Development of a biofidelic computational model of human pelvis for predicting biomechanical responses and pelvic fractures.

BACKGROUND AND OBJECTIVE: The pelvis, a crucial structure for human locomotion, is susceptible to injuries resulting in significant morbidity and disability. This study aims to introduce and validate a biofidelic computational pelvis model, enhancing our understanding of pelvis injury mechanisms under lateral loading conditions. METHODS: The Finite Element (FE) pelvic model, representing a mid-sized male, was developed with variable cortical thickness in pelvis bones. Material properties were determined through a synthesis of existing constitutive models, parametric studies, and multiple validations. Comprehensive validation included various tests, such as load-displacement assessments of sacroiliac joints, quasi-static and dynamic lateral compression on the acetabulum, dynamic side impacts on the acetabulum and iliac wing using defleshed pelvis, and lateral impacts by a rigid plate on the full body's pelvis region. RESULTS: Simulation results demonstrated a reasonable correlation between the pelvis model's overall response and cadaveric testing data. Predicted fracture patterns of the isolated pelvis exhibited fair agreement with experimental results. CONCLUSIONS: This study introduces a credible computational model, providing valuable biomechanical insights into the pelvis' response under diverse lateral loading conditions and fracture patterns. The work establishes a robust framework for developing and enhancing the biofidelity of pelvis FE models through a multi-level validation approach, stimulating further research in modeling, validation, and experimental studies related to pelvic injuries. The findings are expected to offer critical perspectives for predicting, preventing, and mitigating pelvic injuries from vehicular accidents, contributing to advancements in clinical research on medical treatments for pelvic fractures.

Investigating the Effect of Brain Size on Deformation Magnitude Using Subject-Specific Finite Element Models.

Abstract In the last decade, computational models of the brain have become the gold standard tool for investigating traumatic brain injury (TBI) mechanisms and developing novel protective equipment and other safety countermeasures. However, most studies utilizing finite element (FE) models of the brain have been conducted using models developed to represent the average neuroanatomy of a target demographic, such as the 50th percentile male. Although this is an efficient strategy, it neglects normal anatomical variations present within the population and their contributions on the brain's deformation response. As a result, the contributions of structural characteristics of the brain, such as brain volume, on brain deformation are not well understood. The objective of this study was to develop a set of statistical regression models relating measures of the size and shape of the brain to the resulting brain deformation. This was performed using a database of 125 subject-specific models, simulated under six independent head kinematic boundary conditions, spanning a range of impact modes (frontal, oblique, side), severity (non-injurious and injurious), and environments (volunteer, automotive, and American football). Two statistical regression techniques were utilized. First, simple linear regression (SLR) models were trained to relate intracranial volume (ICV) and the 95th percentile of maximum principal strain (MPS-95) for each of the impact cases. Second, a partial least squares regression model was constructed to predict MPS-95 based on the affine transformation parameters from each subject, representing the size and shape of their brain, considering the six impact conditions collectively. Both techniques indicated a strong linear relationship between ICV and MPS-95, with MPS-95 varying by approximately 5% between the smallest and largest brains. This difference represented up to 40% of the mean strain across all subjects. This study represents a comprehensive assessment of the relationships between brain anatomy and deformation, which is crucial for the development of personalized protective equipment, identifying individuals at higher risk of injury, and using computational models to aid clinical diagnostics of TBI.

Generating Human Arm Kinematics Using Reinforcement Learning to Train Active Muscle Behavior in Automotive Research.

Computational human body models (HBMs) are important tools for predicting human biomechanical responses under automotive crash environments. In many scenarios, the prediction of the occupant response will be improved by incorporating active muscle control into the HBMs to generate biofidelic kinematics during different vehicle maneuvers. In this study, we have proposed an approach to develop an active muscle controller based on reinforcement learning (RL). The RL muscle activation control (RL-MAC) approach is a shift from using traditional closed-loop feedback controllers, which can mimic accurate active muscle behavior under a limited range of loading conditions for which the controller has been tuned. Conversely, the RL-MAC uses an iterative training approach to generate active muscle forces for desired joint motion and is analogous to how a child develops gross motor skills. In this study, the ability of a deep deterministic policy gradient (DDPG) RL controller to generate accurate human kinematics is demonstrated using a multibody model of the human arm. The arm model was trained to perform goal-directed elbow rotation by activating the responsible muscles and investigated using two recruitment schemes: as independent muscles or as antagonistic muscle groups. Simulations with the trained controller show that the arm can move to the target position in the presence or absence of externally applied loads. The RL-MAC trained under constant external loads was able to maintain the desired elbow joint angle under a simplified automotive impact scenario, implying the robustness of the motor control approach.

Concussion Prone Scenarios: A Multi-Dimensional Exploration in Impact Directions, Brain Morphology, and Network Architectures Using Computational Models.

While individual susceptibility to traumatic brain injury (TBI) has been speculated, past work does not provide an analysis considering how physical features of an individual's brain (e.g., brain size, shape), impact direction, and brain network features can holistically contribute to the risk of suffering a TBI from an impact. This work investigated each of these features simultaneously using computational modeling and analyses of simulated functional connectivity. Unlike the past studies that assess the severity of TBI based on the quantification of brain tissue damage (e.g., principal strain), we approached the brain as a complex network in which neuronal oscillations orchestrate to produce normal brain function (estimated by functional connectivity) and, to this end, both the anatomical damage location and its topological characteristics within the brain network contribute to the severity of brain function disruption and injury. To represent the variations in the population, we analyzed a publicly available database of brain imaging data and selected five distinct network architectures, seven different brain sizes, and three uniaxial head rotational conditions to study the consequences of 74 virtual impact scenarios. Results show impact direction produces the most significant change in connections across brain areas (structural connectome) and the functional coupling of activity across these brain areas (functional connectivity). Axial rotations were more injurious than those with sagittal and coronal rotations when the head kinematics were the same for each condition. When the impact direction was held constant, brain network architecture showed a significantly different vulnerability across axial and sagittal, but not coronal rotations. As expected, brain size significantly affected the expected change in structural and functional connectivity after impact. Together, these results provided groupings of predicted vulnerability to impact-a subgroup of male brain architectures exposed to axial impacts were most vulnerable, while a subgroup of female brain architectures was the most tolerant to the sagittal impacts studied. These findings lay essential groundwork for subject-specific analyses of concussion and provide invaluable guidance for designing personalized protection equipment.

Quantifying the Effect of Sex and Neuroanatomical Biomechanical Features on Brain Deformation Response in Finite Element Brain Models.

Recent automotive epidemiology studies have concluded that females have significantly higher odds of sustaining a moderate brain injury or concussion than males in a frontal crash after controlling for multiple crash and occupant variables. Differences in neuroanatomical features, such as intracranial volume (ICV), have been shown between male and female subjects, but how these sex-specific neuroanatomical differences affect brain deformation is unknown. This study used subject-specific finite element brain models, generated via registration-based morphing using both male and female magnetic resonance imaging scans, to investigate sex differences of a variety of neuroanatomical features and their effect on brain deformation; additionally, this study aimed to determine the relative importance of these neuroanatomical features and sex on brain deformation metrics for a single automotive loading environment. Based on the Bayesian linear mixed models, sex had a significant effect on ICV, white matter volume and gray matter volume, as well as a section of cortical gray matter regions' thicknesses and volumes; however, after these neuroanatomical features were accounted for in the statistical model, sex was not a significant factor in predicting brain deformation. ICV had the highest relative effect on the brain deformation metrics assessed. Therefore, ICV should be considered when investigating both brain injury biomechanics and injury risk.

Brain architecture-based vulnerability to traumatic injury.

The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups' dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.

Helmet Technology, Head Impact Exposure, and Cortical Thinning Following a Season of High School Football.

The purpose of this study was to compare the effects of wearing older, lower-ranked football helmets (LRank) to wearing newer, higher-ranked football helmets (HRank) on pre- to post-season changes in cortical thickness in response to repetitive head impacts and assess whether changes in cortical thickness are associated with head impact exposure for either helmet type. 105 male high-school athletes (NHRank = 52, NLRank = 53) wore accelerometers affixed behind the left mastoid during all practices and games for one regular season of American football to monitor head impact exposure. Pre- and post-season magnetic resonance imaging (MRI) were completed to assess longitudinal changes in cortical thickness. Significant reductions in cortical thickness (i.e., cortical thinning) were observed pre- to post-season for each group, but these longitudinal alterations were not significantly different between the LRank and HRank groups. Further, significant group-by-head impact exposure interactions were observed when predicting changes in cortical thickness. Specifically, a greater frequency of high magnitude head impacts during the football season resulted in greater cortical thinning for the LRank group, but not for the HRank group. These data provide preliminary in vivo evidence that HRank helmets may provide a buffer between the specific effect of high magnitude head impacts on regional thinning by dissipating forces more evenly throughout the cortex. However, future research with larger sample sizes, increased longitudinal measures and additional helmet technologies is warranted to both expand upon and further validate the present study findings.

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

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

An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks.

The brain is a complex network consisting of neuron cell bodies in the gray matter and their axonal projections, forming the white matter tracts. These neurons are supported by an equally complex vascular network as well as glial cells. Traumatic brain injury (TBI) can lead to the disruption of the structural and functional brain networks due to disruption of both neuronal cell bodies in the gray matter as well as their projections and supporting cells. To explore how an impact can alter the function of brain networks, we integrated a finite element (FE) brain mechanics model with linked models of brain dynamics (Kuramoto oscillator) and vascular perfusion (Balloon-Windkessel) in this study. We used empirical resting-state functional magnetic resonance imaging (MRI) data to optimize the fit of our brain dynamics and perfusion models to clinical data. Results from the FE model were used to mimic injury in these optimized brain dynamics models: injury to the nodes (gray matter) led to a decrease in the nodal oscillation frequency, while damage to the edges (axonal connections/white matter) progressively decreased coupling among connected nodes. A total of 53 cases, including 33 non-injurious and 20 concussive head impacts experienced by professional American football players were simulated using this integrated model. We examined the correlation of injury outcomes with global measures of structural connectivity, neural dynamics, and functional connectivity of the brain networks when using different lesion methods. Results show that injurious head impacts cause significant alterations in global network topology regardless of lesion methods. Changes between the disrupted and healthy functional connectivity (measured by Pearson correlation) consistently correlated well with injury outcomes (AUC≥0.75), although the predictive performance is not significantly different (p>0.05) to that of traditional kinematic measures (angular acceleration). Intriguingly, our lesion model for gray matter damage predicted increases in global efficiency and clustering coefficient with increases in injury risk, while disrupting axonal connections led to lower network efficiency and clustering. When both injury mechanisms were combined into a single injury prediction model, the injury prediction performance depended on the thresholds used to determine neurodegeneration and mechanical tolerance for axonal injury. Together, these results point towards complex effects of mechanical trauma to the brain and provide a new framework for understanding brain injury at a causal mechanistic level and developing more effective diagnostic methods and therapeutic interventions.

Integrating Human and Nonhuman Primate Data to Estimate Human Tolerances for Traumatic Brain Injury.

Traumatic brain injury (TBI) contributes to a significant portion of the injuries resulting from motor vehicle crashes, falls, and sports collisions. The development of advanced countermeasures to mitigate these injuries requires a complete understanding of the tolerance of the human brain to injury. In this study, we developed a new method to establish human injury tolerance levels using an integrated database of reconstructed football impacts, subinjurious human volunteer data, and nonhuman primate data. The human tolerance levels were analyzed using tissue-level metrics determined using harmonized species-specific finite element (FE) brain models. Kinematics-based metrics involving complete characterization of angular motion (e.g., diffuse axonal multi-axial general evaluation (DAMAGE)) showed better power of predicting tissue-level deformation in a variety of impact conditions and were subsequently used to characterize injury tolerance. The proposed human brain tolerances for mild and severe TBI were estimated and presented in the form of injury risk curves based on selected tissue-level and kinematics-based injury metrics. The application of the estimated injury tolerances was finally demonstrated using real-world automotive crash data.

Evaluation of the Effectiveness of Newer Helmet Designs with Emergent Shell and Padding Technologies Versus Older Helmet Models for Preserving White Matter Following a Season of High School Football.

We aimed to objectively compare the effects of wearing newer, higher-ranked football helmets (HRank) vs. wearing older, lower-ranked helmets (LRank) on pre- to post-season alterations to neuroimaging-derived metrics of athletes' white matter. Fifty-four high-school athletes wore an HRank helmet, and 62 athletes wore an LRank helmet during their competitive football season and completed pre- and post-season diffusion tensor imaging (DTI). Longitudinal within- and between-group DTI metrics [fractional anisotropy (FA) and mean/axial/radial diffusivity (MD, AD, RD)] were analyzed using tract-based spatial statistics. The LRank helmet group exhibited significant pre- to post-season reductions in MD, AD, and RD, the HRank helmet group displayed significant pre- to post-season increases in FA, and both groups showed significant pre- to post-season increases in AD (p's < .05 [corrected]). Between-group analyses revealed the pre- to post-season increase in AD was significantly less for athletes wearing HRank compared to LRank (p < .05 [corrected]). These data provide in vivo evidence that wearing an HRank helmet may be efficacious for preserving white matter from head impact exposure during high school football. Future prospective longitudinal investigations with complimentary imaging and behavioral outcomes are warranted to corroborate these initial in vivo findings.

Evaluation and Validation of Thorax Model Responses: A Hierarchical Approach to Achieve High Biofidelity for Thoracic Musculoskeletal System.

As one of the most frequently occurring injuries, thoracic trauma is a significant public health burden occurring in road traffic crashes, sports accidents, and military events. The biomechanics of the human thorax under impact loading can be investigated by computational finite element (FE) models, which are capable of predicting complex thoracic responses and injury outcomes quantitatively. One of the key challenges for developing a biofidelic FE model involves model evaluation and validation. In this work, the biofidelity of a mid-sized male thorax model has been evaluated and enhanced by a multi-level, hierarchical strategy of validation, focusing on injury characteristics, and model improvement of the thoracic musculoskeletal system. At the component level, the biomechanical responses of several major thoracic load-bearing structures were validated against different relevant experimental cases in the literature, including the thoracic intervertebral joints, costovertebral joints, clavicle, sternum, and costal cartilages. As an example, the thoracic spine was improved by accurate representation of the components, material properties, and ligament failure features at tissue level then validated based on the quasi-static response at the segment level, flexion bending response at the functional spinal unit level, and extension angle of the whole thoracic spine. At ribcage and full thorax levels, the thorax model with validated bony components was evaluated by a series of experimental testing cases. The validation responses were rated above 0.76, as assessed by the CORA evaluation system, indicating the model exhibited overall good biofidelity. At both component and full thorax levels, the model showed good computational stability, and reasonable agreement with the experimental data both qualitatively and quantitatively. It is expected that our validated thorax model can predict thorax behavior with high biofidelity to assess injury risk and investigate injury mechanisms of the thoracic musculoskeletal system in various impact scenarios. The relevant validation cases established in this study shall be directly used for future evaluation of other thorax models, and the validation approach and process presented here may provide an insightful framework toward multi-level validating of human body models.

Development and multi-level validation of a computational model to predict traumatic aortic injury.

Traumatic aortic injury (TAI) is one of the leading causes of fatalities in blunt impact. However, there is no consensus on the injury mechanism of TAI in traffic accidents, mainly due to the complexity of occurrence scenarios and limited real-world crash data relevant to TAI. In this study, a computational model of the aorta with nonlinear mechanical characteristics and accurate morphology was developed and integrated within a thorax finite element model that included all major anatomical structures. To maximize the model's capability for predicting TAI, a multi-level process was presented to validate the model comprehensively. At the component level, the in vitro aortic pressurization testing was simulated to mimic the aortic burst pressure. Then, a sled test of a truncated cadaver was modeled to evaluate aorta response under posterior acceleration. The frontal chest pendulum impact was utilized to validate the performance of the aorta within full body model under direct chest compression. A parametric study was implemented to determine an injury tolerance for the aorta under these different loading conditions. The simulated peak pressure before aortic rupture was within the range of the experimental burst pressure. For the sled test, the simulated chest deflection and cross-sectional pressure of the aorta were correlated with the experimental measurement. No aorta injury was observed in simulated results of both sled test and chest pendulum impact, which matched the experimental findings. The present model will be a useful tool for understanding the TAI mechanisms, evaluating injury tolerance, and developing prevention strategies for aortic injuries.

Toward subject-specific evaluation: methods of evaluating finite element brain models using experimental high-rate rotational brain motion.

Computational models of the brain have become the gold standard in biomechanics to understand, predict, and mitigate traumatic brain injuries. Many models have been created and evaluated with limited experimental data and without accounting for subject-specific morphometry of the specimens in the dataset. Recent advancements in the measurement of brain motion using sonomicrometry allow for a comprehensive evaluation of brain model biofidelity using a high-rate, rotational brain motion dataset. In this study, four methods were used to determine the best technique to compare nodal displacement to experimental brain motion, including a new morphing method to match subject-specific inner skull geometry. Three finite element brain models were evaluated in this study: the isotropic GHBMC and SIMon models, as well as an anisotropic model with explicitly embedded axons (UVA-EAM). Using a weighted cross-correlation score (between 0 and 1), the anisotropic model yielded the highest average scores across specimens and loading conditions ranging from 0.53 to 0.63, followed by the isotropic GHBMC with average scores ranging from 0.46 to 0.58, and then the SIMon model with average scores ranging from 0.36 to 0.51. The choice of comparison method did not significantly affect the cross-correlation score, and differences of global strain up to 0.1 were found for the morphed geometry relative to baseline models. The morphed or scaled geometry is recommended when evaluating computational brain models to capture the subject-specific skull geometry of the experimental specimens.

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach.

Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20-40 rad/s), durations (30-60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

The mechanical and microstructural properties of the pediatric skull.

The pediatric skull differs drastically from the adult skull in terms of composition, rigidity, and structure. However, there is limited data which quantifies the mechanical properties of the pediatric skull. The lack of mechanical data may inhibit desired pediatric craniofacial surgical outcomes as current methodologies and materials employed for the pediatric population are adapted from those used for adults. In this study, normally discarded parietal bone tissue from eight pediatric craniosynostosis surgery patients (aged 4 to 10 months) was collected during reconstructive surgery and prepared for microstructural analysis and mechanical testing. Up to 12 individual coupon samples of fresh, never frozen tissue were harvested from each specimen and prepared for four-point bending testing to failure. The microstructure of each sample was analyzed using micro-computed tomography before and after each mechanical test. From this analysis, effective geometric and mechanical properties were determined for each sample (n = 68). Test results demonstrated that the pediatric parietal skull was 2.0 mm (±0.4) thick, with a porosity of 36% (±14). The effective modulus of the tissue samples, determined from the initial slope of the sample stress-strain response using Euler beam theory and a nonlinear Ramberg-Osgood stress-strain relationship, was 4.2 GPa (±2.1), which was approximately three times less stiff than adult skull tissue reported in the literature. Furthermore, the pediatric skull was able to bend up to flexural failure strains of 6.7% (±2.0), which was approximately five times larger than failure strains measured in adult skull. The disparity between the measured mechanical properties of pediatric skull tissue and adult skull tissue points towards the need to reevaluate current surgical technologies, such as pediatric cranial surgical hardware, so that they are more compatible with pediatric tissue.

Evaluation of Tissue-Level Brain Injury Metrics Using Species-Specific Simulations.

Traumatic brain injury (TBI) is a significant public health burden, and the development of advanced countermeasures to mitigate and prevent these injuries during automotive, sports, and military impact events requires an understanding of the intracranial mechanisms related to TBI. In this study, the efficacy of tissue-level injury metrics for predicting TBI was evaluated using finite element reconstructions from a comprehensive, multi-species TBI database. The database consisted of human volunteer tests, laboratory-reconstructed head impacts from sports, in vivo non-human primate (NHP) tests, and in vivo pig tests. Eight tissue-level metrics related to brain tissue strain, axonal strain, and strain-rate were evaluated using survival analysis for predicting mild and severe TBI risk. The correlation between TBI risk and most of the assessed metrics were statistically significant, but when injury data was analyzed by species, the best metric was often inconclusive and limited by the small datasets. When the human and animal datasets were combined, the injury analysis was able to delineate maximum axonal strain as the best predictor of injury for all species and TBI severities, with maximum principal strain as a suitable alternative metric. The current study is the first to provide evidence to support the assumption that brain strain response between human, pig, and NHP result in similar injury outcomes through a multi-species analysis. This assumption is the biomechanical foundation for translating animal brain injury findings to humans. The findings in the study provide fundamental guidelines for developing injury criteria that would contribute towards the innovation of more effective safety countermeasures.

Ranking and Rating Bicycle Helmet Safety Performance in Oblique Impacts Using Eight Different Brain Injury Models.

Bicycle helmets are shown to offer protection against head injuries. Rating methods and test standards are used to evaluate different helmet designs and safety performance. Both strain-based injury criteria obtained from finite element brain injury models and metrics derived from global kinematic responses can be used to evaluate helmet safety performance. Little is known about how different injury models or injury metrics would rank and rate different helmets. The objective of this study was to determine how eight brain models and eight metrics based on global kinematics rank and rate a large number of bicycle helmets (n=17) subjected to oblique impacts. The results showed that the ranking and rating are influenced by the choice of model and metric. Kendall's tau varied between 0.50 and 0.95 when the ranking was based on maximum principal strain from brain models. One specific helmet was rated as 2-star when using one brain model but as 4-star by another model. This could cause confusion for consumers rather than inform them of the relative safety performance of a helmet. Therefore, we suggest that the biomechanics community should create a norm or recommendation for future ranking and rating methods.

The Effect of Muscle Activation on Head Kinematics During Non-injurious Head Impacts in Human Subjects.

In this study, twenty volunteers were subjected to three, non-injurious lateral head impacts delivered by a 3.7 kg padded impactor at 2 m/s at varying levels of muscle activation (passive, co-contraction, and unilateral contraction). Electromyography was used to quantify muscle activation conditions, and resulting head kinematics were recorded using a custom-fit instrumented mouthpiece. A multi-modal battery of diagnostic tests (evaluated using neurocognitive, balance, symptomatic, and neuroimaging based assessments) was performed on each subject pre- and post-impact. The passive muscle condition resulted in the largest resultant head linear acceleration (12.1 ± 1.8 g) and angular velocity (7.3 ± 0.5 rad/s). Compared to the passive activation, increasing muscle activation decreased both peak resultant linear acceleration and angular velocity in the co-contracted (12.1 ± 1.5 g, 6.8 ± 0.7 rad/s) case and significantly decreased in the unilateral contraction (10.7 ± 1.7 g, 6.5 ± 0.7 rad/s) case. The duration of angular velocity was decreased with an increase in neck muscle activation. No diagnostic metric showed a statistically or clinically significant alteration between baseline and post-impact assessments, confirming these impacts were non-injurious. This study demonstrated that isometric neck muscle activation prior to impact can reduce resulting head kinematics. This study also provides the data necessary to validate computational models of head impact.