Evaluation of Head and Brain Injury Risk Functions Using Sub-Injurious Human Volunteer Data

Anatomical Features and Material Properties of Human Surrogate Head Models Affect Spatial and Temporal Brain Motion under Blunt Impact

Traumatic brain injury (TBI) is a biomechanical problem where the initiating event is dynamic loading (blunt, inertial, blast) to the head. To understand the relationship between the mechanical parameters of the injury and the deformation patterns in the brain, we have previously developed a surrogate head (SH) model capable of measuring spatial and temporal deformation in a surrogate brain under blunt impact. The objective of this work was to examine how material properties and anatomical features affect the motion of the brain and the development of injurious deformations. The SH head model was modified to study six variables independently under blunt impact: surrogate brain stiffness, surrogate skull stiffness, inclusion of cerebrospinal fluid (CSF), head/skull size, inclusion of vasculature, and neck stiffness. Each experimental SH was either crown or frontally impacted at 1.3 m/s (3 mph) using a drop tower system. Surrogate brain material, the Hybrid III neck stiffness, and skull stiffness were measured and compared to published properties. Results show that the most significant variables affecting changes in brain deformation are skull stiffness, inclusion of CSF and surrogate brain stiffness. Interestingly, neck stiffness and SH size significantly affected the strain rate only suggesting these parameters are less important in blunt trauma. While the inclusion of vasculature locally created strain concentrations at the interface of the artery and brain, overall deformation was reduced.

Strain Response of an Anatomically Accurate Nonhuman Primate Finite Element Brain Model Under Sagittal Loading

INTRODUCTION

Prevention and treatment of traumatic brain injuries is critical to preserving soldier brain health. Laboratory studies are commonly used to reproduce injuries, understand injury mechanisms, and develop tolerance limits; however, this approach has limitations for studying brain injury, which requires a physiological response. The nonhuman primate (NHP) has been used as an effective model for investigating brain injury for many years. Prior research using the NHP provides a valuable resource to leverage using modern analysis and modeling techniques to improve our understanding of brain injury. The objectives of the present study are to develop an anatomically accurate finite element model of the NHP and determine regional brain responses using previously collected NHP data.

MATERIALS AND METHODS

The finite element model was developed using a neuroimaging-based anatomical atlas of the rhesus macaque that includes both cortical and subcortical structures. Head kinematic data from 10 sagittal NHP experiments, four +Gx (rearward) and six -Gx (frontal), were used to test model stability and obtain brain strain responses from multiple severities and vectors.

RESULTS

For +Gx tests, the whole-brain cumulative strain damage measure exceeding a strain threshold of 0.15 (CSDM15) ranged from 0.28 to 0.89, and 95th percentile of the whole-brain maximum principal strain (MPS95) ranged from 0.21 to 0.59. For -Gx tests, whole-brain CSDM15 ranged from 0.02 to 0.66, and whole-brain MPS95 ranged from 0.08 to 0.39.

CONCLUSIONS

Recognizing that NHPs are the closest surrogate to humans combined with the limitations of conducting brain injury research in the laboratory, a detailed anatomically accurate finite element model of an NHP was developed and exercised using previously collected data from the Naval Biodynamics Laboratory. The presently developed model can be used to conduct additional analyses to act as pilot data for the design of newer experiments with statistical power because of the sensitivity and resources needed to conduct experiments with NHPs.

A Method for Evaluating Brain Deformation Under Sagittal Blunt Impacts Using a Half-Skull Human-Scale Surrogate

Trauma to the brain is a biomechanical problem where the initiating event is a dynamic loading (blunt, inertial, blast) to the head. To understand the relationship between the mechanical parameters of the injury and the spatial and temporal deformation patterns in the brain, there is a need to develop a reusable and adaptable experimental traumatic brain injury (TBI) model that can measure brain motion under varying parameters. In this effort, we aim to directly measure brain deformation (strain and strain rates) in different brain regions in a human head model using a drop tower.

METHODS

Physical head models consisting of a half, sagittal plane skull, brain, and neck were constructed and subjected to crown and frontal impacts at two impact speeds. All tests were recorded with a high-speed camera at 1000 frames per second. Motion of visual markers within brain surrogates were used to track deformations and calculate spatial strain histories in 6 brain regions of interest. Principal strains, strain rates and strain impulses were calculated and reported.

RESULTS

Higher impact velocities corresponded to higher strain values across all impact scenarios. Crown impacts were characterized by high, long duration strains distributed across the parietal, frontal and hippocampal regions whereas frontal impacts were characterized by sharply rising and falling strains primarily found in the parietal, frontal, hippocampal and occipital regions. High strain rates were associated with short durations and impulses indicating fast but short-lived strains. 2.23 m/s (5 mph) crown impacts resulted in 53% of the brain with shear strains higher than 0.15 verses 32% for frontal impacts.

CONCLUSIONS

The results reveal large differences in the spatial and temporal strain responses between crown and forehead impacts. Overall, the results suggest that for the same speed, crown impact leads to higher magnitude strain patterns than a frontal impact. The data provided by this model provides unique insight into the spatial and temporal deformation patterns that have not been provided by alternate surrogate models. The model can be used to investigate how anatomical, material and loading features and parameters can affect deformation patterns in specific regions of interest in the brain.

Influence of surrogate scalp material and thickness on head impact responses: Toward a biofidelic head-brain physical model

Advanced physical head models capable of replicating both global kinematics and intracranial mechanics of the human head are required for head injury research and safety gear assessment. These head surrogates require a complex design to accommodate realistic anatomical details. The scalp is a crucial head component, but its influence on the biomechanical response of such head surrogates remains unclear. This study aimed to evaluate the influence of surrogate scalp material and thickness on head accelerations and intraparenchymal pressures using an advanced physical head-brain model. Scalp pads made from four materials (Vytaflex20, Vytaflex40, Vytaflex50, PMC746) and each material with four thicknesses (2, 4, 6, and 8 mm) were evaluated. The head model attached to the scalp pad was dropped onto a rigid plate from two heights (5 and 19.5 cm) and at three head locations (front, right side, and back). While the selected materials' modulus exhibited a relatively minor effect on head accelerations and coup pressures, the effect of scalp thickness was shown to be major. Moreover, by decreasing the thickness of the head's original scalp by 2 mm and changing the original scalp material from Vytaflex 20 to Vytaflex 40 or Vytaflex 50, the head acceleration biofidelity ratings could improve by 30% and approached the considered rating (0.7) of good biofidelity. This study provides a potential direction for improving the biofidelity of a novel head model that might be a useful tool in head injury research and safety gear tests. This study also has implications for selecting appropriate surrogate scalps in the future design of physical and numerical head models.

Differences in head impact biomechanics between playing positions in Canadian high school football players

The objective of this study was to compare head impact magnitudes and time between impacts among positions in Canadian high-school football. Thirty nine players from two high-school football teams were recruited and assigned a position profile: Profile 1 (quarterback, receiver, defensive back, kicker), Profile 2 (linebacker, running back), and Profile 3 (linemen). Players wore instrumented mouthguards to measure peak magnitudes of linear and angular acceleration and velocity for each head impact throughout the season. A principal component analysis reduced the dimensionality of biomechanical variables, resulting in one principal component (PC1) score assigned to every impact. Time between impacts was calculated by subtracting the timestamps of subsequent head impacts within a session. Significant differences in PC1 scores and time between impacts occurred between playing position profiles (ps<0.001). Post-hoc comparisons determined that PC1 was greatest in Profile 2, followed by Profiles 1 and 3. Time between impacts was lowest in Profile 3, followed by Profiles 2 and 1. This study delivers a new method of reducing the multidimensionality of head impact magnitudes and suggests different Canadian high-school football playing positions experience different head impact magnitudes and frequencies, which is important for monitoring concussion and repetitive head impact exposure.

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.

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.

Integrating Human and Non-Human 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, sub-injurious human volunteer data, and non-human primate data. The human tolerance levels were analyzed using tissue-level metrics determined using harmonized species-specific finite element brain models. Kinematics-based metrics involving complete characterization of angular motion (e.g., 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.

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.

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.

Current state and progress of research on forensic biomechanics in China

Forensic biomechanics gradually has become a significant component of forensic science. Forensic biomechanics is evidence-based science that applies biomechanical principles and methods to forensic practice, which has constituted one of the most potential research areas. In this review, we introduce how finite element techniques can be used to simulate forensic cases, how injury criteria and injury scales can be used to describe injury severity, and how tests of postmortem human subjects and dummy can be used to provide essential validation data. This review also describes research progress and new applications of forensic biomechanics in China.Key pointsThe review shows the main research progress and new applications of forensic biomechanics in China.The review introduces eight cases about the application of forensic biomechanics, including the multiple rigid body reconstruction, the finite element applications, study of mechanical properties, traffic crash reconstruction based on multiple techniques and analysis of morphomechanical mechanism about blood dispersal.Though forensic biomechanics has a great advantage for the evaluation of injury mechanisms, it still has some uncertainties owing to the uniqueness of the human anatomy, the complexity of biological materials, and the uncertainty of injury-causing circumstances.

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.

Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain

Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95^th percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions.

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models

Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subject-specific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subject-specific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinary research between the biomechanics and neuroimaging fields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis.

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.

Biomechanics of the Human Brain during Dynamic Rotation of the Head

Traumatic brain injuries (TBI) are a substantial societal burden. The development of better technologies and systems to prevent and/or mitigate the severity of brain injury requires an improved understanding of the mechanisms of brain injury, and more specifically, how head impact exposure relates to brain deformation. Biomechanical investigations have used computational models to identify these relations, but more experimental brain deformation data are needed to validate these models and support their conclusions. The objective of this study was to generate a dataset describing in situ human brain motion under rotational loading at impact conditions considered injurious. Six head-neck human post-mortem specimens, unembalmed and never frozen, were instrumented with 24 sonomicrometry crystals embedded throughout the parenchyma that can directly measure dynamic brain motion. Dynamic brain displacement, relative to the skull, was measured for each specimen with four loading severities in the three directions of controlled rotation, for a total of 12 tests per specimen. All testing was completed 42-72 h post-mortem for each specimen. The final dataset contains approximately 5,000 individual point displacement time-histories that can be used to validate computational brain models. Brain motion was direction-dependent, with axial rotation resulting in the largest magnitude of displacement. Displacements were largest in the mid-cerebrum, and the inferior regions of the brain-the cerebellum and brainstem-experienced relatively lower peak displacements. Brain motion was also found to be positively correlated to peak angular velocity, and negatively correlated with angular velocity duration, a finding that has implications related to brain injury risk-assessment methods. This dataset of dynamic human brain motion will form the foundation for the continued development and refinement of computational models of the human brain for predicting TBI.

A network-based response feature matrix as a brain injury metric

Conventional brain injury metrics are scalars that treat the whole head/brain as a single unit but do not characterize the distribution of brain responses. Here, we establish a network-based "response feature matrix" to characterize the magnitude and distribution of impact-induced brain strains. The network nodes and edges encode injury risks to the gray matter regions and their white matter interconnections, respectively. The utility of the metric is illustrated in injury prediction using three independent, real-world datasets: two reconstructed impact datasets from the National Football League (NFL) and Virginia Tech, respectively, and measured concussive and non-injury impacts from Stanford University. Injury predictions with leave-one-out cross-validation are conducted using the two reconstructed datasets separately, and then by combining all datasets into one. Using support vector machine, the network-based injury predictor consistently outperforms four baseline scalar metrics including peak maximum principal strain of the whole brain (MPS), peak linear/rotational acceleration, and peak rotational velocity across all five selected performance measures (e.g., maximized accuracy of 0.887 vs. 0.774 and 0.849 for MPS and rotational acceleration with corresponding positive predictive values of 0.938, 0.772, and 0.800, respectively, using the reconstructed NFL dataset). With sufficient training data, real-world injury prediction is similar to leave-one-out in-sample evaluation, suggesting the potential advantage of the network-based injury metric over conventional scalar metrics. The network-based response feature matrix significantly extends scalar metrics by sampling the brain strains more completely, which may serve as a useful framework potentially allowing for other applications such as characterizing injury patterns or facilitating targeted multi-scale modeling in the future.

Suitability of enhanced head injury criteria for vehicle rating

Objective: Euro NCAP is considering the implementation of a new head injury assessment with the introduction of THOR in the mobile progressive deformable barrier frontal impact crash test. The objective of this study is to assess the suitability of enhanced head injury criteria for practical application in consumer rating programs.Method: AIS2+ risk predictions from nine selected head injury criteria where calculated for 27 pairs of crash test results representing small and moderate overlap frontal crashes. The capability of each injury criteria to predict the real-world injury rates of these crash modes was evaluated. Next, the correlation coefficients between the head injury candidates were calculated and individual predictions were compared for all tests in scatter plots.Results: The results show that none of the crash tests head injury assessment predicted the four-times higher head injury rates observed in the accident data for small overlap crashes compared to moderate overlap crashes. Poor correlation was demonstrated between many leading brain injury metrics, and the risk predictions for individual vehicles differ quite substantially depending on the criterion considered. Conclusions: While preliminary, the results of this study demonstrate that more evaluation of the most suitable brain injury criteria is necessary before consideration into a consumer evaluation program. Convergence of the head injury criteria risks for individual cases should be part of the validation process for enhanced head injury criteria, since identical head signals should yield similar injury risks.

Statistical Characterization of Human Brain Deformation During Mild Angular Acceleration Measured In Vivo by Tagged Magnetic Resonance Imaging

Understanding of in vivo brain biomechanical behavior is critical in the study of traumatic brain injury (TBI) mechanisms and prevention. Using tagged magnetic resonance imaging, we measured spatiotemporal brain deformations in 34 healthy human volunteers under mild angular accelerations of the head. Two-dimensional (2D) Lagrangian strains were examined throughout the brain in each subject. Strain metrics peaked shortly after contact with a padded stop, corresponding to the inertial response of the brain after head deceleration. Maximum shear strain of at least 3% was experienced at peak deformation by an area fraction (median±standard error) of 23.5±1.8% of cortical gray matter, 15.9±1.4% of white matter, and 4.0±1.5% of deep gray matter. Cortical gray matter strains were greater in the temporal cortex on the side of the initial contact with the padded stop and also in the contralateral temporal, frontal, and parietal cortex. These tissue-level deformations from a population of healthy volunteers provide the first in vivo measurements of full-volume brain deformation in response to known kinematics. Although strains differed in different tissue type and cortical lobes, no significant differences between male and female head accelerations or strain metrics were found. These cumulative results highlight important kinematic features of the brain's mechanical response and can be used to facilitate the evaluation of computational simulations of TBI.

A reanalysis of football impact reconstructions for head kinematics and finite element modeling

BACKGROUND

Head kinematics generated by laboratory reconstructions of professional football helmet impacts have been applied to computational models to study the biomechanics of concussion. Since the original publication of this data, techniques for evaluating accelerometer consistency and error correction have been developed. This study applies these techniques to the original reconstruction data and reanalyzes the results given the current state of concussion biomechanics.

METHODS

Consistency checks were applied to the sensor data collected in the head of each test dummy. Inconsistent data were corrected using analytical techniques, and head kinematics were recalculated from the corrected data. Reconstruction videos were reviewed to identify artefactual impacts during the reconstruction to establish the region of applicability for simulations. Corrected head kinematics were input into finite element brain models to investigate strain response to the corrected dataset.

FINDINGS

Multiple reconstruction cases had inconsistent sensor arrays caused by a problematic sensor; corrections to the arrays caused changes in calculated rotational head motion. These corrections increased median peak angular velocity for the concussion cases from 35.6 to 41.5 rad/s. Using the original kinematics resulted in an average error of 20% in maximum principal strain results for each case. Simulations of the reconstructions also demonstrated that simulation lengths less than 40 ms did not capture the entire brain strain response and under-predicted strain.

INTERPRETATION

This study corrects data that were used to determine concussion risk, and indicates altered head angular motion and brain strain response for many reconstructions. Conclusions based on the original data should be re-examined based on this new study.

Development of a Metric for Predicting Brain Strain Responses Using Head Kinematics

Diffuse brain injuries are caused by excessive brain deformation generated primarily by rapid rotational head motion. Metrics that describe the severity of brain injury based on head motion often do not represent the governing physics of brain deformation, rendering them ineffective over a broad range of head impact conditions. This study develops a brain injury metric based on the response of a second-order mechanical system, and relates rotational head kinematics to strain-based brain injury metrics: maximum principal strain (MPS) and cumulative strain damage measure (CSDM). This new metric, universal brain injury criterion (UBrIC), is applicable over a broad range of kinematics encountered in automotive crash and sports. Efficacy of UBrIC was demonstrated by comparing it to MPS and CSDM predicted in 1600 head impacts using two different finite element (FE) brain models. Relative to existing metrics, UBrIC had the highest correlation with the FE models, and performed better in most impact conditions. While UBrIC provides a reliable measurement for brain injury assessment in a broad range of head impact conditions, and can inform helmet and countermeasure design, an injury risk function was not incorporated into its current formulation until validated strain-based risk functions can be developed and verified against human injury data.

Initial analysis of archived non-human primate frontal and rear impact data from the biodynamics data resource

OBJECTIVE

The research objective was to conduct an initial analysis of non-human primate (NHP) data from frontal and rear impact events archived in the Biodynamics Data Resource (BDR) records of the Naval Biodynamics Laboratory (NBDL). These rare data, collected between 1973 and 1989, will inform the safety community of upper-end tolerance limits of NHP and may be related to severe crash scenarios.

METHODS

Data from frontal and rear acceleration tests to 93 macaque NHP were examined. Each NHP was fully torso restrained, whereas the head-neck complex was unrestrained. Each NHP underwent between 1 and 21 total runs; 2 total runs was most common-a low-level run and then a high-level run. Following each impact exposure, the NHP was evaluated using a series of medical examinations. Now part of the legacy collection in the BDR, these evaluations were used to assess NHP exposures to be in one of 3 categories: noninjurious, injurious, or fatal. Using reported peak sled acceleration values, data were amenable to survival analysis statistical methodology to derive injury probability curves (IPCs). IPCs were derived for injury and fatality outcomes.

RESULTS

Fatal injuries for both frontal and rear impacts were mostly at the cranio-vertebral junction. In addition to hemorrhage, fatal frontal and rear impact tests both produced predominantly atlanto-occipital dislocations, with and without spinal cord transection. After exclusions, IPCs were derived for frontal and rear impact for both (1) fatal outcome and (2) injurious outcome (any injury including fatal injury). For frontal impact, 53 NHP qualified with 5, 25, and 50% risk for fatality at 89, 105, and 114 peak sled Gs, respectively, and for injurious outcome at 70, 92, and 106 Gs, respectively. For rear impact, 34 NHP qualified with 5, 25, and 50% risk for fatality at 96, 122, 138 peak sled Gs, respectively, and for injurious outcome at 75, 99, and 115 Gs, respectively.

CONCLUSIONS

The majority of injuries were at the cranio-vertebral junction, indicating that the inertial head mass caused a tensile loading mechanism to the cervical spine. These data may be used in conjunction with finite element modeling to estimate risks to the human population. The most direct application in the automotive environment could be to the well-restrained child. The N_ij neck injury criteria, currently based on data from piglet studies, could also benefit because the NHP is a more accurate human surrogate. These types of tests are likely to never be repeated and will form an upper bound of tolerance information valuable to safety system designers.

A Novel Method for Quantifying Human In Situ Whole Brain Deformation under Rotational Loading Using Sonomicrometry

Traumatic brain injuries (TBI) are one of the least understood injuries to the body. Finite element (FE) models of the brain have been crucial for understanding concussion and for developing injury mitigation systems; however, the experimental brain deformation data currently used to validate these models are limited. The objective of this study was to develop a methodology for the investigation of in situ three-dimensional brain deformation during pure rotational loading of the head, using sonomicrometry. Sonomicrometry uses ultrasonic pulses to measure the dynamic distances between piezoelectric crystals implanted in any sound-transmitting media. A human cadaveric head-neck specimen was acquired 14 h postmortem and was instrumented with an array of 32 small sonomicrometry crystals embedded in the head: 24 crystals were implanted in the brain, and 8 were fixed to the inner skull. A dynamic rotation was then applied to the head using a closed-loop controlled test device. Four pulses with different severity levels were applied around three orthogonal anatomical axes of rotation. A repeated test of the highest severity rotation was conducted in each axis to assess repeatability. All tests were completed within 56 h postmortem. Overall, the combined experimental and sonomicrometry methods were demonstrated to reliably and repeatedly capture three-dimensional dynamic deformation of an intact human brain. These methods provide a framework for using sonomicrometry to acquire multidimensional experimental data required for FE model development and validation, and will lend insight into the deformations sustained by the brain during impact.