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.

Development of a Low-Power Instrumented Mouthpiece for Directly Measuring Head Acceleration in American Football.

Instrumented mouthpieces (IM) offer a means of measuring head impacts that occur in sport. Direct measurement of angular head kinematics is preferential for accuracy; however, existing IMs measure angular velocity and differentiate the measurement to calculate angular acceleration, which can limit bandwidth and consume more power. This study presents the development and validation of an IM that uses new, low-power accelerometers for direct measurement of linear and angular acceleration over a broad range of head impact conditions in American football. IM sensor accuracy for measuring six-degree-of-freedom head kinematics was assessed using two helmeted headforms instrumented with a custom-fit IM and reference sensor instrumentation. Head impacts were performed at 10 locations and 6 speeds representative of the on-field conditions associated with injurious and non-injurious impacts in American football. Sensor measurements from the IM were highly correlated with those from the reference instrumentation located at the maxilla and skull center of gravity. Based on pooled data across headform and impact location, R2 ≥ 0.94, mean absolute error (AE) ≤ 7%, and mean relative impact angle ≤ 11° for peak linear and angular acceleration and angular velocity while R2 ≥ 0.90 and mean AE ≤ 7% for kinematic-based injury metrics used in helmet tests.

Development and Evaluation of a Test Method for Assessing the Performance of American Football Helmets.

As more is learned about injury mechanisms of concussion and scenarios under which injuries are sustained in football games, methods used to evaluate protective equipment must adapt. A combination of video review, videogrammetry, and laboratory reconstructions was used to characterize concussive impacts from National Football League games during the 2015-2017 seasons. Test conditions were generated based upon impact locations and speeds from this data set, and a method for scoring overall helmet performance was created. Head kinematics generated using a linear impactor and sliding table fixture were comparable to those from laboratory reconstructions of concussive impacts at similar impact conditions. Impact tests were performed on 36 football helmet models at two laboratories to evaluate the reproducibility of results from the resulting test protocol. Head acceleration response metric, a head impact severity metric, varied 2.9-5.6% for helmet impacts in the same lab, and 3.8-6.0% for tests performed in a separate lab when averaged by location for the models tested. Overall inter-lab helmet performance varied by 1.1 ± 0.9%, while the standard deviation in helmet performance score was 7.0%. The worst helmet performance score was 33% greater than the score of the best-performing helmet evaluated by this study.

On-Field Performance of an Instrumented Mouthguard for Detecting Head Impacts in American Football.

Wearable sensors that accurately record head impacts experienced by athletes during play can enable a wide range of potential applications including equipment improvements, player education, and rule changes. One challenge for wearable systems is their ability to discriminate head impacts from recorded spurious signals. This study describes the development and evaluation of a head impact detection system consisting of a mouthguard sensor and machine learning model for distinguishing head impacts from spurious events in football games. Twenty-one collegiate football athletes participating in 11 games during the 2018 and 2019 seasons wore a custom-fit mouthguard instrumented with linear and angular accelerometers to collect kinematic data. Video was reviewed to classify sensor events, collected from instrumented players that sustained head impacts, as head impacts or spurious events. Data from 2018 games were used to train the ML model to classify head impacts using kinematic data features (127 head impacts; 305 non-head impacts). Performance of the mouthguard sensor and ML model were evaluated using an independent test dataset of 3 games from 2019 (58 head impacts; 74 non-head impacts). Based on the test dataset results, the mouthguard sensor alone detected 81.6% of video-confirmed head impacts while the ML classifier provided 98.3% precision and 100% recall, resulting in an overall head impact detection system that achieved 98.3% precision and 81.6% recall.

A Multibody Model for Predicting Spatial Distribution of Human Brain Deformation Following Impact Loading.

With an increasing focus on long-term consequences of concussive brain injuries, there is a new emphasis on developing tools that can accurately predict the mechanical response of the brain to impact loading. Although finite element models (FEM) estimate the brain response under dynamic loading, these models are not capable of delivering rapid (∼seconds) estimates of the brain's mechanical response. In this study, we develop a multibody spring-mass-damper model that estimates the regional motion of the brain to rotational accelerations delivered either about one anatomic axis or across three orthogonal axes simultaneously. In total, we estimated the deformation across 120 locations within a 50th percentile human brain. We found the multibody model (MBM) correlated, but did not precisely predict, the computed finite element response (average relative error: 18.4 ± 13.1%). We used machine learning (ML) to combine the prediction from the MBM and the loading kinematics (peak rotational acceleration, peak rotational velocity) and significantly reduced the discrepancy between the MBM and FEM (average relative error: 9.8 ± 7.7%). Using an independent sports injury testing set, we found the hybrid ML model also correlated well with predictions from a FEM (average relative error: 16.4 ± 10.2%). Finally, we used this hybrid MBM-ML approach to predict strains appearing in different locations throughout the brain, with average relative error estimates ranging from 8.6% to 25.2% for complex, multi-axial acceleration loading. Together, these results show a rapid and reasonably accurate method for predicting the mechanical response of the brain for single and multiplanar inputs, and provide a new tool for quickly assessing the consequences of impact loading throughout the brain.

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.

Brain tissue strains vary with head impact location: A possible explanation for increased concussion risk in struck versus striking football players.

BACKGROUND: On-field football helmet impacts over a large range of severities have caused concussions in some players but not in other players. One possible explanation for this variability is the struck player's helmet impact location. METHODS: We examined the effect of impact location on regional brain tissue strain when input energy was held constant. Laboratory impacts were performed at 12 locations distributed over the helmet and the resulting head kinematics were simulated in two finite element models of the brain: the Simulated Injury Monitor and the Global Human Body Model Consortium brain model. FINDINGS: Peak kinematics, injury metrics and brain strain varied significantly with impact location. Differences in impact location explained 33 to 37% of the total variance in brain strain for the whole brain and cerebrum, considerably more than the variance explained by impact location for the peak resultant head kinematics (8 to 23%) and slightly more than half of the variance explained by the difference in closing speed (57 to 61%). Both finite element models generated similar strain results, with minor variations for impacts that generated multi-axial rotations, larger variations in brainstem strains for some impact locations and a small bias for the cerebellum. INTERPRETATION: Based on this experimental and computational simulation study, impact location on the football helmet has a large effect on regional brain tissue strain. We also found that the lowest strains consistently occurred in impacts to the crown and forehead, helmet locations commonly associated with the striking player.

Development of a Second-Order System for Rapid Estimation of Maximum Brain Strain.

Diffuse brain injuries are assessed with deformation-based criteria that utilize metrics based on rotational head kinematics to estimate brain injury severity. Although numerous metrics have been proposed, many are based on empirically-derived models that use peak kinematics, which often limit their applicability to a narrow range of head impact conditions. However, over a broad range of impact conditions, brain deformation response to rotational head motion behaves similarly to a second-order mechanical system, which utilizes the full kinematic time history of a head impact. This study describes a new brain injury metric called Diffuse Axonal Multi-Axis General Evaluation (DAMAGE). DAMAGE is based on the equations of motion of a three-degree-of-freedom, coupled 2nd-order system, and predicts maximum brain strain using the directionally dependent angular acceleration time-histories from a head impact. Parameters for the effective mass, stiffness, and damping were determined using simplified rotational pulses which were applied multiaxially to a 50th percentile adult human male finite element model. DAMAGE was then validated with a separate database of 1747 head impacts including helmet, crash, and sled tests and human volunteer responses. Relative to existing rotational brain injury metrics that were evaluated in this study, DAMAGE was found to be the best predictor of maximum brain strain.

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.

Development of a Single-Degree-of-Freedom Mechanical Model for Predicting Strain-Based Brain Injury Responses.

Linking head kinematics to injury risk has been the focus of numerous brain injury criteria. Although many early forms were developed using mechanics principles, recent criteria have been developed using empirical methods based on subsets of head impact data. In this study, a single-degree-of-freedom (sDOF) mechanical analog was developed to parametrically investigate the link between rotational head kinematics and brain deformation. Model efficacy was assessed by comparing the maximum magnitude of displacement to strain-based brain injury predictors from finite element (FE) human head models. A series of idealized rotational pulses covering a broad range of acceleration and velocity magnitudes (0.1-15 krad/s2 and 1-100 rad/s) with durations between 1 and 3000 ms were applied to the mechanical models about each axis of the head. Results show that brain deformation magnitude is governed by three categories of rotational head motion each distinguished by the duration of the pulse relative to the brain's natural period: for short-duration pulses, maximum brain deformation depended primarily on angular velocity magnitude; for long-duration pulses, brain deformation depended primarily on angular acceleration magnitude; and for pulses relatively close to the natural period, brain deformation depended on both velocity and acceleration magnitudes. These results suggest that brain deformation mechanics can be adequately explained by simple mechanical systems, since FE model responses and experimental brain injury tolerances exhibited similar patterns to the sDOF model. Finally, the sDOF model was the best correlate to strain-based responses and highlighted fundamental limitations with existing rotational-based brain injury metrics.

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

Risk assessment models are developed to estimate the probability of brain injury during head impact using mechanical response variables such as head kinematics and brain tissue deformation. Existing injury risk functions have been developed using different datasets based on human volunteer and scaled animal injury responses to impact. However, many of these functions have not been independently evaluated with respect to laboratory-controlled human response data. In this study, the specificity of 14 existing brain injury risk functions was assessed by evaluating their ability to correctly predict non-injurious response using previously conducted sled tests with well-instrumented human research volunteers. Six degrees-of-freedom head kinematics data were obtained for 335 sled tests involving subjects in frontal, lateral, and oblique sled conditions up to 16 Gs peak sled acceleration. A review of the medical reports associated with each individual test indicated no clinical diagnosis of mild or moderate brain injury in any of the cases evaluated. Kinematic-based head and brain injury risk probabilities were calculated directly from the kinematic data, while strain-based risks were determined through finite element model simulation of the 335 tests. Several injury risk functions substantially over predict the likelihood of concussion and diffuse axonal injury; proposed maximum principal strain-based injury risk functions predicted nearly 80 concussions and 14 cases of severe diffuse axonal injury out of the 335 non-injurious cases. This work is an important first step in assessing the efficacy of existing brain risk functions and highlights the need for more predictive injury assessment models.

Assessment of Kinematic Brain Injury Metrics for Predicting Strain Responses in Diverse Automotive Impact Conditions.

Numerous injury criteria have been developed to predict brain injury using the kinematic response of the head during impact. Each criterion utilizes a metric that is some mathematical combination of the velocity and/or acceleration components of translational and/or rotational head motion. Early metrics were based on linear acceleration of the head, but recent injury criteria have shifted towards rotational-based metrics. Currently, there is no universally accepted metric that is suitable for a diverse range of head impacts. In this study, we assessed the capability of fifteen existing kinematic-based metrics for predicting strain-based brain response using four different automotive impact conditions. Tissue-level strains were obtained through finite element model simulation of 660 head impacts including occupant and pedestrian crash tests, and pendulum head impacts. Correlations between head kinematic metrics and predicted brain strain-based metrics were evaluated. Correlations between brain strain and metrics based on angular velocity were highest among those evaluated, while metrics based on linear acceleration were least correlative. BrIC and RVCI were the kinematic metrics with the highest overall correlation; however, each metric had limitations in certain impact conditions. The results of this study suggest that rotational head kinematics are the most important parameters for brain injury criteria.