A viscoelastic analysis of the P56 mouse brain under large-deformation dynamic indentation

Validation of a computational biomechanical mouse brain model for rotational head acceleration

Recent mouse brain injury experiments examine diffuse axonal injury resulting from accelerative head rotations. Evaluating brain deformation during these events would provide valuable information on tissue level thresholds for brain injury, but there are many challenges to imaging the brain's mechanical response during dynamic loading events, such as a blunt head impact. To address this shortcoming, we present an experimentally validated computational biomechanics model of the mouse brain that predicts tissue deformation, given the motion of the mouse head during laboratory experiments. First, we developed a finite element model of the mouse brain that computes tissue strains, given the same head rotations as previously conducted in situ hemicephalic mouse brain experiments. Second, we calibrated the model using a single brain segment, and then validated the model based on the spatial and temporal strain responses of other regions. The result is a computational tool that will provide researchers with the ability to predict brain tissue strains that occur during mouse laboratory experiments, and to link the experiments to the resulting neuropathology, such as diffuse axonal injury.

Dynamic strain fields of the mouse brain during rotation

Mouse models are used to better understand brain injury mechanisms in humans, yet there is a limited understanding of biomechanical relevance, beginning with how the murine brain deforms when the head undergoes rapid rotation from blunt impact. This problem makes it difficult to translate some aspects of diffuse axonal injury from mouse to human. To address this gap, we present the two-dimensional strain field of the mouse brain undergoing dynamic rotation in the sagittal plane. Using a high-speed camera with digital image correlation measurements of the exposed mid-sagittal brain surface, we found that pure rotations (no direct impact to the skull) of 100-200 rad/s are capable of producing complex strain fields that evolve over time with respect to rotational acceleration and deceleration. At the highest rotational velocity tested, the largest tensile strains (≥ 21% elongation) in selected regions of the mouse brain approach strain thresholds previously associated with axonal injury in prior work. These findings provide a benchmark to validate the mechanical response in biomechanical computational models predicting diffuse axonal injury, but much work remains in correlating tissue deformation patterns from computational models with underlying neuropathology.

Mechanical behavior and microstructure of porcine brain tissues under pulsed electric fields

Pulsed electric fields are extensively utilized in clinical treatments, such as subthalamic deep brain stimulation, where electric field loading is in direct contact with brain tissue. However, the alterations in brain tissue's mechanical properties and microstructure due to changes in electric field parameters have not received adequate attention. In this study, the mechanical properties and microstructure of the brain tissue under pulsed electric fields were focused on. Herein, a custom indentation device was equipped with a module for electric field loading. Parameters such as pulse amplitude and frequency were adjusted. The results demonstrated that following an indentation process lasting 5 s and reaching a depth of 1000 μm, and a relaxation process of 175 s, the average shear modulus of brain tissue was reduced, and viscosity decreased. At the same amplitude, high-frequency pulsed electric fields had a smaller effect on brain tissue than low-frequency ones. Furthermore, pulsed electric fields induced cell polarization and reduced the proteoglycan concentration in brain tissue. As pulse frequency increased, cell polarization diminished, and proteoglycan concentration decreased significantly. High-frequency pulsed electric fields applied to brain tissue were found to reduce impedance fluctuation amplitude. This study revealed the effect of pulsed electric fields on the mechanical properties and microstructure of ex vivo brain tissue, providing essential information to promote the advancement of brain tissue electrotherapy in clinical settings.

Brain Stiffness Follows Cuprizone-Induced Variations in Local Myelin Content

Brain maturation and neurological diseases are intricately linked to microstructural changes that inherently affect the brain's mechanical behavior. Animal models are frequently used to explore relative brain stiffness changes as a function of underlying microstructure. Here, we are using the cuprizone mouse model to study indentation-derived stiffness changes resulting from acute and chronic demyelination during a 15-week observation period. We focus on the corpus callosum, cingulum, and cortex which undergo different degrees of de- and remyelination and, therefore, result in region-specific stiffness changes. Mean stiffness of the corpus callosum starts at 1.1 ± 0.3 kPa in untreated mice, then cuprizone treatment causes stiffness to drop to 0.6 ± 0.1 kPa by week 3, temporarily increase to 0.9 ± 0.3 kPa by week 6, and ultimately stabilize around 0.7 ± 0.1 kPa by week 9 for the rest of the observation period. The cingulum starts at 3.2 ± 0.9 kPa, then drops to 1.6 ± 0.4 kPa by week 3, and then gradually stabilizes around 1.4 ± 0.3 kPa by week 9. Cortical stiffness exhibits less stiffness variations overall; it starts at 4.2 ± 1.3 kPa, drops to 2.4 ± 0.6 kPa by week 3, and stabilizes around 2.7 ± 0.9 kPa by week 6. We also assess the impact of tissue fixation on indentation-based mechanical tissue characterization. On the one hand, fixation drastically increases untreated mean tissue stiffness by a factor of 3.3 for the corpus callosum, 2.9 for the cingulum, and 3.6 for the cortex; on the other hand, fixation influences interregional stiffness ratios during demyelination, thus suggesting that fixation affects individual brain tissues differently. Lastly, we determine the spatial correlation between stiffness measurements and myelin density and observe a region-specific proportionality between myelin content and tissue stiffness. STATEMENT OF SIGNIFICANCE: Despite extensive work, the relationship between microstructure and mechanical behavior in the brain remains mostly unknown. Additionally, the existing variation of measurement results reported in literature requires in depth investigation of the impact of individual cell and protein populations on tissue stiffness and interregional stiffness ratios. Here, we used microindentation measurements to show that brain stiffness changes with myelin density in the cuprizone-based demyelination mouse model. Moreover, we explored the impact of tissue fixation prior to mechanical characterization because of conflicting results reported in literature. We observe that fixation has a distinctly different impact on our three regions of interest, thus causing region-specific tissue stiffening and, more importantly, changing interregional stiffness ratios.

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.

A Bayesian method with nonlinear noise model to calibrate constitutive parameters of soft tissue

The measured mechanical responses of soft tissue exhibit large variability and errors, especially for the softest brain tissue, while calibrating its constitutive parameters in a deterministic way remains a common practice. Here we implement a Bayesian method considering the nonlinear noise model to calibrate constitutive parameters of brain tissue. A probability model is first developed based on the measured experimental data, likelihood function, and prior function, from which the posterior distributions of model parameters are formulated. The likelihood function considers the nonlinear behaviors of the constitutive response and noise distribution of the experimentally measured data. Meanwhile, the prior predictive distribution is computed to check the probability model preliminarily. Secondly, the Markov Chain Monte Carlo (MCMC) method is used to compute the posterior distributions of model parameters, enabling assessment of parameter uncertainty, correlation, and model calibration error. Finally, the posterior predictive distributions of the overall response, constitutive response, and noise response are computed to validate the probabilistic model, all of which are consistent with the corresponding data. Furthermore, the effect of the prior distribution, experimental data, and noise model on posterior distribution is studied. Our study provides a general approach to calibrating constitutive parameters of soft tissue despite errors and large variability in experimental data.

Acute brain slice elastic modulus decreases over time

A common benchmark in the brain tissue mechanics literature is that the properties of acute brain slices should be measured within 8 h of the experimental animal being sacrificed. The core assumption is that-since there is no substantial protein degradation during this time-there will be no change to elastic modulus. This assumption overlooks the possibility of other effects (such as osmotic swelling) that may influence the mechanical properties of the tissue. To achieve consistent and accurate analysis of brain mechanics, it is important to account for or mitigate these effects. Using atomic force microscopy (AFM), tissue hydration and volume measurements, we find that acute brain slices in oxygenated artificial cerebrospinal fluid (aCSF) with a standard osmolarity of 300 mOsm/l experience rapid swelling, softening, and increases in hydration within the first 2 hours after slicing. Reductions in elastic modulus can be partly mitigated by addition of chondroitinase ABC enzyme (CHABC). Increasing aCSF osmolarity to 400 mOsm/l does not prevent softening but may hasten equilibration of samples to a point where measurements of relative elastic modulus are consistent across experiments.

Development, validation and a case study: The female finite element head model (FeFEHM)

BACKGROUND AND OBJECTIVE

Traumatic brain injuries are one of the leading causes of death and disability in the world. To better understand the interactions and forces applied in different constituents of the human head, several finite element head models have been developed throughout the years, for offering a good cost-effective and ethical approach compared to experimental tests. Once validated, the female finite element head model (FeFEHM) will allow a better understanding of injury mechanisms resulting in neuronal damage, which can later evolve into neurodegenerative diseases.

METHODS

This work encompasses the approached methodology starting from medical images and finite element modelling until the validation process using novel experimental data of brain displacements conducted on human cadavers. The material modelling of the brain is performed using an age-specific characterization of the brain using microindentation at dynamic rates and under large deformation, with a similar age to the patient used to model the FeFEHM.

RESULTS

The numerical displacement curves are in good accordance with the experimental data, displaying similar peak times and values, in all three anatomical planes. The case study result shows a similarity between the pressure fields of the FeFEHM compared to another model, highlighting the future potential of the model.

CONCLUSIONS

The initial objective was met, and a new female finite element head model has been developed with biofidelic brain motion. This model will be used for the assessment of repetitive impact scenarios and its repercussions on the female brain.

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.

Region-Dependent Viscoelastic Properties of Human Brain Tissue Under Large Deformations

This study characterizes the mechanical properties of human brain tissue resected during the course of surgery under multistep indentation loading up to 30% strain. The experimental characterization using fresh, post-operative, human brain tissue is highly advantageous since postmortem times can affect its biomechanical behavior. Although the quasilinear theory of viscoelasticity (QLV) approach has been widely used to model brain tissue mechanical properties, our analysis concluded that the linear viscoelastic approach provided a better fit to the experimental data overall. The only statistically significant regional difference in observed stiffness was between the cortex gray and dentate gyrus. There were no statistically significant age or sex dependent differences, although the data suggested that the cortex white matter in males was stiffer than that in females. Our results can help improve the accuracy of finite element models of brain tissue deformation to predict its response to traumatic brain injury.

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.

Mechanical characterisation of hydrophobic and hydrophilic acrylates used in intraocular lenses through depth sensing indentation

In this work, the mechanical behaviour of hydrophilic and hydrophobic acrylates has been characterised by depth sensing indentation. Time-dependent behaviour has been studied using load-relaxation tests. Experiments have been simulated with a finite element software using a visco-hyperelastic material model. The parameters of this model have been determined using deep learning techniques. The developed material models have been used to mechanically simulate a standard compression test of a prototype intraocular lens.

Glycolytic preconditioning in astrocytes mitigates trauma-induced neurodegeneration

Concussion is associated with a myriad of deleterious immediate and long-term consequences. Yet the molecular mechanisms and genetic targets promoting the selective vulnerability of different neural subtypes to dysfunction and degeneration remain unclear. Translating experimental models of blunt force trauma in C. elegans to concussion in mice, we identify a conserved neuroprotective mechanism in which reduction of mitochondrial electron flux through complex IV suppresses trauma-induced degeneration of the highly vulnerable dopaminergic neurons. Reducing cytochrome C oxidase function elevates mitochondrial-derived reactive oxygen species, which signal through the cytosolic hypoxia inducing transcription factor, Hif1a, to promote hyperphosphorylation and inactivation of the pyruvate dehydrogenase, PDHE1α. This critical enzyme initiates the Warburg shunt, which drives energetic reallocation from mitochondrial respiration to astrocyte-mediated glycolysis in a neuroprotective manner. These studies demonstrate a conserved process in which glycolytic preconditioning suppresses Parkinson-like hypersensitivity of dopaminergic neurons to trauma-induced degeneration via redox signaling and the Warburg effect.

Concussion is a type of traumatic brain injury that results from a sudden blow or jolt to the head. Symptoms can include a passing headache, dizziness, confusion or sensitivity to light, but experiencing multiple concussions can have drastic repercussions in later life.

Studies of professional athletes have shown that those who experience one or more concussions are prone to developing Alzheimer’s and Parkinson’s disease, two well-known neurodegenerative diseases. Both conditions involve the progressive loss or breakdown of nerve cells, called neurons. But exactly how this so-called neurodegeneration of brain cells stems from the original, physical injury remains unclear.

Head trauma may cause damage to the structural support of a cell or disrupt the flow of electrical impulses through neurons. Energy use and production in damaged cells could shift into overdrive to repair the damage. The chemical properties of different types of brain cells could also make some more vulnerable to trauma than others. Besides neurons, star-shaped support cells in the brain called astrocytes, which may have some protective ability, could also be affected.

To investigate which cells may be more susceptible to traumatic injuries, Solano Fonseca et al. modelled the impacts of concussion-like head trauma in roundworms (C. elegans) and mice. In both animals, one type of neuron was extremely vulnerable to cell death after trauma. Neurons that release dopamine, a chemical involved in cell-to-cell communication and the brain’s reward system, showed signs of cell damage and deteriorated after injury. Dopaminergic cells, as these cells are called, are involved in motor coordination, and the loss of dopaminergic cells has been linked to both Alzheimer’s and Parkinson’s disease.

Astrocytes, however, had a role in reducing the death of dopaminergic neurons after trauma. In experiments, astrocytes appeared to restore the balance of energy production to meet the increased energy demands of impacted neurons. Single-cell analyses showed that genes involved in metabolism were switched on in astrocytes to produce energy via an alternative pathway. This energetic shift facilitated via astrocytes may help mitigate against some damage to dopamine-producing neurons after trauma, reducing cell death.

This work furthers our understanding of cellular changes in the concussed brain. More research will be required to better characterise how this immediate trauma to cells, and the subsequent loss of dopaminergic neurons, impacts brain health long-term. Efforts to design effective therapies to slow or reverse these changes could then follow.

Mechanosensation in traumatic brain injury

Traumatic brain injury (TBI) is distinct from other neurological disorders because it is induced by a discrete event that applies extreme mechanical forces to the brain. This review describes how the brain senses, integrates, and responds to forces under both normal conditions and during injury. The response to forces is influenced by the unique mechanical properties of brain tissue, which differ by region, cell type, and sub-cellular structure. Elements such as the extracellular matrix, plasma membrane, transmembrane receptors, and cytoskeleton influence its properties. These same components also act as force-sensors, allowing neurons and glia to respond to their physical environment and maintain homeostasis. However, when applied forces become too large, as in TBI, these components may respond in an aberrant manner or structurally fail, resulting in unique pathological sequelae. This so-called "pathological mechanosensation" represents a spectrum of cellular responses, which vary depending on the overall biomechanical parameters of the injury and may be compounded by repetitive injuries. Such aberrant physical responses and/or damage to cells along with the resulting secondary injury cascades can ultimately lead to long-term cellular dysfunction and degeneration, often resulting in persistent deficits. Indeed, pathological mechanosensation not only directly initiates secondary injury cascades, but this post-physical damage environment provides the context in which these cascades unfold. Collectively, these points underscore the need to use experimental models that accurately replicate the biomechanics of TBI in humans. Understanding cellular responses in context with injury biomechanics may uncover therapeutic targets addressing various facets of trauma-specific sequelae.

A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact

Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost while maintaining a computational accuracy comparable with that of the high-fidelity model is desired. In this study, we constructed magnetic resonance (MR) image-based finite element (FE) models of a mouse brain for simulations of controlled cortical impact. The anatomical details were kept by mapping each image voxel to a corresponding FE mesh element. We constructed a super-resolution neural network that could produce computational results of a refined FE model with a mesh size of 70 μm from a coarse FE model with a mesh size of 280 μm. The peak signal-to-noise ratio of the reconstructed results was 33.26 dB, while the computational speed was increased by 50-fold. This proof-of-concept study showed that using machine learning techniques, MR image-based computational modeling could be applied and evaluated in a timely fashion. This paved ways for fast FE modeling and computation based on MR images. Results also support the potential clinical applications of MR image-based computational modeling of the human brain in a variety of scenarios such as brain impact and intervention.Graphical abstract MR image-based FE models with different mesh sizes were generated for CCI. The training and testing data sets were computed with 5 different impact locations and 3 different impact velocities. High-resolution strain maps were estimated using a SR neural network with greatly reduced computational cost.

A knowledge map analysis of brain biomechanics: Current evidence and future directions

Although brain, one of the most complex organs in the mammalian body, has been subjected to many studies from physiological and pathological points of view, there remain significant gaps in the available knowledge regarding its biomechanics. This article reviews the research trends in brain biomechanics with a focus on injury. We used published scientific articles indexed by Web of Science database over the past 40 years and tried to address the gaps that still exist in this field. We analyzed the data using VOSviewer, which is a software tool designed for scientometric studies. The results of this study showed that the response of brain tissue to external forces has been one of the significant research topics among biomechanicians. These studies have addressed the effects of mechanical forces on the brain and mechanisms of traumatic brain injury, as well as characterized changes in tissue behavior under trauma and other neurological diseases to provide new diagnostic and monitoring methods. In this study, some challenges in the field of brain injury biomechanics have been identified and new directions toward understanding the gaps in this field are suggested.

Tissue mechanics, an important regulator of development and disease

A growing body of work describes how physical forces in and around cells affect their growth, proliferation, migration, function and differentiation into specialized types. How cells receive and respond biochemically to mechanical signals is a process termed mechanotransduction. Disease may arise if a disruption occurs within this mechanism of sensing and interpreting mechanics. Cancer, cardiovascular diseases and developmental defects, such as during the process of neural tube formation, are linked to changes in cell and tissue mechanics. A breakdown in normal tissue and cellular forces activates mechanosignalling pathways that affect their function and can promote disease progression. The recent advent of high-resolution techniques enables quantitative measurements of mechanical properties of the cell and its extracellular matrix, providing insight into how mechanotransduction is regulated. In this review, we will address the standard methods and new technologies available to properly measure mechanical properties, highlighting the challenges and limitations of probing different length-scales. We will focus on the unique environment present throughout the development and maintenance of the central nervous system and discuss cases where disease, such as brain cancer, arises in response to changes in the mechanical properties of the microenvironment that disrupt homeostasis. This article is part of a discussion meeting issue 'Forces in cancer: interdisciplinary approaches in tumour mechanobiology'.

Biomechanical simulation of traumatic brain injury in the rat

BACKGROUND

Traumatic brain injury poses an enormous clinical challenge. Rats are the animals most widely used in pre-clinical experiments. Biomechanical simulations of these experiments predict the distribution of mechanical stress and strain across key tissues. It is in theory possible to dramatically increase our understanding of traumatic brain injury pathophysiology by correlating stress and strain with histological and functional injury outcomes. This review summarizes the state of the art in biomechanical simulation of traumatic brain injury in the rat. It also places this body of knowledge in the context of the wider effort to understand traumatic brain injury in rats and in humans.

METHODS

Peer-reviewed research articles on biomechanical simulation of traumatic brain injury in the rat were reviewed and summarized.

FINDINGS

When mathematical models of traumatic brain injury in the rat first emerged, they relied on scant data regarding biomechanical properties. The data on relevant biomechanical properties has increased recently. However, experimental models of traumatic brain injury in the rat have also become less homogeneous. New and modified models have emerged that are biomechanically distinct from traditional models.

INTERPRETATION

Important progress in mathematical modeling and measurement of biomechanical properties has led to credible, predictive simulations of traditional, experimental models of traumatic brain injury in the rat, such as controlled cortical impact. However, recent trends such as the increasing popularity of closed head models and blast models create new biomechanical challenges. Investigators studying rat brain biomechanics must continue to innovate to keep pace with these developments.

Region and species dependent mechanical properties of adolescent and young adult brain tissue

Traumatic brain injuries, the leading cause of death and disability in children and young adults, are the result of a rapid acceleration or impact of the head. In recent years, a global effort to better understand the biomechanics of TBI has been undertaken, with many laboratories creating detailed computational models of the head and brain. For these models to produce realistic results they require accurate regional constitutive data for brain tissue. However, there are large differences in the mechanical properties reported in the literature. These differences are likely due to experimental parameters such as specimen age, brain region, species, test protocols, and fiber direction which are often not reported. Furthermore, there is a dearth of reported viscoelastic properties for brain tissue at large-strain and high rates. Mouse, rat, and pig brains are impacted at 10/s to a strain of ~36% using a custom-built micro-indenter with a 125 μm radius. It is shown that the resultant mechanical properties are dependent on specimen-age, species, and region, under identical experimental parameters.

Tissue mechanics regulate brain development, homeostasis and disease

All cells sense and integrate mechanical and biochemical cues from their environment to orchestrate organismal development and maintain tissue homeostasis. Mechanotransduction is the evolutionarily conserved process whereby mechanical force is translated into biochemical signals that can influence cell differentiation, survival, proliferation and migration to change tissue behavior. Not surprisingly, disease develops if these mechanical cues are abnormal or are misinterpreted by the cells - for example, when interstitial pressure or compression force aberrantly increases, or the extracellular matrix (ECM) abnormally stiffens. Disease might also develop if the ability of cells to regulate their contractility becomes corrupted. Consistently, disease states, such as cardiovascular disease, fibrosis and cancer, are characterized by dramatic changes in cell and tissue mechanics, and dysregulation of forces at the cell and tissue level can activate mechanosignaling to compromise tissue integrity and function, and promote disease progression. In this Commentary, we discuss the impact of cell and tissue mechanics on tissue homeostasis and disease, focusing on their role in brain development, homeostasis and neural degeneration, as well as in brain cancer.

Protection of cortex by overlying meninges tissue during dynamic indentation of the adolescent brain

Traumatic brain injury (TBI) has become a recent focus of biomedical research with a growing international effort targeting material characterization of brain tissue and simulations of trauma using computer models of the head and brain to try to elucidate the mechanisms and pathogenesis of TBI. The meninges, a collagenous protective tri-layer, which encloses the entire brain and spinal cord has been largely overlooked in these material characterization studies. This has resulted in a lack of accurate constitutive data for the cranial meninges, particularly under dynamic conditions such as those experienced during head impacts. The work presented here addresses this lack of data by providing for the first time, in situ large deformation material properties of the porcine dura-arachnoid mater composite under dynamic indentation. It is demonstrated that this tissue is substantially stiffer (shear modulus, μ=19.10±8.55kPa) and relaxes at a slower rate (τ₁=0.034±0.008s, τ₂=0.336±0.077s) than the underlying brain tissue (μ=6.97±2.26kPa, τ₁=0.021±0.007s, τ₂=0.199±0.036s), reducing the magnitudes of stress by 250% and 65% for strains that arise during indentation-type deformations in adolescent brains.

STATEMENT OF SIGNIFICANCE

We present the first mechanical analysis of the protective capacity of the cranial meninges using in situ micro-indentation techniques. Force-relaxation tests are performed on in situ meninges and cortex tissue, under large strain dynamic micro-indentation. A quasi-linear viscoelastic model is used subsequently, providing time-dependent mechanical properties of these neural tissues under loading conditions comparable to what is experienced in TBI. The reported data highlights the large differences in mechanical properties between these two tissues. Finite element simulations of the indentation experiments are also performed to investigate the protective capacity of the meninges. These simulations show that the meninges protect the underlying brain tissue by reducing the overall magnitude of stress by 250% and up to 65% for strains.