation. Our findings show that PNIPAM hydrogel cerebral infusion can form a mass effect similar to haematoma and minimize the interference of blood, and the establishment of a mass effect ICH model is beneficial for understanding the mechanism of primary brain injury and the role of mass effects in secondary brain damage after ICH.

A New Mass Effect Research Rat Model to Explore the Occuping Effect on Secondary Brain Injuries after ICH

The mechanical response of brain tissue closely relates to cerebral blood flow and brain diseases. During intracerebral haemorrhage (ICH), a mass effect occurs during the initial bleeding. As the hematoma increases, the haema... | Find, read and cite all the research you need on Tech Science Press

A composite hydrogel for brain tissue phantoms

Synthetic phantoms are valuable tools for training, research and development in traditional and computer aided surgery, but complex organs, such as the brain, are difficult to replicate. Here, we present the development of a new composite hydrogel capable of mimicking the mechanical response of brain tissue under loading. Our results demonstrate how the combination of two different hydrogels, whose synergistic interaction results in a highly tunable blend, produces a hybrid material that closely matches the strongly dynamic and non-linear response of brain tissue. The new synthetic material is inexpensive, simple to prepare, and its constitutive components are both widely available and biocompatible. Our investigation of the properties of this engineered tissue, using both small scale testing and life-sized brain phantoms, shows that it is suitable for reproducing the brain shift phenomenon and brain tissue response to indentation and palpation.

In Vivo Investigation of the Effectiveness of a Hyper-viscoelastic Model in Simulating Brain Retraction.

Intraoperative brain retraction leads to a misalignment between the intraoperative positions of the brain structures and their previous positions, as determined from preoperative images. In vitro swine brain sample uniaxial tests showed that the mechanical response of brain tissue to compression and extension could be described by the hyper-viscoelasticity theory. The brain retraction caused by the mechanical process is a combination of brain tissue compression and extension. In this paper, we first constructed a hyper-viscoelastic framework based on the extended finite element method (XFEM) to simulate intraoperative brain retraction. To explore its effectiveness, we then applied this framework to an in vivo brain retraction simulation. The simulation strictly followed the clinical scenario, in which seven swine were subjected to brain retraction. Our experimental results showed that the hyper-viscoelastic XFEM framework is capable of simulating intraoperative brain retraction and improving the navigation accuracy of an image-guided neurosurgery system (IGNS).

Computational simulation of the mechanical response of brain tissue under blast loading.

In the present study, numerical simulations of nonlinear wave propagation and shock formation in brain tissue have been presented and a new mechanism of injury for blast-induced neurotrauma (BINT) is proposed. A quasilinear viscoelastic (QLV) constitutive material model was used that encompasses the nonlinearity as well as the rate dependence of the tissue relevant to BINT modeling. A one-dimensional model was implemented using the discontinuous Galerkin finite element method and studied with displacement- and pressure-input boundary conditions. The model was validated against LS-DYNA finite element code and theoretical results for specific conditions that resulted in shock wave formation. It was shown that a continuous wave can become a shock wave as it propagates in the QLV brain tissue when the initial changes in acceleration are beyond a certain limit. The high spatial gradient of stress and strain at the shock front cause large relative motions at the cellular scale at high temporal rates even when the maximum stresses and strains are relatively low. This gradient-induced local deformation may occur away from the boundary and is proposed as a contributing factor to the diffuse nature of BINT.

Mechanical response of brain tissue under blast loading

In this study, a framework for understanding the propagation of stress waves in brain tissue under blast loading has been developed. It was shown that tissue nonlinearity and rate dependence are the key parameters in predicting the mechanical behavior under such loadings, as they determine whether traveling waves could become steeper and eventually evolve into shock discontinuities. To investigate this phenomenon, in the present study, brain tissue has been characterized as a quasi-linear viscoelastic (QLV) material and a nonlinear constitutive model has been developed for the tissue that spans from medium loading rates up to blast rates. It was shown that development of shock waves is possible inside the head in response to high rate compressive pressure waves. Finally, it was argued that injury to the nervous tissue at the microstructural level could be partly attributed to the high stress gradients with high rates generated at the shock front and this was proposed as a mechanism of injury in brain tissue.

Dynamic mechanical response of brain tissue in indentation in vivo, in situ and in vitro

Characterizing the dynamic mechanical properties of brain tissue is deemed important for developing a comprehensive knowledge of the mechanisms underlying brain injury. The results gathered to date on the tissue properties have been mostly obtained in vitro. Learning how these results might differ quantitatively from those encountered in vivo is a critical step towards the development of biofidelic brain models. The present study provides novel and unique experimental results on, and insights into, brain biorheology in vivo, in situ and in vitro, at large deformations, in the quasi-static and dynamic regimes. The nonlinear dynamic response of the cerebral cortex was measured in indentation on the exposed frontal and parietal lobes of anesthetized porcine subjects. Load–unload cycles were applied to the tissue surface at sinusoidal frequencies of 10, 1, 0.1 and 0.01 Hz. Ramp–relaxation tests were also conducted to assess the tissue viscoelastic behavior at longer times. After euthanasia, the indentation test sequences were repeated in situ on the exposed cortex maintained in its native configuration within the cranium. Mixed gray and white matter samples were subsequently excised from the superior cortex to be subjected to identical indentation test segments in vitro within 6–7 h post mortem. The main response features (e.g. nonlinearities, rate dependencies, hysteresis and conditioning) were measured and contrasted in vivo, in situ and in vitro. The indentation response was found to be significantly stiffer in situ than in vivo. The consistent, quantitative set of mechanical measurements thereby collected provides a preliminary experimental database, which may be used to support the development of constitutive models for the study of mechanically mediated pathways leading to traumatic brain injury.

Applying DTI white matter orientations to finite element head models to examine diffuse TBI under high rotational accelerations

The in-vivo mechanical response of neural tissue during impact loading of the head is simulated using geometrically accurate finite element (FE) head models. However, current FE models do not account for the anisotropic elastic material behaviour of brain tissue. In soft biological tissue, there is a correlation between internal microscopic structure and macroscopic mechanical properties. Therefore, constitutive equations are important for the numerical analysis of the soft biological tissues. By exploiting diffusion tensor techniques the anisotropic orientation of neural tissue is incorporated into a non-linear viscoelastic material model for brain tissue and implemented in an explicit FE analysis. The viscoelastic material parameters are derived from published data and the viscoelastic model is used to describe the mechanical response of brain tissue. The model is formulated in terms of a large strain viscoelastic framework and considers non-linear viscous deformations in combination with non-linear elastic behaviour. The constitutive model was applied in the University College Dublin brain trauma model (UCDBTM) (i.e. three-dimensional finite element head model) to predict the mechanical response of the intra-cranial contents due to rotational injury.

Effect of inter-species, gender, and breeding on the mechanical behavior of brain tissue

Mechanical response of brain tissue deforming at high rates are needed to build high-fidelity computer models for traumatic brain injury (TBI) studies. Different types of mammalian brains have been used to obtain the constitutive behavior of tissue. It is necessary to examine how these different brains compare to each other in order to determine which animal might be the best surrogate for human brain tissue. In this experimental study, fresh brain tissue from three different mammals, two types of porcine breeds, and genders were loaded under uniaxial compression over a wide range of strain rates. The experiments at higher rates were conducted with a Kolsky bar modified for soft tissue characterization, whereas lower rate experiments were performed on a conventional hydraulic material test frame. Experimental results did not show any significant difference in high-rate compressive response of the brain tissue of different animals, different breeds, and different genders. However, there was significant rate dependence for all tissues tested, especially in the Kolsky bar range. Further investigation is necessary to identify the source of the rate effects.

A micromechanical hyperelastic modeling of brain white matter under large deformation

A finite element based micromechanical model has been developed for analyzing and characterizing the microstructural as well as homogenized mechanical response of brain tissue under large deformation. The model takes well-organized soft tissue as a fiber-reinforced composite with nonlinear and anisotropic behavior assumption for the fiber as well as the matrix of composite matter. The procedure provides a link between the macroscopic scale and microscopic scale as brain tissue undergoes deformation. It can be used to better understand how macroscopic stresses are transferred to the microstructure or cellular structure of the brain. A repeating unit cell (RUC) is created to stand as a representative volume element (RVE) of the hyperelastic material with known properties of the constituents. The model imposes periodicity constraints on the RUC. The RUC is loaded kinematically by imposing displacements on it to create the appropriate normal and shear stresses. The homogenized response of the composite, the average stresses carried within each of the constituents, and the maximum local stresses are all obtained. For each of the normal and shear loading scenarios, the impact of geometrical variables such as the axonal fiber volume fraction and undulation of the axons are evaluated. It was found that axon undulation has significant impact on the stiffness and on how stresses were distributed between the axon and the matrix. As axon undulation increased, the maximum stress and stress in the matrix increased while the stress in the axons decreased. The axon volume fraction was found to have an impact on the tissue stiffness as higher axon volume fractions lead to higher stresses both in the composite and in the constituents. The direction of loading clearly has a large impact on how stresses are distributed amongst the constituents. This micromechanics tool provides the detailed micromechanics stresses and deformations, as well as the average homogenized behavior of the RUC, which can be efficiently used in mechanical characterization of brain tissue.

Inelastic Behavior in Repeated Shearing of Bovine White Matter

Understanding the brain's response to multiple loadings requires knowledge of how straining changes the mechanical response of brain tissue. We studied the inelastic behavior of bovine white matter and found that when this tissue is stretched beyond a critical strain threshold, its reloading stiffness drops. An upper bound for this strain threshold was characterized, and was found to be strain rate dependent at low strain rates and strain rate independent at higher strain rates. Results suggest that permanent changes to tissue mechanics can occur at strains below those believed to cause physiological disruption or rupture of axons. Such behavior is characteristic of disentanglement in fibrous-networked solids, in which strain-induced mechanical changes may result from fiber realignment rather than fiber breakage.