scholarly journals Insights into the Microstructural Origin of Brain Viscoelasticity

2021 ◽  
Author(s):  
Nina Reiter ◽  
Biswaroop Roy ◽  
Friedrich Paulsen ◽  
Silvia Budday
Keyword(s):  
Brain Tissue ◽  
Computational Models ◽  
Mechanical Response ◽  
Constitutive Models ◽  
Brain Regions ◽  
Tissue Mechanics ◽  
Linear Functions ◽  
Model Parameters ◽  
Microstructural Parameters ◽  
Viscoelastic Constitutive Model

AbstractMechanical aspects play an important role in brain development, function, and disease. Therefore, continuum-mechanics-based computational models are a valuable tool to advance our understanding of mechanics-related physiological and pathological processes in the brain. Currently, mainly phenomenological material models are used to predict the behavior of brain tissue numerically. The model parameters often lack physical interpretation and only provide adequate estimates for brain regions which have a similar microstructure and age as those used for calibration. These issues can be overcome by establishing advanced constitutive models that are microstructurally motivated and account for regional heterogeneities through microstructural parameters.In this work, we perform simultaneous compressive mechanical loadings and microstructural analyses of porcine brain tissue to identify the microstructural mechanisms that underlie the macroscopic nonlinear and time-dependent mechanical response. Based on experimental insights into the link between macroscopic mechanics and cellular rearrangements, we propose a microstructure-informed finite viscoelastic constitutive model for brain tissue. We determine a relaxation time constant from cellular displacement curves and introduce hyperelastic model parameters as linear functions of the cell density, as determined through histological staining of the tested samples. The model is calibrated using a combination of cyclic loadings and stress relaxation experiments in compression. The presented considerations constitute an important step towards microstructure-based viscoelastic constitutive models for brain tissue, which may eventually allow us to capture regional material heterogeneities and predict how microstructural changes during development, aging, and disease affect macroscopic tissue mechanics.

scholarly journals A unified rheological model for cells and cellularised materials

2020 ◽  
Vol 7 (1) ◽  
pp. 190920 ◽  
Author(s):  
A. Bonfanti ◽  
J. Fouchard ◽  
N. Khalilgharibi ◽  
G. Charras ◽  
A. Kabla
Keyword(s):  
Power Law ◽  
Computational Models ◽  
Mechanical Response ◽  
Single Cells ◽  
Relaxation Modulus ◽  
Predictive Modelling ◽  
Tissue Mechanics ◽  
Cell Cortex ◽  
Model Parameters ◽  
Complex Behaviour

The mechanical response of single cells and tissues exhibits a broad distribution of time-scales that often gives rise to a distinctive power-law rheology. Such complex behaviour cannot be easily captured by traditional rheological approaches, making material characterisation and predictive modelling very challenging. Here, we present a novel model combining conventional viscoelastic elements with fractional calculus that successfully captures the macroscopic relaxation response of epithelial monolayers. The parameters extracted from the fitting of the relaxation modulus allow prediction of the response of the same material to slow stretch and creep, indicating that the model captured intrinsic material properties. Two characteristic times, derived from the model parameters, delimit different regimes in the materials response. We compared the response of tissues with the behaviour of single cells as well as intra and extra-cellular components, and linked the power-law behaviour of the epithelium to the dynamics of the cell cortex. Such a unified model for the mechanical response of biological materials provides a novel and robust mathematical approach to consistently analyse experimental data and uncover similarities and differences in reported behaviour across experimental methods and research groups. It also sets the foundations for more accurate computational models of tissue mechanics.

Development of a Biofidelic Material Model for Brain Tissue

2011 ◽  
Author(s):  
Sandeep Kulathu ◽  
David L. Littlefield
Keyword(s):  
Brain Tissue ◽  
Grey Matter ◽  
Mechanical Response ◽  
Constitutive Models ◽  
Material Model ◽  
Small Sample ◽  
Material Models ◽  
Computational Mesh ◽  
Injury Mechanisms ◽  
The Brain

Computational simulations of brain injury mechanisms have advanced to a level of sophistication where in addition to capturing different anatomic regions, the computational mesh is capable of distinguishing white and grey matter in the brain. Brain tissue is typically modeled as an isotropic, viscoelastic material. Experiments have shown that the mechanical response of brain tissue to an external load varies depending on the location from which the tissue is harvested and also the direction of loading. Some researchers have developed anisotropic constitutive models by appealing to the composite material case wherein cylindrical axon fibers are immersed in a cellular matrix. Though such material models have been developed over a small sample, they have not been applied over the entire brain for simulation purposes.

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

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Miao Na ◽  
Timothy J. Beavers ◽  
Abhijit Chandra ◽  
Sarah A. Bentil
Keyword(s):  
Brain Tissue ◽  
Mechanical Response ◽  
Brain Regions ◽  
Von Mises Stress ◽  
Fe Model ◽  
Brain Response ◽  
Fe Method ◽  
Angular Velocities ◽  
Von Mises ◽  
The Brain

Abstract 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.

A Biphasic Viscoelastic Constitutive Model for Brain Tissue

2019 ◽  
Author(s):  
Mohammad Hosseini Farid ◽  
Mohammadreza Ramzanpour ◽  
Mariusz Ziejewski ◽  
Ghodrat Karami
Keyword(s):  
Constitutive Model ◽  
Brain Tissue ◽  
Mechanical Response ◽  
Solid Phase ◽  
Saturated Porous Medium ◽  
Bovine Brain ◽  
Coefficient Of Determination ◽  
Solid Matrix ◽  
Biphasic Model ◽  
Viscoelastic Constitutive Model

Abstract In this study, a rate-dependent biphasic model will be introduced to account for phenomenological behavior of brain tissue. For this purpose, a poro-hyper viscoelastic constitutive model is developed. The tissue is treated as a fluid-saturated porous medium, modeled as biphasic matter constituting of a solid matrix and interstitial liquids fill the porous spaces. The interactions between the two phases are assumed to be governed by Darcy’s law. This suggested model is calibrated with the experimental results of the bovine brain tissue, tested under high deformation rates (10, 100, 1000 mm/sec). The model will successfully take care of the detailed mechanical responses for solid and fluid phases, and their contributions to morphological behavior of this biological tissue. The material parameters of the model have been examined to agree well (R2 ≥ 0.96, where R is the coefficient of determination) with various deformation rates. In addition to representing the complete mechanical response and deformation of the solid phase, this biphasic model demonstrates the flow and diffusion of the liquid through the tissue networks.

The effect of large deformation on Poisson’s ratio of brain white matter: An experimental study

2020 ◽  
pp. 095441192098402
Author(s):  
Faezeh Eskandari ◽  
Zahra Rahmani ◽  
Mehdi Shafieian
Keyword(s):  
Brain Tissue ◽  
Poisson’S Ratio ◽  
Computational Models ◽  
Mechanical Response ◽  
Incompressible Material ◽  
Material Behavior ◽  
Poisson's Ratio ◽  
Unconfined Compression ◽  
Injury Threshold ◽  
Brain White Matter

A more Accurate description of the mechanical behavior of brain tissue could improve the results of computational models. While most studies have assumed brain tissue as an incompressible material with constant Poisson’s ratio of almost 0.5 and constructed their modeling approach according to this assumption, the relationship between this ratio and levels of applied strains has not yet been studied. Since the mechanical response of the tissue is highly sensitive to the value of Poisson’s ratio, this study was designed to investigate the characteristics of the Poisson’s ratio of brain tissue at different levels of applied strains. Samples were extracted from bovine brain tissue and tested under unconfined compression at strain values of 5%, 10%, and 30%. Using an image processing method, the axial and transverse strains were measured over a 60-s period to calculate the Poisson’s ratio for each sample. The results of this study showed that the Poisson’s ratio of brain tissue at strain levels of 5% and 10% was close to 0.5, and assuming brain tissue as an incompressible material is a valid assumption at these levels of strain. For samples under 30% compression, this ratio was higher than 0.5, which could suggest that under strains higher than the brain injury threshold (approximately 18%), tissue integrity was impaired. Based on these observations, it could be concluded that for strain levels higher than the injury threshold, brain tissue could not be assumed as an incompressible material, and new material models need to be proposed to predict the material behavior of the tissue. In addition, the results showed that brain tissue under unconfined compression uniformly stretched in the transverse direction, and the bulging in the samples is negligible.

scholarly journals Experimental analysis and biaxial biomechanical behaviour of ex-vivo sheep trachea

2021 ◽  
Author(s):  
Fulufhelo Nemavhola ◽  
Harry Ngwangwa ◽  
Thanyani Pandelani
Keyword(s):  
Material Properties ◽  
Computational Models ◽  
Ex Vivo ◽  
Constitutive Models ◽  
Longitudinal Direction ◽  
Effective Area ◽  
Model Parameters ◽  
Engineering Strain ◽  
Tracheal Diseases ◽  
Tracheal Tissue

Purpose: The purpose of this study is to investigate the mechanical behaviour of the tracheal tissue under biaxial tensile loading. Furthermore, the study examines the material properties of the tissue through a study of the model parameters for six constitutive models. Materials and methods: The fourteen (n = 13) trachea sheep (Vleis Merino) pieces of tissues measured to be ~ 30 x 20 mm where only the effective area subjected to engineering strain was ~ 25 x 16 mm. In this study, we assume that the tracheal tissue is anisotropic and incompressible, therefore we apply and study the material parameters from six models namely the Fung, Choi-Vito, Holzapfel (2000), Holzapfel (2005), Polynomial (Anisotropic) and Four-Fiber Family models. Results: The results show that the trachea tissue is twice as stiff along the circumferential direction as it is along the longitudinal direction. It is also observed that the material properties are different (non-homogeneous) along the trachea. Conclusions: The findings of this study will benefit computational models for the study of tracheal diseases or injuries. Furthermore, these findings will assist in the development of regenerative medicine for different tracheal pathologies and in the bioengineering of replacement tissue in cases of damage.

Constitutive Modeling of Brain Tissue: Current Perspectives

2016 ◽  
Vol 68 (1) ◽  
Author(s):  
Rijk de Rooij ◽  
Ellen Kuhl
Keyword(s):  
Brain Tissue ◽  
Mechanical Response ◽  
Deformation Gradient ◽  
Constitutive Models ◽  
Time Dependent ◽  
Elastic Behavior ◽  
Integral Approach ◽  
Mechanical Stimuli ◽  
The Past ◽  
The Brain

Modeling the mechanical response of the brain has become increasingly important over the past decades. Although mechanical stimuli to the brain are small under physiological conditions, mechanics plays a significant role under pathological conditions including brain development, brain injury, and brain surgery. Well calibrated and validated constitutive models for brain tissue are essential to accurately simulate these phenomena. A variety of constitutive models have been proposed over the past three decades, but no general consensus on these models exists. Here, we provide a comprehensive and structured overview of state-of-the-art modeling of the brain tissue. We categorize the different features of existing models into time-independent, time-dependent, and history-dependent contributions. To model the time-independent, elastic behavior of the brain tissue, most existing models adopt a hyperelastic approach. To model the time-dependent response, most models either use a convolution integral approach or a multiplicative decomposition of the deformation gradient. We evaluate existing constitutive models by their physical motivation and their practical relevance. Our comparison suggests that the classical Ogden model is a well-suited phenomenological model to characterize the time-independent behavior of the brain tissue. However, no consensus exists for mechanistic, physics-based models, neither for the time-independent nor for the time-dependent response. We anticipate that this review will provide useful guidelines for selecting the appropriate constitutive model for a specific application and for refining, calibrating, and validating future models that will help us to better understand the mechanical behavior of the human brain.

scholarly journals Identifiability of Tissue Material Parameters from Uniaxial Tests using Multi-start Optimization

2020 ◽  
Author(s):  
Babak N. Safa ◽  
Michael H. Santare ◽  
C. Ross Ethier ◽  
Dawn M. Elliott
Keyword(s):  
Experimental Data ◽  
Constitutive Model ◽  
Constitutive Models ◽  
Data Fitting ◽  
Tissue Mechanics ◽  
Lateral Strain ◽  
Model Parameters ◽  
Fiber Reinforced ◽  
Material Parameters ◽  
Tissue Material

AbstractDetermining tissue biomechanical material properties from mechanical test data is frequently required in a variety of applications, e.g. tissue engineering. However, the validity of the resulting constitutive model parameters is the subject of debate in the field. Common methods to perform fitting, such as nonlinear least-squares, are known to be subject to several limitations, most notably the uniqueness of the fitting results. Parameter optimization in tissue mechanics often comes down to the “identifiability” or “uniqueness” of constitutive model parameters; however, despite advances in formulating complex constitutive relations and many classic and creative curve-fitting approaches, there is no accessible framework to study the identifiability of tissue material parameters. Our objective was to assess the identifiability of material parameters for established constitutive models of fiber-reinforced soft tissues, biomaterials, and tissue-engineered constructs. To do so, we generated synthetic experimental data by simulating uniaxial tension and compression tests, commonly used in biomechanics. We considered tendon and sclera as example tissues, using constitutive models that describe these fiber-reinforced tissues. We demonstrated that not all of the model parameters of these constitutive models were identifiable from uniaxial mechanical tests, despite achieving virtually identical fits to the stress-stretch response. We further show that when the lateral strain was considered as an additional fitting criterion, more parameters are identifiable, but some remain unidentified. This work provides a practical approach for addressing parameter identifiability in tissue mechanics.Statement of SignificanceData fitting is a powerful technique commonly used to extract tissue material parameters from experimental data, and which thus has applications in tissue biomechanics and engineering. However, the problem of “uniqueness” or “identifiability” of the fit parameters is a significant issue, limiting the fit results’ validity. Here we provide a novel method to evaluate data fitting and assess the uniqueness of results in the tissue mechanics constitutive models. Our results indicate that the uniaxial stress-stretch experimental data are not adequate to identify all the tissue material parameters. This study is of potential interest to a wide range of readers because of its application for the characterization of other engineering materials, while addressing the problem of uniqueness of the fitted results.

scholarly journals Mechanical alterations of the hippocampus in the APP/PS1 Alzheimer’s disease mouse model

2020 ◽  
Author(s):  
Nelda Antonovaite ◽  
Lianne A. Hulshof ◽  
Christiaan F.M. Huffels ◽  
Elly M. Hol ◽  
Wytse J. Wadman ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mouse Model ◽  
Brain Tissue ◽  
Mechanical Testing ◽  
Brain Slices ◽  
Brain Regions ◽  
Tissue Mechanics ◽  
Testing Procedures ◽  
The Brain

AbstractThere is increasing evidence of altered tissue mechanics in neurodegeneration. However, due to difficulties in mechanical testing procedures and the complexity of the brain, there is still little consensus on the role of mechanics in the onset and progression of neurodegenerative diseases. In the case of Alzheimer’s disease (AD), magnetic resonance elastography (MRE) studies have indicated viscoelastic differences in the brain tissue of AD and healthy patients. However, there is a lack of viscoelastic data from contact mechanical testing at higher spatial resolution. Therefore, we report viscoelastic maps of the hippocampus obtained by a dynamic indentation on brain slices from the APP/PS1 mouse model where individual brain regions are resolved. A comparison of viscoelastic parameters shows that regions in the hippocampus of the APP/PS1 mice are significantly stiffer than wild-type (WT) mice and have increased viscous dissipation. Furthermore, indentation mapping at the cellular scale directly on the plaques and their surroundings did not show local alterations in stiffness although overall mechanical heterogeneity of the tissue was high (SD~40%). Therefore, reported mechanical alterations at a tissue scale indicates global remodeling of the brain tissue structure.

scholarly journals A unified rheological model for cells and cellularised materials

2019 ◽  
Author(s):  
A Bonfanti ◽  
J Fouchard ◽  
N Khalilgharibi ◽  
G Charras ◽  
A Kabla
Keyword(s):  
Power Law ◽  
Computational Models ◽  
Mechanical Response ◽  
Single Cells ◽  
Relaxation Modulus ◽  
Predictive Modelling ◽  
Diagnostic Methods ◽  
Cell Cortex ◽  
Model Parameters ◽  
Complex Behaviour

The mechanical response of single cells and tissues exhibits a broad distribution of time scales that gives often rise to a distinctive power-law regime. Such complex behaviour cannot be easily captured by traditional rheological approaches, making material characterisation and predictive modelling very challenging. Here, we present a novel model combining conventional viscoelastic elements with fractional calculus that successfully captures the macroscopic relaxation response of epithelial monolayers. The parameters extracted from the fitting of the relaxation modulus allow prediction of the response of the same material to slow stretch and creep, indicating that the model captured intrinsic material properties. Two characteristic times can be derived from the model parameters, and together these explain different qualitative behaviours observed in creep after genetic and chemical treatments. We compared the response of tissues with the behaviour of single cells as well as intra and extra-cellular components, and linked the power-law behaviour of the epithelium to the dynamics of the cell cortex. Such a unified model for the mechanical response of biological materials provides a novel and robust mathematical approach for diagnostic methods based on mechanical traits as well as more accurate computational models of tissues mechanics.

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