Properties Of Human Brain Tissue Research Articles

Identifying composition-mechanics relations in human brain tissue based on neural-network-enhanced inverse parameter identification

The mechanical properties of human brain tissue remain far from being fully understood. One aspect that has gained more attention recently is their regional dependency, as the brain’s microstructure varies significantly from one region to another. Understanding the correlation between tissue components and the mechanical behavior is an important step toward better understanding how human brain tissue properties change in space and time and to develop highly spatially resolved constitutive models for large-scale brain simulations. Here, we analyze the correlation between human brain tissue components quantified through enzyme-linked immunosorbent assays (ELISA) and material parameters obtained through an inverse parameter identification scheme based on a hyperelastic Ogden model and multimodal mechanical testing data for eight regions of the brain. We use neural networks as a metamodel to save computational costs. The networks are trained on finite element simulation outputs and are able to replace the simulations in the initial optimization step. We identified strong dependencies between mechanical properties and Iba1 associated with microglia cells, collagen VI, GFAP associated with astrocytes, and collagen IV. These results advance our understanding of microstructure-mechanics relations in human brain tissue and will contribute to the development of highly spatially resolved microstructure-informed constitutive models.

Hydrogels with brain tissue-like mechanical properties in complex environments

In surgical training and experimental research, brain tissues immersed in cerebrospinal fluid often exhibit complex deformation and strain rate effects that can compromise their reliability and stability. Therefore, it is essential to develop a high-fidelity human brain tissue simulant material that serves as a physical surrogate model to understand its mechanical behavior, such as traumatic brain injury (TBI). However, the existing simulant materials have failed to meet the required mechanical properties. This study presents a composite hydrogel consisting of both a rigid polysaccharides network (Sodium alginate and Pectin) and a flexible polyacrylamide network, exhibiting brain tissue-like mechanical properties under various solution environments and strain rates. The results show that nonlinear mechanical behavior and good similarity under various external environments (artificial cerebrospinal fluid, normal saline, deionized water, and air environments) and different strain rates (0.001 s−1,900 s−1,1700 s−1). By analyzing the experimental data and theoretical analysis, we examine the effects of complex environments on the mechanical behavior of composite hydrogel and porcine brain tissue. Given that the properties of human brain tissue resemble those of porcine brain tissue, our work has significant reference value in realizing surgical training and advancing related research in biomedical engineering.

Development and mechanical characterization of artificial surrogates for brain tissues

In the 21st century, trauma injury due to direct head impact in accidents and collisions or blast exposure is the most important research area, which is poorly understood to date. Due to ethical and biosafety concerns, limited studies have reported on the experimental research on brain tissue and have investigated the mechanics and mechanical properties of human brain tissue. In this work, novel surrogates were fabricated using a castable and low-cost material system to imitate the mechanical behavior of the human brain tissue. The tissue surrogates were developed through iterative mixing of a four-part polymeric material at specific ratios to mimic the mechanical properties of the natural human brain tissue. The tissue surrogates were tested at six different displacement rates (low to high) to simulate a range of loading scenarios. Hyperelastic material models were employed to characterize the non-linear behavior of the fabricated brain tissue surrogates. Such tissue surrogates with precisely characterized mechanical properties would be beneficial for studying a wide range of traumatic brain injuries and simulating brain surgeries for training purposes.

Effect of static offsets on the nonlinear dynamic mechanical properties of human brain tissue

This study focuses on the variations in the brain tissue dynamic behaviour pointing out new insight into the material nonlinear viscoelasticity. Shear dynamic response curves are obtained in different working conditions in terms of strain sweep and superimposed static compression offsets (SCO) applied in orthogonal direction to the shear. The strain sweep mode is used to study the storage and loss moduli dependence on the amplitude of the applied strain. It is found that the material exhibits linear viscoelastic behaviour up to about 0.1% strain amplitude. Above this critical threshold, the storage modulus G′ decreases rapidly with increasing dynamic strain amplitude and this effect is gradually intensified as the SCO are increased. In addition, it is observed that the loss factor (G’’/G′) increases by increasing the SCO applied to the specimens. The dynamic strain amplitude results of the storage modulus reveal that the elastic component of the brain tissue's stiffness (G′) evaluated at low strain strongly increases with increasing static superimposed compression strain while the loss factor in the same strain range appears to be SCO independent. Finally, dynamic stiffness recovery after a large strain deformation is considered. The reduction in low amplitude dynamic modulus and subsequent recovery kinetics due to a perturbation is found to be independent of the level of the SCO. The same assessments were carried out on 5 consecutive strain sweep cycle loading. It has been noticed that at the last cycle, the dissipation peak is reduced, and the non-linearity of the curve begins earlier. This could be explained by the effects of cerebral edema on cells and their surrounding environment.

Dissecting the complexities of Alzheimer disease with in vitro models of the human brain.

Alzheimer disease (AD) is the most prevalent type of dementia. It is marked by severe memory loss and cognitive decline, and currently has limited effective treatment options. Although individuals with AD have common neuropathological hallmarks, emerging data suggest that the disease has a complex polygenic aetiology, and more than 25 genetic loci have been linked to an elevated risk of AD and dementia. Nevertheless, our ability to decipher the cellular and molecular mechanisms that underlie genetic susceptibility to AD, and its progression and severity, remains limited. Here, we discuss ongoing efforts to leverage genomic data from patients using cellular reprogramming technologies to recapitulate complex brain systems and build in vitro discovery platforms. Much attention has already been given to methodologies to derive major brain cell types from pluripotent stem cells. We therefore focus on technologies that combine multiple cell types to recreate anatomical and physiological properties of human brain tissue in vitro. We discuss the advances in the field for modelling four domains that have come into view as key contributors to the pathogenesis of AD: the blood-brain barrier, myelination, neuroinflammation and neuronal circuits. We also highlight opportunities for the field to further interrogate the complex genetic and environmental factors of AD using in vitro models.

Towards animal surrogates for characterising large strain dynamic mechanical properties of human brain tissue

The regional dynamic mechanical properties of mouse, rat, pig, and human brain tissue were compared directly in this first-of-its-kind study. Our results suggest the use of pig or mouse brain tissue as suitable surrogates to characterise human brain tissue. The importance of this work is highlighted by the extensive use of constitutive data from animal brain tissue in traumatic brain injury research in the absence of human brain tissue data without any direct evidence supporting their use. Indentation force-relaxation experiments were performed on mouse, rat, pig, and human brains at 10/s strain rate up to 35% strain to determine the dynamic mechanical properties of brain tissue. Finite element models have become useful tools to investigate the biomechanics of traumatic brain injury - a global leading cause of death and disability and a risk factor for developing neurodegenerative diseases. However, these models require accurate constitutive data for brain tissue to produce reliable results. The results presented here provide validation for the use of pig and mouse brain tissue data in such models. Statement of SignificanceThe significance of this work is underscored by the extensive use of animal brain tissue as a surrogate for human brain tissue without any direct evidence supporting the validity of their use. For the first time ever, we demonstrate that porcine and murine brain tissue can be used as surrogates for human brain tissue under dynamic loading conditions. These findings will allow researchers to select appropriate animal surrogates for human brain tissue under dynamic loading conditions. Furthermore, our findings support the use of animal surrogate data to improve the fidelity of computational models of the human brain, and provide experimental data to develop constitutive models of brain tissue.

Regional characterization of the dynamic mechanical properties of human brain tissue by microindentation

Traumatic brain injury (TBI) is an important cause of mortality and morbidity worldwide. Finite element models of the human head are used widely to simulate TBI loading scenarios, to improve the understanding of the mechanical pathogenesis of head trauma. The reliability of such computational models depends strongly on the accuracy of the mechanical properties of the different components of the brain. Here, we address the shortage of high-quality data on the region-specific properties of human brain tissue at dynamic rates and under large deformation. We mechanically characterized 12 different regions of the human brain including cerebrum, cerebellum and brainstem through n=595 force-relaxation experiments with a custom-built micro-indentation apparatus. We imposed up to 35% strain at 10/s strain rate, i.e. values representative for TBI. Pronounced differences in mechanical response were observed across the brain. This work both highlights and addresses the need to assign accurate, region-specific viscoelastic properties to different brain regions in finite element head models.

Development of phantom material that resembles compression properties of human brain tissue for training models

There is a need to quantify and reproduce the mechanical behavior of brain tissue for a variety of applications from designing proper training models for surgeons to enabling research on the effectiveness of personal protective gear, such as football helmets. The mechanical response of several candidate phantom materials, including hydrogels and emulsions, was characterized and compared to porcine brain tissue under similar strains and strain rates. Some candidate materials were selected since their compositions were similar to brain tissue, such as emulsions that mimic the high content of lipids. Others, like silicone, were included since these are currently used as phantom materials. The mechanical response of the emulsion was closer to that of the native porcine brain tissue than the other candidates. The emulsions, created by addition of oil to a hydrogel, were able to withstand compressive strain greater than 40%. The addition of lipids in the emulsions also prevented the syneresis typically seen with hydrogel materials. This allowed the emulsion material to undergo freeze-thaw cycles with no significant change in their mechanical properties.

Mechanical Characterization of Human Brain Tissue and Soft Dynamic Gels Exhibiting Electromechanical Neuro-Mimicry.

Synthetic hydrogels are an important class of materials in tissue engineering, drug delivery, and other biomedical fields. Their mechanical and electrical properties can be tuned to match those of biological tissues. In this work, hydrogels that exhibit both mechanical and electrical biomimicry are reported. The presented dual networks consist of supramolecular networks formed from 2:1 homoternary complexes of imidazolium-based guest molecules in cucubit[8]uril and covalent networks of oligoethylene glycol-(di)methacrylate. The viscoelastic properties of human brain tissues are also investigated. The mechanical properties of the dual network gels are benchmarked against the human tissue, and it is found that they both are neuro-mimetic and exhibit cytocompatibility in a neural stem cell model.

Effect of bulk modulus on deformation of the brain under rotational accelerations.

Traumatic brain injury such as that developed as a consequence of blast is a complex injury with a broad range of symptoms and disabilities. Computational models of brain biomechanics hold promise for illuminating the mechanics of traumatic brain injury (TBI) and for developing preventive devices. However, reliable material parameters are needed for models to be predictive. Unfortunately, the properties of human brain tissue are difficult to measure, and the bulk modulus of brain tissue in particular is not well-characterized. Thus, a wide range of bulk modulus values are used in computational models of brain biomechanics, spanning up to three orders of magnitude in the differences between values. However, the sensitivity of these variations on computational predictions is not known. In this work, we study the sensitivity of a 3D computational human head model to various bulk modulus values. A subject-specific human head model was constructed from T1-weighted MRI images at 2 mm3 voxel resolution. Diffusion tensor imaging provided data on spatial distribution and orientation of axonal fiber-bundles for modeling white-matter anisotropy. Non-injurious, full-field brain deformations in a human volunteer were used to assess the simulated predictions. The comparison suggests that a bulk modulus value on the order of GPa gives the best agreement with experimentally measured in vivo deformation in the human brain. Further, simulations of injurious loading suggest that bulk modulus values on the order of GPa provide the closest match with the clinical findings in terms of predicated injured regions and extent of injury.

Characterizing the optical properties of human brain tissue with high numerical aperture optical coherence tomography.

Quantification of tissue optical properties with optical coherence tomography (OCT) has proven to be useful in evaluating structural characteristics and pathological changes. Previous studies primarily used an exponential model to analyze low numerical aperture (NA) OCT measurements and obtain the total attenuation coefficient for biological tissue. In this study, we develop a systematic method that includes the confocal parameter for modeling the depth profiles of high NA OCT, when the confocal parameter cannot be ignored. This approach enables us to quantify tissue optical properties with higher lateral resolution. The model parameter predictions for the scattering coefficients were tested with calibrated microsphere phantoms. The application of the model to human brain tissue demonstrates that the scattering and back-scattering coefficients each provide unique information, allowing us to differentially identify laminar structures in primary visual cortex and distinguish various nuclei in the midbrain. The combination of the two optical properties greatly enhances the power of OCT to distinguish intricate structures in the human brain beyond what is achievable with measured OCT intensity information alone, and therefore has the potential to enable objective evaluation of normal brain structure as well as pathological conditions in brain diseases. These results represent a promising step for enabling the quantification of tissue optical properties from high NA OCT.

Regional mechanical properties of human brain tissue for computational models of traumatic brain injury.

We present the first mechanical characterization of fresh, post-operative human brain tissue using an indentation loading mode. Indentation generates highly localized data, allowing structure-specific mechanical properties to be determined from small tissue samples resected during surgery. It also avoids pitfalls of cadaveric tissue and allows data to be collected before degenerative processes alter mechanical properties. To correctly predict traumatic brain injury, finite element models must calculate intracranial deformation during head impact. The functional consequences of injury depend on the anatomical structures injured. Therefore, morbidity depends on the distribution of deformation across structures. Accurate prediction of structure-specific deformation requires structure-specific mechanical properties. This data will facilitate deeper understanding of the physical mechanisms that lead to traumatic brain injury.

Surgeon assessment versus laboratory measurement: brain tissue mechanics quantified by expert opinion

Transcranial ultrasound (tUS) is a plausible means of measuring motion of the brain due to traumatic force or structural abnormalities. However, limited data in the literature on the mechanical properties of human brain tissue make it practically impossible to develop a mechanically realistic ultrasound phantom that can mimic the brain under these conditions. The present study aimed to determine, by indirect qualitative assessment, the stiffness of in-vivo brain tissue. Six surgeon examiners under blinded conditions physically examined 16 gelatin-based tissue phantoms (0.020-0.095 g/mL) to determine which sample best matched the stiffness of brain tissue in-vivo. Ten cylindrical phantom samples, acoustically matched to human brain tissue, were prepared based on the examiners’ responses (0.030±0.008 g/mL), and stress-strain behavior was characterized under compression up to 50% strain. The average compressive stress at 50% strain was 10.3±1.4 kPa. These results correlate well with those previously reported in the literature, and add a practical measure of brain stiffness to the paucity of information available on human brain tissue mechanics. Furthermore, it is potentially an initial step in developing mechanically realistic models of structural disorders of the brain and traumatic brain injury to evaluate theories on the brain’s mechanical behavior under these circumstances.

Investigation of noise effect on image quality in microwave head imaging systems

The effect of noise on the ability to detect strokes in the human head using microwave-based systems is investigated using both simulations and measurements. The simulations, which are implemented using the full-wave electromagnetic solver (CST), utilise a realistic numerical head model generated from magnetic resonance imaging slices. On the other hand, the experiments are performed using a wideband microwave system with a frequency range of 1-4 GHz. The experiments use a head phantom consisting of materials that accurately emulate (within 3% of reported properties of human brain tissues) the main tissues found in the human brain. It is shown, in both of the simulations and measurements, that a minimum signal-to-noise ratio of around 10 dB is required to accurately detect the presence of a stroke. Below this level, the microwave-based system either does not detect or falsely indicates the location of the stroke. Based on the presented results, the required microwave power to achieve acceptable detection is well within the recommended safe levels.

Fitted hyperelastic parameters for Human brain tissue from reported tension, compression, and shear tests

The mechanical properties of human brain tissue are the subject of interest because of their use in understanding brain trauma and in developing therapeutic treatments and procedures. To represent the behavior of the tissue, we have developed hyperelastic mechanical models whose parameters are fitted in accordance with experimental test results. However, most studies available in the literature have fitted parameters with data of a single type of loading, such as tension, compression, or shear. Recently, Jin et al. (Journal of Biomechanics 46:2795−2801, 2013) reported data from ex vivo tests of human brain tissue under tension, compression, and shear loading using four strain rates and four different brain regions. However, they do not report parameters of energy functions that can be readily used in finite element simulations. To represent the tissue behavior for the quasi-static loading conditions, we aimed to determine the best fit of the hyperelastic parameters of the hyperfoam, Ogden, and polynomial strain energy functions available in ABAQUS for the low strain rate data, while simultaneously considering all three loading modes. We used an optimization process conducted in MATLAB, calling iteratively three finite element models developed in ABAQUS that represent the three loadings. Results showed a relatively good fit to experimental data in all loading modes using two terms in the energy functions. Values for the shear modulus obtained in this analysis (897−1653Pa) are in the range of those presented in other studies. These energy-function parameters can be used in brain tissue simulations using finite element models.

A comprehensive experimental study on material properties of human brain tissue

A comprehensive study on the biomechanical response of human brain tissue is necessary to investigate traumatic brain injury mechanisms. Published brain material property studies have been mostly performed under a specific type of loading, which is insufficient to develop accurate brain tissue constitutive equations. In addition, inconsistent or contradictory data in the literature made it impossible for computational model developers to create a single brain material model that can fit most, if not all, experimental results.In the current study, a total of 240 brain tissue specimens were tested under tension (n=72), compression (n=72), and shear (n=96) loading modes at varying strain rates. Gray–white matter difference, regional difference, and directional difference within white matter were also investigated.Stress–strain relationships of human brain tissue were obtained up to 50% of engineering strain. Strain rate dependency was observed under all three loading modes. White matter was stiffer than gray matter in compression and shear. Corona radiata was found to be stiffer than cortex, thalamus, and corpus callosum in tension and compression. Directional dependency of white matter was observed under shear loading.

Towards child versus adult brain mechanical properties

The characterization of brain tissue mechanical properties is of crucial importance in the development of realistic numerical models of the human head. While the mechanical behavior of the adult brain has been extensively investigated in several studies, there is a considerable paucity of data concerning the influence of age on mechanical properties of the brain. Therefore, the implementation of child and infant head models often involves restrictive assumptions like properties scaling from adult or animal data. The present study presents a step towards the investigation of the effects of age on viscoelastic properties of human brain tissue from a first set of dynamic oscillatory shear experiments. Tests were also performed on three different locations of brain (corona radiata, thalamus and brainstem) in order to investigate regional differences. Despite the limited number of child brain samples a significant increase in both storage and loss moduli occurring between the age of 5 months and the age of 22 months was found, confirmed by statistical Student’s t-tests (p=0.104,0.038 and 0.054 for respectively corona radiata, thalamus and brain stem samples locations respectively). The adult brain appears to be 3–4 times stiffer than the young child one. Moreover, the brainstem was found to be approximately 2–3 times stiffer than both gray and white matter from corona radiata and thalamus. As a tentative conclusion, this study provides the first rheological data on the human brain at different ages and brain regions. This data could be implemented in numerical models of the human head, especially in models concerning pediatric population.

Poster — Thur Eve — 09: Effects of Small Sample Size on Diffuse Reflectance Spectroscopy for the Identification of Brain Tumours

An optical system using diffuse reflectance spectroscopy (DRS) and time‐resolved laser induced fluorescence spectroscopy (TR‐LIFS) is being developed to identify surgical margins during brain tumour resection. Since initial trials will be performed ex vivo on small samples, we investigated our ability to perform DRS for these small sample volumes. We examined both the effects of decreasing sample volume on the DRS signal and decreasing probe size on our ability to recover optical properties. Sample volume effects were studied by performing DRS measurements on bovine brain tissue samples of various sizes and comparing the signals to those for very large samples. A decrease in DRS signal strength was observed at sample volumes of less than 500 mm3. Probe size effects were studied using Monte Carlo simulations to obtain the spatially‐resolved reflectance using the optical properties of human brain tissue. Varying amounts of noise were added to the reflectance, and optical properties were recovered by fitting to Monte Carlo generated reflectance curves. The recovered properties were compared to their actual values as the maximum radius used for fitting was reduced. Our ability to recover the absorption coefficient was improved when fitting to absolute reflectance data.

Imaging electric properties of human brain tissues by B1 mapping: A simulation study

The electric properties (EPs) of biological tissues, i.e., the electric conductivity and permittivity, can provide important diagnostic information in cancer detection and characterization. The conductivity also plays an important role in specific absorption rate (SAR) calculation, which is a major concern in high-field Magnetic Resonance Imaging (MRI). In MRI systems the radio frequency (RF) radiation is dependent on the object's EPs by Maxwell Equations, and we can reconstruct the EPs distribution by measuring the active transverse component of the applied rotating magnetic field in MRI systems (known as B1-mapping). Applying previously proposed Dual-excitation algorithm, we conducted a series of computer simulations on a three-dimensional five-tissue anatomically accurate head model using the Finite Element Method (FEM). The reconstruction results utilizing several RF coils were examined and compared, and the present simulation results indicate that the TEM coil could produce the optimal result. The Specific energy Absorption Rate (SAR) within the head was also estimated using the reconstructed electric properties distribution, and the promising results suggest our approach might provide an efficient way for in-vivo SAR determination in high field MRI.

A mixture of ferritin and magnetite nanoparticles mimics the magnetic properties of human brain tissue

Magnetic properties of a two-component system, consisting of horse spleen ferritin (HoSF) which contains a $5--8\phantom{\rule{0.3em}{0ex}}\mathrm{nm}$ sized antiferromagnetic ferrihydrite $(5{\mathrm{Fe}}_{2}{\mathrm{O}}_{3}∙9{\mathrm{H}}_{2}\mathrm{O})$ core and ferrimagnetic magnetite $({\mathrm{Fe}}_{3}{\mathrm{O}}_{4})$ nanoparticles (MNP) with an average size of $10--20\phantom{\rule{0.3em}{0ex}}\mathrm{nm}$, have been investigated by using four different methods: induced magnetization versus (1) temperature and (2) field; (3) AC susceptibility; and (4) first-order reversal curves (FORC). All measurements were done on a mixed system of HoSF and MNP, as well as separately on the individual components. The average blocking temperature $({T}_{\mathrm{B}})$ of the mixed system at $50\phantom{\rule{0.3em}{0ex}}\mathrm{mT}$ is $15.6\phantom{\rule{0.3em}{0ex}}\mathrm{K}$, which is a shift towards higher temperatures compared to pure HoSF $({T}_{\mathrm{B}}=12\phantom{\rule{0.3em}{0ex}}\mathrm{K})$. The contribution of the MNP component to magnetic ordering is evident only as a separation of the zero-field-cooled and field-cooled measurement curves. ac susceptibility is dominated by the ferrimagnetic MNP and shows strong frequency dependence. The peak ac susceptibilities can be described by the Vogel-Fulcher law, indicating the influence of interactions within the system. Hysteresis measurements at $5\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ show a wasp-waisted shape due to the mixture of a high coercivity phase (HoSF) with a low coercivity phase (MNP). Initial magnetization curves above ${T}_{\mathrm{B}}$ can be fitted by a sum of Langevin functions, showing superparamagnetic behavior of both components. FORC diagrams are effective in illustrating the change from that of blocked MNP particles together with the superparamagnetic HoSF at $20\phantom{\rule{0.3em}{0ex}}\mathrm{K}$ to purely superparamagnetic behavior in both components above $50\phantom{\rule{0.3em}{0ex}}\mathrm{K}$. We conclude that the mixed nanoparticle system is a good model for complex natural samples, such as human brain tissue.