Exploring Piezo1, Piezo2, and TMEM150C in human brain tissues and their correlation with brain biomechanical characteristics

Unraveling the intricate relationship between mechanical factors and brain activity is a pivotal endeavor, yet the underlying mechanistic model of signaling pathways in brain mechanotransduction remains enigmatic. To bridge this gap, we introduced an in situ multi-scale platform, through which we delineate comprehensive brain biomechanical traits in white matter (WM), grey-white matter junctions (GW junction), and the pons across human brain tissue from four distinct donors. We investigate the three-dimensional expression patterns of Piezo1, Piezo2, and TMEM150C, while also examining their associated histological features and mechanotransduction signaling networks, particularly focusing on the YAP/β-catenin axis. Our results showed that the biomechanical characteristics (including stiffness, spring term, and equilibrium stress) associated with Piezo1 vary depending on the specific region. Moving beyond Piezo1, our result demonstrated the significant positive correlations between Piezo2 expression and stiffness in the WM. Meanwhile, the expression of Piezo2 and TMEM150C was shown to be correlated to viscoelastic properties in the pons and WM. Given the heterogeneity of brain tissue, we investigated the three-dimensional expression of Piezo1, Piezo2, and TMEM150C. Our results suggested that three mechanosensitive proteins remained consistent across different vertical planes within the tissue sections. Our findings not only establish Piezo1, Piezo2, and TMEM150C as pivotal mechanosensors that regulate the region-specific mechanotransduction activities but also unveil the paradigm connecting brain mechanical properties and mechanotransduction activities and the variations between individuals.

Measurement of relative motion of the brain and skull in the mini-pig in-vivo

The mechanical role of the skull-brain interface is critical to the pathology of concussion and traumatic brain injury (TBI) and may evolve with age. Here we characterize the skull-brain interface in juvenile, female Yucatan mini-pigs from 3 to 6 months old using techniques from magnetic resonance elastography (MRE). The displacements of the skull and brain were measured by a motion-sensitive MR imaging sequence during low-amplitude harmonic motion of the head. Each animal was scanned four times at 1-month intervals. Harmonic motion at 100 Hz was excited by three different configurations of a jaw actuator in order to vary the direction of loading. Rigid-body linear motions of the brain and skull were similar, although brain rotations were consistently smaller than corresponding skull rotations. Relative displacements between the brain and skull were estimated for voxels on the surface of the brain. Amplitudes of relative displacements between skull and brain were 1-3 μm, approximately 25-50% of corresponding skull displacements. Maps of relative displacement showed variations by anatomical region, and the normal component of relative displacement was consistently 25-50% of the tangential component. These results illuminate the mechanics of the skull-brain interface in a gyrencephalic animal model relevant to human brain injury and development.

A sensor-enabled cloud-based computing platform for computational brain biomechanics

BACKGROUND AND OBJECTIVES

Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as "instrumented" or "smart" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion.

METHODS

We have built a brain modeling platform using several of Amazon's Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline.

RESULTS

The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D.

CONCLUSION

We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.

A transversely isotropic viscohyperelastic-damage model for the brain tissue with strain rate sensitivity

Understanding the mechanical behaviors and properties of brain tissue are crucial to study the mechanisms of traumatic brain injury (TBI). Such injury may be associated with high rate loading conditions and the large deformation of brain tissue. Thus, constitutive models that consider the rate dependent large deformation of brain tissue and its possible damage initiation and evolution may help uncover the related mechanisms of TBI. Motivated from this, in this paper we present a fully three-dimensional large strain viscohyperelastic-damage model with the purpose of reproducing the experimentally observed rate sensitive elastic and damage-induced stress softening behaviors of brain tissue. The parameters of the proposed model can be identified using the experimental data from simple monotonic tests such as uniaxial tension, compression and simple shear. The proposed model is validated by comparing its prediction with experimental data. Good agreement between predictive results and experimental data is achieved indicating the potential of the proposed model in characterizing the mechanical behaviors of brain tissue considering rate dependence and damage effect.

Characterization of material properties and deformation in the ANGUS phantom during mild head impacts using MRI

Traumatic brain injury (TBI) is a major health concern affecting both military and civilian populations. Despite notable advances in TBI research in recent years, there remains a significant gap in linking the impulsive loadings from a blast or a blunt impact to the clinical injury patterns observed in TBI. Synthetic head models or phantoms can be used to establish this link as they can be constructed with geometry, anatomy, and material properties that match the human brain, and can be used as an alternative to animal models. This study presents one such phantom called the Anthropomorphic Neurologic Gyrencephalic Unified Standard (ANGUS) phantom, which is an idealized gyrencephalic brain phantom composed of polyacrylamide gel. Here we mechanically characterized the ANGUS phantom using tagged magnetic resonance imaging (MRI) and magnetic resonance elastography (MRE), and then compared the outcomes to data obtained in healthy volunteers. The direct comparison between the phantom's response and the data from a cohort of in vivo human subjects demonstrate that the ANGUS phantom may be an appropriate model for bulk tissue response and gyral dynamics of the human brain under small amplitude linear impulses. However, the phantom's response differs from that of the in vivo human brain under rotational impacts, suggesting avenues for future improvements to the phantom.

Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports

Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes.

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.

Simulation of harmonic shear waves in the human brain and comparison with measurements from magnetic resonance elastography

Magnetic Resonance Elastography (MRE) provides a non-invasive method to characterize the mechanical response of the living brain subjected to harmonic loading conditions. The peak magnitude of the harmonic strain is small and the excitation results in harmless deformation waves propagating through the brain. In this paper, we describe a three-dimensional computational model of the brain for comparison of simulated harmonic deformations of the brain with MRE measurements. Relevant substructures of the head were constructed from MRI scans. Harmonic wave motions in a live human brain obtained in an MRE experiment were used to calibrate the viscoelastic properties at 50 Hz and assess accuracy of the computational model by comparing the measured and the simulated harmonic response of the brain. Quantitative comparison of strain field from simulations with measured data from MRE shows that the harmonic deformation of the brain tissue is responsive to changes in the viscoelastic properties, loss and storage moduli, of the brain. The simulation results demonstrate, in agreement with MRE measurements, that the presence of the falx and tentorium membranes alter the spatial distribution of harmonic deformation field and peak strain amplitudes in the computational model of the brain.

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.

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.

A fully nonlinear viscohyperelastic model for the brain tissue applicable to dynamic rates

Understanding the mechanical response of the brain to external loadings is of critical importance in investigating the pathological conditions of this tissue during injurious conditions. Such injurious loadings may occur at high rates, for example among others, during road traffic or sport accidents, falls, or due to explosions. Hence, investigating the injury mechanism and design of protective devices for the brain requires constitutive modeling of this tissue at such rates. Accordingly, this paper is aimed at critically investigating the physical background for viscohyperelastic modeling of the brain tissue with scrutinizing the elastic fields pertinent to large, time dependent deformations, and developing a fully nonlinear multimode Maxwell model that can mathematically explain such deformations. The proposed model can be calibrated using the simple monotonic uniaxial deformation of the sample extracted from the tissue, and does not require additional information from relaxation or creep experiments. The performance of the proposed model is examined using the experimental results of two different studies, which reveals a desirable agreement. The usefulness, limitations, and future developments of the proposed model are discussed in this paper.