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

An evaluation of the performance of medical helmets used in healthcare for the protection of vulnerable patients

Introduction

Medical helmets (MHs) are used by individuals with an increased vulnerability to falls and are essentially unregulated in the UK; therefore, their impact performance is unproven. This study investigated the performance of a selection of medical helmets available to clinicians using general techniques to determine their protective performance against impacts. Additionally, clinicians have stated that medical helmets need to consider focal vulnerabilities to impact (often a postsurgical site of a decompressive craniectomy); therefore, novel techniques were specifically employed for measuring the protection of a focal site.

Materials and Methods

A freefall drop test methodology was used to assess six medical helmets (MH1-6) and two sports helmets (SH1 and SH2). The headform was instrumented with six degrees of freedom instrumentation to quantify global kinematics metrics related to injury risk (peak linear acceleration (PLA), peak angular velocity (PAV), peak angular acceleration (PAA), head injury criterion (HIC), and brain injury criterion (BrIC)), and a thin-film contact pressure measurement system was used to quantify the contact area (above a threshold of 560 kPa) focal to the impact. Due to the advanced nature of these measurements, a novel biofidelic headform was used to more accurately represent local deformation. Additionally, impact performance was plotted against two proxy measures of comfort.

Results

The difference in performance between the worst and best helmets ranged from 90% to 2844%, showing a substantial variation. HIC, PLA, and PAA showed the largest range, whereas PAV showed the smallest range. Nonetheless, there was good agreement between each kinematic metric regarding the rank order of the medical helmets. The contact pressure was a consistent outlier. Each metric included at least one injury threshold, which MH4 and MH6 consistently exceeded (15/18 occasions).

Discussion

MH2 and MH3 were the only medical helmets comparable to sports helmets in terms of both comfort and performance. MH1 showed excellent performance metrics but exhibited possible discomfort, while MH4 was above average across both measurement categories. MH4 and MH6 were significantly deficient compared to the sample of helmets. These results highlight the need for standardisation.

Effects of anatomy and head motion on spatial patterns of deformation in the human brain

PURPOSE

To determine how the biomechanical vulnerability of the human brain is affected by features of individual anatomy and loading.

METHODS

To identify the features that contribute most to brain vulnerability, we imparted mild harmonic acceleration to the head and measured the resulting brain motion and deformation using magnetic resonance elastography (MRE). Oscillatory motion was imparted to the heads of adult participants using a lateral actuator (n = 24) or occipital actuator (n = 24) at 20 Hz, 30 Hz, and 50 Hz. Displacement vector fields and strain tensor fields in the brain were obtained from MRE measurements. Anatomical images, as well as displacement and strain fields from each participant were rigidly and deformably aligned to a common atlas (MNI-152). Vulnerability of the brain to deformation was quantified by the ratio of strain energy (SE) to kinetic energy (KE) for each participant. Similarity of deformation patterns between participants was quantified using strain field correlation (CV). Linear regression models were used to identify the effect of similarity of brain size, shape, and age, as well as similarity of loading, on CV.

RESULTS

The SE/KE ratio decreased with frequency and was larger for participants undergoing lateral, rather than occipital, actuation. Head rotation about the inferior-superior axis was correlated with larger SE/KE ratio. Strain field correlations were primarily affected by the similarity of rigid-body motion.

CONCLUSION

The motion applied to the skull is the most important factor in determining both the vulnerability of the brain to deformation and the similarity between strain fields in different individuals.

Development of a repetitive traumatic brain injury risk function based on real-world accident reconstruction and wavelet packet energy analysis

Existing evaluation criteria for pedestrian head impact injuries focus only on single impacts, with less attention given to repetitive traumatic brain injury (rTBI), which is common in motor vehicle collisions, falls, and sports. Improving pedestrian collision protection safety requires a complete understanding of the tolerance of the repeated collisions of the human brain to injury. Therefore, this study aimed to collect data from 72 pedestrian collisions that were reconstructed using MADYMO and THUMS finite element head models (version 4.0.2). The evaluation metrics for rTBI were developed by integrating brain injury criteria based on time-domain features, including the head injury criterion (HIC), brain injury criterion (BrIC), diffuse axonal multi-axial general evaluation (DAMAGE), and maximum principal strain (MPS), with frequency-domain features obtained from wavelet packet transform energy analysis of head motion responses. The proposed brain tolerance for mild and severe rTBI was estimated through parametric survival analysis and presented as injury risk curves based on the selected injury metrics. The results showed a significant difference in brain injury tolerance between repetitive and single collisions. For the 50% probability of mild and severe brain injury in real accidents, the thresholds for rTBI metrics based on BrIC and DAMAGE were 1.085 and 1.513 and 0.494 and 0.678, respectively, all higher than the thresholds of single-impact reported in previous studies. However, the thresholds for repetitive head injury criteria based on MPS were 0.604 and 0.838, which were lower than the thresholds of single impact reported in previous studies, implying that the prediction of tolerance to repetitive brain more consistent with tissue-level than head kinematics level. This study developed injury risk functions (IRFs) for rTBI by integrating the amplitude-frequency characteristics of head responses and brain injury criteria. This knowledge further provides crucial support for understanding the tolerance to rTBI and enhancing pedestrian safety.

Distribution of position-specific head impact severities among professional and Division I collegiate American football athletes during games

Objective

To compare the severity of head impacts between professional and Division I (D-I) collegiate football games for the purpose of improving protective equipment.

Methods

A total of 243 football players from the National Football League (NFL) and from D-I of the National Collegiate Athletic Association (NCAA) were equipped with instrumented mouthpieces capable of measuring six degrees-of-freedom head kinematics. Head impacts were processed using a custom algorithm and combined with game period descriptors to produce a curated dataset for analysis. Head impact severity distributions for several kinematic-based metrics were compared within position groupings between leagues.

Results

A total of 11 038 head impacts greater than 10 g from 1208 player-games were collected during 286 player-seasons (2019-2022). No significant differences were found between leagues in the distributions of kinematic-based metrics for all investigated position groupings (p≥0.320). The median and IQRs for peak linear acceleration for NFL and NCAA were 17.2 (9.3) g and 17.0 (8.6) g for linemen, 20.7 (13.8) g and 20.0 (13.5) g for hybrid and 21.0 (17.0) g and 20.8 (15.5) g for speed position groupings, respectively.

Conclusion

The absence of statistically significant differences in the distributions of head impact severity between professional and D-I collegiate football players indicates that these data can be combined for the purpose of understanding the range of loading conditions for which new protective equipment, such as position-specific helmets, should be designed. This observation underscores the potential for knowledge transfer regarding biomechanical factors affecting head loading across professional and D-I college football, highlighting crucial implications for innovation in protective equipment.

Brain injury metrics and their risk functions in frontal automotive collisions

OBJECTIVES

The objective of this study was to develop abbreviated injury scale (AIS) 1, AIS2, AIS3 and AIS4+ injury risk functions (IRFs) for traumatic brain injuries (TBIs) as estimated by the rotational kinematics of the head, in accordance with AIS1998. The effectiveness of the IRFs was investigated by comparisons with real-world accident data of frontal crash configurations. In addition, links of the IRFs developed in accordance with AIS1998 to other AIS versions were discussed.

METHODS

AIS1, AIS2, AIS3 and AIS4+ IRFs based on finite element analysis (FEA)-based metrics in this study were developed using a TBI database used for developing mild TBI (concussion) and severe TBI (diffuse axonal injury (DAI) and intracerebral hemorrhage (ICH)) IRFs in our previous study. The TBI database includes head kinematics, clinical outcomes, and FEA-based metrics such as maximum principal strain (MPS) obtained from reconstructions using harmonized species-specific finite element (FE) brain models. In this study, TBI severities in the TBI database were reclassified in accordance with AIS1998 to evaluate IRFs in comparison with field accident data for application to automotive safety. IRFs based on kinematics-based metrics were developed by transforming FEA-based IRFs via linear regression models between the FEA-based and kinematics-based metrics. The FEA-based and kinematics-based IRFs were evaluated by comparing TBI risk predictions using frontal crash test data with real-world TBI rates in similar crash configurations.

RESULTS

The MPS95 IRFs exhibited better quality (lower quality index (QI) values) and better goodness of fit with the TBI database (lower AIC value) among the FEA-based IRFs. Kinematics-based metrics exhibited the greatest coefficients of determination (R2) with MPS95. The accident data evaluation demonstrated that the MPS95 IRFs and kinematics-based IRFs derived from the MPS95 IRFs generally overpredicted most frontal crash configurations, with the full engagement conditions tending to have smaller errors and the oblique crash conditions having the largest overprediction.

CONCLUSIONS

The TBI risks predicted by the MPS95 IRFs and kinematics-based IRFs derived from the MPS95 IRFs were relatively more aligned with the real-world TBI rates for drivers in the full engagement crash configuration. However, further investigations are needed to minimize the gap between predicted TBI risks and real-world TIB rates. In addition, AIS coding of TBIs has changed through version upgrades, especially for concussion. This change in AIS coding has affected IRFs for AIS1 and AIS2. Further revisions of TBI IRFs will be required in the future if the AIS definitions change.

Peaks and Distributions of White Matter Tract-related Strains in Bicycle Helmeted Impacts: Implication for Helmet Ranking and Optimization

Traumatic brain injury (TBI) in cyclists is a growing public health problem, with helmets being the major protection gear. Finite element head models have been increasingly used to engineer safer helmets often by mitigating brain strain peaks. However, how different helmets alter the spatial distribution of brain strain remains largely unknown. Besides, existing research primarily used maximum principal strain (MPS) as the injury parameter, while white matter fiber tract-related strains, increasingly recognized as effective predictors for TBI, have rarely been used for helmet evaluation. To address these research gaps, we used an anatomically detailed head model with embedded fiber tracts to simulate fifty-one helmeted impacts, encompassing seventeen bicycle helmets under three impact locations. We assessed the helmet performance based on four tract-related strains characterizing the normal and shear strain oriented along and perpendicular to the fiber tract, as well as the prevalently used MPS. Our results showed that both the helmet model and impact location affected the strain peaks. Interestingly, we noted that different helmets did not alter strain distribution, except for one helmet under one specific impact location. Moreover, our analyses revealed that helmet ranking outcome based on strain peaks was affected by the choice of injury metrics (Kendall's Tau coefficient: 0.58-0.93). Significant correlations were noted between tract-related strains and angular motion-based injury metrics. This study provided new insights into computational brain biomechanics and highlighted the helmet ranking outcome was dependent on the choice of injury metrics. Our results also hinted that the performance of helmets could be augmented by mitigating the strain peak and optimizing the strain distribution with accounting the selective vulnerability of brain subregions and more research was needed to develop region-specific injury criteria.

Assessing bicycle helmet protection for head and neck in E-scooter falls

OBJECTIVES

The rapid increase in E-scooter usage has led to more scooter-related head and neck injuries. Yet, experimental data on head impacts and helmet effectiveness during crashes are scarce. The objectives of this study are to experimentally evaluate bicycle helmets in E-scooter falls, assessing head kinematics, impact conditions, and injury risks in two crash scenarios with and without helmets.

METHODS

Six E-scooter forward falls, induced by a curb collision at 20 km/h, were simulated in sled tests using a Hybrid III 50th anthropomorphic test device with and without a helmet. The curb was positioned either perpendicularly or at a 55° angle to the E-scooter's trajectory. Head velocity, head acceleration, neck load, chest acceleration, and chest deflection were measured.

RESULTS

The average normal and tangential head velocities at impact were 5.9 m/s and 3.7 m/s, respectively. In configurations without helmet, both head accelerations and neck loads exceeded some injury thresholds, indicating a risk of severe injury. Using a helmet significantly reduced peak head linear (143 g vs. 571 g) and rotational (9.8 krad/s2 vs. 23.1 krad/s2) accelerations, and Head Injury Criterion (HIC) (792 vs. 5868). However, it did not significantly affect peak head rotational velocity (44.5 rad/s vs. 41.5 rad/s), neck load (in flexion-compression) nor Neck Injury Criterion (NIJ) (1.2 vs. 1.0).

CONCLUSION

The bicycle helmet significantly reduced most head injury metrics. Yet, the risk of severe head and neck injuries remains high. These results offer valuable data for evaluating head protection and developing and validating numerical crash test reconstructions for further investigations.

Concussion biomechanics, head acceleration exposure and brain injury criteria in sport: a review

There are mounting concerns surrounding the risk of neurodegenerative diseases and complications associated with concussion incidence and repetitive head acceleration events (HAE) in sport. The aim of this review is to provide an overview of concussion biomechanics, head acceleration exposure and brain injury criteria in sport. Rotational head motion appears to be the primary contributor to brain injury risk due to the unique mechanical properties of the brain and its location within the body. There is a growing evidence base of different biomechanical brain injury mechanisms, including those involving repetitive HAE. Historically, many studies on concussion biomechanics, head acceleration exposure and brain injury criteria in sport have been limited by validity of the biomechanical approaches undertaken. Biomechanical approaches such as instrumented mouthguards and subject-specific finite element (FE) brain models provide a unique opportunity to develop greater brain injury criteria and aid in on-field athlete removal. Implementing these approaches on a large-scale can gain insight into potential risk factors within sports and certain athletes/cohorts who sustain a greater number and/or severity of HAE throughout their playing career. These findings could play a key role in the development of concussion prevention strategies and techniques that mitigate the severity of HAE in sport.

Assessing head injury risks in electric scooter accidents: A multi-body simulation study with insights into sex differences

E-scooters have become increasingly popular for short-distance travel in urban areas, but this rise in usage also brings about an increased risk of accidents. Studies have shown that approximately 40% of electric scooter accident victims admitted to hospitals suffer head injuries. Therefore, it is crucial to implement safety measures and improve safety systems and equipment to mitigate these risks. One approach to gaining insights into the injuries users face is through simulations using the multi-body method. This method allows for the reconstruction of accidents by modeling and analyzing the dynamic behavior of interconnected bodies. This study aims to assess the impacts on the user's head and the injuries they may sustain in electric scooter accidents using numerical methods. Initially, a reference scenario was established based on a YouTube video, with the assumption that the user was an average-height man. Simulations were conducted for various percentiles, including both males and females. Different velocities were simulated to determine the threshold velocity at which survival becomes practically impossible. Two scenarios were considered: one where the car braked for 0.333 s and another where the distance between the start the braking task and the collision was kept constant. The location of the first head impact on the vehicle was also examined. Injury assessment was conducted using two criteria: Head Injury Criterion (HIC) and Brain Injury Criterion (BrIC). The study found that smaller individuals are more vulnerable to severe injuries, and higher car velocities correlate with more severe user injuries. Furthermore, the location of the first impact varies between genders, with women more likely to experience impacts in the lower part of the windshield, while men tend to experience impacts in the central zone. This study highlights the importance of considering user characteristics and accident dynamics in assessing injury risks associated with e-scooters.

Biofidelity and Limitations of Instrumented Mouthguard Systems for Assessment of Rigid Body Head Kinematics

Instrumented mouthguard systems (iMGs) are commonly used to study rigid body head kinematics across a variety of athletic environments. Previous work has found good fidelity for iMGs rigidly fixed to anthropomorphic test device (ATD) headforms when compared to reference systems, but few validation studies have focused on iMG performance in human cadaver heads. Here, we examine the performance of two boil-and-bite style iMGs in helmeted cadaver heads. Three unembalmed human cadaver heads were fitted with two instrumented boil-and-bite mouthguards [Prevent Biometrics and Diversified Technical Systems (DTS)] per manufacturer instructions. Reference sensors were rigidly fixed to each specimen. Specimens were fitted with a Riddell SpeedFlex American football helmet and impacted with a rigid impactor at three velocities and locations. All impact kinematics were compared at the head center of gravity. The Prevent iMG performed comparably to the reference system up to ~ 60 g in linear acceleration, but overall had poor correlation (CCC = 0.39). Prevent iMG angular velocity and BrIC generally well correlated with the reference, while underestimating HIC and overestimating HIC duration. The DTS iMG consistently overestimated the reference across all measures, with linear acceleration error ranging from 10 to 66%, and angular acceleration errors greater than 300%. Neither iMG demonstrated consistent agreement with the reference system. While iMG validation efforts have utilized ATD testing, this study highlights the need for cadaver testing and validation of devices intended for use in-vivo, particularly when considering realistic (non-idealized) sensor-skull coupling, when accounting for interactions with the mandible and when subject-specific anatomy may affect device performance.

Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings

Protective headgear effects measured in the laboratory may not always translate to the field. In this study, we evaluated the impact attenuation capabilities of a commercially available padded helmet shell cover in the laboratory and on the field. In the laboratory, we evaluated the padded helmet shell cover's efficacy in attenuating impact magnitude across six impact locations and three impact velocities when equipped to three different helmet models. In a preliminary on-field investigation, we used instrumented mouthguards to monitor head impact magnitude in collegiate linebackers during practice sessions while not wearing the padded helmet shell covers (i.e., bare helmets) for one season and whilst wearing the padded helmet shell covers for another season. The addition of the padded helmet shell cover was effective in attenuating the magnitude of angular head accelerations and two brain injury risk metrics (DAMAGE, HARM) across most laboratory impact conditions, but did not significantly attenuate linear head accelerations for all helmets. Overall, HARM values were reduced in laboratory impact tests by an average of 25% at 3.5 m/s (range: 9.7 to 39.6%), 18% at 5.5 m/s (range: - 5.5 to 40.5%), and 10% at 7.4 m/s (range: - 6.0 to 31.0%). However, on the field, no significant differences in any measure of head impact magnitude were observed between the bare helmet impacts and padded helmet impacts. Further laboratory tests were conducted to evaluate the ability of the padded helmet shell cover to maintain its performance after exposure to repeated, successive impacts and across a range of temperatures. This research provides a detailed assessment of padded helmet shell covers and supports the continuation of in vivo helmet research to validate laboratory testing results.

Instrumented mouthguards in elite-level men's and women's rugby union: characterising tackle-based head acceleration events

Objectives

To examine the propensity of tackle height and the number of tacklers that result in head acceleration events (HAEs) in elite-level male and female rugby tackles.

Methods

Instrumented mouthguard data were collected from women (n=67) and men (n=72) elite-level rugby players from five elite and three international teams. Peak linear acceleration and peak angular acceleration were extracted from HAEs. Propensities for HAEs at a range of thresholds were calculated as the proportion of tackles/carries that resulted in an HAE exceeding a given magnitude for coded tackle height (low, medium, high) and number of tacklers. Propensity ratios with 95% CIs were calculated for tackle heights and number of tacklers.

Results

High tackles had a 32.7 (95% CI=6.89 to 155.02) and 41.2 (95% CI=9.22 to 184.58) propensity ratio to cause ball carrier HAEs>30 g compared with medium tackles for men and women, respectively. Low tackles had a 2.6 (95% CI=1.91 to 3.42) and 5.3 (95% CI=3.28 to 8.53) propensity ratio to cause tackler HAEs>30 g compared with medium tackles for men and women, respectively. In men, multiple tacklers had a higher propensity ratio (6.1; 95% CI=3.71 to 9.93) than singular tacklers to cause ball carrier HAEs>30 g but a lower propensity ratio (0.4; 95% CI=0.29 to 0.56) to cause tackler HAEs>30 g. No significant differences were observed in female tacklers or carriers for singular or multiple tacklers.

Conclusion

To limit HAE exposure, rule changes and coaching interventions that promote tacklers aiming for the torso (medium tackle) could be explored, along with changes to multiple tackler events in the male game.

The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model

In contact sports such as rugby, players are at risk of sustaining traumatic brain injuries (TBI) due to high-intensity head impacts that generate high linear and rotational accelerations of the head. Previous studies have established a clear link between high-intensity head impacts and brain strains that result in concussions. This study presents a novel approach to investigating the effect of a range of laboratory controlled drop test parameters on regional peak and mean maximum principal strain (MPS) predictions within the brain using a trained convolutional neural network (CNN). The CNN is publicly available at https://github.com/Jilab-biomechanics/CNN-brain-strains . The results of this study corroborate previous findings that impacts to the side of the head result in significantly higher regional MPS than forehead impacts. Forehead impacts tend to result in the lowest region-averaged MPS values for impacts where the surface angle was at 0° and 45°, while side impacts tend to result in higher regional peak and mean MPS. The absence of a neck in drop tests resulted in lower regional peak and mean MPS values. The results indicated that the relationship between drop test parameters and resulting regional peak and mean MPS predictions is complex. The study's findings offer valuable insights into how deep learning models can be used to provide more detailed insights into how drop test conditions impact regional MPS. The novel approach used in this paper to predict brain strains can be applied in the development of better methods to reduce the brain strain resulting from head accelerations such as protective sports headgear.

When to Pull the Trigger: Conceptual Considerations for Approximating Head Acceleration Events Using Instrumented Mouthguards

Head acceleration events (HAEs) are acceleration responses of the head following external short-duration collisions. The potential risk of brain injury from a single high-magnitude HAE or repeated occurrences makes them a significant concern in sport. Instrumented mouthguards (iMGs) can approximate HAEs. The distinction between sensor acceleration events, the iMG datum for approximating HAEs and HAEs themselves, which have been defined as the in vivo event, is made to highlight limitations of approximating HAEs using iMGs. This article explores the technical limitations of iMGs that constrain the approximation of HAEs and discusses important conceptual considerations for stakeholders interpreting iMG data. The approximation of HAEs by sensor acceleration events is constrained by false positives and false negatives. False positives occur when a sensor acceleration event is recorded despite no (in vivo) HAE occurring, while false negatives occur when a sensor acceleration event is not recorded after an (in vivo) HAE has occurred. Various mechanisms contribute to false positives and false negatives. Video verification and post-processing algorithms offer effective means for eradicating most false positives, but mitigation for false negatives is less comprehensive. Consequently, current iMG research is likely to underestimate HAE exposures, especially at lower magnitudes. Future research should aim to mitigate false negatives, while current iMG datasets should be interpreted with consideration for false negatives when inferring athlete HAE exposure.

Evaluation of DAMAGE Algorithm in Frontal Crashes

With the current trend of including the evaluation of the risk of brain injuries in vehicle crashes due to rotational kinematics of the head, two injury criteria have been introduced since 2013 - BrIC and DAMAGE. BrIC was developed by NHTSA in 2013 and was suggested for inclusion in the US NCAP for frontal and side crashes. DAMAGE has been developed by UVa under the sponsorship of JAMA and JARI and has been accepted tentatively by the EuroNCAP. Although BrIC in US crash testing is known and reported, DAMAGE in tests of the US fleet is relatively unknown. The current paper will report on DAMAGE in NCAP-like tests and potential future frontal crash tests involving substantial rotation about the three axes of occupant heads. Distribution of DAMAGE of three-point belted occupants without airbags will also be discussed. Prediction of brain injury risks from the tests have been compared to the risks in the real world. Although DAMAGE correlates well with MPS in the human brain model across several test scenarios, the predicted risk of AIS2+ brain injuries are too high compared to real-world experience. The prediction of AIS4+ brain injury risk in lower velocity crashes is good, but too high in NCAP-like and high speed angular frontal crashes.

Instrumented Mouthguards in Elite-Level Men's and Women's Rugby Union: The Incidence and Propensity of Head Acceleration Events in Matches

OBJECTIVES

The aim of this study was to examine head acceleration event (HAE) propensity and incidence during elite-level men's and women's rugby union matches.

METHODS

Instrumented mouthguards (iMGs) were fitted in 92 male and 72 female players from nine elite-level clubs and three international teams. Data were collected during 406 player matches (239 male, 167 female) using iMGs and video analysis. Incidence was calculated as the number of HAEs per player hour and propensity as the proportion of contact events resulting in an HAE at a range of linear and angular thresholds.

RESULTS

HAE incidence above 10 g was 22.7 and 13.2 per hour in men's forwards and backs and 11.8 and 7.2 per hour in women's forwards and backs, respectively. Propensity varied by contact event, with 35.6% and 35.4% of men's tackles and carries and 23.1% and 19.6% of women's tackles and carries producing HAEs above 1.0 krad/s2. Tackles produced significantly more HAEs than carries, and incidence was greater in forwards compared with backs for both sexes and in men compared with women. Women's forwards were 1.6 times more likely to experience a medium-magnitude HAE from a carry than women's backs. Propensity was similar from tackles and carries, and between positional groups, while significantly higher in men than women. The initial collision stage of the tackle had a higher propensity than other stages.

CONCLUSION

This study quantifies HAE exposures in elite rugby union players using iMGs. Most contact events in rugby union resulted in lower-magnitude HAEs, while higher-magnitude HAEs were comparatively rare. An HAE above 40 g occurred once every 60-100 min in men and 200-300 min in women. Future research on mechanisms for HAEs may inform strategies aimed at reducing HAEs.

Characteristics of head frequency response in blunt impacts: a biomechanical modeling study

Existing evaluation criteria for head impact injuries are typically based on time-domain features, and less attention has been paid to head frequency responses for head impact injury assessment. The purpose of the current study is, therefore, to understand the characteristics of human body head frequency response in blunt impacts via finite element (FE) modeling and the wavelet packet analysis method. FE simulation results show that head frequency response in blunt impacts could be affected by the impact boundary condition. The head energy peak and its frequency increase with the increase in impact; a stiffer impact block is associated with a higher head energy peak, and a bigger impact block could result in a high proportion of the energy peak. Regression analysis indicates that only the head energy peak has a high correlation with exiting head injury criteria, which implies that the amplitude-frequency aggregation characteristic but not the frequency itself of the head acceleration response has predictability for head impact injury in blunt impacts. The findings of the current study may provide additional criteria for head impact injury evaluation and new ideas for head impact injury protection.

Assessment of brain response in operators subject to recoil force from firing long-range rifles

Mild traumatic brain injury (mTBI) may be caused by occupational hazards military personnel encounter, such as falls, shocks, exposure to blast overpressure events, and recoil from weapon firing. While it is important to protect against injurious head impacts, the repeated exposure of Canadian Armed Forces (CAF) service members to sub-concussive events during the course of their service may lead to a significant reduction in quality of life. Symptoms may include headaches, difficulty concentrating, and noise sensitivity, impacting how personnel complete their duties and causing chronic health issues. This study investigates how the exposure to the recoil force of long-range rifles results in head motion and brain deformation. Direct measurements of head kinematics of a controlled population of military personnel during firing events were obtained using instrumented mouthguards. The experimentally measured head kinematics were then used as inputs to a finite element (FE) head model to quantify the brain strains observed during each firing event. The efficacy of a concept recoil mitigation system (RMS), designed to mitigate loads applied to the operators was quantified, and the RMS resulted in lower loading to the operators. The outcomes of this study provide valuable insights into the magnitudes of head kinematics observed when firing long-range rifles, and a methodology to quantify effects, which in turn will help craft exposure guidelines, guide training to mitigate the risk of injury, and improve the quality of lives of current and future CAF service members and veterans.

A technique for in situ intracranial strain measurement within a helmeted deformable headform

Despite the broad use of helmets, incidence of concussion remains high. Current methods for helmet evaluation focus on the measurement of head kinematics as the primary tool for quantifying risk of brain injury. Though the primary cause of mild Traumatic Brain Injury (mTBI) is thought to be intracranial strain, helmet testing methodologies are not able to directly resolve these parameters. Computational injury models and impact severity measures are currently used to approximate intracranial strains from head kinematics and predict injury outcomes. Advancing new methodologies that enable experimental intracranial strain measurements in a physical model would be useful in the evaluation of helmet performance. This study presents a proof-of-concept head surrogate and novel helmet evaluation platform that allows for the measurement of intracranial strain using high-speed X-ray digital image correlation (XDIC). In the present work, the head surrogate was subjected to a series of bare and helmeted impacts using a pneumatically-driven linear impactor. Impacts were captured at 5,000 fps using a high-speed X-ray cineradiography system, and strain fields were computed using digital image correlation. This test platform, once validated, will open the door to using brain tissue-level measurements to evaluate helmet performance, providing a tool that can be translated to represent mTBI injury mechanisms, benefiting the helmet design processes.

Holistic pedestrian safety assessment for average males and females

Objective

An integrated assessment framework that enables holistic safety evaluations addressing vulnerable road users (VRU) is introduced and applied in the current study. The developed method enables consideration of both active and passive safety measures and distributions of real-world crash scenario parameters.

Methods

The likelihood of a specific virtual testing scenario occurring in real life has been derived from accident databases scaled to European level. Based on pre-crash simulations, it is determined how likely it is that scenarios could be avoided by a specific Autonomous Emergency Braking (AEB) system. For the unavoidable cases, probabilities for specific collision scenarios are determined, and the injury risk for these is determined, subsequently, from in-crash simulations with the VIVA+ Human Body Models combined with the created metamodel for an average male and female model. The integrated assessment framework was applied for the holistic assessment of car-related pedestrian protection using a generic car model to assess the safety benefits of a generic AEB system combined with current passive safety structures.

Results

In total, 61,914 virtual testing scenarios have been derived from the different car-pedestrian cases based on real-world crash scenario parameters. Considering the occurrence probability of the virtual testing scenarios, by implementing an AEB, a total crash risk reduction of 81.70% was achieved based on pre-crash simulations. It was shown that 50 in-crash simulations per load case are sufficient to create a metamodel for injury prediction. For the in-crash simulations with the generic vehicle, it was also shown that the injury risk can be reduced by implementing an AEB, as compared to the baseline scenarios. Moreover, as seen in the unavoidable cases, the injury risk for the average male and female is the same for brain injuries and femoral shaft fractures. The average male has a higher risk of skull fractures and fractures of more than three ribs compared to the average female. The average female has a higher risk of proximal femoral fractures than the average male.

Conclusions

A novel methodology was developed which allows for movement away from the exclusive use of standard-load case assessments, thus helping to bridge the gap between active and passive safety evaluations.

Finite element evaluation of an American football helmet featuring liquid shock absorbers for protecting against concussive and subconcussive head impacts

Introduction: Concern has grown over the potential long-term effects of repeated head impacts and concussions in American football. Recent advances in impact engineering have yielded the development of soft, collapsible, liquid shock absorbers, which have demonstrated the ability to dramatically attenuate impact forces relative to existing helmet shock absorbers. Methods: To further explore how liquid shock absorbers can improve the efficacy of an American football helmet, we developed and optimized a finite element (FE) helmet model including 21 liquid shock absorbers spread out throughout the helmet. Using FE models of an anthropomorphic test headform and linear impactor, a previously published impact test protocol representative of concussive National Football League impacts (six impact locations, three velocities) was performed on the liquid FE helmet model and four existing FE helmet models. We also evaluated the helmets at three lower impact velocities representative of subconcussive football impacts. Head kinematics were recorded for each impact and used to compute the Head Acceleration Response Metric (HARM), a metric factoring in both linear and angular head kinematics and used to evaluate helmet performance. The head kinematics were also input to a FE model of the head and brain to calculate the resulting brain strain from each impact. Results: The liquid helmet model yielded the lowest value of HARM at 33 of the 36 impact conditions, offering an average 33.0% (range: -37.5% to 56.0%) and 32.0% (range: -2.2% to 50.5%) reduction over the existing helmet models at each impact condition in the subconcussive and concussive tests, respectively. The liquid helmet had a Helmet Performance Score (calculated using a summation of HARM values weighted based on injury incidence data) of 0.71, compared to scores ranging from 1.07 - 1.21 from the other four FE helmet models. Resulting brain strains were also lower in the liquid helmet. Discussion: The results of this study demonstrate the promising ability of liquid shock absorbers to improve helmet safety performance and encourage the development of physical prototypes of helmets featuring this technology. The implications of the observed reductions on brain injury risk are discussed.

Modeling of active skeletal muscles: a 3D continuum approach incorporating multiple muscle interactions

Skeletal muscles have a highly organized hierarchical structure, whose main function is to generate forces for movement and stability. To understand the complex heterogeneous behaviors of muscles, computational modeling has advanced as a non-invasive approach to evaluate relevant mechanical quantities. Aiming to improve musculoskeletal predictions, this paper presents a framework for modeling 3D deformable muscles that includes continuum constitutive representation, parametric determination, model validation, fiber distribution estimation, and integration of multiple muscles into a system level for joint motion simulation. The passive and active muscle properties were modeled based on the strain energy approach with Hill-type hyperelastic constitutive laws. A parametric study was conducted to validate the model using experimental datasets of passive and active rabbit leg muscles. The active muscle model with calibrated material parameters was then implemented to simulate knee bending during a squat with multiple quadriceps muscles. A computational fluid dynamics (CFD) fiber simulation approach was utilized to estimate the fiber arrangements for each muscle, and a cohesive contact approach was applied to simulate the interactions among muscles. The single muscle simulation results showed that both passive and active muscle elongation responses matched the range of the testing data. The dynamic simulation of knee flexion and extension showed the predictive capability of the model for estimating the active quadriceps responses, which indicates that the presented modeling pipeline is effective and stable for simulating multiple muscle configurations. This work provided an effective framework of a 3D continuum muscle model for complex muscle behavior simulation, which will facilitate additional computational and experimental studies of skeletal muscle mechanics. This study will offer valuable insight into the future development of multiscale neuromuscular models and applications of these models to a wide variety of relevant areas such as biomechanics and clinical research.

Head injury criteria assessment using head kinematics from crash tests and accident reconstructions

OBJECTIVE

The aim of this study was to assess head injury criteria based on their correlation to brain strain in a Finite Element (FE) head model (the KTH Royal Institute of Technology model), by simulation of head kinematics data from frontal and side crash tests with Anthropomorphic Test Devices (ATDs), and from Human Body Model (HBM) accident reconstructions.

METHODS

Six Degrees of Freedom (DoF) head kinematic data was extracted from 221 crash tests, consisting of frontal impacts with the THOR-50M ATD, near-side and far-side impacts with the WorldSID-50M ATD, and from 19 FE HBM accident reconstructions. The head injury criteria HIC₁₅, HIP, BrIC, UBrIC, DAMAGE and CIBIC were calculated, and FE head model simulations were conducted using the six DoF kinematics data. The 100th, 99th, and 95th percentile Maximum Principal Strains (MPS) of the brain were extracted and linear regression models with respect to the injury criteria were created. The injury criteria were then evaluated based on the coefficient of determination, R², and the Normalized Root Mean Square Error (NRMSE) of each regression model.

RESULTS

For all the data sets combined and for the WorldSID far-side data, CIBIC had the best goodness of fit, with R² of 0.76 and 0.85. For frontal impacts with THOR and the combined ATD data set, DAMAGE had highest R², 0.83 and 0.78, respectively. Injury criteria including translational accelerations were ranked lower, and BrIC were among the three lowest ranked for most data sets evaluated. UBrIC generally ranked after DAMAGE and CIBIC with respect to the goodness of fit but had the lowest NRMSE for all data sets.

CONCLUSIONS

The two mass-spring-damper brain surrogate model criteria, DAMAGE and CIBIC, were best in capturing the head model MPS response for both the THOR and WorldSID data sets. BrIC had lower correlation to the head model MPS and performed marginally better than the linear acceleration only criteria for all the data sets combined. This study supports the suitability of DAMAGE and CIBIC as brain injury criteria to be used with THOR-50M and WorldSID-50M in vehicle crash test conditions, as they outperform BrIC.

Analyzing pericytes under mild traumatic brain injury using 3D cultures and dielectric elastomer actuators

Traumatic brain injury (TBI) is defined as brain damage due to an external force that negatively impacts brain function. Up to 90% of all TBI are considered in the mild severity range (mTBI) but there is still no therapeutic solution available. Therefore, further understanding of the mTBI pathology is required. To assist with this understanding, we developed a cell injury device (CID) based on a dielectric elastomer actuator (DEA), which is capable of modeling mTBI via injuring cultured cells with mechanical stretching. Our injury model is the first to use patient-derived brain pericyte cells, which are ubiquitous cells in the brain involved in injury response. Pericytes were cultured in our CIDs and mechanically strained up to 40%, and by at least 20%, prior to gene expression analysis. Our injury model is a platform capable of culturing and stretching primary human brain pericytes. The heterogeneous response in gene expression changes in our result may suggest that the genes implicated in pathological changes after mTBI could be a patient-dependent response, but requires further validation. The results of this study demonstrate that our CID is a suitable tool for simulating mTBI as an in vitro stretch injury model, that is sensitive enough to induce responses from primary human brain pericytes due to mechanical impacts.

A Review of Head Injury Metrics Used in Automotive Safety and Sports Protective Equipment

Despite advances in the understanding of human tolerances to brain injury, injury metrics used in automotive safety and protective equipment standards have changed little since they were first implemented nearly a half-century ago. Although numerous metrics have been proposed as improvements over the ones currently used, evaluating the predictive capability of these metrics is challenging. The purpose of this review is to summarize existing head injury metrics that have been proposed for both severe head injuries, such as skull fractures and traumatic brain injuries (TBI), and mild traumatic brain injuries (mTBI) including concussions. Metrics have been developed based on head kinematics or intracranial parameters such as brain tissue stress and strain. Kinematic metrics are either based on translational motion, rotational motion, or a combination of the two. Tissue-based metrics are based on finite element model simulations or in vitro experiments. This review concludes with a discussion of the limitations of current metrics and how improvements can be made in the future.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

SIGNIFICANCE

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

Neurotrauma Prevention Review: Improving Helmet Design and Implementation

Neurotrauma continues to contribute to significant mortality and disability. The need for better protective equipment is apparent. This review focuses on improved helmet design and the necessity for continued research. We start by highlighting current innovations in helmet design for sport and subsequent utilization in the lay community for construction. The current standards by sport and organization are summarized. We then address current standards within the military environment. The pathophysiology is discussed with emphasis on how helmets provide protection. As innovative designs emerge, protection against secondary injury becomes apparent. Much research is needed, but this focused paper is intended to serve as a catalyst for improvement in helmet design and implementation to provide more efficient and reliable neuroprotection across broad arenas.

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.

American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism

Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future. The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains .

Impact and workload are dominating on-field data monitoring techniques to track health and well-being of team-sports athletes

Participation in sports has become an essential part of healthy living in today's world. However, injuries can often occur during sports participation. With advancements in sensor technology and data analytics, many sports have turned to technology-aided, data-driven, on-field monitoring techniques to help prevent injuries and plan better player management. This review searched three databases, Web of Science, IEEE, and PubMed, for peer-reviewed articles on on-field data monitoring techniques that are aimed at improving the health and well-being of team-sports athletes. It was found that most on-field data monitoring methods can be categorized as either player workload tracking or physical impact monitoring. Many studies covered during this review attempted to establish correlations between captured physical and physiological data, as well as injury risk. In these studies, workloads are frequently tracked to optimize training and prevent overtraining in addition to overuse injuries, while impacts are most often tracked to detect and investigate traumatic injuries. This review found that current sports monitoring practices often suffer from a lack of standard metrics and definitions. Furthermore, existing data-analysis models are created on data that are limited in both size and diversity. These issues need to be addressed to create ecologically valid approaches in the future.

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.

Influence of Friction at the Head-Helmet Interface on Advanced Combat Helmet (ACH) Blunt Impact Kinematic Performance

INTRODUCTION

The purpose of this study was to compare the rotational blunt impact performance of an anthropomorphic test device (ATD: male 50% Hybrid III head and neck) headform donning an Advanced Combat Helmet (ACH) between conditions in which the coefficient of static friction (μs) at the head-to-helmet pad interface varied.

MATERIALS AND METHODS

Two ACHs (size large) were used in this study and friction was varied using polytetrafluoroethylene (PTFE), human hair, skullcap, and the native vinyl skin of the ATD. A condition in which hook and loop material adhered the headform to the liner system was also tested, resulting in a total of five conditions: PTFE, Human Hair, Skullcap, Vinyl, and Hook. Blunt impact tests with each helmet in each of the five conditions were conducted on a pneumatic linear impactor at 4.3 m/s. The ATD donning the ACH was impacted in seven locations (Crown, Front, Rear, Left Side, Right Side, Left Nape, and Right Nape). The peak resultant angular acceleration (PAA), velocity (PAV), and the Diffuse Axonal Multi-Axis, General Evaluation (DAMAGE) metric were compared between conditions.

RESULTS

No pairwise differences were observed between conditions for PAA. A positive correlation was observed between mean μs and PAA at the Front (τ = 0.28; P = .044) and Rear (τ = 0.31; P = .024) impact locations. The Hook condition had a mean PAV value that was often less than the other conditions (P ≤ .024). A positive correlation was observed between mean μs and PAV at the Front (τ = 0.32; P = .019) and Right Side (τ = 0.57; P < .001) locations. The Hook condition tended to have the lowest DAMAGE value compared to the other conditions (P ≤ .032). A positive correlation was observed between the mean μs and DAMAGE at the Rear (τ = 0.60; P < .001) location. A negative correlation was observed at the Left Side (τ = -0.28; P = .040), Right Side (τ = -0.58; P < .001) and Left Nape (τ = -0.56; P < .001) locations.

CONCLUSIONS

The results of this study indicate that at some impact locations kinematic responses can vary as a function of the friction at the head-to-helmet pad interface. However, a reduction in the coupling of the head-helmet pad interface did not consistently reduce head angular kinematics or measures of brain strain across impact locations. Thus, for the ACH during collision-type impacts, impact location as opposed to μs seems to have a greater influence on head kinematics and rotational-based measures of brain strain.

Assessment of Nanobag as a New Safety System in the Frontal Sled Test

Objective: The future mobility challenges lead to considering new safety systems to protect vehicle passengers in non-standard and complex seating configurations. The objective of this study is to assess the performance of a brand new safety system called nanobag and to compare it to traditional airbag performance in the frontal sled test scenario. Methods: The nanobag technology is assessed in the frontal crash test scenario and compared with the standard airbag by numerical simulation. The previously identified material model is used to assemble the nanobag numerical model. The paper exploits an existing validated human body model to assess the performance of the nanobag safety system. Using both the new nanobag and the standard airbag, the sled test numerical simulations with the variation of human bodies were performed in 30 km/h and 50 km/h frontal impacts. Results: The sled test results for both the nanobag and the standard airbag based on injury criteria show a good and acceptable performance of the nanobag safety system compared to the traditional airbag. Conclusions: The results show that the nanobag system’s performance is comparable to the standard airbag’s, which means that, thanks to the design, the nanobag safety system has high potential and an extended application for multi-directional protection against impact.

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.

Instantaneous Brain Strain Estimation for Automotive Head Impacts via Deep Learning

Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (v_rot) and the corresponding rotational acceleration (a_rot) profiles were concatenated as static images to serve as CNN input. Three training strategies were evaluated: 1) "baseline", using random initial weights; 2) "transfer learning", using weight transfer from a previous CNN model trained on head impacts drawn from contact sports; and 3) "combined training", combining previous training data from contact sports (N=5661) for training. The combined training achieved the best performances. For peak MPS, the CNN achieved a coefficient of determination (R²) of 0.932 and root mean squared error (RMSE) of 0.031 for the real-world testing dataset. It also achieved a success rate of 60.5% and 94.8% for elementwise MPS, where the linear regression slope, k, and correlation coefficient, r, between estimated and simulated MPS did not deviate from 1.0 (when identical) by more than 0.1 and 0.2, respectively. Cumulative strain damage measure (CSDM) from the CNN estimation was also highly accurate compared to those from direct simulation across a range of thresholds (R² of 0.899-0.943 with RMSE of 0.054-0.069). Finally, the CNN achieved an average k and r of 0.98±0.12 and 0.90±0.07, respectively, for six reconstructed car crash impacts drawn from two other sources independent of the training dataset. Importantly, the CNN is able to efficiently estimate elementwise MPS with sufficient accuracy while conventional kinematic injury metrics cannot. Therefore, the CNN has the potential to supersede current kinematic injury metrics that can only approximate a global peak MPS or CSDM. The CNN technique developed here may offer enhanced utility in the design and development of head protective countermeasures, including in the automotive industry. This is the first study aimed at instantly estimating spatially detailed brain strains for automotive head impacts, which employs >8.8 thousand impact simulations generated from ~1.5 years of nonstop computations on a high-performance computing platform.

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

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

Head Kinematics and Injury Metrics for Laboratory Hockey-Relevant Head Impact Experiments

Investigating head responses during hockey-related blunt impacts and hence understanding how to mitigate brain injury risk from such impacts still needs more exploration. This study used the recently developed hockey helmet testing methodology, known as the Hockey Summation of Tests for the Analysis of Risk (Hockey STAR), to collect 672 laboratory helmeted impacts. Brain strains were then calculated from the according 672 simulations using the detailed Global Human Body Models Consortium (GHBMC) finite element head model. Experimentally measured head kinematics and brain strains were used to calculate head/brain injury metrics including peak linear acceleration, peak rotational acceleration, peak rotational velocity, Gadd Severity Index (GSI), Head Injury Criteria (HIC₁₅), Generalized Acceleration Model for Brain Injury Threshold (GAMBIT), Brain Injury Criteria (BrIC), Universal Brain Injury Criterion (UBrIC), Diffuse Axonal Multi-Axis General Equation (DAMAGE), average maximum principal strain (MPS) and cumulative strain damage measure (CSDM). Correlation analysis of kinematics-based and strain-based metrics highlighted the importance of rotational velocity. Injury metrics that use rotational velocity correlated highly to average MPS and CSDM with UBrIC yielding the strongest correlation. In summary, a comprehensive analysis for kinematics-based and strain-based injury metrics was conducted through a hybrid experimental (672 impacts) and computational (672 simulations) approach. The results can provide references for adopting brain injury metrics when using the Hockey STAR approach and guide ice hockey helmet designs that help reduce brain injury risks.

Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football

Wearable devices have been shown to effectively measure the head's movement during impacts in sports like American football. When a head impact occurs, the device is triggered to collect and save the kinematic measurements during a predefined time window. Then, based on the collected kinematics, finite element (FE) head models can calculate brain strain and strain rate, which are used to evaluate the risk of mild traumatic brain injury. To find a time window that can provide a sufficient duration of kinematics for FE analysis, we investigated 118 on-field video-confirmed football head impacts collected by the Stanford Instrumented Mouthguard. The simulation results based on the kinematics truncated to a shorter time window were compared with the original to determine the minimum time window needed for football. Because the individual differences in brain geometry influence these calculations, we included six representative brain geometries and found that larger brains need a longer time window of kinematics for accurate calculation. Among the different sizes of brains, a pre-trigger time of 40 ms and a post-trigger time of 70 ms were found to yield calculations of brain strain and strain rate that were not significantly different from calculations using the original 200 ms time window recorded by the mouthguard. Therefore, approximately 110 ms is recommended for complete modeling of impacts for football.

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.

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.

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.

Effective Head Impact Kinematics to Preserve Brain Strain

Conventional kinematics-based brain injury metrics often approximate peak maximum principal strain (MPS) of the whole brain but ignore the anatomical location of occurrence. In this study, we develop effective impact kinematics consisting of peak rotational velocity and the associated rotational axis to preserve not only peak MPS but also spatially detailed MPS. A pre-computed brain response atlas (pcBRA) serves as a common reference. A training dataset (N = 3069) is used to develop a convolutional neural network (CNN) to automate impact simplification. When preserving peak MPS alone, the CNN-estimated effective peak rotational velocity achieves a coefficient of determination ([Formula: see text]) of ~ 0.96 relative to the directly identified counterpart, far outperforming nominal peak velocity from the resultant profiles ([Formula: see text] of ~ 0.34). Impacts from a subset of data (N = 1900) are also successfully matched with pcBRA idealized impacts based on elementwise MPS, where their regression slope and Pearson correlation coefficient do not deviate from 1.0 (when identical) by more than 0.1. The CNN-estimated effective peak rotation velocity and rotational axis are sufficiently accurate for ~ 73.5% of the impacts. This is not possible for the nominal peak velocity or any other conventional injury metric. The performance may be further improved by expanding the pcBRA to include deceleration and focusing on region-wise strains. This study establishes a new avenue to reduce an arbitrary head impact into an idealized but actual "impact mode" characterized by triplets of basic kinematic variables. They retain specific physical interpretations of head impact and may be an advancement over state-of-the-art kinematics-based scalar metrics for more effective impact comparison in the future.

An Experimental Platform Generating Simulated Blunt Impacts to the Head Due to Rearward Falls

Impacts to the back of the head due to rearward falls, also referred to as "backfall" events, represent a common source of TBI for athletes and soldiers. A new experimental apparatus is described for replicating the linear and rotational kinematics of the head during backfall events. An anthropomorphic test device (ATD) with a head-borne sensor suite was configured to fall backwards from a standing height, inducing contact between the rear of the head and a ground surface simulant. A pivoting swing arm and release strap were used to generate consistent and realistic head kinematics. Backfall experiments were performed with the ATD fitted with an American football helmet and the resulting linear and rotational head kinematics, as well as calculated injury metrics, compared favorably with those of football players undergoing similar impacts during games or play reconstructions. This test method complements existing blunt impact helmet performance experiments, such as drop tower and pneumatic ram test methods, which may not be able to fully reproduce head-neck-torso kinematics during a backfall event.

The relationship between brain injury criteria and brain strain across different types of head impacts can be different

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

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.

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.

Instantaneous Whole-Brain Strain Estimation in Dynamic Head Impact

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

Rapid Estimation of Entire Brain Strain Using Deep Learning Models

OBJECTIVE

Many recent studies have suggested that brain deformation resulting from a head impact is linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even though several finite element (FE) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these FE head models is limited due to the time-consuming nature of FE simulations. This work aims to accelerate the process of brain deformation calculation and thus improve the potential for clinical applications.

METHODS

We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 2511 total head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts.

RESULTS

The proposed deep learning head model can calculate the maximum principal strain (Green Lagrange) for every element in the entire brain in less than 0.001s with an average root mean squared error of 0.022, and with a standard deviation of 0.001 over twenty repeats with random data partition and model initialization.

CONCLUSION

Trained and tested using the dataset of 2511 head impacts, this model can be applied to various sports in the calculation of brain strain with accuracy, and its applicability can even further be extended by incorporating data from other types of head impacts.

SIGNIFICANCE

In addition to the potential clinical application in real-time brain deformation monitoring, this model will help researchers estimate the brain strain from a large number of head impacts more efficiently than using FE models.