Finite element model predictions of intracranial hemorrhage from non-impact, rapid head rotations in the piglet

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.

In vivo measurement of human brain material properties under quasi-static loading

Computational modelling of the brain requires accurate representation of the tissues concerned. Mechanical testing has numerous challenges, in particular for low strain rates, like neurosurgery, where redistribution of fluid is biomechanically important. A finite-element (FE) model was generated in FEBio, incorporating a spring element/fluid-structure interaction representation of the pia-arachnoid complex (PAC). The model was loaded to represent gravity in prone and supine positions. Material parameter identification and sensitivity analysis were performed using statistical software, comparing the FE results to human in vivo measurements. Results for the brain Ogden parameters µ, α and k yielded values of 670 Pa, -19 and 148 kPa, supporting values reported in the literature. Values of the order of 1.2 MPa and 7.7 kPa were obtained for stiffness of the pia mater and out-of-plane tensile stiffness of the PAC, respectively. Positional brain shift was found to be non-rigid and largely driven by redistribution of fluid within the tissue. To the best of our knowledge, this is the first study using in vivo human data and gravitational loading in order to estimate the material properties of intracranial tissues. This model could now be applied to reduce the impact of positional brain shift in stereotactic neurosurgery.

Development and global validation of a 1-week-old piglet head finite element model for impact simulations

PURPOSE

Child head injury under impact scenarios (e.g. falling, vehicle crashing, etc.) is an important topic in the field of injury biomechanics. The head of piglet was commonly used as the surrogate to investigate the biomechanical response and mechanisms of pediatric head injuries because of the similar cellular structures and material properties. However, up to date, piglet head models with accurate geometry and material properties, which have been validated by impact experiments, are seldom. We aimed to develop such a model for future research.

METHODS

In this study, first, the detailed anatomical structures of the piglet head, including the skull, suture, brain, pia mater, dura mater, cerebrospinal fluid, scalp, and soft tissue, were constructed based on CT scans. Then, a structured butterfly method was adopted to mesh the complex geometries of the piglet head to generate high-quality elements and each component was assigned corresponding constitutive material models. Finally, the guided drop tower tests were conducted and the force-time histories were ectracted to validate the piglet head finite element model.

RESULTS

Simulations were conducted on the developed finite element model under impact conditions and the simulation results were compared with the experimental data from the guided drop tower tests and the published literature. The average peak force and duration of the guide drop tower test were similar to that of the simulation, with an error below 10%. The inaccuracy was below 20%. The average peak force and duration reported in the literature were comparable to those of the simulation, with the exception of the duration for an impact energy of 11 J. The results showed that the model was capable to capture the response of the pig head.

CONCLUSION

This study can provide an effective tool for investigating child head injury mechanisms and protection strategies under impact loading conditions.

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.

Viscoelastic Characterization of Parasagittal Bridging Veins and Implications for Traumatic Brain Injury: A Pilot Study

Many previous studies on the mechanical properties of Parasagittal Bridging Veins (PSBVs) found that strain rate had a significant effect on some mechanical properties, but did not extensively study the viscoelastic effects, which are difficult to detect with uniaxial simple tensile tests. In this study, relaxation tests and tests under cyclic loading were performed, and it was found that PSBVs do indeed exhibit clear viscoelastic effects. In addition, a complete viscoelastic model for the PSBVs is proposed and data from relaxation, cyclic load and load-unload tests for triangular loads are used to find reference values that characterize the viscoelastic behavior of the PSBVs. Although such models have been proposed for other types of blood vessels, this is the first study that clearly demonstrates the existence of viscoelastic effects from an experimental point of view and also proposes a specific model to explain the data obtained. Finally, this study provides reference values for the usual viscoelastic properties, which would allow more accurate numerical simulation of PSBVs by means of computational models.

A Computational Study of Liquid Shock Absorption for Prevention of Traumatic Brain Injury

Mild traumatic brain injury (mTBI), more colloquially known as concussion, is common in contact sports such as American football, leading to increased scrutiny of head protective gear. Standardized laboratory impact testing, such as the yearly National Football League (NFL) helmet test, is used to rank the protective performance of football helmets, motivating new technologies to improve the safety of helmets relative to existing equipment. In this work, we hypothesized that a helmet which transmits a nearly constant minimum force will result in a reduced risk of mTBI. To evaluate the plausibility of this hypothesis, we first show that the optimal force transmitted to the head, in a reduced order model of the brain, is in fact a constant force profile. To simulate the effects of a constant force within a helmet, we conceptualize a fluid-based shock absorber system for use within a football helmet. We integrate this system within a computational helmet model and simulate its performance on the standard NFL helmet test impact conditions. The simulated helmet is compared with other helmet designs with different technologies. Computer simulations of head impacts with liquid shock absorption predict that, at the highest impact speed (9.3 m/s), the average brain tissue strain is reduced by 27.6% ± 9.3 compared to existing helmet padding when tested on the NFL helmet protocol. This simulation-based study puts forth a target benchmark for the future design of physical manifestations of this technology.

Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts

Because of the rigid coupling between the upper dentition and the skull, instrumented mouthguards have been shown to be a viable way of measuring head impact kinematics for assisting in understanding the underlying biomechanics of concussions. This has led various companies and institutions to further develop instrumented mouthguards. However, their use as a research tool for understanding concussive impacts makes quantification of their accuracy critical, especially given the conflicting results from various recent studies. Here we present a study that uses a pneumatic impactor to deliver impacts characteristic to football to a Hybrid III headform, in order to validate and compare five of the most commonly used instrumented mouthguards. We found that all tested mouthguards gave accurate measurements for the peak angular acceleration, the peak angular velocity, brain injury criteria values (mean average errors < 13, 8, 13%, respectively), and the mouthguards with long enough sampling time windows are suitable for a convolutional neural network-based brain model to calculate the brain strain (mean average errors < 9%). Finally, we found that the accuracy of the measurement varies with the impact locations yet is not sensitive to the impact velocity for the most part.

Multi-Scale White Matter Tract Embedded Brain Finite Element Model Predicts the Location of Traumatic Diffuse Axonal Injury

Finite element models (FEMs) are used increasingly in the traumatic brain injury (TBI) field to provide an estimation of tissue responses and predict the probability of sustaining TBI after a biomechanical event. However, FEM studies have mainly focused on predicting the absence/presence of TBI rather than estimating the location of injury. In this study, we created a multi-scale FEM of the pig brain with embedded axonal tracts to estimate the sites of acute (≤6 h) traumatic axonal injury (TAI) after rapid head rotation. We examined three finite element (FE)-derived metrics related to the axonal bundle deformation and three FE-derived metrics based on brain tissue deformation for prediction of acute TAI location. Rapid head rotations were performed in pigs, and TAI neuropathological maps were generated and colocalized to the FEM. The distributions of the FEM-derived brain/axonal deformations spatially correlate with the locations of acute TAI. For each of the six metric candidates, we examined a matrix of different injury thresholds (th_x) and distance to actual TAI sites (d_s) to maximize the average of two optimization criteria. Three axonal deformation-related TAI candidates predicted the sites of acute TAI within 2.5 mm, but no brain tissue metric succeeded. The optimal range of thresholds for maximum axonal strain, maximum axonal strain rate, and maximum product of axonal strain and strain rate were 0.08-0.14, 40-90, and 2.0-7.5 s^-1, respectively. The upper bounds of these thresholds resulted in higher true-positive prediction rate. In summary, this study confirmed the hypothesis that the large axonal-bundle deformations occur on/close to the areas that sustained TAI.

A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact

Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost while maintaining a computational accuracy comparable with that of the high-fidelity model is desired. In this study, we constructed magnetic resonance (MR) image-based finite element (FE) models of a mouse brain for simulations of controlled cortical impact. The anatomical details were kept by mapping each image voxel to a corresponding FE mesh element. We constructed a super-resolution neural network that could produce computational results of a refined FE model with a mesh size of 70 μm from a coarse FE model with a mesh size of 280 μm. The peak signal-to-noise ratio of the reconstructed results was 33.26 dB, while the computational speed was increased by 50-fold. This proof-of-concept study showed that using machine learning techniques, MR image-based computational modeling could be applied and evaluated in a timely fashion. This paved ways for fast FE modeling and computation based on MR images. Results also support the potential clinical applications of MR image-based computational modeling of the human brain in a variety of scenarios such as brain impact and intervention.Graphical abstract MR image-based FE models with different mesh sizes were generated for CCI. The training and testing data sets were computed with 5 different impact locations and 3 different impact velocities. High-resolution strain maps were estimated using a SR neural network with greatly reduced computational cost.

Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury

With the growing rate of traumatic brain injury (TBI), there is an increasing interest in validated tools to predict and prevent brain injuries. Finite element models (FEM) are valuable tools to estimate tissue responses, predict probability of TBI, and guide the development of safety equipment. In this study, we developed and validated an anisotropic pig brain multi-scale FEM by explicitly embedding the axonal tract structures and utilized the model to simulate experimental TBI in piglets undergoing dynamic head rotations. Binary logistic regression, survival analysis with Weibull distribution, and receiver operating characteristic curve analysis, coupled with repeated k-fold cross-validation technique, were used to examine 12 FEM-derived metrics related to axonal/brain tissue strain and strain rate for predicting the presence or absence of traumatic axonal injury (TAI). All 12 metrics performed well in predicting of TAI with prediction accuracy rate of 73-90%. The axonal-based metrics outperformed their rival brain tissue-based metrics in predicting TAI. The best predictors of TAI were maximum axonal strain times strain rate (MASxSR) and its corresponding optimal fraction-based metric (AF-MASxSR7.5) that represents the fraction of axonal fibers exceeding MASxSR of 7.5 s-1. The thresholds compare favorably with tissue tolerances found in in-vitro/in-vivo measurements in the literature. In addition, the damaged volume fractions (DVF) predicted using the axonal-based metrics, especially MASxSR (DVF = 0.05-4.5%), were closer to the actual DVF obtained from histopathology (AIV = 0.02-1.65%) in comparison with the DVF predicted using the brain-related metrics (DVF = 0.11-41.2%). The methods and the results from this study can be used to improve model prediction of TBI in humans.

Predictions of neonatal porcine bridging vein rupture and extra-axial hemorrhage during rapid head rotations

When the head is rotated rapidly, the movement of the brain lags that of the skull. Intracranial contents between the brain and skull include meninges, cerebrospinal fluid (CSF), and cerebral vasculature. Among the cerebral vasculature in this space are the parasagittal bridging veins (BVs), which drain blood from the brain into the superior sagittal sinus (SSS), which is housed within the falx cerebri, adhered to the inner surface of the skull. Differential motion between the brain and skull that may occur during a traumatic event is thought to stretch BVs, causing damage and producing extra-axial hemorrhage (EAH). Finite element (FE) modeling is a technique often used to aid in the understanding and prediction of traumatic brain injury (TBI), and estimation of tissue deformation during traumatic events provides insight into kinematic injury thresholds. Using a FE model of the newborn porcine head with neonatal porcine brain and BV properties, single and cyclic rapid head rotations without impact were simulated. Measured BV failure properties were used to predict BV rupture. By comparing simulation outputs to observations of EAH in a development group of in vivo studies of rapid non-impact head rotations in the piglet, it was determined that failure of 16.7% of BV elements was associated with a 50% risk of EAH, and showed in a separate validation group that this threshold predicted the occurrence of EAH with 100% sensitivity and 100% specificity for single rapid non-impact rotations. This threshold for failed BV elements performed with 90% overall correct prediction in simulations of cyclic rotational head injuries. A 50% risk of EAH was associated with head angular velocities of 94.74 rad/s and angular accelerations of 29.60 krad/s² in the newborn piglet. Future studies may build on these findings for BV failure in the piglet to develop predictive models for BV failure in human infants.

Thresholds for the assessment of inflicted head injury by shaking trauma in infants: a systematic review

In order to investigate potential causal relations between the shaking of infants and injuries, biomechanical studies compare brain and skull dynamic behavior during shaking to injury thresholds. However, performing shaking tolerance research on infants, either in vivo or ex vivo, is extremely difficult, if not impossible. Therefore, infant injury thresholds are usually estimated by scaling or extrapolating adult or animal data obtained from crash tests or whiplash experiments. However, it is doubtful whether such data accurately matches the biomechanics of shaking in an infant. Hence some thresholds may be inappropriate to be used for the assessment of inflicted head injury by shaking trauma in infants. A systematic literature review was conducted to 1) provide an overview of existing thresholds for head- and neck injuries related to violent shaking, and 2) to identify and discuss which thresholds have been used or could be used for the assessment of inflicted head injury by shaking trauma in infants. Key findings: The majority of studies establishing or proposing injury thresholds were found to be based on loading cycle durations and loading cycle repetitions that did not resemble those occurring during shaking, or had experimental conditions that were insufficiently documented in order to evaluate the applicability of such thresholds. Injury thresholds that were applied in studies aimed at assessing whether an injury could occur under certain shaking conditions were all based on experiments that did not properly replicate the loading characteristics of shaking. Somewhat validated threshold scaling methods only exist for scaling concussive injury thresholds from adult primate to adult human. Scaling methods that have been used for scaling other injuries, or for scaling adult injury thresholds to infants were not validated. There is a clear and urgent need for new injury thresholds established by accurately replicating the loading characteristics of shaking.

Modeling of inflicted head injury by shaking trauma in children: what can we learn? : Part II: A systematic review of mathematical and physical models

Various types of complex biomechanical models have been published in the literature to better understand processes related to inflicted head injury by shaking trauma (IHI-ST) in infants. In this systematic review, a comprehensive overview of these models is provided. A systematic review was performed in MEDLINE and Scopus for articles using physical (e.g. dolls) and mathematical (e.g. computer simulations) biomechanical models for IHI-ST. After deduplication, the studies were independently screened by two researchers using PRISMA methodology and data extracted from the papers is represented in a “7-steps description”, addressing the different processes occurring during IHI-ST. Eleven papers on physical models and 23 papers on mathematical models were included after the selection process. In both categories, some models focus on describing gross head kinematics during IHI-ST events, while others address the behavior of internal head- and eye structures in various levels of detail. In virtually all physical and mathematical models analyzed, injury thresholds are derived from scaled non-infant data. Studies focusing on head kinematics often use injury thresholds derived from impact studies. It remains unclear to what extent these thresholds reflect the failure thresholds of infant biological material. Future research should therefore focus on investigating failure thresholds of infant biological material as well as on possible alternative injury mechanism and alternative injury criteria for IHI-ST.

The pig as a preclinical traumatic brain injury model: current models, functional outcome measures, and translational detection strategies

Traumatic brain injury (TBI) is a major contributor of long-term disability and a leading cause of death worldwide. A series of secondary injury cascades can contribute to cell death, tissue loss, and ultimately to the development of functional impairments. However, there are currently no effective therapeutic interventions that improve brain outcomes following TBI. As a result, a number of experimental TBI models have been developed to recapitulate TBI injury mechanisms and to test the efficacy of potential therapeutics. The pig model has recently come to the forefront as the pig brain is closer in size, structure, and composition to the human brain compared to traditional rodent models, making it an ideal large animal model to study TBI pathophysiology and functional outcomes. This review will focus on the shared characteristics between humans and pigs that make them ideal for modeling TBI and will review the three most common pig TBI models-the diffuse axonal injury, the controlled cortical impact, and the fluid percussion models. It will also review current advances in functional outcome assessment measures and other non-invasive, translational TBI detection and measurement tools like biomarker analysis and magnetic resonance imaging. The use of pigs as TBI models and the continued development and improvement of translational assessment modalities have made significant contributions to unraveling the complex cascade of TBI sequela and provide an important means to study potential clinically relevant therapeutic interventions.

Establishment of a precise novel brain trauma model in a large animal based on injury of the cerebral motor cortex

BACKGROUND

Animal models are essential in simulating clinical diseases and facilitating relevant studies.

NEW METHOD

We established a precise canine model of traumatic brain injury (TBI) based on cerebral motor cortex injury which was confirmed by neuroimaging, electrophysiology, and a series of motor function assessment methods. Twelve beagles were divided into control, sham, and model groups. The cerebral motor cortex was identified by diffusion tensor imaging (DTI), a simple marker method, and intraoperative electrophysiological measurement. Bony windows were designed by magnetic resonance imaging (MRI) scan and DTI. During the operation, canines in the control group were under general anesthesia. The canines were operated via bony window craniotomy and dura mater opening in the sham group. After opening of the bony window and dura mater, the motor cortex was impacted by a modified electronic cortical contusion impactor (eCCI) in the model group.

RESULTS

Postoperative measurements revealed damage to the cerebral motor cortex and functional defects. Comparisons between preoperative and postoperative results demonstrated that the established model was successful.

COMPARISON WITH EXISTING METHOD(S)

Compared with conventional models, this is the first brain trauma model in large animal that was constructed based on injury to the cerebral motor cortex under the guidance of DTI, a simple marker method, and electrophysiology.

CONCLUSION

The method used to establish this model can be standardized to enhance reproducibility and provide a stable and precise large animal model of TBI for clinical and basic research.

Mechanistic Insights into Human Brain Impact Dynamics through Modal Analysis

Although concussion is one of the greatest health challenges today, our physical understanding of the cause of injury is limited. In this Letter, we simulated football head impacts in a finite element model and extracted the most dominant modal behavior of the brain's deformation. We showed that the brain's deformation is most sensitive in low frequency regimes close to 30 Hz, and discovered that for most subconcussive head impacts, the dynamics of brain deformation is dominated by a single global mode. In this Letter, we show the existence of localized modes and multimodal behavior in the brain as a hyperviscoelastic medium. This dynamical phenomenon leads to strain concentration patterns, particularly in deep brain regions, which is consistent with reported concussion pathology.

Relationships between scalp, brain, and skull motion estimated using magnetic resonance elastography

The objective of this study was to characterize the relationships between motion in the scalp, skull, and brain. In vivo estimates of motion transmission from the skull to the brain may illuminate the mechanics of traumatic brain injury. Because of challenges in directly sensing skull motion, it is useful to know how well motion of soft tissue of the head, i.e., the scalp, can approximate skull motion or predict brain tissue deformation. In this study, motion of the scalp and brain were measured using magnetic resonance elastography (MRE) and separated into components due to rigid-body displacement and dynamic deformation. Displacement estimates in the scalp were calculated using low motion-encoding gradient strength in order to reduce "phase wrapping" (an ambiguity in displacement estimates caused by the 2 π-periodicity of MRE phase contrast). MRE estimates of scalp and brain motion were compared to skull motion estimated from three tri-axial accelerometers. Comparison of the relative amplitudes and phases of harmonic motion in the scalp, skull, and brain of six human subjects indicate that data from scalp-based sensors should be used with caution to estimate skull kinematics, but that fairly consistent relationships exist between scalp, skull, and brain motion. In addition, the measured amplitude and phase relationships of scalp, skull, and brain can be used to evaluate and improve mathematical models of head biomechanics.

Measurement and Finite Element Model Validation of Immature Porcine Brain-Skull Displacement during Rapid Sagittal Head Rotations

Computational models are valuable tools for studying tissue-level mechanisms of traumatic brain injury, but to produce more accurate estimates of tissue deformation, these models must be validated against experimental data. In this study, we present in situ measurements of brain-skull displacement in the neonatal piglet head (n = 3) at the sagittal midline during six rapid non-impact rotations (two rotations per specimen) with peak angular velocities averaging 51.7 ± 1.4 rad/s. Marks on the sagittally cut brain and skull/rigid potting surfaces were tracked, and peak values of relative brain-skull displacement were extracted and found to be significantly less than values extracted from a previous axial plane model. In a finite element model of the sagittally transected neonatal porcine head, the brain-skull boundary condition was matched to the measured physical experiment data. Despite smaller sagittal plane displacements at the brain-skull boundary, the corresponding finite element boundary condition optimized for sagittal plane rotations is far less stiff than its axial counterpart, likely due to the prominent role of the boundary geometry in restricting interface movement. Finally, bridging veins were included in the finite element model. Varying the bridging vein mechanical behavior over a previously reported range had no influence on the brain-skull boundary displacements. This direction-specific sagittal plane boundary condition can be employed in finite element models of rapid sagittal head rotations.

Large animal models of traumatic brain injury

Purpose/Aim: Animal models of traumatic brain injury (TBI) provide powerful tools to study TBI in a controlled, rigorous and cost-efficient manner. The mostly used animals in TBI studies so far are rodents. However, compared with rodents, large animals (e.g. swine, rabbit, sheep, ferret, etc.) show great advantages in modeling TBI due to the similarity of their brains to human brain. The aim of our review was to summarize the development and progress of common large animal TBI models in past 30 years.

MATERIALS AND METHODS

Mixed published articles and books associated with large animal models of TBI were researched and summarized.

RESULTS

We majorly sumed up current common large animal models of TBI, including discussion on the available research methodologies in previous studies, several potential therapies in large animal trials of TBI as well as advantages and disadvantages of these models.

CONCLUSIONS

Large animal models of TBI play crucial role in determining the underlying mechanisms and screening putative therapeutic targets of TBI.

Improved prediction of direction-dependent, acute axonal injury in piglets

To guide development of safety equipment that reduces sports-related head injuries, we sought to enhance predictive relationships between head movement and acute axonal injury severity. The severity of traumatic brain injury (TBI) is influenced by the magnitude and direction of head kinematics. Previous studies have demonstrated correlation between rotational head kinematics and symptom severity in the adult. More recent studies have demonstrated brain injury age- and direction-dependence, relating head kinematics to white matter tract-oriented strains. We have recently developed and assessed novel rotational head kinematic parameters as predictors of white matter damage in the female immature piglet. We show that many previously published rotational kinematic injury predictor metrics poorly predict acute axonal pathology induced by rapid, non-impact head rotations and that inclusion of cerebral moments of inertia (MOI) in rotational head injury metrics refines prediction of diffuse axonal injury following rapid head rotations for two immature age groups. Rotational Work (RotWork) was the best significant predictor of traumatic axonal injury in both newborn and pre-adolescent piglets following head rotations in the axial, coronal, and sagittal planes. An improvement over current metrics, we find that RotWork, which incorporates head rotation rate, direction, and brain shape, significantly enhanced acute traumatic axonal injury prediction. For similar injury extent, the RotWork threshold is lower for the newborn piglet than the pre-adolescent.

Biofidelic white matter heterogeneity decreases computational model predictions of white matter strains during rapid head rotations

The finite element (FE) brain model is used increasingly as a design tool for developing technology to mitigate traumatic brain injury. We developed an ultra high-definition FE brain model (>4 million elements) from CT and MRI scans of a 2-month-old pre-adolescent piglet brain, and simulated rapid head rotations. Strain distributions in the thalamus, coronal radiata, corpus callosum, cerebral cortex gray matter, brainstem and cerebellum were evaluated to determine the influence of employing homogeneous brain moduli, or distinct experimentally derived gray and white matter property representations, where some white matter regions are stiffer and others less stiff than gray matter. We find that constitutive heterogeneity significantly lowers white matter deformations in all regions compared with homogeneous properties, and should be incorporated in FE model injury prediction.

A Porcine Model of Traumatic Brain Injury via Head Rotational Acceleration

Unique from other brain disorders, traumatic brain injury (TBI) generally results from a discrete biomechanical event that induces rapid head movement. The large size and high organization of the human brain makes it particularly vulnerable to traumatic injury from rotational accelerations that can cause dynamic deformation of the brain tissue. Therefore, replicating the injury biomechanics of human TBI in animal models presents a substantial challenge, particularly with regard to addressing brain size and injury parameters. Here we present the historical development and use of a porcine model of head rotational acceleration. By scaling up the rotational forces to account for difference in brain mass between swine and humans, this model has been shown to produce the same tissue deformations and identical neuropathologies found in human TBI. The parameters of scaled rapid angular accelerations applied for the model reproduce inertial forces generated when the human head suddenly accelerates or decelerates in falls, collisions, or blunt impacts. The model uses custom-built linkage assemblies and a powerful linear actuator designed to produce purely impulsive non-impact head rotation in different angular planes at controlled rotational acceleration levels. Through a range of head rotational kinematics, this model can produce functional and neuropathological changes across the spectrum from concussion to severe TBI. Notably, however, the model is very difficult to employ, requiring a highly skilled team for medical management, biomechanics, neurological recovery, and specialized outcome measures including neuromonitoring, neurophysiology, neuroimaging, and neuropathology. Nonetheless, while challenging, this clinically relevant model has proven valuable for identifying mechanisms of acute and progressive neuropathologies as well as for the evaluation of noninvasive diagnostic techniques and potential neuroprotective treatments following TBI.

Six Degree-of-Freedom Measurements of Human Mild Traumatic Brain Injury

This preliminary study investigated whether direct measurement of head rotation improves prediction of mild traumatic brain injury (mTBI). Although many studies have implicated rotation as a primary cause of mTBI, regulatory safety standards use 3 degree-of-freedom (3DOF) translation-only kinematic criteria to predict injury. Direct 6DOF measurements of human head rotation (3DOF) and translation (3DOF) have not been previously available to examine whether additional DOFs improve injury prediction. We measured head impacts in American football, boxing, and mixed martial arts using 6DOF instrumented mouthguards, and predicted clinician-diagnosed injury using 12 existing kinematic criteria and 6 existing brain finite element (FE) criteria. Among 513 measured impacts were the first two 6DOF measurements of clinically diagnosed mTBI. For this dataset, 6DOF criteria were the most predictive of injury, more than 3DOF translation-only and 3DOF rotation-only criteria. Peak principal strain in the corpus callosum, a 6DOF FE criteria, was the strongest predictor, followed by two criteria that included rotation measurements, peak rotational acceleration magnitude and Head Impact Power (HIP). These results suggest head rotation measurements may improve injury prediction. However, more 6DOF data is needed to confirm this evaluation of existing injury criteria, and to develop new criteria that considers directional sensitivity to injury.

Microstructural Characterization of the Pia-Arachnoid Complex Using Optical Coherence Tomography

Traumatic brain injury (TBI) is one of the leading causes of death and disability in the world, and is often identified by the presence of subdural and/or subarachnoid hemorrhages that develop from ruptured cortical vessels during brain-skull displacement. The pia-arachnoid complex (PAC), also known as the leptomeninges, is the major mechanical connection between the brain and skull, and influences cortical vessel deformation and rupture following brain trauma. This complex consists of cerebrospinal fluid, arachnoid trabeculae, and subarachnoid vasculature sandwiched between the arachnoid and pia mater membranes. Remarkably, studies of the tissues in the PAC are largely qualitative and do not provide numerical metrics of population density and variability of the arachnoid trabeculae and subarachnoid vasculature. In this study, microstructural imaging was performed on the PAC to numerically quantify these metrics. Five porcine brains were perfusion-fixed and imaged in situ using optical coherence tomography with micrometer resolution. Image processing was performed to estimate the volume fraction (VF) of the arachnoid trabeculae and subarachnoid vasculature in 12 regions of the brain. High regional variability was found within each brain, with individual brains exhibiting up to a 38.4 percentage-point range in VF. Regions with high VF were often next to regions with low VF. This suggests that some areas of the brain may be mechanically weaker, increasing their susceptibility to hemorrhage during TBI events. This study provides the first quantifiable data of arachnoid trabeculae and subarachnoid vasculature distribution within the PAC and will be valuable to understanding brain biomechanics during head trauma.

Cellular biomechanics of central nervous system injury

With a growing interest in how the brain responds and remodels itself following a traumatic injury, this chapter outlines the major organizing principles of how to study these injuries in the laboratory and extend these findings back into the clinic. A new repertoire of models is available to examine the response of isolated circuits of the brain in vitro, and to study precisely how mechanical forces applied to even small regions of these circuits can disrupt the entire circuit dysfunction. We review the existing knowledge garnered from these models and our current understanding of mechanically sensitive receptors and channels activated immediately following trauma. In turn, we point to the emergence of in silico models of network function that will lead to an improved understanding of the principles for the remodeling of circuit structure after traumatic, possibly pointing out new biological rules for circuit reassembly that would help guide new therapies for reconstructing brain circuits after trauma.

White matter tract-oriented deformation predicts traumatic axonal brain injury and reveals rotational direction-specific vulnerabilities

A systematic correlation between finite element models (FEMs) and histopathology is needed to define deformation thresholds associated with traumatic brain injury (TBI). In this study, a FEM of a transected piglet brain was used to reverse engineer the range of optimal shear moduli for infant (5 days old, 553-658 Pa) and 4-week-old toddler piglet brain (692-811 Pa) from comparisons with measured in situ tissue strains. The more mature brain modulus was found to have significant strain and strain rate dependencies not observed with the infant brain. Age-appropriate FEMs were then used to simulate experimental TBI in infant (n=36) and preadolescent (n=17) piglets undergoing a range of rotational head loads. The experimental animals were evaluated for the presence of clinically significant traumatic axonal injury (TAI), which was then correlated with FEM-calculated measures of overall and white matter tract-oriented tissue deformations, and used to identify the metric with the highest sensitivity and specificity for detecting TAI. The best predictors of TAI were the tract-oriented strain (6-7%), strain rate (38-40 s(-1), and strain times strain rate (1.3-1.8 s(-1) values exceeded by 90% of the brain. These tract-oriented strain and strain rate thresholds for TAI were comparable to those found in isolated axonal stretch studies. Furthermore, we proposed that the higher degree of agreement between tissue distortion aligned with white matter tracts and TAI may be the underlying mechanism responsible for more severe TAI after horizontal and sagittal head rotations in our porcine model of nonimpact TAI than coronal plane rotations.

Improved measurement of brain deformation during mild head acceleration using a novel tagged MRI sequence

In vivo measurements of human brain deformation during mild acceleration are needed to help validate computational models of traumatic brain injury and to understand the factors that govern the mechanical response of the brain. Tagged magnetic resonance imaging is a powerful, noninvasive technique to track tissue motion in vivo which has been used to quantify brain deformation in live human subjects. However, these prior studies required from 72 to 144 head rotations to generate deformation data for a single image slice, precluding its use to investigate the entire brain in a single subject. Here, a novel method is introduced that significantly reduces temporal variability in the acquisition and improves the accuracy of displacement estimates. Optimization of the acquisition parameters in a gelatin phantom and three human subjects leads to a reduction in the number of rotations from 72 to 144 to as few as 8 for a single image slice. The ability to estimate accurate, well-resolved, fields of displacement and strain in far fewer repetitions will enable comprehensive studies of acceleration-induced deformation throughout the human brain in vivo.

Functional tolerance to mechanical deformation developed from organotypic hippocampal slice cultures

In this study, we measured changes in electrophysiological activity after mechanical deformation of living organotypic hippocampal brain slice cultures at tissue strains and strain rates relevant to traumatic brain injury (TBI). Electrophysiological activity was measured throughout the hippocampus with a 60-electrode microelectrode array. Electrophysiological parameters associated with unstimulated spontaneous activity (neural event firing rate, duration, and magnitude), stimulated evoked responses (the maximum response [Formula: see text], the stimulus current necessary for a half-maximal response [Formula: see text], and the electrophysiological parameter m that is representative of firing uniformity), and paired-pulse responses (paired-pulse ratio at varying interstimulus intervals) were quantified for each hippocampal region (CA1, CA3, and DG). We present functional tolerance criteria for the hippocampus in the form of mathematical relationships between the input tissue-level injury parameters (strain and strain rate) and altered neuronal network function. Most changes in electrophysiology were dependent on strain and strain rate in a complex fashion, independent of hippocampal anatomy, with the notable exception of [Formula: see text]. Until it becomes possible to directly measure brain tissue deformation in vivo, finite element (FE) models will be necessary to simulate and predict the in vivo consequences of TBI. One application of our study is to provide functional relationships that can be incorporated into these FE models to enhance their biofidelity of accident and collision reconstructions by predicting biological outcomes in addition to mechanical responses.

The mechanics of traumatic brain injury: a review of what we know and what we need to know for reducing its societal burden

Traumatic brain injury (TBI) is a significant public health problem, on pace to become the third leading cause of death worldwide by 2020. Moreover, emerging evidence linking repeated mild traumatic brain injury to long-term neurodegenerative disorders points out that TBI can be both an acute disorder and a chronic disease. We are at an important transition point in our understanding of TBI, as past work has generated significant advances in better protecting us against some forms of moderate and severe TBI. However, we still lack a clear understanding of how to study milder forms of injury, such as concussion, or new forms of TBI that can occur from primary blast loading. In this review, we highlight the major advances made in understanding the biomechanical basis of TBI. We point out opportunities to generate significant new advances in our understanding of TBI biomechanics, especially as it appears across the molecular, cellular, and whole organ scale.

Influences of developmental age on the resolution of diffuse traumatic intracranial hemorrhage and axonal injury

This study investigated the age-dependent injury response of diffuse traumatic axonal injury (TAI) and regional subdural and subarachnoid intracranial hemorrhage (ICH) in two pediatric age groups using a porcine head injury model. Fifty-five 5-day-old and 40 four-week-old piglets-which developmentally correspond to infants and toddlers, respectively-underwent either a sham injury or a single rapid non-impact rotational injury in the sagittal plane and were grouped by post-TBI survival time (sham, 3-8 h, one day, 3-4 days, and 5-6 days). Both age groups exhibited similar initial levels of ICH and a significant reduction of ICH over time (p<0.0001). However, ICH took longer to resolve in the five-day-old age group. At 5-6 days post-injury, ICH in the cerebrum had returned to sham levels in the four-week-old piglets, while the five-day-olds still had significantly elevated cerebral ICH (p=0.012). Both ages also exhibited similar resolution of axonal injury with a peak in TAI at one day post-injury (p<0.03) and significantly elevated levels even at 5-6 days after the injury (p<0.008), which suggests a window of vulnerability to a second insult at one day post-injury that may extend for a prolonged period of time. However, five-day-old piglets had significantly more TAI than four-week-olds overall (p=0.016), which presents some evidence for an increased vulnerability to brain injury in this age group. These results provide insight into an optimal window for clinical intervention, the period of increased susceptibility to a second injury, and an age dependency in brain injury tolerance within the pediatric population.