Magnetic resonance elastography of the brain: A comparison between pigs and humans

Machine learning reveals correlations between brain age and mechanics

Our brain undergoes significant micro- and macroscopic changes throughout its life cycle. It is therefore crucial to understand the effect of aging on the mechanical properties of the brain in order to develop accurate personalized simulations and diagnostic tools. Here we systematically probed the mechanical behavior of n=439 brain tissue samples in tension and compression, in different anatomical regions, for different axon orientations, across five age groups. We used Bayesian statistics to characterize the relation between brain age and mechanical properties and quantify uncertainties. Our results, based on our experimental data and material parameters for the isotropic Ogden and the anisotropic Gasser-Ogden-Holzapfel models, reveal a non-linear relationship between age and mechanics across the life cycle of the porcine brain. Both tensile and compressive shear moduli reached peak values ranging from 0.4-1.0 kPa in tension to 0.16-0.32 kPa in compression at three years of age. Anisotropy was most pronounced at six months, and then decreased. These results represent an important step in understanding age-dependent changes in the mechanical properties of brain tissue and provide the scientific basis for more accurate and realistic computational brain simulations. STATEMENT OF SIGNIFICANCE: In this paper, we investigate the age-dependent mechanical properties of brain tissue based on different deformation modes, anatomical regions, and axon orientations. Hierarchical Bayesian modeling was used to identify isotropic and anisotropic material parameters. The study reveals a nonlinear relationship between shear modulus, degree of anisotropy, and tension-compression asymmetry over the life cycle of the brain. By demonstrating the non-linearity of these relationships, the study fills a significant knowledge gap in current research. This work is a fundamental step in accurately characterizing the complex relationship between brain aging and mechanical properties.

Development of Neural Cells and Spontaneous Neural Activities in Engineered Brain-Like Constructs for Transplantation

Stem cell transplantation has demonstrated efficacy in treating neurological disorders by generating functional cells and secreting beneficial factors. However, challenges remain for current cell suspension injection therapy, including uncontrollable cell distribution, the potential for tumor formation, and limited ability to treat spatial defects. Therefore, implants with programmable cell development, tailored 3D structure, and functionalized biomaterials have the potential to both control cell distribution and reduce or heal spatial defects. Here, a biomimetic material system comprising gelatin, alginate, and fibrinogen has been developed for neural progenitor cell constructs using 3D printing. The resulting constructs exhibit excellent formability, stability, and developmental functions in vitro, as well as biocompatibility and integration into the hippocampus in vivo. The controllability, reproducibility, and material composition of the constructs show potential for use in personalized stem cell-based therapies for defective neurological disorders, neural development research, disease modeling, and organoid-derived intelligent systems.

Application of Data-Driven computing to patient-specific prediction of the viscoelastic response of human brain under transcranial ultrasound stimulation

We present a class of model-free Data-Driven solvers that effectively enable the utilization of in situ and in vivo imaging data directly in full-scale calculations of the mechanical response of the human brain to sonic and ultrasonic stimulation, entirely bypassing the need for analytical modeling or regression of the data. The well-posedness of the approach and its convergence with respect to data are proven analytically. We demonstrate the approach, including its ability to make detailed spatially resolved patient-specific predictions of wave patterns, using public-domain MRI images, MRE data and commercially available solid-mechanics software.

Evaluating the Effects of Pulsed Electrical Stimulation on the Mechanical Behavior and Microstructure of Medulla Oblongata Tissues

The development of remote surgery hinges on comprehending the mechanical properties of the tissue at the surgical site. Understanding the mechanical behavior of the medulla oblongata tissue is instrumental for precisely determining the remote surgery implementation site. Additionally, exploring this tissue's response under electric fields can inform the creation of electrical stimulation therapy regimens. This could potentially reduce the extent of medulla oblongata tissue damage from mechanical compression. Various types of pulsed electric fields were integrated into a custom-built indentation device for this study. Experimental findings suggested that applying pulsed electric fields amplified the shear modulus of the medulla oblongata tissue. In the electric field, the elasticity and viscosity of the tissue increased. The most significant influence was noted from the low-frequency pulsed electric field, while the burst pulsed electric field had a minimal impact. At the microstructural scale, the application of an electric field led to the concentration of myelin in areas distant from the surface layer in the medulla oblongata, and the orderly structure of proteoglycans became disordered. The alterations observed in the myelin and proteoglycans under an electric field were considered to be the fundamental causes of the changes in the mechanical behavior of the medulla oblongata tissue. Moreover, cell polarization and extracellular matrix cavitation were observed, with transmission electron microscopy results pointing to laminar separation within the myelin at the ultrastructure scale. This study thoroughly explored the impact of electric field application on the mechanical behavior and microstructure of the medulla oblongata tissue, delving into the underlying mechanisms. This investigation delved into the changes and mechanisms in the mechanical behavior and microstructure of medulla oblongata tissue under the influence of electric fields. Furthermore, this study could serve as a reference for the development of electrical stimulation regimens in the central nervous system. The acquired mechanical behavior data could provide valuable baseline information to aid in the evolution of remote surgery techniques involving the medulla oblongata tissue.

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.

Transversely-isotropic brain in vivo MR elastography with anisotropic damping

Measuring tissue parameters from increasingly sophisticated mechanical property models may uncover new contrast mechanisms with clinical utility. Building on previous work on in vivo brain MR elastography (MRE) with a transversely-isotropic with isotropic damping (TI-ID) model, we explore a new transversely-isotropic with anisotropic damping (TI-AD) model that involves six independent parameters describing direction-dependent behavior for both stiffness and damping. The direction of mechanical anisotropy is determined by diffusion tensor imaging and we fit three complex-valued moduli distributions across the full brain volume to minimize differences between measured and modeled displacements. We demonstrate spatially accurate property reconstruction in an idealized shell phantom simulation, as well as an ensemble of 20 realistic, randomly-generated simulated brains. We characterize the simulated precisions of all six parameters across major white matter tracts to be high, suggesting that they can be measured independently with acceptable accuracy from MRE data. Finally, we present in vivo anisotropic damping MRE reconstruction data. We perform t-tests on eight repeated MRE brain exams on a single-subject, and find that the three damping parameters are statistically distinct for most tracts, lobes and the whole brain. We also show that population variations in a 17-subject cohort exceed single-subject measurement repeatability for most tracts, lobes and whole brain, for all six parameters. These results suggest that the TI-AD model offers new information that may support differential diagnosis of brain diseases.

Automated model discovery for human brain using Constitutive Artificial Neural Networks

The brain is our softest and most vulnerable organ, and understanding its physics is a challenging but significant task. Throughout the past decade, numerous competing models have emerged to characterize its response to mechanical loading. However, selecting the best constitutive model remains a heuristic process that strongly depends on user experience and personal preference. Here we challenge the conventional wisdom to first select a constitutive model and then fit its parameters to data. Instead, we propose a new strategy that simultaneously discovers both model and parameters. We integrate more than a century of knowledge in thermodynamics and state-of-the-art machine learning to build a Constitutive Artificial Neural Network that enables automated model discovery. Our design paradigm is to reverse engineer the network from a set of functional building blocks that are, by design, a generalization of popular constitutive models, including the neo Hookean, Blatz Ko, Mooney Rivlin, Demiray, Gent, and Holzapfel models. By constraining input, output, activation functions, and architecture, our network a priori satisfies thermodynamic consistency, objectivity, symmetry, and polyconvexity. We demonstrate that-out of more than 4000 models-our network autonomously discovers the model and parameters that best characterize the behavior of human gray and white matter under tension, compression, and shear. Importantly, our network weights translate naturally into physically meaningful parameters, such as shear moduli of 1.82kPa, 0.88kPa, 0.94kPa, and 0.54kPa for the cortex, basal ganglia, corona radiata, and corpus callosum. Our results suggest that Constitutive Artificial Neural Networks have the potential to induce a paradigm shift in soft tissue modeling, from user-defined model selection to automated model discovery. Our source code, data, and examples are available at https://github.com/LivingMatterLab/CANN. STATEMENT OF SIGNIFICANCE: Human brain is ultrasoft, difficult to test, and challenging to model. Numerous competing constitutive models exist, but selecting the best model remains a matter of personal preference. Here we automate the process of model selection. We formulate the problem of autonomous model discovery as a neural network and capitalize on the powerful optimizers in deep learning. However, rather than using a conventional neural network, we reverse engineer our own Constitutive Artificial Neural Network from a set of modular building blocks, which we rationalize from common constitutive models. When trained with tension, compression, and shear experiments of gray and white matter, our network simultaneously discovers both model and parameters that describes the data better than any existing invariant-based model. Our network could induce a paradigm shift from user-defined model selection to automated model discovery.

Correlating the microstructural architecture and macrostructural behaviour of the brain

The computational simulation of pathological conditions and surgical procedures, for example the removal of cancerous tissue, can contribute crucially to the future of medicine. Especially for brain surgery, these methods can be important, as the ultra-soft tissue controls vital functions of the body. However, the microstructural interactions and their effects on macroscopic material properties remain incompletely understood. Therefore, we investigated the mechanical behaviour of brain tissue under three different deformation modes, axial tension, compression, and semi-confined compression, in different anatomical regions, and for varying axon orientation. In addition, we characterised the underlying microstructure in terms of myelin, cells, glial cells and neuron area fraction, and density. The correlation of these quantities with the material parameters of the anisotropic Ogden model reveals a decrease in shear modulus with increasing myelin area fraction. Strikingly, the tensile shear modulus correlates positively with cell and neuronal area fraction (Spearman's correlation coefficient of r_s=0.40 and r_s=0.33), whereas the compressive shear modulus decreases with increasing glial cell area (r_s=-0.33). Our study finds that tissue non-linearity significantly depends on the myelin area fraction (r_s=0.47), cell density (r_s=0.41) and glial cell area (r_s=0.49). Our results provide an important step towards understanding the micromechanical load transfer that leads to the non-linear macromechanical behaviour of the brain. STATEMENT OF SIGNIFICANCE: Within this article, we investigate the mechanical behaviour of brain tissue under three different deformation modes, in different anatomical regions, and for varying axon orientation. Further, we characterise the underlying microstructure in terms of various constituents. The correlation of these quantities with the material parameters of the anisotropic Ogden model reveals a decrease in shear modulus with increasing myelin area fraction. Strikingly, the tensile shear modulus correlates positively with cell and neuronal area fraction, whereas the compressive shear modulus decreases with increasing glial cell area. Our study finds that tissue non-linearity significantly depends on the myelin area fraction, cell density, and glial cell area. Our results provide an important step towards understanding the micromechanical load transfer that leads to the non-linear macromechanical behaviour of the brain.

Mechanical Properties of the Extracellular Environment of Human Brain Cells Drive the Effectiveness of Drugs in Fighting Central Nervous System Cancers

The evaluation of nanomechanical properties of tissues in health and disease is of increasing interest to scientists. It has been confirmed that these properties, determined in part by the composition of the extracellular matrix, significantly affect tissue physiology and the biological behavior of cells, mainly in terms of their adhesion, mobility, or ability to mutate. Importantly, pathophysiological changes that determine disease development within the tissue usually result in significant changes in tissue mechanics that might potentially affect the drug efficacy, which is important from the perspective of development of new therapeutics, since most of the currently used in vitro experimental models for drug testing do not account for these properties. Here, we provide a summary of the current understanding of how the mechanical properties of brain tissue change in pathological conditions, and how the activity of the therapeutic agents is linked to this mechanical state.

Novel Brain-Stiffness-Mimicking Matrix Gel Enables Comprehensive Invasion Analysis of 3D Cultured GBM Cells

Glioblastoma (GBM) is the most common malignant primary brain tumor in adults, which is fast growing and tends to invade surrounding normal brain tissues. Uncovering the molecular and cellular mechanisms of GBM high invasion potential is of great importance for the treatment and prognostic prediction. However, the commonly used two-dimensional (2D) cell culture and analysis system suffers from lack of the heterogeneity and in vivo property of brain tissues. Here, we established a three-dimensional (3D) cell culture-based analysis system that could better recapitulate the heterogeneity of GBM and mimic the in vivo conditions in the brain. The GBM cell lines, DBTRG and U251, were cultured by hanging drop culture into the GBM multicellular spheroids, which were embedded in the optimized 3D brain-stiffness-mimicking matrix gel (0.5 mg/ml Collagen Ⅰ + 3 mg/ml Matrigel+ 3.3 mg/ml Hyaluronic Acid (HA)). The biochemical composition of the optimized matrix gel is similar to that of the brain microenvironment, and the elastic modulus is close to that of the brain tissue. The dynamics of the GBM spheroids was examined using high-content imaging for 60 h, and four metrics including invasion distance, invasion area, single-cell invasion velocity, and directionality were employed to quantify the invasion capacity. The result showed that DBTRG cells possess higher invasion capacity than U251 cells, which was consistent with the results of the classic transwell test. Transcriptome analysis of both cell lines was performed to explore the underlying molecular mechanisms. Our novel brain-stiffness-mimicking matrix gel enables comprehensive invasion analysis of the 3D cultured GBM cells and provides a model basis for in-depth exploration of the mechanisms regulating GBM invasion including the interaction between GBM cells and brain stroma.

Lose the stress: Viscoelastic materials for cell engineering

Biomaterials are widely used to study and control a variety of cell behaviors, including stem cell differentiation, organogenesis, and tumor invasion. While considerable attention has historically been paid to biomaterial elastic (storage) properties, it has recently become clear that viscous (loss) properties can also powerfully influence cell behavior. Here we review advances in viscoelastic materials for cell engineering. We begin by discussing collagen, an abundant naturally occurring biomaterial that derives its viscoelastic properties from its fibrillar architecture, which enables dissipation of applied stresses. We then turn to two other naturally occurring biomaterials that are more frequently modified for engineering applications, alginate and hyaluronic acid, whose viscoelastic properties may be tuned by modulating network composition and crosslinking. We also discuss the potential of exploiting engineered fibrous materials, particularly electrospun fiber-based materials, to control viscoelastic properties. Finally, we review mechanisms through which cells process viscous and viscoelastic cues as they move along and within these materials. The ability of viscoelastic materials to relax cell-imposed stresses can dramatically alter migration on two-dimensional surfaces and confinement-imposed barriers to engraftment and infiltration in three-dimensional scaffolds. STATEMENT OF SIGNIFICANCE: Most tissues and many biomaterials exhibit some viscous character, a property that is increasingly understood to influence cell behavior in profound ways. This review discusses the origin and significance of viscoelastic properties of common biomaterials, as well as how these cues are processed by cells to influence migration. A deeper understanding of the mechanisms of viscoelastic behavior in biomaterials and how cells interpret these inputs should aid the design and selection of biomaterials for specific applications.

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.

Construction of tissue-customized hydrogels from cross-linkable materials for effective tissue regeneration

Hydrogels are prevalent scaffolds for tissue regeneration because of their hierarchical architectures along with outstanding biocompatibility and unique rheological and mechanical properties. For decades, researchers have found that many materials (natural, synthetic, or hybrid) can form hydrogels using different cross-linking strategies. Traditional strategies for fabricating hydrogels include physical, chemical, and enzymatical cross-linking methods. However, due to the diverse characteristics of different tissues/organs to be regenerated, tissue-customized hydrogels need to be developed through precisely controlled processes, making the manufacture of hydrogels reliant on novel cross-linking strategies. Thus, hybrid cross-linkable materials are proposed to tackle this challenge through hybrid cross-linking strategies. Here, different cross-linkable materials and their associated cross-linking strategies are summarized. From the perspective of the major characteristics of the target tissues/organs, we critically analyze how different cross-linking strategies are tailored to fit the regeneration of such tissues and organs. To further advance this field, more appropriate cross-linkable materials and cross-linking strategies should be investigated. In addition, some innovative technologies, such as 3D bioprinting, the internet of medical things (IoMT), and artificial intelligence (AI), are also proposed to improve the development of hydrogels for more efficient tissue regeneration.

Obesity Animal Models for Acupuncture and Related Therapy Research Studies

Obesity and related diseases are considered as pandemic representing a worldwide threat for health. Animal models are critical to validate the effects and understand the mechanisms related to classical or innovative preventive and therapeutic strategies. It is, therefore, important to identify the best animal models for translational research, using different evaluation criteria such as the face, construct, and predictive validity. Because the pharmacological treatments and surgical interventions currently used for treating obesity often present many undesirable side effects, relatively high relapse probabilities, acupuncture, electroacupuncture (EA), and related therapies have gained more popularity and attention. Many kinds of experimental animal models have been used for obesity research studies, but in the context of acupuncture, most of the studies were performed in rodent obesity models. Though, are these obesity rodent models really the best for acupuncture or related therapies research studies? In this study, we review different obesity animal models that have been used over the past 10 years for acupuncture and EA research studies. We present their respective advantages, disadvantages, and specific constraints. With the development of research on acupuncture and EA and the increasing interest regarding these approaches, proper animal models are critical for preclinical studies aiming at developing future clinical trials in the human. The aim of the present study is to provide researchers with information and guidance related to the preclinical models that are currently available to investigate the outcomes of acupuncture and related therapies.

Normative Data of Axial Length, Retinal Thickness Measurements, Visual Evoked Potentials, and Full-Field Electroretinography in Female, Wild-Type Minipigs

Purpose

The purpose of this study was to present normative data of optical coherence tomography (OCT), electrophysiological, and ocular biometry parameters and their correlation in minipigs.

Methods

Eighty-eight eyes of 44 minipigs underwent full-field electroretinogram (ERG) recording and ocular biometry. However, 10 eyes of 6 minipigs were excluded because of poor OCT image quality. The thickness of the retinal sublayers was measured on a vertical line at 5 locations with a 1 mm interval from the disc margin to the dorsal periphery and at 10 locations on the visual streak. Visual evoked potentials (VEPs) were measured in 15 eyes of 8 minipigs.

Results

All minipigs were female with a mean age and axial length of 13.83 ± 10.56 months and 20.33 ± 0.88 mm, respectively. The implicit time of the a-wave and b-wave in scotopic 3.0 ERGs was longer than that in photopic 3.0 ERG. The implicit time of the n2-wave and p2-wave in VEP was 25.67 ± 7.41 ms and 52.96 ± 10.38 ms, respectively. The total retinal layer (TRL) and nerve fiber layer (NFL) became thinner near the periphery. The inner retinal sublayers near the visual streak were thicker than those at other locations. Central TRL and NFL thickness on visual streak was 223.06 ± 23.19 µm and 74.03 ± 13.93 µm, respectively. The temporal TRL and NFL on the visual streak were thicker than those on the nasal side.

Conclusions

The normative electrophysiological and OCT parameters used in our study can be used as reference data in further pig studies.

Translational Relevance

This study presents normative data of minipigs, which are adequate animal models for preclinical studies.

Brain-mimicking phantom for biomechanical validation of motion sensitive MR imaging techniques

Motion sensitive MR imaging techniques allow for the non-invasive evaluation of biological tissues by using different excitation schemes, including physiological/intrinsic motions caused by cardiac pulsation or respiration, and vibrations caused by an external actuator. The mechanical biomarkers extracted through these imaging techniques have been shown to hold diagnostic value for various neurological disorders and conditions. Amplified MRI (aMRI), a cardiac gated imaging technique, can help track and quantify low frequency intrinsic motion of the brain. As for high frequency actuation, the mechanical response of brain tissue can be measured by applying external high frequency actuation in combination with a motion sensitive MR imaging sequence called Magnetic Resonance Elastography (MRE). Due to the frequency-dependent behavior of brain mechanics, there is a need to develop brain phantom models that can mimic the broadband mechanical response of the brain in order to validate motion-sensitive MR imaging techniques. Here, we have designed a novel phantom test setup that enables both the low and high frequency responses of a brain-mimicking phantom to be captured, allowing for both aMRI and MRE imaging techniques to be applied on the same phantom model. This setup combines two different vibration sources: a pneumatic actuator, for low frequency/intrinsic motion (1 Hz) for use in aMRI, and a piezoelectric actuator for high frequency actuation (30-60 Hz) for use in MRE. Our results show that in MRE experiments performed from 30 Hz through 60 Hz, propagating shear waves attenuate faster at higher driving frequencies, consistent with results in the literature. Furthermore, actuator coupling has a substantial effect on wave amplitude, with weaker coupling causing lower amplitude wave field images, specifically shown in the top-surface shear loading configuration. For intrinsic actuation, our results indicate that aMRI linearly amplifies motion up to at least an amplification factor of 9 for instances of both visible and sub-voxel motion, validated by varying power levels of pneumatic actuation (40%-80% power) under MR, and through video analysis outside the MRI scanner room. While this investigation used a homogeneous brain-mimicking phantom, our setup can be used to study the mechanics of non-homogeneous phantom configurations with bio-interfaces in the future.

Mesenchymal stem cells and three-dimensional-osteoconductive scaffold regenerate calvarial bone in critical size defects in swine

Craniofacial bones protect vital organs, perform important physiological functions, and shape facial identity. Critical‐size defects (CSDs) in calvarial bones, which will not heal spontaneously, are caused by trauma, congenital defects, or tumor resections. They pose a great challenge for patients and physicians, and significantly compromise quality of life. Currently, calvarial CSDs are treated either by allogenic or autologous grafts, metal or other synthetic plates that are associated with considerable complications. While previous studies have explored tissue regeneration for calvarial defects, most have been done in small animal models with limited translational value. Here we define a swine calvarial CSD model and show a novel approach to regenerate high‐quality bone in these defects by combining mesenchymal stem cells (MSCs) with a three‐dimensional (3D)‐printed osteoconductive HA/TCP scaffold. Specifically, we have compared the performance of dental pulp neural crest MSCs (DPNCCs) to bone marrow aspirate (BMA) combined with a 3D‐printed HA/TCP scaffold to regenerate bone in a calvarial CSD (>7.0 cm²). Both DPNCCs and BMA loaded onto the 3D‐printed osteoconductive scaffold support the regeneration of calvarial bone with density, compression strength, and trabecular structures similar to native bone. Our study demonstrates a novel application of an original scaffold design combined with DPNCCs or BMA to support regeneration of high‐quality bone in a newly defined and clinically relevant swine calvarial CSD model. This discovery may have important impact on bone regeneration beyond the craniofacial region and will ultimately benefit patients who suffer from debilitating CSDs.

Cryogel biomaterials for neuroscience applications

Biomaterials in the form of 3D polymeric scaffolds have been used to create structurally and functionally biomimetic constructs of nervous system tissue. Such constructs can be used to model defects and disease or can be used to supplement neuronal tissue regeneration and repair. One such group of biomaterial scaffolds are hydrogels, which have been widely investigated for cell/tissue culture and as cell or molecule delivery systems in the field of neurosciences. However, a subset of hydrogels called cryogels, have shown to possess several distinct structural advantages over conventional hydrogel networks. Their macroporous structure, created via the time and resource efficient fabrication process (cryogelation) not only allows mass fluid transport throughout the structure, but also creates a high surface area to volume ratio for cell growth or drug loading. In addition, the macroporous structure of cryogels is ideal for applications in the central nervous system as they are very soft and spongey, yet also robust, which makes them a user-friendly and reproducible tool to address neuroscience challenges. In this review, we aim to provide the neuroscience community, who may not be familiar with the fundamental concepts of cryogels, an accessible summary of the basic information that pertain to their use in the brain and nervous tissue. We hope that this review shall initiate creative ways that cryogels could be further adapted and employed to tackle unsolved neuroscience challenges.

3D Printed Hydrogels with Aligned Microchannels to Guide Neural Stem Cell Migration

Following traumatic or ischemic brain injury, rapid cell death and extracellular matrix degradation lead to the formation of a cavity at the brain lesion site, which is responsible for prolonged neurological deficits and permanent disability. Transplantation of neural stem/progenitor cells (NSCs) represents a promising strategy for reconstructing the lesion cavity and promoting tissue regeneration. In particular, the promotion of neuronal migration, organization, and integration of transplanted NSCs is critical to the success of stem cell-based therapy. This is particularly important for the cerebral cortex, the most common area involved in brain injuries, because the highly organized structure of the cerebral cortex is essential to its function. Biomaterials-based strategies show some promise for conditioning the lesion site microenvironment to support transplanted stem cells, but the progress in demonstrating organized cell engraftment and integration into the brain is very limited. An effective approach to sufficiently address these challenges has not yet been developed. Here, we have implemented a digital light-processing-based 3D printer and printed hydrogel scaffolds with a designed shape, uniaxially aligned microchannels, and tunable mechanical properties. We demonstrated the capacity to achieve high shape precision to the lesion site with brain tissue-matching mechanical properties. We also established spatial control of bioactive molecule distribution within 3D printed hydrogel scaffolds. These printed hydrogel scaffolds have shown high neuro-compatibility with aligned neuronal outgrowth along with the microchannels. This study will provide a biomaterial-based approach that can serve as a protective and guidance vehicle for transplanted NSC organization and integration for brain tissue regeneration after injuries.

Viscoelasticity of children and adolescent brains through MR elastography

Magnetic Resonance Elastography (MRE) is an elasticity imaging technique that allows a safe, fast, and non-invasive evaluation of the mechanical properties of biological tissues in vivo. Since mechanical properties reflect a tissue's composition and arrangement, MRE is a powerful tool for the investigation of the microstructural changes that take place in the brain during childhood and adolescence. The goal of this study was to evaluate the viscoelastic properties of the brain in a population of healthy children and adolescents in order to identify potential age and sex dependencies. We hypothesize that because of myelination, age dependent changes in the mechanical properties of the brain will occur during childhood and adolescence. Our sample consisted of 26 healthy individuals (13 M, 13 F) with age that ranged from 7-17 years (mean: 11.9 years). We performed multifrequency MRE at 40, 60, and 80 Hz actuation frequencies to acquire the complex-valued shear modulus G = G' + iG″ with the fundamental MRE parameters being the storage modulus (G'), the loss modulus (G″), and the magnitude of complex-valued shear modulus (|G|). We fitted a springpot model to these frequency-dependent MRE parameters in order to obtain the parameter α, which is related to tissue's microstructure, and the elasticity parameter k, which was converted to a shear modulus parameter (μ) through viscosity (η). We observed no statistically significant variation in the parameter μ, but a significant increase of the microstructural parameter α of the white matter with increasing age (p < 0.05). Therefore, our MRE results suggest that subtle microstructural changes such as neural tissue's enhanced alignment and geometrical reorganization during childhood and adolescence could result in significant biomechanical changes. In line with previously reported MRE data for adults, we also report significantly higher shear modulus (μ) for female brains when compared to males (p < 0.05). The data presented here can serve as a clinical baseline in the analysis of the pediatric and adolescent brain's viscoelasticity over this age span, as well as extending our understanding of the biomechanics of brain development.

Dynamic mechanical characterization and viscoelastic modeling of bovine brain tissue

Brain tissue is vulnerable and sensitive, predisposed to potential damage under various conditions of mechanical loading. Although its material properties have been investigated extensively, the frequency-dependent viscoelastic characterization is currently limited. Computational models can provide a non-invasive method by which to analyze brain injuries and predict the mechanical response of the tissue. The brain injuries are expected to be induced by dynamic loading, mostly in compression and measurement of dynamic viscoelastic properties are essential to improve the accuracy and variety of finite element simulations on brain tissue. Thus, the aim of this study was to investigate the compressive frequency-dependent properties of brain tissue and present a mathematical model in the frequency domain to capture the tissue behavior based on experimental results. Bovine brain specimens, obtained from four locations of corona radiata, corpus callosum, basal ganglia and cortex, were tested under compression using dynamic mechanical analysis over a range of frequencies between 0.5 and 35 Hz to characterize the regional and directional response of the tissue. The compressive dynamic properties of bovine brain tissue were heterogenous for regions but not sensitive to orientation showing frequency dependent statistical results, with viscoelastic properties increasing with frequency. The mean storage and loss modulus were found to be 12.41 kPa and 5.54 kPa, respectively. The material parameters were obtained using the linear viscoelastic model in the frequency domain and the numeric simulation can capture the compressive mechanical behavior of bovine brain tissue across a range of frequencies. The frequency-dependent viscoelastic characterization of brain tissue will improve the fidelity of the computational models of the head and provide essential information to the prediction and analysis of brain injuries in clinical treatments.

nNOS-expressing interneurons control basal and behaviorally evoked arterial dilation in somatosensory cortex of mice

Cortical neural activity is coupled to local arterial diameter and blood flow. However, which neurons control the dynamics of cerebral arteries is not well understood. We dissected the cellular mechanisms controlling the basal diameter and evoked dilation in cortical arteries in awake, head-fixed mice. Locomotion drove robust arterial dilation, increases in gamma band power in the local field potential (LFP), and increases calcium signals in pyramidal and neuronal nitric oxide synthase (nNOS)-expressing neurons. Chemogenetic or pharmocological modulation of overall neural activity up or down caused corresponding increases or decreases in basal arterial diameter. Modulation of pyramidal neuron activity alone had little effect on basal or evoked arterial dilation, despite pronounced changes in the LFP. Modulation of the activity of nNOS-expressing neurons drove changes in the basal and evoked arterial diameter without corresponding changes in population neural activity.

Enhanced complex local frequency elastography method for tumor viscoelastic shear modulus reconstruction

BACKGROUND AND OBJECTIVES

The Mayo Clinic provides a magnetic resonance (MR) elastography software named MRE Wave, which uses the conventional local frequency elastography (LFE) method. However, MRE Wave is unable to supply complex viscoelasticity maps for elastography. We sought to improve the local frequency estimation algorithm used in LFE, which we refer to as the Enhanced Complex Local Frequency Elastography (EC-LFE) algorithm.

METHODS

The proposed algorithm uses wave equations under the hypotheses of being linear, isotropic, and locally homogeneous. Two 2D simulation models were used to investigate the accuracy and sensitivity of the EC-LFE algorithm for detecting small tumors. The corresponding statistical parameters were the relative root mean square (RMS) error and contrast-to-noise ratio (CNR). EC-LFE was investigated with two different parameter sets, one with an optimally chosen parameter ξ (EC-LFE Adj, for short) and the other with ξ = 0 (EC-LFE0). We compared the MRE Wave and the EC-LFE using series signal-to-noise (SNR) wave data.

RESULTS

The elasticity RMS error of the MRE Wave software was about 1%, and that of the EC-LFE0 and EC-LFE Adj were about 0.2%. The elasticity standard deviation of the MRE Wave software was about 3% of the mean value, and those of the EC-LFE0 and EC-LFE Adj were about 1% of the mean value. The elasticity CNR value of EC-LFE0 reached 1.93 times that of the MRE Wave in the region of small tumors (less than 10-point sampling). The viscosity RMS errors of the EC-LFE0 could be less than 5%.

CONCLUSION

Compared to conventional methods, the EC-LFE was more accurate and sensitive for small tumor detection and exhibited higher noise immunity. The improved algorithm output more parameters and outperformed than the MRE Wave, thereby rendering them more suitable for clinical applications.

Requirements for accurate estimation of shear modulus by magnetic resonance elastography: A computational comparative study

BACKGROUND

Magnetic resonance (MR) elastography is a non-destructive method of measuring biological tissue and is conducive to the early detection of tumors. Researchers usually set different assumptions according to different research objects, then establish and solve wave equations to estimate the shear modulus. Establishing a more reasonable model for a measured object estimates a more accurate shear modulus. Different assumptions of the mathematical model, and the method used to solve the wave equation causes deviation of the estimation.

OBJECTIVE

This study focused on shear modulus deviations caused by differences in calculation methods. The author demonstrated a method to ensure that the measuring range of the selected reconstruction algorithm with selected drive frequency covers the elasticity range of the target tissue. It is hoped to arouse the interest of researchers to introduce new transform domain methods to the field of MR elastography.

METHOD

In linear, isotropic and local homogeneity assumptions, the typical representative of two different calculation methods are algebraic inversion of the differential equation (AIDE) algorithm and local frequency elastography (LFE) algorithm. To compare the accuracy of these calculation methods, the author adopted a digital phantom that can set the parameter values accurately. It is assumed that the phantom tissue was linear and isotropic, and that the driving wave was sinusoidal. The displacement distribution of waves in the tissue was calculated by the finite element simulation method in two different resolutions with the signal-to-noise ratio (SNR) set to 40 dB and the threshold of relative mean error (RME) no more than 10%. The wavelength-to-pixel-size ratios of the two methods under the setting threshold of RME were compared.

RESULTS

The lower threshold of wavelength-to-pixel-size ratio for AIDE was close to 10, while that for LFE was nearly 2 (the limitation of Shannon's law) under the setting precision. Thus, the measuring range of the AIDE method was less than that of LFE at the same experimental conditions.

CONCLUSION

The driving frequency selection range of the spatial frequency domain method is wider than that of the spatial domain method. It is worthwhile for researchers to devote more time to introducing new transformation domain method for MR elastography.

MR elastography frequency-dependent and independent parameters demonstrate accelerated decrease of brain stiffness in elder subjects

OBJECTIVES

To analyze the mechanical properties in different regions of the brain in healthy adults in a wide age range: 26 to 76 years old.

METHODS

We used a multifrequency magnetic resonance elastography (MRE) protocol to analyze the effect of age on frequency-dependent (storage and loss moduli, G' and G″, respectively) and frequency-independent parameters (μ₁, μ₂, and η, as determined by a standard linear solid model) of the cerebral parenchyma, cortical gray matter (GM), white matter (WM), and subcortical GM structures of 46 healthy male and female subjects. The multifrequency behavior of the brain and frequency-independent parameters were analyzed across different age groups.

RESULTS

The annual change rate ranged from - 0.32 to - 0.36% for G' and - 0.43 to - 0.55% for G″ for the cerebral parenchyma, cortical GM, and WM. For the subcortical GM, changes in G' ranged from - 0.18 to - 0.23%, and G″ changed - 0.43%. Interestingly, males exhibited decreased elasticity, while females exhibited decreased viscosity with respect to age in some regions of subcortical GM. Significantly decreased values were also found in subjects over 60 years old.

CONCLUSION

Values of G' and G″ at 60 Hz and the frequency-independent μ₂ of the caudate, putamen, and thalamus may serve as parameters that characterize the aging effect on the brain. The decrease in brain stiffness accelerates in elderly subjects.

KEY POINTS

• We used a multifrequency MRE protocol to assess changes in the mechanical properties of the brain with age. • Frequency-dependent (storage moduli G' and loss moduli G″) and frequency-independent (μ₁, μ₂, and η) parameters can bequantitatively measured by our protocol. • The decreased value of viscoelastic properties due to aging varies in different regions of subcortical GM in males and females, and the decrease in brain stiffness is accelerated in elderly subjects over 60 years old.

Towards brain-tissue-like biomaterials

Many biomaterials have been developed which aim to match the elastic modulus of the brain for improved interfacing. However, other properties such as ultimate toughness, tensile strength, poroviscoelastic responses, energy dissipation, conductivity, and mass diffusivity also need to be considered.

Tension Strain-Softening and Compression Strain-Stiffening Behavior of Brain White Matter

Brain, the most important component of the central nervous system (CNS), is a soft tissue with a complex structure. Understanding the role of brain tissue microstructure in mechanical properties is essential to have a more profound knowledge of how brain development, disease, and injury occur. While many studies have investigated the mechanical behavior of brain tissue under various loading conditions, there has not been a clear explanation for variation reported for material properties of brain tissue. The current study compares the ex-vivo mechanical properties of brain tissue under two loading modes, namely compression and tension, and aims to explain the differences observed by closely examining the microstructure under loading. We tested bovine brain samples under uniaxial tension and compression loading conditions, and fitted hyperelastic material parameters. At 20% strain, we observed that the shear modulus of brain tissue in compression is about 6 times higher than in tension. In addition, we observed that brain tissue exhibited strain-stiffening in compression and strain-softening in tension. In order to investigate the effect of loading modes on the tissue microstructure, we fixed the samples using a novel method that enabled keeping the samples at the loaded stage during the fixation process. Based on the results of histology, we hypothesize that during compressive loading, the strain-stiffening behavior of the tissue could be attributed to glial cell bodies being pushed against surroundings, contacting each other and resisting compression, while during tension, cell connections are detached and the tissue displays softening behavior.

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.

Mechanical regulation of oligodendrocyte biology

Oligodendrocytes (OL) are a subset of glial cells in the central nervous system (CNS) comprising the brain and spinal cord. The CNS environment is defined by complex biochemical and biophysical cues during development and response to injury or disease. In the last decade, significant progress has been made in understanding some of the key biophysical factors in the CNS that modulate OL biology, including their key role in myelination of neurons. Taken together, those studies offer translational implications for remyelination therapies, pharmacological research, identification of novel drug targets, and improvements in methods to generate human oligodendrocyte progenitor cells (OPCs) and OLs from donor stem cells in vitro. This review summarizes current knowledge of how various physical and mechanical cues affect OL biology and its implications for disease, therapeutic approaches, and generation of human OPCs and OLs.

Non-invasive Quantitative Assessment of Muscle Force Based on Ultrasonic Shear Wave Elastography

The objective of this study was to investigate the feasibility of using shear wave elastography (SWE) to indirectly measure passive muscle force and to examine the effects of muscle mass and scan angle. We measured the Young's moduli of 24 specimens from six muscles of four swine at different passive muscle loads under different scan angles (0°, 30°, 60° and 90°) using SWE. Highly linear relationships between Young's modulus E and passive muscle force F were found for all 24 muscle specimens at 0^o scan angle with coefficients of determination R² ranging from 0.984 to 0.999. The results indicate that the muscle mass has no significant effect on the muscle E-F relationship, whereas E-F linearity decreases disproportionately with increased scan angle. These findings suggest that SWE, when carefully applied, can provide a highly reliable tool to measure muscle Young's modulus, and could be used to assess the muscle force quantitatively.

Magnetic Resonance Elastography of Rodent Brain

Magnetic resonance elastography (MRE) is a non-invasive imaging technique, using the propagation of mechanical waves as a probe to palpate biological tissues. It consists in three main steps: production of shear waves within the tissue; encoding subsequent tissue displacement in magnetic resonance images; and extraction of mechanical parameters based on dedicated reconstruction methods. These three steps require an acoustic-frequency mechanical actuator, magnetic resonance imaging acquisition, and a post-processing tool for which no turnkey technology is available. The aim of the present review is to outline the state of the art of reported set-ups to investigate rodent brain mechanical properties. The impact of experimental conditions in dimensioning the set-up (wavelength and amplitude of the propagated wave, spatial resolution, and signal-to-noise ratio of the acquisition) on the accuracy and precision of the extracted parameters is discussed, as well as the influence of different imaging sequences, scanners, electromagnetic coils, and reconstruction algorithms. Finally, the performance of MRE in demonstrating viscoelastic differences between structures constituting the physiological rodent brain, and the changes in brain parameters under pathological conditions, are summarized. The recently established link between biomechanical properties of the brain as obtained on MRE and structural factors assessed by histology is also studied. This review intends to give an accessible outline on how to conduct an elastography experiment, and on the potential of the technique in providing valuable information for neuroscientists.

Brain stiffens post mortem

Alterations in brain rheology are increasingly recognized as a diagnostic marker for various neurological conditions. Magnetic resonance elastography now allows us to assess brain rheology repeatably, reproducibly, and non-invasively in vivo. Recent elastography studies suggest that brain stiffness decreases one percent per year during normal aging, and is significantly reduced in Alzheimer’s disease and multiple sclerosis. While existing studies successfully compare brain stiffnesses across different populations, they fail to provide insight into changes within the same brain. Here we characterize rheological alterations in one and the same brain under extreme metabolic changes: alive and dead. Strikingly, the storage and loss moduli of the cerebrum increased by 26% and 60% within only three minutes post mortem and continued to increase by 40% and 103% within 45 minutes. Immediate post mortem stiffening displayed pronounced regional variations; it was largest in the corpus callosum and smallest in the brainstem. We postulate that post mortem stiffening is a manifestation of alterations in polarization, oxidation, perfusion, and metabolism immediately after death. Our results suggest that the stiffness of our brain–unlike any other organ–is a dynamic property that is highly sensitive to the metabolic environment Our findings emphasize the importance of characterizing brain tissue in vivo and question the relevance of ex vivo brain tissue testing as a whole. Knowing the true stiffness of the living brain has important consequences in diagnosing neurological conditions, planning neurosurgical procedures, and modeling the brain’s response to high impact loading.

Glial Tissue Mechanics and Mechanosensing by Glial Cells

Understanding the mechanical behavior of human brain is critical to interpret the role of physical stimuli in both normal and pathological processes that occur in CNS tissue, such as development, inflammation, neurodegeneration, aging, and most common brain tumors. Despite clear evidence that mechanical cues influence both normal and transformed brain tissue activity as well as normal and transformed brain cell behavior, little is known about the links between mechanical signals and their biochemical and medical consequences. A multi-level approach from whole organ rheology to single cell mechanics is needed to understand the physical aspects of human brain function and its pathologies. This review summarizes the latest achievements in the field.

Rheological characterization of human brain tissue

The rheology of ultrasoft materials like the human brain is highly sensitive to regional and temporal variations and to the type of loading. While recent experiments have shaped our understanding of the time-independent, hyperelastic response of human brain tissue, its time-dependent behavior under various loading conditions remains insufficiently understood. Here we combine cyclic and relaxation testing under multiple loading conditions, shear, compression, and tension, to understand the rheology of four different regions of the human brain, the cortex, the basal ganglia, the corona radiata, and the corpus callosum. We establish a family of finite viscoelastic Ogden-type models and calibrate their parameters simultaneously for all loading conditions. We show that the model with only one viscoelastic mode and a constant viscosity captures the essential features of brain tissue: nonlinearity, pre-conditioning, hysteresis, and tension-compression asymmetry. With stiffnesses and time constants of μ_∞=0.7kPa, μ₁=2.0kPa, and τ₁=9.7s in the gray matter cortex and μ_∞=0.3kPa, μ₁=0.9kPa and τ₁=14.9s in the white matter corona radiata combined with negative parameters α_∞ and α₁, this five-parameter model naturally accounts for pre-conditioning and tissue softening. Increasing the number of viscoelastic modes improves the agreement between model and experiment, especially across the entire relaxation regime. Strikingly, two cycles of pre-conditioning decrease the gray matter stiffness by up to a factor three, while the white matter stiffness remains almost identical. These new insights allow us to better understand the rheology of different brain regions under mixed loading conditions. Our family of finite viscoelastic Ogden-type models for human brain tissue is simple to integrate into standard nonlinear finite element packages. Our simultaneous parameter identification of multiple loading modes can inform computational simulations under physiological conditions, especially at low to moderate strain rates. Understanding the rheology of the human brain will allow us to more accurately model the behavior of the brain during development and disease and predict outcomes of neurosurgical procedures.

STATEMENT OF SIGNIFICANCE

While recent experiments have shaped our understanding of the time-independent, hyperelastic response of human brain tissue, its time-dependent behavior at finite strains and under various loading conditions remains insufficiently understood. In this manuscript, we characterize the rheology of human brain tissue through a family of finite viscoelastic Ogdentype models and identify their parameters for multiple loading modes in four different regions of the brain. We show that even the simplest model of this family, with only one viscoelastic mode and five material parameters, naturally captures the essential features of brain tissue: its characteristic nonlinearity, pre-conditioning, hysteresis, and tension-compression asymmetry. For the first time, we simultaneously identify a single parameter set for shear, compression, tension, shear relaxation, and compression relaxation loading. This parameter set is significant for computational simulations under physiological conditions, where loading is naturally of mixed mode nature. Understanding the rheology of the human brain will help us predict neurosurgical procedures, inform brain injury criteria, and improve the design of protective devices.