Strain and rate-dependent neuronal injury in a 3D in vitro compression model of traumatic brain injury

The White Matter Fiber Tract Deforms Most in the Perpendicular Direction During In Vivo Volunteer Impacts

White matter (WM) tract-related strains are increasingly used to quantify brain mechanical responses, but their dynamics in live human brains during in vivo impact conditions remain largely unknown. Existing research primarily looked into the normal strain along the WM fiber tracts (i.e., tract-oriented normal strain), but it is rarely the case that the fiber tract only endures tract-oriented normal strain during impacts. In this study, we aim to extend the in vivo measurement of WM fiber deformation by quantifying the normal strain perpendicular to the fiber tract (i.e., tract-perpendicular normal strain) and the shear strain along and perpendicular to the fiber tract (i.e., tract-oriented shear strain and tract-perpendicular shear strain, respectively). To achieve this, we combine the three-dimensional strain tensor from the tagged magnetic resonance imaging with the diffusion tensor imaging (DTI) from an open-access dataset, including 44 volunteer impacts under two head loading modes, i.e., neck rotations (N = 30) and neck extensions (N = 14). The strain tensor is rotated to the coordinate system with one axis aligned with DTI-revealed fiber orientation, and then four tract-related strain measures are calculated. The results show that tract-perpendicular normal strain peaks are the largest among the four strain types (p < 0.05, Friedman's test). The distribution of tract-related strains is affected by the head loading mode, of which laterally symmetric patterns with respect to the midsagittal plane are noted under neck extensions, but not under neck rotations. Our study presents a comprehensive in vivo strain quantification toward a multifaceted understanding of WM dynamics. We find that the WM fiber tract deforms most in the perpendicular direction, illuminating new fundamentals of brain mechanics. The reported strain images can be used to evaluate the fidelity of computational head models, especially those intended to predict fiber deformation under noninjurious conditions.

Cortical spheroids show strain-dependent cell viability loss and neurite disruption following sustained compression injury

Sustained compressive injury (SCI) in the brain is observed in numerous injury and pathological scenarios, including tumors, ischemic stroke, and traumatic brain injury-related tissue swelling. Sustained compressive injury is characterized by tissue loading over time, and currently, there are few in vitro models suitable to study neural cell responses to strain-dependent sustained compressive injury. Here, we present an in vitro model of sustained compressive neural injury via centrifugation. Spheroids were made from neonatal rat cortical cells seeded at 4000 cells/spheroid and cultured for 14 days in vitro. A subset of spheroids was centrifuged at 104, 209, 313 or 419 rads/s for 2 minutes. Modeling the physical deformation of the spheroids via finite element analyses, we found that spheroids centrifuged at the aforementioned angular velocities experienced pressures of 10, 38, 84 and 149 kPa, respectively, and compressive (resp. tensile) strains of 10% (5%), 18% (9%), 27% (14%) and 35% (18%), respectively. Quantification of LIVE-DEAD assay and Hoechst 33342 nuclear staining showed that centrifuged spheroids subjected to pressures above 10 kPa exhibited significantly higher DNA damage than control spheroids at 2, 8, and 24 hours post-injury. Immunohistochemistry of β3-tubulin networks at 2, 8, and 24 hours post-centrifugation injury showed increasing degradation of microtubules over time with increasing strain. Our findings show that cellular injuries occur as a result of specific levels and timings of sustained tissue strains. This experimental SCI model provides a high throughput in vitro platform to examine cellular injury, to gain insights into brain injury that could be targeted with therapeutic strategies.

Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-Based Machine Learning Approach

INTRODUCTION

Computational head injury models are promising tools for understanding and predicting traumatic brain injuries. However, most available head injury models are "average" models that employ a single set of head geometry (e.g., 50th-percentile U.S. male) without considering variability in these parameters across the human population. A significant variability of head shapes exists in U.S. Army soldiers, evident from the Anthropometric Survey of U.S. Army Personnel (ANSUR II). The objective of this study is to elucidate the effects of head shape on the predicted risk of traumatic brain injury from computational head injury models.

MATERIALS AND METHODS

Magnetic resonance imaging scans of 25 human subjects are collected. These images are registered to the standard MNI152 brain atlas, and the resulting transformation matrix components (called head shape parameters) are used to quantify head shapes of the subjects. A generative machine learning model is used to generate 25 additional head shape parameter datasets to augment our database. Head injury models are developed for these head shapes, and a rapid injurious head rotation event is simulated to obtain several brain injury predictor variables (BIPVs): Peak cumulative maximum principal strain (CMPS), average CMPS, and the volume fraction of brain exceeding an injurious CMPS threshold. A Gaussian process regression model is trained between head shape parameters and BIPVs, which is then used to study the relative sensitivity of the various BIPVs on individual head shape parameters. We distinguish head shape parameters into 2 types: Scaling components ${T_{xx}}$, ${T_{yy}}$, and ${T_{zz}}$ that capture the breadth, length, and height of the head, respectively, and shearing components (${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$) that capture the relative skewness of the head shape.

RESULTS

An overall positive correlation is evident between scaling components and BIPVs. Notably, a very high, positive correlation is seen between the BIPVs and the head volume. As an example, a 57% increase in peak CMPS was noted between the smallest and the largest investigated head volume parameters. The variation in shearing components ${T_{xy}},{T_{xz}},{T_{yx}},{T_{yz}},{T_{zx}}$, and ${T_{zy}}$ on average does not cause notable changes in the BIPVs. From the Gaussian process regression model, all 3 BIPVs showed an increasing trend with each of the 3 scaling components, but the BIPVs are found to be most sensitive to the height dimension of the head. From the Sobol sensitivity analysis, the ${T_{zz}}$ scaling parameter contributes nearly 60% to the total variance in peak and average CMPS; ${T_{yy}}$ contributes approximately 20%, whereas ${T_{xx}}$ contributes less than 5%. The remaining contribution is from the 6 shearing components. Unlike peak and average CMPS, the VF-CMPS BIPV is associated with relatively evenly distributed Sobol indices across the 3 scaling parameters. Furthermore, the contribution of shearing components on the total variance in this case is negligible.

CONCLUSIONS

Head shape has a considerable influence on the injury predictions of computational head injury models. Available "average" head injury models based on a 50th-percentile U.S. male are likely associated with considerable uncertainty. In general, larger head sizes correspond to greater BIPV magnitudes, which point to potentially a greater injury risk under rapid neck rotation for people with larger heads.

A mechanics theory for the exploration of a high-throughput, sterile 3D in vitro traumatic brain injury model

Brain injuries resulting from mechanical trauma represent an ongoing global public health issue. Several in vitro and in vivo models for traumatic brain injury (TBI) continue to be developed for delineating the various complex pathophysiological processes involved in its onset and progression. Developing an in vitro TBI model that is based on cortical spheroids is especially of great interest currently because they can replicate key aspects of in vivo brain tissue, including its electrophysiology, physicochemical microenvironment, and extracellular matrix composition. Being able to mechanically deform the spheroids are a key requirement in any effective in vitro TBI model. The spheroids' shape and size, however, make mechanically loading them, especially in a high-throughput, sterile, and reproducible manner, quite challenging. To address this challenge, we present an idea for a spheroid-based, in vitro TBI model in which the spheroids are mechanically loaded by being spun by a centrifuge. (An experimental demonstration of this new idea will be published shortly elsewhere.) An issue that can limit its utility and scope is that imaging techniques used in 2D and 3D in vitro TBI models cannot be readily applied in it to determine spheroid strains. In order to address this issue, we developed a continuum mechanics-based theory to estimate the spheroids' strains when they are being spun at a constant angular velocity. The mechanics theory, while applicable here to a special case of the centrifuge-based TBI model, is also of general value since it can help with the further exploration and development of TBI models.

Frequency and time dependent viscoelastic characterization of pediatric porcine brain tissue in compression

Understanding the viscoelastic behavior of pediatric brain tissue is critical to interpret how external mechanical forces affect head injury in children. However, knowledge of the viscoelastic properties of pediatric brain tissue is limited, and this reduces the biofidelity of developed numeric simulations of the pediatric head in analysis of brain injury. Thus, it is essential to characterize the viscoelastic behavior of pediatric brain tissue in various loading conditions and to identify constitutive models. In this study, the pediatric porcine brain tissue was investigated in compression with determine the viscoelasticity under small and large strain, respectively. A range of frequencies between 0.1 and 40 Hz was applied to determine frequency-dependent viscoelastic behavior via dynamic mechanical analysis, while brain samples were divided into three strain rate groups of 0.01/s, 1/s and 10/s for compression up to 0.3 strain level and stress relaxation to obtain time-dependent viscoelastic properties. At frequencies above 20 Hz, the storage modulus did not increase, while the loss modulus increased continuously. With strain rate increasing from 0.01/s to 10/s, the mean stress at 0.1, 0.2 and 0.3 strain increased to approximate 6.8, 5.6 and 4.4 times, respectively. The brain compressive response was sensitive to strain rate and frequency. The characterization of brain tissue will be valuable for development of head protection systems and prediction of brain injury.

Impact of prior axonal injury on subsequent injury during brain tissue stretching - A mesoscale computational approach

Epidemiology studies of traumatic brain injury (TBI) show individuals with a prior history of TBI experience an increased risk of future TBI with a significantly more detrimental outcome. But the mechanisms through which prior head injuries may affect risks of injury during future head insults have not been identified. In this work, we show that prior brain tissue injury in the form of mechanically induced axonal injury and glial scar formation can facilitate future mechanically induced tissue injury. To achieve this, we use finite element computational models of brain tissue and a history-dependent pathophysiology-based mechanically-induced axonal injury threshold to determine the evolution of axonal injury and scar tissue formation and their effects on future brain tissue stretching. We find that due to the reduced stiffness of injured tissue and glial scars, the existence of prior injury can increase the risk of future injury in the vicinity of prior injury during future brain tissue stretching. The softer brain scar tissue is shown to increase the strain and strain rate in its vicinity by as much as 40% in its vicinity during dynamic stretching that reduces the global strain required to induce injury by 20% when deformed at 15 s-1 strain rate. The results of this work highlight the need to account for patient history when determining the risk of brain injury.

Dynamic strain fields of the mouse brain during rotation

Mouse models are used to better understand brain injury mechanisms in humans, yet there is a limited understanding of biomechanical relevance, beginning with how the murine brain deforms when the head undergoes rapid rotation from blunt impact. This problem makes it difficult to translate some aspects of diffuse axonal injury from mouse to human. To address this gap, we present the two-dimensional strain field of the mouse brain undergoing dynamic rotation in the sagittal plane. Using a high-speed camera with digital image correlation measurements of the exposed mid-sagittal brain surface, we found that pure rotations (no direct impact to the skull) of 100-200 rad/s are capable of producing complex strain fields that evolve over time with respect to rotational acceleration and deceleration. At the highest rotational velocity tested, the largest tensile strains (≥ 21% elongation) in selected regions of the mouse brain approach strain thresholds previously associated with axonal injury in prior work. These findings provide a benchmark to validate the mechanical response in biomechanical computational models predicting diffuse axonal injury, but much work remains in correlating tissue deformation patterns from computational models with underlying neuropathology.

Shear shock wave injury in vivo: High frame-rate ultrasound observation and histological assessment

Using high frame-rate ultrasound and ¡1μm sensitive motion tracking we previously showed that shear waves at the surface of ex vivo and in situ brains develop into shear shock waves deep inside the brain, with destructive local accelerations. However post-mortem tissue cannot develop injuries and has different viscoelastodynamic behavior from in vivo tissue. Here we present the ultrasonic measurement of the high-rate shear shock biomechanics in the in vivo porcine brain, and histological assessment of the resulting axonal pathology. A new biomechanical model of brain injury was developed consisting of a perforated mylar surface attached to the brain and vibrated using an electromechanical shaker. Using a custom sequence with 8 interleaved wide beam emissions, brain imaging and motion tracking were performed at 2900 images/s. Shear shock waves were observed for the first time in vivo wherein the shock acceleration was measured to be 2.6 times larger than the surface acceleration ( 95g vs. 36g). Histopathology showed axonal damage in the impacted side of the brain from the brain surface, accompanied by a local shock-front acceleration of >70g. This shows that axonal injury occurs deep in the brain even though the shear excitation was at the brain surface, and the acceleration measurements support the hypothesis that shear shock waves are responsible for deep traumatic brain injuries.

Bedside to bench: the outlook for psychedelic research

There has recently been a resurgence of interest in psychedelic compounds based on studies demonstrating their potential therapeutic applications in treating post-traumatic stress disorder, substance abuse disorders, and treatment-resistant depression. Despite promising efficacy observed in some clinical trials, the full range of biological effects and mechanism(s) of action of these compounds have yet to be fully established. Indeed, most studies to date have focused on assessing the psychological mechanisms of psychedelics, often neglecting the non-psychological modes of action. However, it is important to understand that psychedelics may mediate their therapeutic effects through multi-faceted mechanisms, such as the modulation of brain network activity, neuronal plasticity, neuroendocrine function, glial cell regulation, epigenetic processes, and the gut-brain axis. This review provides a framework supporting the implementation of a multi-faceted approach, incorporating in silico, in vitro and in vivo modeling, to aid in the comprehensive understanding of the physiological effects of psychedelics and their potential for clinical application beyond the treatment of psychiatric disorders. We also provide an overview of the literature supporting the potential utility of psychedelics for the treatment of brain injury (e.g., stroke and traumatic brain injury), neurodegenerative diseases (e.g., Parkinson's and Alzheimer's diseases), and gut-brain axis dysfunction associated with psychiatric disorders (e.g., generalized anxiety disorder and major depressive disorder). To move the field forward, we outline advantageous experimental frameworks to explore these and other novel applications for psychedelics.

Characterization of neural mechanotransduction response in human traumatic brain injury organoid model

The ability to model physiological systems through 3D neural in-vitro systems may enable new treatments for various diseases while lowering the need for challenging animal and human testing. Creating such an environment, and even more impactful, one that mimics human brain tissue under mechanical stimulation, would be extremely useful to study a range of human-specific biological processes and conditions related to brain trauma. One approach is to use human cerebral organoids (hCOs) in-vitro models. hCOs recreate key cytoarchitectural features of the human brain, distinguishing themselves from more traditional 2D cultures and organ-on-a-chip models, as well as in-vivo animal models. Here, we propose a novel approach to emulate mild and moderate traumatic brain injury (TBI) using hCOs that undergo strain rates indicative of TBI. We subjected the hCOs to mild (2 s[Formula: see text]) and moderate (14 s[Formula: see text]) loading conditions, examined the mechanotransduction response, and investigated downstream genomic effects and regulatory pathways. The revealed pathways of note were cell death and metabolic and biosynthetic pathways implicating genes such as CARD9, ENO1, and FOXP3, respectively. Additionally, we show a steeper ascent in calcium signaling as we imposed higher loading conditions on the organoids. The elucidation of neural response to mechanical stimulation in reliable human cerebral organoid models gives insights into a better understanding of TBI in humans.

Mechanical mechanism and indicator of diffuse axonal injury under blast-type acceleration

Diffuse axonal injury (DAI) caused by acceleration is one of the most prominent forms of blast-induced Traumatic Brain Injury. However, the mechanical mechanism and indicator of axonal deformation-induced injury under blast-type acceleration with high peak and short duration are unclear. This study constructed a multilayer head model that can reflect the response characteristics of translational and rotational acceleration (the peak time of which is within 0.5 ms). Based on von Mises stress, axonal strain and axonal strain rate indicators, the physical process of axonal injury is studied, and the vulnerable area under blast-type acceleration load is given. In the short term (within 1.75 ms), dominated by sagittal rotational acceleration peaks, the constraint of falx and tentorium rapidly imposes the inertial load on the brain tissue, resulting in a high-rate deformation of axons (axonal strain rate of which exceed 100 s-1). For a long term (after 1.75 ms), fixed-point rotation of the brain following the head causes excessive distortion of brain tissue (von Mises stress of which exceeds 15 kPa), resulting in a large axonal stretch strain where the main axonal orientation coincides with the principal strain direction. It is found that the axonal strain rate can better indicate the pathological axonal injury area and coincides with external inertial loading in the risk areas, which suggests that DAI under blast-type acceleration overload is mainly caused by the rapid axonal deformation instead of by the excessive axonal strain. The research in this paper helps understand and diagnose blast-induced DAI.

Dynamic biophysical responses of neuronal cell nuclei and cytoskeletal structure following high impulse loading

Cells are continuously exposed to dynamic environmental cues that influence their behavior. Mechanical cues can influence cellular and genomic architecture, gene expression, and intranuclear mechanics, providing evidence of mechanosensing by the nucleus, and a mechanoreciprocity between the nucleus and environment. Force disruption at the tissue level through aging, disease, or trauma, propagates to the nucleus and can have lasting consequences on proper functioning of the cell and nucleus. While the influence of mechanical cues leading to axonal damage has been well studied in neuronal cells, the mechanics of the nucleus following high impulse loading is still largely unexplored. Using an in vitro model of traumatic neural injury, we show a dynamic nuclear behavioral response to impulse stretch (up to 170% strain per second) through quantitative measures of nuclear movement, including tracking of rotation and internal motion. Differences in nuclear movement were observed between low and high strain magnitudes. Increased exposure to impulse stretch exaggerated the decrease in internal motion, assessed by particle tracking microrheology, and intranuclear displacements, assessed through high-resolution deformable image registration. An increase in F-actin puncta surrounding nuclei exposed to impulse stretch additionally demonstrated a corresponding disruption of the cytoskeletal network. Our results show direct biophysical nuclear responsiveness in neuronal cells through force propagation from the substrate to the nucleus. Understanding how mechanical forces perturb the morphological and behavioral response can lead to a greater understanding of how mechanical strain drives changes within the cell and nucleus, and may inform fundamental nuclear behavior after traumatic axonal injury. STATEMENT OF SIGNIFICANCE: The nucleus of the cell has been implicated as a mechano-sensitive organelle, courting molecular sensors and transmitting physical cues in order to maintain cellular and tissue homeostasis. Disruption of this network due to disease or high velocity forces (e.g., trauma) can not only result in orchestrated biochemical cascades, but also biophysical perturbations. Using an in vitro model of traumatic neural injury, we aimed to provide insight into the neuronal nuclear mechanics and biophysical responses at a continuum of strain magnitudes and after repetitive loads. Our image-based methods demonstrate mechanically-induced changes in cellular and nuclear behavior after high intensity loading and have the potential to further define mechanical thresholds of neuronal cell injury.

Beyond symptomatic diagnosis of mild traumatic brain injury

It is commonly assumed that there is no brain injury if there are no noticeable symptoms following a head impact. There is growing evidence that traumatic brain injuries can occur with no outward symptoms and that the damage from these injuries can accumulate over time resulting in disease and impairment later in life. It is time to rethink the role that symptoms play in traumatic brain injury and adopt a quantitative understanding of brain health at the cellular level to improve the way we diagnose, prevent, and ultimately heal brain injury.

Mechanisms of mechanical load transfer through brain tissue

Brain injuries are often characterized by diffusely distributed axonal and vascular damage invisible to medical imaging techniques. The spatial distribution of mechanical stresses and strains plays an important role, but is not sufficient to explain the diffuse distribution of brain lesions. It remains unclear how forces are transferred from the organ to the cell scale and why some cells are damaged while neighboring cells remain unaffected. To address this knowledge gap, we subjected histologically stained fresh human and porcine brain tissue specimens to compressive loading and simultaneously tracked cell and blood vessel displacements. Our experiments reveal different mechanisms of load transfer from the organ or tissue scale to single cells, axons, and blood vessels. Our results show that cell displacement fields are inhomogeneous at the interface between gray and white matter and in the vicinity of blood vessels-locally inducing significant deformations of individual cells. These insights have important implications to better understand injury mechanisms and highlight the importance of blood vessels for the local deformation of the brain's cellular structure during loading.

Head Impact Modeling to Support a Rotational Combat Helmet Drop Test

INTRODUCTION

The Advanced Combat Helmet (ACH) military specification (mil-spec) provides blunt impact acceleration criteria that must be met before use by the U.S. warfighter. The specification, which requires a helmeted magnesium Department of Transportation (DOT) headform to be dropped onto a steel hemispherical target, results in a translational headform impact response. Relative to translations, rotations of the head generate higher brain tissue strains. Excessive strain has been implicated as a mechanical stimulus leading to traumatic brain injury (TBI). We hypothesized that the linear constrained drop test method of the ACH specification underreports the potential for TBI.

MATERIALS AND METHODS

To establish a baseline of translational acceleration time histories, we conducted linear constrained drop tests based on the ACH specification and then performed simulations of the same to verify agreement between experiment and simulation. We then produced a high-fidelity human head digital twin and verified that biological tissue responses matched experimental results. Next, we altered the ACH experimental configuration to use a helmeted Hybrid III headform, a freefall cradle, and an inclined anvil target. This new, modified configuration allowed both a translational and a rotational headform response. We applied this experimental rotation response to the skull of our human digital twin and compared brain deformation relative to the translational baseline.

RESULTS

The modified configuration produced brain strains that were 4.3 times the brain strains from the linear constrained configuration.

CONCLUSIONS

We provide a scientific basis to motivate revision of the ACH mil-spec to include a rotational component, which would enhance the test's relevance to TBI arising from severe head impacts.

Simulating traumatic brain injury in vitro: developing high throughput models to test biomaterial based therapies

Traumatic brain injuries are serious clinical incidents associated with some of the poorest outcomes in neurological practice. Coupled with the limited regenerative capacity of the brain, this has significant implications for patients, carers, and healthcare systems, and the requirement for life-long care in some cases. Clinical treatment currently focuses on limiting the initial neural damage with long-term care/support from multidisciplinary teams. Therapies targeting neuroprotection and neural regeneration are not currently available but are the focus of intensive research. Biomaterial-based interventions are gaining popularity for a range of applications including biomolecule and drug delivery, and to function as cellular scaffolds. Experimental investigations into the development of such novel therapeutics for traumatic brain injury will be critically underpinned by the availability of appropriate high throughput, facile, ethically viable, and pathomimetic biological model systems. This represents a significant challenge for researchers given the pathological complexity of traumatic brain injury. Specifically, there is a concerted post-injury response mounted by multiple neural cell types which includes microglial activation and astroglial scarring with the expression of a range of growth inhibitory molecules and cytokines in the lesion environment. Here, we review common models used for the study of traumatic brain injury (ranging from live animal models to in vitro systems), focusing on penetrating traumatic brain injury models. We discuss their relative advantages and drawbacks for the developmental testing of biomaterial-based therapies.

Morphological changes in glial cells arrangement under mechanical loading: A quantitative study

The mechanical properties and microstructure of brain tissue, as its two main physical parameters, could be affected by mechanical stimuli. In previous studies, microstructural alterations due to mechanical loading have received less attention than the mechanical properties of the tissue. Therefore, the current study aimed to investigate the effect of ex-vivo mechanical forces on the micro-architecture of brain tissue including axons and glial cells. A three-step loading protocol (i.e., loading-recovery-loading) including eight strain levels from 5% to 40% was applied to bovine brain samples with axons aligned in one preferred direction (each sample experienced only one level of strain). After either the first or secondary loading step, the samples were fixed, cut in planes parallel and perpendicular to the loading direction, and stained for histology. The histological images were analyzed to measure the end-to-end length of axons and glial cell-cell distances. The results showed that after both loading steps, as the strain increased, the changes in the cell nuclei arrangement in the direction parallel to axons were more significant compared to the other two perpendicular directions. Based on this evidence, we hypothesized that the spatial pattern of glial cells is highly affected by the orientation of axonal fibers. Moreover, the results revealed that in both loading steps, the maximum cell-cell distance occurred at 15% strain, and this distance decreased for higher strains. Since 15% strain is close to the previously reported brain injury threshold, this evidence could suggest that at higher strains, the axons start to rupture, causing a reduction in the displacement of glial cells. Accordingly, it was concluded that more attention to glial cells' architecture during mechanical loading may lead to introduce a new biomarker for brain injury.

Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis

OBJECTIVE

Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM).

METHODS

We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021).

RESULTS

PC1 explained variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy.

CONCLUSION

The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance.

SIGNIFICANCE

The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.

A biomechanical-based approach to scale blast-induced molecular changes in the brain

Animal studies provide valuable insights on how the interaction of blast waves with the head may injure the brain. However, there is no acceptable methodology to scale the findings from animals to humans. Here, we propose an experimental/computational approach to project observed blast-induced molecular changes in the rat brain to the human brain. Using a shock tube, we exposed rats to a range of blast overpressures (BOPs) and used a high-fidelity computational model of a rat head to correlate predicted biomechanical responses with measured changes in glial fibrillary acidic protein (GFAP) in rat brain tissues. Our analyses revealed correlates between model-predicted strain rate and measured GFAP changes in three brain regions. Using these correlates and a high-fidelity computational model of a human head, we determined the equivalent BOPs in rats and in humans that induced similar strain rates across the two species. We used the equivalent BOPs to project the measured GFAP changes in the rat brain to the human. Our results suggest that, relative to the rat, the human requires an exposure to a blast wave of a higher magnitude to elicit similar brain-tissue responses. Our proposed methodology could assist in the development of safety guidelines for blast exposure.

Modelling transcranial ultrasound neuromodulation: an energy-based multiscale framework

Several animal and human studies have now established the potential of low intensity, low frequency transcranial ultrasound (TUS) for non-invasive neuromodulation. Paradoxically, the underlying mechanisms through which TUS neuromodulation operates are still unclear, and a consensus on the identification of optimal sonication parameters still remains elusive. One emerging hypothesis based on thermodynamical considerations attributes the acoustic-induced nerve activity alterations to the mechanical energy and/or entropy conversions occurring during TUS action. Here, we propose a multiscale modelling framework to examine the energy states of neuromodulation under TUS. First, macroscopic tissue-level acoustic simulations of the sonication of a whole monkey brain are conducted under different sonication protocols. For each one of them, mechanical loading conditions of the received waves in the anterior cingulate cortex region are recorded and exported into a microscopic cell-level 3D viscoelastic finite element model of neuronal axon embedded extracellular medium. Pulse-averaged elastically stored and viscously dissipated energy rate densities during axon deformation are finally computed under different sonication incident angles and are mapped against distinct combinations of sonication parameters of the TUS. The proposed multiscale framework allows for the analysis of vibrational patterns of the axons and its comparison against the spectrograms of stimulating ultrasound. The results are in agreement with literature data on neuromodulation, demonstrating the potential of this framework to identify optimised acoustic parameters in TUS neuromodulation. The proposed approach is finally discussed in the context of multiphysics energetic considerations, argued here to be a promising avenue towards a scalable framework for TUS in silico predictions. STATEMENT OF SIGNIFICANCE: Low-intensity transcranial ultrasound (TUS) is poised to become a leading neuromodulation technique for the treatment of neurological disorders. Paradoxically, how it operates at the cellular scale remains unknown, hampering progress in personalised treatment. To this end, models of the multiphysics of neurons able to upscale results to the organ scale are required. We propose here to achieve this by considering an axon submitted to an ultrasound wave extracted from a simulation at the organ scale. Doing so, information pertaining to both stored and dissipated axonal energies can be extracted for a given head/brain morphology. This two-scale multiphysics energetic approach is a promising scalable framework for in silico predictions in the context of personalised TUS treatment.

Cavitation induced fracture of intact brain tissue

Nonpenetrating traumatic brain injuries (TBIs) are linked to cavitation. The structural organization of the brain makes it particularly susceptible to tears and fractures from these cavitation events, but limitations in existing characterization methods make it difficult to understand the relationship between fracture and cavitation in this tissue. More broadly, fracture energy is an important, yet often overlooked, mechanical property of all soft tissues. We combined needle-induced cavitation with hydraulic fracture models to induce and quantify fracture in intact brains at precise locations. We report here the first measurements of the fracture energy of intact brain tissue that range from 1.5 to 8.9 J/m2, depending on the location in the brain and the model applied. We observed that fracture consistently occurs along interfaces between regions of brain tissue. These fractures along interfaces allow cavitation-related damage to propagate several millimeters away from the initial injury site. Quantifying the forces necessary to fracture brain and other soft tissues is critical for understanding how impact and blast waves damage tissue in vivo and has implications for the design of protective gear and tissue engineering.

Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact

Real-time dynamic simulation remains a significant challenge for spatiotemporal data of high dimension and resolution. In this study, we establish a transformer neural network (TNN) originally developed for natural language processing and a separate convolutional neural network (CNN) to estimate five-dimensional (5D) spatiotemporal brain-skull relative displacement resulting from impact (isotropic spatial resolution of 4 mm with temporal resolution of 1 ms). Sequential training is applied to train (N = 5184 samples) the two neural networks for estimating the complete 5D displacement across a temporal duration of 60 ms. We find that TNN slightly but consistently outperforms CNN in accuracy for both displacement and the resulting voxel-wise four-dimensional (4D) maximum principal strain (e.g., root mean squared error (RMSE) of ~1.0% vs. ~1.6%, with coefficient of determination, R 2 >0.99 vs. >0.98, respectively, and normalized RMSE (NRMSE) at peak displacement of 2%-3%, based on an independent testing dataset; N = 314). Their accuracies are similar for a range of real-world impacts drawn from various published sources (dummy, helmet, football, soccer, and car crash; average RMSE/NRMSE of ~0.3 mm/~4%-5% and average R 2 of ~0.98 at peak displacement). Sequential training is effective for allowing instantaneous estimation of 5D displacement with high accuracy, although TNN poses a heavier computational burden in training. This work enables efficient characterization of the intrinsically dynamic brain strain in impact critical for downstream multiscale axonal injury model simulation. This is also the first application of TNN in biomechanics, which offers important insight into how real-time dynamic simulations can be achieved across diverse engineering fields.

Protective Performance of Helmets and Goggles in Mitigating Brain Biomechanical Response to Primary Blast Exposure

The current combat helmets are primarily designed to mitigate blunt impacts and ballistic loadings. Their protection against primary blast wave is not well studied. In this paper, we comprehensively assessed the protective capabilities of the advanced combat helmet and goggles against blast waves with different intensity and directions. Using a high-fidelity human head model, we compared the intracranial pressure (ICP), cerebrospinal fluid (CSF) cavitation, and brain strain and strain rate predicted from bare head, helmet-head and helmet-goggles-head simulations. The helmet was found to be effective in mitigating the positive ICP (24–57%) and strain rate (5–34%) in all blast scenarios. Goggles were found to be effective in mitigating the positive ICP in frontal (6–16%) and lateral (5–7%) blast exposures. However, the helmet and goggles had minimal effects on mitigating CSF cavitation and even increased brain strain. Further investigation showed that wearing a helmet leads to higher risk of cavitation. In addition, their presence increased the head kinetic energy, leading to larger strains in the brain. Our findings can improve our understanding of the protective effects of helmets and goggles and guide the design of helmet pads to mitigate brain responses to blast.

Simulated traumatic brain injury in in-vitro mouse neuronal and brain endothelial cell culture models

Traumatic brain injury can lead to fatal outcomes such as disability and death. Every year, it affects many patients all over the world. Not only the primary ischemic event, but also the subsequent reperfusion can cause severe brain injury. This so-called ischemia/reperfusion injury combined with mechanical forces lead to cellular disruption. Hence, this paper describes a special in-vitro model, mimicking traumatic brain injury by combining both hypoxia/reoxygenation and compression to simulate ischemia/reperfusion injury as well as the mechanical effects that occur concurrently when suffering traumatic brain injury. Through this approach, stroke, concussion, and traumatic brain injury can be studied on different cell lines in a simplified way. We used two primary mouse brain cell cultures, namely neurons and endothelial cells. Our results show that for the different cell types, different timelines of hypoxia and compression need to be explored to achieve the optimal amount of cellular damage in order to effectively mimic traumatic brain injury. Thus, this model will be useful to test potential treatments of brain injury in future in-vitro studies.

The Emergence of Model Systems to Investigate the Link Between Traumatic Brain Injury and Alzheimer's Disease

Numerous epidemiological studies have demonstrated that individuals who have sustained a traumatic brain injury (TBI) have an elevated risk for developing Alzheimer's disease and Alzheimer's-related dementias (AD/ADRD). Despite these connections, the underlying mechanisms by which TBI induces AD-related pathology, neuronal dysfunction, and cognitive decline have yet to be elucidated. In this review, we will discuss the various in vivo and in vitro models that are being employed to provide more definite mechanistic relationships between TBI-induced mechanical injury and AD-related phenotypes. In particular, we will highlight the strengths and weaknesses of each of these model systems as it relates to advancing the understanding of the mechanisms that lead to TBI-induced AD onset and progression as well as providing platforms to evaluate potential therapies. Finally, we will discuss how emerging methods including the use of human induced pluripotent stem cell (hiPSC)-derived cultures and genome engineering technologies can be employed to generate better models of TBI-induced AD.

A multilayer network model of neuron-astrocyte populations in vitro reveals mGluR5 inhibition is protective following traumatic injury

Astrocytes communicate bidirectionally with neurons, enhancing synaptic plasticity and promoting the synchronization of neuronal microcircuits. Despite recent advances in understanding neuron-astrocyte signaling, little is known about astrocytic modulation of neuronal activity at the population level, particularly in disease or following injury. We used high-speed calcium imaging of mixed cortical cultures in vitro to determine how population activity changes after disruption of glutamatergic signaling and mechanical injury. We constructed a multilayer network model of neuron-astrocyte connectivity, which captured distinct topology and response behavior from single-cell-type networks. mGluR₅ inhibition decreased neuronal activity, but did not on its own disrupt functional connectivity or network topology. In contrast, injury increased the strength, clustering, and efficiency of neuronal but not astrocytic networks, an effect that was not observed in networks pretreated with mGluR₅ inhibition. Comparison of spatial and functional connectivity revealed that functional connectivity is largely independent of spatial proximity at the microscale, but mechanical injury increased the spatial-functional correlation. Finally, we found that astrocyte segments of the same cell often belong to separate functional communities based on neuronal connectivity, suggesting that astrocyte segments function as independent entities. Our findings demonstrate the utility of multilayer network models for characterizing the multiscale connectivity of two distinct but functionally dependent cell populations.

Astrocytes communicate bidirectionally with neurons, enhancing synaptic plasticity and promoting the synchronization of neuronal microcircuits. We constructed a multilayer network model of neuron-astrocyte connectivity based on calcium activity in mixed cortical cultures, and used this model to evaluate the effect of glutamatergic inhibition and mechanical injury on network topology. We found that injury increased the strength, clustering, and efficiency of neuronal but not astrocytic networks, an effect that was not observed in injured networks pretreated with a glutamate receptor antagonist. Our findings demonstrate the utility of multilayer network models for characterizing the multiscale connectivity of two distinct but functionally dependent cell populations.

Lipopolysaccharide-induced neuroinflammation disrupts functional connectivity and community structure in primary cortical microtissues

Three-dimensional (3D) neural microtissues are a powerful in vitro paradigm for studying brain development and disease under controlled conditions, while maintaining many key attributes of the in vivo environment. Here, we used primary cortical microtissues to study the effects of neuroinflammation on neural microcircuits. We demonstrated the use of a genetically encoded calcium indicator combined with a novel live-imaging platform to record spontaneous calcium transients in microtissues from day 14-34 in vitro. We implemented graph theory analysis of calcium activity to characterize underlying functional connectivity and community structure of microcircuits, which are capable of capturing subtle changes in network dynamics during early disease states. We found that microtissues cultured for 34 days displayed functional remodeling of microcircuits and that community structure strengthened over time. Lipopolysaccharide, a neuroinflammatory agent, significantly increased functional connectivity and disrupted community structure 5-9 days after exposure. These microcircuit-level changes have broad implications for the role of neuroinflammation in functional dysregulation of neural networks.

Mechanical Stretching-Induced Traumatic Brain Injury Is Mediated by the Formation of GSK-3β-Tau Complex to Impair Insulin Signaling Transduction

Traumatic brain injury confers a significant and growing public health burden. It is a major environmental risk factor for dementia. Nonetheless, the mechanism by which primary mechanical injury leads to neurodegeneration and an increased risk of dementia-related diseases is unclear. Thus, we aimed to investigate the effect of stretching on SH-SY5Y neuroblastoma cells that proliferate in vitro. These cells retain the dopamine-β-hydroxylase activity, thus being suitable for neuromechanistic studies. SH-SY5Y cells were cultured on stretchable membranes. The culture conditions contained two groups, namely non-stretched (control) and stretched. They were subjected to cyclic stretching (6 and 24 h) and 25% elongation at 1 Hz. Following stretching at 25% and 1 Hz for 6 h, the mechanical injury changed the mitochondrial membrane potential and triggered oxidative DNA damage at 24 h. Stretching decreased the level of brain-derived neurotrophic factors and increased amyloid-β, thus indicating neuronal stress. Moreover, the mechanical injury downregulated the insulin pathway and upregulated glycogen synthase kinase 3β (GSK-3β)^S9/p-Tau protein levels, which caused a neuronal injury. Following 6 and 24 h of stretching, GSK-3β^S9 was directly bound to p-Tau^S396. In contrast, the neuronal injury was improved using GSK-3β inhibitor TWS119, which downregulated amyloid-β/p-Taus396 phosphorylation by enhancing ERK1/2T202/Y204 and AktS473 phosphorylation. Our findings imply that the neurons were under stress and that the inactivation of the GSK3β could alleviate this defect.

Biofidelic dynamic compression of human cortical spheroids reproduces neurotrauma phenotypes

Fundamental questions about patient heterogeneity and human-specific pathophysiology currently obstruct progress towards a therapy for traumatic brain injury (TBI). Human in vitro models have the potential to address these questions. 3D spheroidal cell culture protocols for human-origin neural cells have several important advantages over their 2D monolayer counterparts. Three dimensional spheroidal cultures may mature more quickly, develop more biofidelic electrophysiological activity and/or reproduce some aspects of brain architecture. Here, we present the first human in vitro model of non-penetrating TBI employing 3D spheroidal cultures. We used a custom-built device to traumatize these spheroids in a quantifiable, repeatable and biofidelic manner and correlated the heterogeneous, mechanical strain field with the injury phenotype. Trauma reduced cell viability, mitochondrial membrane potential and spontaneous, synchronous, electrophysiological activity in the spheroids. Electrophysiological deficits emerged at lower injury severities than changes in cell viability. Also, traumatized spheroids secreted lactate dehydrogenase, a marker of cell damage, and neurofilament light chain, a promising clinical biomarker of neurotrauma. These results demonstrate that 3D human in vitro models can reproduce important phenotypes of neurotrauma in vitro.

Cerebral Vasculature Influences Blast-Induced Biomechanical Responses of Human Brain Tissue

Multiple finite-element (FE) models to predict the biomechanical responses in the human brain resulting from the interaction with blast waves have established the importance of including the brain-surface convolutions, the major cerebral veins, and using non-linear brain-tissue properties to improve model accuracy. We hypothesize that inclusion of a more detailed network of cerebral veins and arteries can further enhance the model-predicted biomechanical responses and help identify correlates of blast-induced brain injury. To more comprehensively capture the biomechanical responses of human brain tissues to blast-wave exposure, we coupled a three-dimensional (3-D) detailed-vasculature human-head FE model, previously validated for blunt impact, with a 3-D shock-tube FE model. Using the coupled model, we computed the biomechanical responses of a human head facing an incoming blast wave for blast overpressures (BOPs) equivalent to 68, 83, and 104 kPa. We validated our FE model, which includes the detailed network of cerebral veins and arteries, the gyri and the sulci, and hyper-viscoelastic brain-tissue properties, by comparing the model-predicted intracranial pressure (ICP) values with previously collected data from shock-tube experiments performed on cadaver heads. In addition, to quantify the influence of including a more comprehensive network of brain vessels, we compared the biomechanical responses of our detailed-vasculature model with those of a reduced-vasculature model and a no-vasculature model for the same blast-loading conditions. For the three BOPs, the predicted ICP values matched well with the experimental results in the frontal lobe, with peak-pressure differences of 4-11% and phase-shift differences of 9-13%. As expected, incorporating the detailed cerebral vasculature did not influence the ICP, however, it redistributed the peak brain-tissue strains by as much as 30% and yielded peak strain differences of up to 7%. When compared to existing reduced-vasculature FE models that only include the major cerebral veins, our high-fidelity model redistributed the brain-tissue strains in most of the brain, highlighting the importance of including a detailed cerebral vessel network in human-head FE models to more comprehensively account for the biomechanical responses induced by blast exposure.

Toward a Comprehensive Delineation of White Matter Tract-Related Deformation

Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) recently has been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly proposed strain peaks along with the previously used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.

Investigation of the Compressive Viscoelastic Properties of Brain Tissue Under Time and Frequency Dependent Loading Conditions

The mechanical characterization of brain tissue has been generally analyzed in the frequency and time domain. It is crucial to understand the mechanics of the brain under realistic, dynamic conditions and convert it to enable mathematical modelling in a time domain. In this study, the compressive viscoelastic properties of brain tissue were investigated under time and frequency domains with the same physical conditions and the theory of viscoelasticity was applied to estimate the prediction of viscoelastic response in the time domain based on frequency-dependent mechanical moduli through Finite Element models. Storage and loss modulus were obtained from white and grey matter, of bovine brains, using dynamic mechanical analysis and time domain material functions were derived based on a Prony series representation. The material models were evaluated using brain testing data from stress relaxation and hysteresis in the time dependent analysis. The Finite Element models were able to represent the trend of viscoelastic characterization of brain tissue under both testing domains. The outcomes of this study contribute to a better understanding of brain tissue mechanical behaviour and demonstrate the feasibility of deriving time-domain viscoelastic parameters from frequency-dependent compressive data for biological tissue, as validated by comparing experimental tests with computational simulations.

Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation

Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in (1) the derivative order (angular velocity, angular acceleration, angular jerk), (2) the direction and (3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics about three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.

In Vitro Models of Traumatic Brain Injury: A Systematic Review

Traumatic brain injury (TBI) is a major public health challenge that is also the third leading cause of death worldwide. It is also the leading cause of long-term disability in children and young adults worldwide. Despite a large body of research using predominantly in vivo and in vitro rodent models of brain injury, there is no medication that can reduce brain damage or promote brain repair mainly due to our lack of understanding in the mechanisms and pathophysiology of the TBI. The aim of this review is to examine in vitro TBI studies conducted from 2008-2018 to better understand the TBI in vitro model available in the literature. Specifically, our focus was to perform a detailed analysis of the in vitro experimental protocols used and their subsequent biological findings. Our review showed that the uniaxial stretch is the most frequently used way of load application, accounting for more than two-thirds of the studies reviewed. The rate and magnitude of the loading were varied significantly from study to study but can generally be categorized into mild, moderate, and severe injuries. The in vitro studies reviewed here examined key processes in TBI pathophysiology such as membrane disruptions leading to ionic dysregulation, inflammation, and the subsequent damages to the microtubules and axons, as well as cell death. Overall, the studies examined in this review contributed to the betterment of our understanding of TBI as a disease process. Yet, our review also revealed the areas where more work needs to be done such as: 1) diversification of load application methods that will include complex loading that mimics in vivo head impacts; 2) more widespread use of human brain cells, especially patient-matched human cells in the experimental set-up; and 3) need for building a more high-throughput system to be able to discover effective therapeutic targets for TBI.

Orientation of neurites influences severity of mechanically induced tau pathology

Chronic traumatic encephalopathy is a neurodegenerative disease associated with repeated traumatic brain injury (TBI). Chronic traumatic encephalopathy is a tauopathy, in which cognitive decline is accompanied by the accumulation of neurofibrillary tangles of the protein tau in patients' brains. We recently found that mechanical force alone can induce tau mislocalization to dendritic spines and loss of synaptic function in in vitro neuronal cultures with random cell organization. However, in the brain, neurons are highly aligned, so here we aimed to determine how neuronal organization influences early-stage tauopathy caused by mechanical injury. Using microfabricated cell culture constructs to control the growth of neurites and an in vitro simulated TBI device to apply controlled mechanical deformation, we found that neuronal orientation with respect to the direction of a uniaxial high-strain-rate stretch injury influences the degree of tau pathology in injured neurons. We found that a mechanical stretch applied parallel to the neurite alignment induces greater mislocalization of tau proteins to dendritic spines than does a stretch with the same strain applied perpendicular to the neurites. Synaptic function, characterized by the amplitude of miniature excitatory postsynaptic currents, was similarly decreased in neurons with neurites aligned parallel to stretch, whereas in neurons aligned perpendicular to stretch, it had little to no functional loss. Experimental injury parameters (strain, strain rate, direction of stretch) were combined with a standard viscoelastic solid model to show that in our in vitro model, neurite work density during stretch correlates with tau mislocalization. These findings suggest that in a TBI, the magnitude of brain deformation is not wholly predictive of neurodegenerative consequences of TBI but that deformation relative to local neuronal architecture and the neurite mechanical energy during injury are better metrics for predicting trauma-induced tauopathy.

Modeling links softening of myelin and spectrin scaffolds of axons after a concussion to increased vulnerability to repeated injuries

Damage to the microtubule lattice, which serves as a rigid cytoskeletal backbone for the axon, is a hallmark mechanical initiator of pathophysiology after concussion. Understanding the mechanical stress transfer from the brain tissue to the axonal cytoskeleton is essential to determine the microtubule lattice's vulnerability to mechanical injury. Here, we develop an ultrastructural model of the axon's cytoskeletal architecture to identify the components involved in the dynamic load transfer during injury. Corroborative in vivo studies were performed using a gyrencephalic swine model of concussion via single and repetitive head rotational acceleration. Computational analysis of the load transfer mechanism demonstrates that the myelin sheath and the actin/spectrin cortex play a significant role in effectively shielding the microtubules from tissue stress. We derive failure maps in the space spanned by tissue stress and stress rate to identify physiological conditions in which the microtubule lattice can rupture. We establish that a softer axonal cortex leads to a higher susceptibility of the microtubules to failure. Immunohistochemical examination of tissue from the swine model of single and repetitive concussion confirms the presence of postinjury spectrin degradation, with more extensive pathology observed following repetitive injury. Because the degradation of myelin and spectrin occurs over weeks following the first injury, we show that softening of the myelin layer and axonal cortex exposes the microtubules to higher stress during repeated incidences of traumatic brain injuries. Our predictions explain how mechanical injury predisposes axons to exacerbated responses to repeated injuries, as observed in vitro and in vivo.

Characterizing viscoelastic materials via ensemble-based data assimilation of bubble collapse observations

Viscoelastic material properties at high strain rates are needed to model many biological and medical systems. Bubble cavitation can induce such strain rates, and the resulting bubble dynamics are sensitive to the material properties. Thus, in principle, these properties can be inferred via measurements of the bubble dynamics. Estrada et al. (2018) demonstrated such bubble-dynamic high-strain-rate rheometry by using least-squares shooting to minimize the difference between simulated and experimental bubble radius histories. We generalize their technique to account for additional uncertainties in the model, initial conditions, and material properties needed to uniquely simulate the bubble dynamics. Ensemble-based data assimilation minimizes the computational expense associated with the bubble cavitation model, providing a more efficient and scalable numerical framework for bubble-collapse rheometry. We test an ensemble Kalman filter (EnKF), an iterative ensemble Kalman smoother (IEnKS), and a hybrid ensemble-based 4D-Var method (En4D-Var) on synthetic data, assessing their estimations of the viscosity and shear modulus of a Kelvin-Voigt material. Results show that En4D-Var and IEnKS provide better moduli estimates than EnKF. Applying these methods to the experimental data of Estrada et al. (2018) yields similar material property estimates to those they obtained, but provides additional information about uncertainties. In particular, the En4D-Var yields lower viscosity estimates for some experiments, and the dynamic estimators reveal a potential mechanism that is unaccounted for in the model, whereby the apparent viscosity is reduced in some cases due to inelastic behavior, possibly in the form of material damage occurring at bubble collapse.

A Methodology to Compare Biomechanical Simulations With Clinical Brain Imaging Analysis Utilizing Two Blunt Impact Cases

According to the US Defense and Veterans Brain Injury Center (DVBIC) and Centers for Disease Control and Prevention (CDC), mild traumatic brain injury (mTBI) is a common form of head injury. Medical imaging data provides clinical insight into tissue damage/injury and injury severity, and helps medical diagnosis. Computational modeling and simulation can predict the biomechanical characteristics of such injury, and are useful for development of protective equipment. Integration of techniques from computational biomechanics with medical data assessment modalities (e.g., magnetic resonance imaging or MRI) has not yet been used to predict injury, support early medical diagnosis, or assess effectiveness of personal protective equipment. This paper presents a methodology to map computational simulations with clinical data for interpreting blunt impact TBI utilizing two clinically different head injury case studies. MRI modalities, such as T1, T2, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC), were used for simulation comparisons. The two clinical cases have been reconstructed using finite element analysis to predict head biomechanics based on medical reports documented by a clinician. The findings are mapped to simulation results using image-based clinical analyses of head impact injuries, and modalities that could capture simulation results have been identified. In case 1, the MRI results showed lesions in the brain with skull indentation, while case 2 had lesions in both coup and contrecoup sides with no skull deformation. Simulation data analyses show that different biomechanical measures and thresholds are needed to explain different blunt impact injury modalities; specifically, strain rate threshold corresponds well with brain injury with skull indentation, while minimum pressure threshold corresponds well with coup–contrecoup injury; and DWI has been found to be the most appropriate modality for MRI data interpretation. As the findings from these two cases are substantiated with additional clinical studies, this methodology can be broadly applied as a tool to support injury assessment in head trauma events and to improve countermeasures (e.g., diagnostics and protective equipment design) to mitigate these injuries.

Design and Evaluation of an In Vitro Mild Traumatic Brain Injury Modeling System Using 3D Printed Mini Impact Device on the 3D Cultured Human iPSC Derived Neural Progenitor Cells

Despite significant progress in understanding the disease mechanism of traumatic brain injury (TBI), promising preclinical therapeutics have seldom been translated into successful clinical outcomes, partially because the model animals have physiological and functional differences in the central nervous system (CNS) compared to humans. Human relevant models are thus urgently required. Here, an in vitro mild TBI (mTBI) modeling system is reported based on 3D cultured human induced pluripotent stem cells (iPSC) derived neural progenitor cells (iPSC-NPCs) to evaluate consequences of single and repetitive mTBI using a 3D printed mini weight-drop impact device. Computational simulation is performed to understand the single/cumulative effects of weight-drop impact on the NPC differentiated neurospheres. Experimental results reveal that neurospheres show reactive astrogliosis and glial scar formation after repetitive (10 hits) mild impacts, while no astrocyte activation is found after one or two mild impacts. A 3D co-culture model of human microglia cells with neurospheres is further developed. It is found that astrocyte response is promoted even after two mild impacts, possibly caused by the chronic neuroinflammation after microglia activation. The in vitro mTBI modeling system recapitulates several hallmarks of the brain impact injury and might serve as a good platform for future drug screening.

Microphysiological models of the central nervous system with fluid flow

There are over 1,000 described neurological and neurodegenerative disorders affecting nearly 100 million Americans - roughly one third of the U.S. population. Collectively, treatment of neurological conditions is estimated to cost $800 billion every year. Lowering this societal burden will require developing better model systems in which to study these diverse disorders. Microphysiological systems are promising tools for modeling healthy and diseased neural tissues to study mechanisms and treatment of neuropathology. One major benefit of microphysiological systems is the ability to incorporate biophysical forces, namely the forces derived from biological fluid flow. Fluid flow in the central nervous system (CNS) is a complex but important element of physiology, and pathologies as diverse as traumatic or ischemic injury, cancer, neurodegenerative disease, and natural aging have all been found to alter flow pathways. In this review, we summarize recent advances in three-dimensional microphysiological systems for studying the biology and therapy of CNS disorders and highlight the ability and growing need to incorporate biological fluid flow in these miniaturized model systems.

Instantaneous Whole-Brain Strain Estimation in Dynamic Head Impact

Head injury models are notoriously time consuming and resource demanding in simulations, which prevents routine application. Here, we extend a convolutional neural network (CNN) to instantly estimate element-wise distribution of peak maximum principal strain (MPS) of the entire brain (>36 k speedup accomplished on a low-end computing platform). To achieve this, head impact rotational velocity and acceleration temporal profiles are combined into two-dimensional images to serve as CNN input for training and prediction of MPS. Compared with the directly simulated counterparts, the CNN-estimated responses (magnitude and distribution) are sufficiently accurate for 92.1% of the cases via 10-fold cross-validation using impacts drawn from the real world (n = 5661; range of peak rotational velocity in augmented data extended to 2-40 rad/sec). The success rate further improves to 97.1% for "in-range" impacts (n = 4298). When using the same CNN architecture to train (n = 3064) and test on an independent, reconstructed National Football League (NFL) impact dataset (n = 53; 20 concussions and 33 non-injuries), 51 out of 53, or 96.2% of the cases, are sufficiently accurate. The estimated responses also achieve virtually identical concussion prediction performances relative to the directly simulated counterparts, and they often outperform peak MPS of the whole brain (e.g., accuracy of 0.83 vs. 0.77 via leave-one-out cross-validation). These findings support the use of CNN for accurate and efficient estimation of spatially detailed brain strains across the vast majority of head impacts in contact sports. Our technique may hold the potential to transform traumatic brain injury (TBI) research and the design and testing standards of head protective gears by facilitating the transition from acceleration-based approximation to strain-based design and analysis. This would have broad implications in the TBI biomechanics field to accelerate new scientific discoveries. The pre-trained CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains.

Human mini-brain models

Engineered human mini-brains, made possible by knowledge from the convergence of precision microengineering and cell biology, permit systematic studies of complex neurological processes and of pathogenesis beyond what can be done with animal models. By culturing human brain cells with physiological microenvironmental cues, human mini-brain models reconstitute the arrangement of structural tissues and some of the complex biological functions of the human brain. In this Review, we highlight the most significant developments that have led to microphysiological human mini-brain models. We introduce the history of mini-brain development, review methods for creating mini-brain models in static conditions, and discuss relevant state-of-the-art dynamic cell-culture systems. We also review human mini-brain models that reconstruct aspects of major neurological disorders under static or dynamic conditions. Engineered human mini-brains will contribute to advancing the study of the physiology and aetiology of neurological disorders, and to the development of personalized medicines for them.

A computational investigation of strain concentration in the brain in response to a rapid temperature rise

Following the mysterious health attacks on U.S. diplomats in Cuba in 2016, the cause of concussion-like symptoms concurrent with strange noises heard by the diplomats remains undetermined. A wide range of possible causes of the sensations have been proposed: pulsed microwave exposure, infrasound acoustic devices, pesticides/neurotoxins, and even mass hysteria (psychogenic illness). Here, we numerically examine the pulsed microwave exposure hypothesis and the simulated mechanical response of brain tissue. A computational model is used to examine the influence of various spatially varying temperature gradients and pulse durations on the mechanical response of brain tissue. We show that a stress-focusing effect due to a rapid temperature increase may result in brain tissue strains larger than the initially applied thermal strains.

SIRT1 Inhibits High Shear Stress-Induced Apoptosis in Rat Cortical Neurons

INTRODUCTION

Sirtuin1 (SIRT1), one of NAD+-dependent protein deacetylases, is proved to be neuroprotective in aging diseases, but its effect on neuronal apoptosis has not been clarified. To investigate the role of SIRT1 in inhibiting neuronal apoptosis, SIRT1 was interfered or overexpressed in cortical neurons.

METHODS

We exerted overloading laminar shear stress with 10 dyn/cm2 for 4, 8, and 12 h on neurons to cause cortical neuronal apoptosis, and the apoptosis percentage was tested by TUNEL assay. The adenovirus plasmids containing SIRT1 RNA interference or SIRT1 wild type gene were transfected into neurons before shear stress loading. SIRT1 mRNA and protein level were tested by Real-time PCR, immunofluorescence and western blots assay.

RESULTS

SIRT1 was primarily expressed in nucleus of cortical neurons, and its mRNA level was significantly increased after 4 h stimulation. SIRT1 RNAi cortical neurons had higher TUNEL positive cells, while SIRT1 overexpression significantly decreased the percentage of died cells induced by shear stress compared to control group.

CONCLUSIONS

SIRT1 plays a neuroprotective role in shear stress induced apoptosis and could be as potential pharmacological targets against neuronal degeneration in future.

Mechanosensation in traumatic brain injury

Traumatic brain injury (TBI) is distinct from other neurological disorders because it is induced by a discrete event that applies extreme mechanical forces to the brain. This review describes how the brain senses, integrates, and responds to forces under both normal conditions and during injury. The response to forces is influenced by the unique mechanical properties of brain tissue, which differ by region, cell type, and sub-cellular structure. Elements such as the extracellular matrix, plasma membrane, transmembrane receptors, and cytoskeleton influence its properties. These same components also act as force-sensors, allowing neurons and glia to respond to their physical environment and maintain homeostasis. However, when applied forces become too large, as in TBI, these components may respond in an aberrant manner or structurally fail, resulting in unique pathological sequelae. This so-called "pathological mechanosensation" represents a spectrum of cellular responses, which vary depending on the overall biomechanical parameters of the injury and may be compounded by repetitive injuries. Such aberrant physical responses and/or damage to cells along with the resulting secondary injury cascades can ultimately lead to long-term cellular dysfunction and degeneration, often resulting in persistent deficits. Indeed, pathological mechanosensation not only directly initiates secondary injury cascades, but this post-physical damage environment provides the context in which these cascades unfold. Collectively, these points underscore the need to use experimental models that accurately replicate the biomechanics of TBI in humans. Understanding cellular responses in context with injury biomechanics may uncover therapeutic targets addressing various facets of trauma-specific sequelae.