Tissue-Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

From structure to mechanics: exploring the role of axons and interconnections in anisotropic behavior of brain white matter

According to various experimental studies, the role of axons in the brain's white matter (WM) is still a subject of debate: Is the role of axons in brain white matter (WM) limited to their functional significance, or do they also play a pivotal mechanical role in defining its anisotropic behavior? Micromechanics and computational models provide valuable tools for scientists to comprehend the underlying mechanisms of tissue behavior, taking into account the contribution of microstructures. In this review, we delve into the consideration of strain level, strain rates, and injury threshold to determine when WM should be regarded as anisotropic, as well as when the assumption of isotropy can be deemed acceptable. Additionally, we emphasize the potential mechanical significance of interconnections between glial cells-axons and glial cells-vessels. Moreover, we elucidate the directionality of WM stiffness under various loading conditions and define the possible roles of microstructural components in each scenario. Ultimately, this review aims to shed light on the significant mechanical contributions of axons in conjunction with glial cells, paving the way for the development of future multiscale models capable of predicting injuries and facilitating the discovery of applicable treatments.

The pulsing brain: state of the art and an interdisciplinary perspective

Understanding the pulsing dynamics of tissue and fluids in the intracranial environment is an evolving research theme aimed at gaining new insights into brain physiology and disease progression. This article provides an overview of related research in magnetic resonance imaging, ultrasound medical diagnostics and mathematical modelling of biological tissues and fluids. It highlights recent developments, illustrates current research goals and emphasizes the importance of collaboration between these fields.

Mechanical properties of fresh rhesus monkey brain tissue

Studying brain tissue mechanics is critical for understanding how the brain's physical properties influence its biological functions. Non-human primates, such as rhesus monkeys, are a key translational model for human neuroscience research, yet their brain tissue mechanics remain poorly understood. We report the mechanical properties of rhesus monkey white (corona radiata, CR) and gray (basal ganglia, BG) matter during compression relaxation, tension relaxation, tension-compression cycling (strain = 0.15, nCR = 21, nBG = 14), and shear cycling (strain = 0.3, nCR = 17, nBG = 9). Compression relaxation yields short and long-term time constants of 1.13 ± 0.041 s and 26.3 ± 0.68 s for CR and 1.22 ± 0.046 s and 28.3 ± 0.70 s for BG. Tension relaxation yields short and long-term time constants of 1.10 ± 0.052 s and 28.2 ± 0.82 s for CR and 1.19 ± 0.052 s and 29 ± 1.3 s for BG. Tension-compression cycling yields elastic moduli (E₁, E₂, E₃) of 36 ± 3.8 kPa, 0.61 ± 0.096 kPa, and 9.3 ± 0.90 kPa for CR and 27 ± 4.8 kPa, 0.68 ± 0.092 kPa, and 8 ± 1.0 kPa for BG. Shear cycling yields E₁, E₂, and E₃ of 3.9 ± 0.77 kPa, 0.19 ± 0.034 kPa, and 3.1 ± 0.40 kPa for CR and 2.8 ± 0.52 kPa, 0.18 ± 0.058 kPa, and 3.2 ± 0.53 kPa for BG. Hysteresis areas are also captured during tension-compression and shear cycling. These findings extend the translatability of rhesus monkey models for neuroscience. STATEMENT OF SIGNIFICANCE: While rhesus monkeys are a valuable translational model in human neuroscience research, there is a huge gap in knowledge about rhesus monkey brain tissue mechanics. This study serves to increase our understanding of rhesus monkey brain tissue mechanics and is the first to report the stiffness, time constant, and hysteresis parameters for rhesus monkey brain tissue in compression, tension, and shear for both the corona radiata and basal ganglia. The data is available in an open-source format, allowing others to fit and validate their mechanical models.

Systematic analysis of constitutive models of brain tissue materials based on compression tests

It's crucial to understand the biomechanical properties of brain tissue to comprehend the potential mechanisms of traumatic brain injury. This study, distinct from homogeneous models, integrates axonal coupling in both axial and transverse compressive experiments within a continuum mechanics framework to capture its intricate mechanical behaviors. Fresh porcine brains underwent unconfined compression at strain rates of 0.001/s and 0.1/s to 0.3 strain, allowing for a comprehensive statistical analysis of the directional, regional, and strain-rate-dependent mechanical properties of brain tissue. The established constitutive model, fitted to experimental data, delineates material parameters providing intuitive insights into the stiffness of gray/white matter isotropic matrices and neural fibers. Additionally, it predicts the mechanical performance of white matter matrix and axonal fibers under compressive loading. Results reveal that gray matter is insensitive to loading direction, exhibiting insignificant stiffness variations within regions. White matter, however, displays no significant differences in mechanical properties under axial and transverse loading, with an overall higher average stress than gray matter and a more pronounced strain-rate effect. Stress-strain curves indicate that, under axial compression, white matter axons primarily resist the load before transitioning to a matrix-dominated response. Under transverse loading, axonal fibers exhibit weaker resistance to lateral pressure. The mechanical behavior of brain tissue is highly dependent on loading rate, region, direction, and peak strain. This study, by combining experimentation with phenomenological modeling, elucidates certain phenomena, contributing valuable insights for the development of precise computational models.

Deep Indentation Tests of Soft Materials Using Mobile and Stationary Devices

Measurements of the properties of soft materials are important from the point of view of medical diagnostics of soft tissues as well as testing the quality of food products and many technical materials. One of the frequently used techniques for testing such materials, attractive due to its non-invasive nature, is the indentation technique, which does not puncture the material. The difficulty of testing soft materials, which affects the objectivity of the results, is related to the problems of stable positioning of the studied material in relation to the indentation apparatus, especially with a device held by the operator. This work concerns the comparison of test results using an indentation apparatus mounted on mobile and stationary handles. The tested materials are cylindrical samples of polyurethane foams with three different stiffnesses and the same samples with a 0.5 or 1 mm thick silicone layer. The study presented uses an apparatus with a flat cylindrical indenter, with a surface area of 1 cm2, pressed to a depth of 10 mm (so-called deep tests). Based on the recorded force changes over time, five descriptors of the indentation test were determined and compared for both types of handles. The tests performed showed that the elastic properties of foam materials alone and with a silicone layer can be effectively characterized by the maximum forces during recessing and retraction and the slopes of the recessing and retraction curves. In the case of two-layer materials, these descriptors reflect both the characteristics of the foams and the silicone layer. The results show that the above property of the deep indentation method distinguishes it from the shallow indentation method. The repeatability of the tests performed in the mobile and stationary holders were determined to be comparable.

The Networking Brain: How Extracellular Matrix, Cellular Networks, and Vasculature Shape the In Vivo Mechanical Properties of the Brain

Mechanically, the brain is characterized by both solid and fluid properties. The resulting unique material behavior fosters proliferation, differentiation, and repair of cellular and vascular networks, and optimally protects them from damaging shear forces. Magnetic resonance elastography (MRE) is a noninvasive imaging technique that maps the mechanical properties of the brain in vivo. MRE studies have shown that abnormal processes such as neuronal degeneration, demyelination, inflammation, and vascular leakage lead to tissue softening. In contrast, neuronal proliferation, cellular network formation, and higher vascular pressure result in brain stiffening. In addition, brain viscosity has been reported to change with normal blood perfusion variability and brain maturation as well as disease conditions such as tumor invasion. In this article, the contributions of the neuronal, glial, extracellular, and vascular networks are discussed to the coarse-grained parameters determined by MRE. This reductionist multi-network model of brain mechanics helps to explain many MRE observations in terms of microanatomical changes and suggests that cerebral viscoelasticity is a suitable imaging marker for brain disease.

Insights into the Mechanical Characterization of Mouse Brain Tissue Using Microindentation Testing

Indentation testing is the most common approach to quantify mechanical brain tissue properties. Despite a myriad of studies conducted already, reported stiffness values vary extensively and continue to be subject of study. Moreover, the growing interest in the relationship between the brain's spatially heterogeneous microstructure and local tissue stiffness warrants the development of standardized measurement protocols to enable comparability between studies and assess repeatability of reported data. Here, we present three individual protocols that outline (1) sample preparation of a 1000-µm thick coronal slice, (2) a comprehensive list of experimental parameters associated with the FemtoTools FT-MTA03 Micromechanical Testing System for spherical indentation, and (3) two different approaches to derive the elastic modulus from raw force-displacement data. Lastly, we demonstrate that our protocols deliver a robust experimental framework that enables us to determine the spatially heterogeneous microstructural properties of (mouse) brain tissue. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Mouse brain sample preparation Basic Protocol 2: Indentation testing of mouse brain tissue using the FemtoTools FT-MTA03 Micromechanical Testing and Assembly System Basic Protocol 3: Tissue stiffness identification from force-displacement data.

Biofabrication methods for reconstructing extracellular matrix mimetics

In the human body, almost all cells interact with extracellular matrices (ECMs), which have tissue and organ-specific compositions and architectures. These ECMs not only function as cellular scaffolds, providing structural support, but also play a crucial role in dynamically regulating various cellular functions. This comprehensive review delves into the examination of biofabrication strategies used to develop bioactive materials that accurately mimic one or more biophysical and biochemical properties of ECMs. We discuss the potential integration of these ECM-mimics into a range of physiological and pathological in vitro models, enhancing our understanding of cellular behavior and tissue organization. Lastly, we propose future research directions for ECM-mimics in the context of tissue engineering and organ-on-a-chip applications, offering potential advancements in therapeutic approaches and improved patient outcomes.

Inverse identification of region-specific hyperelastic material parameters for human brain tissue

The identification of material parameters accurately describing the region-dependent mechanical behavior of human brain tissue is crucial for computational models used to assist, e.g., the development of safety equipment like helmets or the planning and execution of brain surgery. While the division of the human brain into different anatomical regions is well established, knowledge about regions with distinct mechanical properties remains limited. Here, we establish an inverse parameter identification scheme using a hyperelastic Ogden model and experimental data from multi-modal testing of tissue from 19 anatomical human brain regions to identify mechanically distinct regions and provide the corresponding material parameters. We assign the 19 anatomical regions to nine governing regions based on similar parameters and microstructures. Statistical analyses confirm differences between the regions and indicate that at least the corpus callosum and the corona radiata should be assigned different material parameters in computational models of the human brain. We provide a total of four parameter sets based on the two initial Poisson's ratios of 0.45 and 0.49 as well as the pre- and unconditioned experimental responses, respectively. Our results highlight the close interrelation between the Poisson's ratio and the remaining model parameters. The identified parameters will contribute to more precise computational models enabling spatially resolved predictions of the stress and strain states in human brains under complex mechanical loading conditions.

Poro-viscoelastic material parameter identification of brain tissue-mimicking hydrogels

Understanding and characterizing the mechanical and structural properties of brain tissue is essential for developing and calibrating reliable material models. Based on the Theory of Porous Media, a novel nonlinear poro-viscoelastic computational model was recently proposed to describe the mechanical response of the tissue under different loading conditions. The model contains parameters related to the time-dependent behavior arising from both the viscoelastic relaxation of the solid matrix and its interaction with the fluid phase. This study focuses on the characterization of these parameters through indentation experiments on a tailor-made polyvinyl alcohol-based hydrogel mimicking brain tissue. The material behavior is adjusted to ex vivo porcine brain tissue. An inverse parameter identification scheme using a trust region reflective algorithm is introduced and applied to match experimental data obtained from the indentation with the proposed computational model. By minimizing the error between experimental values and finite element simulation results, the optimal constitutive model parameters of the brain tissue-mimicking hydrogel are extracted. Finally, the model is validated using the derived material parameters in a finite element simulation.

Simulation Analysis of Organic–Inorganic Interface Failure of Scallop under Ultra-High Pressure

Shell is a typical biomineralized inorganic–organic composite material. The essence of scallop deshelling is caused by the fracture failure at the interface of the organic and inorganic–organic matter composites. The constitutive equations were solved so that the stress distributions of the adductor in the radial, circumferential, and axial directions were obtained as σr = σθ = P, σz = 2(2 − ν)P/(2ν − 1), and the shear stress was τzr = 0. Using the method of finite element simulation analysis, the stress distribution laws at different interface states were obtained. The experimental results show that when the amplitude is constant, the undulation period is smaller than the diameter of the adductor or the angle between the bus of the adductor, and the reference horizontal plane gradually decreases, so the interface is more likely to yield. After the analysis, the maximum stress for the yielding of the scallop interface was about 247 MPa, and the whole deshelling process was gradually spread from the outer edge of the interface to the center. The study analyzed the scallop organic–inorganic material interface from the perspective of mechanics, and the mechanical model and simulation analysis results were consistent with the parameter optimization results, which can provide some theoretical basis for the composite material interface failure and in-depth research.