Mechanical Behavior Of Brain Tissue Research Articles

Viscoelastic properties of the ferret brain measured in vivo at multiple frequencies by magnetic resonance elastography

Characterization of the dynamic mechanical behavior of brain tissue is essential for understanding and simulating the mechanisms of traumatic brain injury (TBI). Changes in mechanical properties may also reflect changes in the brain due to aging or disease. In this study, we used magnetic resonance elastography (MRE) to measure the viscoelastic properties of ferret brain tissue in vivo. Three-dimensional (3D) displacement fields were acquired during wave propagation in the brain induced by harmonic excitation of the skull at 400Hz, 600Hz and 800Hz. Shear waves with wavelengths in the order of millimeters were clearly visible in the displacement field, in strain fields, and in the curl of displacement field (which contains no contributions from longitudinal waves). Viscoelastic parameters (storage and loss moduli) governing dynamic shear deformation were estimated in gray and white matter for these excitation frequencies. To characterize the reproducibility of measurements, two ferrets were studied on three different dates each. Estimated viscoelastic properties of white matter in the ferret brain were generally similar to those of gray matter and consistent between animals and scan dates. In both tissue types G′ increased from approximately 3kPa at 400Hz to 7kPa at 800Hz and G″ increased from approximately 1kPa at 400Hz to 2kPa at 800Hz. These measurements of shear wave propagation in the ferret brain can be used to both parameterize and validate finite element models of brain biomechanics.

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.

Age-Dependent Regional Mechanical Properties of the Rat Hippocampus and Cortex

Age-dependent outcomes following traumatic brain injury motivate the study of brain injury biomechanics in experimental animal models at different stages of development. Finite element models of the rat brain are used to better understand the mechanical mechanisms behind these age-dependent outcomes; however, age- and region-specific rat brain tissue mechanical properties are required for biofidelity in modeling. Here, we have used the atomic force microscope (AFM) to measure region-dependent mechanical properties for subregions of the cortex and hippocampus in P10, P17, and adult rats. Apparent elastic modulus increased nonlinearly with indentation strain, and a nonlinear Ogden hyperelastic model was used to fit the force-deflection data. Subregional heterogeneous distributions of mechanical properties changed significantly with age. Apparent elastic modulus was also found to increase overall with age, increasing by >100% between P10 and adult rats. Unconfined compression tests (epsilon=-0.3) were performed on whole slices of the hippocampus and cortex of P10, P17, and adult rats to verify the mechanical properties measured with the AFM. Mean apparent elastic modulus at an indentation strain of 30% from AFM measurements for each region and age correlated well with the long-term elastic modulus measured from 30% unconfined compression tests (slope not significantly different from 1, p>0.05). Protein, lipid, and sulfated glycosaminoglycan content of the brain increased with age and were positively correlated with tissue stiffness, whereas water content decreased with age and was negatively correlated with tissue stiffness. These correlations can be used to hypothesize mechanistic models for describing the mechanical behavior of brain tissue as well as to predict relative differences between brain tissue mechanical properties of other species, at different ages, and for different regions based on differences in tissue composition.

A Biphasic Hyperelastic Model for the Analysis of Fluid and Mass Transport in Brain Tissue

A biphasic hyperelastic finite element model is proposed for the description of the mechanical behavior of brain tissue. The model takes into account finite deformations through an Ogden-type hyperelastic compressible function and a hydraulic conductivity dependent on deformation. The biphasic equations, implemented here for spherical symmetry using an updated Lagrangian algorithm, yielded radial coordinates and fluid velocities that were used with the convective-diffusive equation in order to predict mass transport in the brain. Results of the model were equal to those of a closed-form solution under infinitesimal deformations, however, for a wide range of material parameters, the model predicted important increments in the infusion sphere, reductions of the fluid velocities, and changes in the species content distribution. In addition, high localized deformation and stresses were obtained at the infusion sphere. Differences with the infinitesimal solution may be mainly attributed to geometrical nonlinearities related to the increment of the infusion sphere and not to material nonlinearities.

Assessment of in vivo and post-mortem mechanical behavior of brain tissue using magnetic resonance elastography

The knowledge of in vivo brain tissue mechanical properties is essential in several biomedical engineering fields, such as injury biomechanics and neurosurgery simulation. Almost all existing available data have been obtained in vitro by invasive experimental protocols. However, the difference between in vivo and post-mortem mechanical properties remains poorly known, essentially due to the lack of a common method that could measure them both in vivo and ex vivo. In this study, we report the use of magnetic resonance elastography (MRE) for the non-invasive assessment of in vivo brain tissue viscoelastic properties and for the investigation of their evolution after the death. Experiments were performed on seven adult male rats. Shear storage and loss moduli were measured in vivo, just after death and at post-mortem time of ≈ 24 h . A significant increase in shear storage modulus G ′ of approximately 100% was found to occur just after death ( p = 0.002 ), whereas no significant difference was found between in vivo G ′ and G ′ at 24 h post-mortem time. No significant difference was found between shear loss modulus G ″ in vivo and just after death, whereas a decrease of about 50% was found to occur after 24 h ( p = 0.02 ). These results illustrate the ability of MRE to investigate some of the critical soft tissue biomechanics-related issues, as it can be used as a non-invasive tool for measuring soft tissue viscoelastic properties.

Characterisation of the mechanical behaviour of brain tissue in compression and shear

No validated, generally accepted data set on the mechanical properties of brain tissue exists, not even for small strains. Most of the experimental and methodological issues have previously been addressed for linear shear loading. The objective of this work was to obtain a consistent data set for the mechanical response of brain tissue to either compression or shear. Results for these two deformation modes were obtained from the same samples to reduce the effect of inter-sample variation. Since compression tests are not very common, the influence of several experimental conditions for the compression measurements was analysed in detail. Results with and without initial contact of the sample with the loading plate were compared. The influence of a fluid layer surrounding the sample and the effect of friction were examined and were found to play an important role during compression measurements.To validate the non-linear viscoelastic constitutive model of brain tissue that was developed in Hrapko et al. (Biorheology 43 (2006), 623-636) and has shown to provide a good prediction of the shear response, the model has been implemented in the explicit Finite Element code MADYMO. The model predictions were compared to compression relaxation results up to 15% strain of porcine brain tissue samples. Model simulations with boundary conditions varying within the physical ranges of friction, initial contact and compression rate are used to interpret the compression results.

Mechanical Characterization of Brain Tissue in High-Rate Compression

Mechanical properties of brain tissue in high strain region are indispensable for the analysis of brain damage during traffic accidents. However, accurate data on the mechanical behavior of brain tissue under impact loading condition are sparse. In this study, mechanical properties of porcine brain tissues were characterized in their cylindrical samples cored out from their surface. The samples were compressed in their axial direction at strain rates ranging from 1 to 50 s-1. Stress relaxation test was also conducted following rapid compression with a rise time of ∼30 ms to different strain levels (20-70%). Brain tissue exhibited stiffer responses under higher impact rates: initial elastic modulus was 5.7±1.6, 11.9±3.3, 23.8±10.5 kPa (mean±SD) for strain rate of 1, 10, 50 s-1, respectively. We found that stress relaxation K(t,e) could be analysed in time and strain domains separately. The relaxation response could be expressed as the product of two mutually independent functions of time and strain as: K(t,e)=G(t)σe(e), where σe(e) is an elastic response, i.e., the peak stress in response to a step input of strain e, and G(t) is a reduced relaxation function: G(t)=0.642e-t/0.0207+0.142e-t/0.482+0.216e-t/18.9, i.e., the time-dependent stress response normalized by the peak stress. The reduced relaxation function obtained here will serve as a useful tool to predict mechanical behavior of brain tissue in compression with strain rate greater than 10 s-1.