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

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

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

Influences of impact scenarios and vehicle front-end design on head injury risk of motorcyclist

Motorcycle to vehicle collision is one of the most common accidents in the world and usually leads to serious or fatal head injuries to motorcyclists. This study aims to investigate the influences of impact scenarios and vehicle front-end design parameters on head injury risk of the motorcyclist. Five general vehicle types and different impact scenarios were selected for a parametric analysis. Impact scenarios were set according to ISO, 13232 regulation considering impact angles and impact speeds. Five vehicle types of Sedan, MPV (Multi-Purpose Vehicle), SUV (Sport Utility Vehicle), EV (Electric Vehicle) and 1-Box vehicle were included. HIC₁₅ (Head Injury Criterion), head angular acceleration and CSDM (Cumulative Strain Damage Measure) were calculated to evaluate head injury risk of the motorcyclist. The results show that the critical impact speed for HIC₁₅ and head angular acceleration was around 15 m/s, while the critical speed for CSDM was approximately 10 m/s. Impact angle of 45° show extremely high injury risk to the motorcyclist head. Bonnet leading edge height and its combination with other parameter present high influences on motorcyclist head injuries, and the increasing the bonnet leading edge height can potentially reduce head injury risk of motorcyclists. In summary, the present research results provide some theoretic bases for determining the test speed in motorcycle-vehicle crash regulation and design consideration for typical vehicle front end shape.

Effect of foam densification and impact velocity on the performance of a football helmet using computational modeling

The NFL recently released validated helmet-impact models to study the performance of currently used helmets. This study used the model of a Riddell Speed Classic helmet to determine the influence of the properties of protective foam padding on acceleration and deformation at two common impact locations to cause concussions. The performance of the helmet was measured before and after manipulating the material properties of the protective foam liner material using FEA software. The densification strain was adjusted by using the scale factor tool in LS-DYNA to create four material categories - soft, standard, stiff, and rigid. The helmet was tested under side and rear impacts using the four material properties at 2.0, 5.5, 7.4, 9.3 and 12.3 m/s impact speeds using the NOCSAE linear impactor model. This study suggests that the standard foam material compresses to a range that could be considered to have "bottomed out" at impact speeds at 5.5 m/s for side impacts. Despite testing a wide range of material properties, the measured accelerations did not vary dramatically across material properties. Rather, impact speed played the dominant role on measured acceleration. This is the first study to demonstrate how open-source impact models can be used to run a design of experiments and investigate the role between different materials used inside a helmet and football helmet performance.

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.

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

Biomechanics of Periventricular Injury

Periventricular injury is frequently noted as one aspect of severe traumatic brain injury (TBI) and the presence of the ventricles has been hypothesized to be a primary pathogenesis associated with the prevalence of periventricular injury in patients with TBI. Although substantial endeavors have been made to elucidate the potential mechanism, a thorough explanation for this hypothesis appears lacking. In this study, a three-dimensional (3D) finite element (FE) model of the human head with an accurate representation of the cerebral ventricles is developed accounting for the fluid properties of the intraventricular cerebrospinal fluid (CSF) as well as its interaction with the brain. An additional model is developed by replacing the intraventricular CSF with a substitute with brain material. Both models are subjected to rotational accelerations with magnitudes suspected to induce severe diffuse axonal injury. The results reveal that the presence of the ventricles leads to increased strain in the periventricular region, providing a plausible explanation for the vulnerability of the periventricular region. In addition, the strain-exacerbation effect associated with the presence of the ventricles is also noted in the paraventricular region, although less pronounced than that in the periventricular region. The current study advances the understanding of the periventricular injury mechanism as well as the detrimental effects that the ventricles exert on the periventricular and paraventricular brain tissue.

Biomechanics of the Human Brain during Dynamic Rotation of the Head

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

Finite Element Model of a Deformable American Football Helmet Under Impact

Despite the use of helmets in American football, brain injuries are still prevalent. To reduce the burden of these injuries, novel impact mitigation systems are needed. The Vicis Zero1 (VZ1) American football helmet is unique in its use of multi-directional buckling structures sandwiched between a deformable outer shell and a stiff inner shell. The objective of this study was to develop a model of the VZ1 and to assess this unique characteristic for its role in mitigating head kinematics. The VZ1 model was developed using a bottom-up framework that emphasized material testing, constitutive model calibration, and component-level validation. Over 50 experimental tests were simulated to validate the VZ1 model. CORrelation and Analysis (CORA) was used to quantify the similarity between experimental and model head kinematics, neck forces, and impactor accelerations and forces. The VZ1 model demonstrated good correlation with an overall mean CORA score of 0.86. A parametric analysis on helmet compliance revealed that the outer shell and column stiffness influenced translational head kinematics more than rotational. For the material parameters investigated, head linear acceleration ranged from 80 to 220 g, whereas angular velocity ranged from 37 to 40 rad/s. This helmet model is open-source and serves as an in silico design platform for helmet innovation.

Protection performance of bicycle helmets

INTRODUCTION

The evaluation of head protection systems needs proper knowledge of the head impact conditions in terms of impact speed and angle, as well as a realistic estimation of brain tolerance limits. In current bicycle helmet test procedures, both of these aspects should be improved.

METHOD

The present paper suggests a bicycle helmet evaluation methodology based on realistic impact conditions and consideration of tissue level brain injury risk, in addition to well known headform kinematic parameters. The method is then applied to a set of 32 existing helmets, leading to a total of 576 experimental impact tests followed by 576 numerical simulations of the brain response.

RESULTS

It is shown that the most critical impacts are the linear-lateral ones as well as the oblique impact leading to rotation around the vertical axis (ZRot), leading both to around 50% risks of moderate neurological injuries. Based on this test method, the study enables us to compare the protection capability of a given helmet and eventually to compare helmets via a dedicated rating system.

Convolutional neural network for efficient estimation of regional brain strains

Head injury models are important tools to study concussion biomechanics but are impractical for real-world use because they are too slow. Here, we develop a convolutional neural network (CNN) to estimate regional brain strains instantly and accurately by conceptualizing head rotational velocity profiles as two-dimensional images for input. We use two impact datasets with augmentation to investigate the CNN prediction performances with a variety of training-testing configurations. Three strain measures are considered, including maximum principal strain (MPS) of the whole brain, MPS of the corpus callosum, and fiber strain of the corpus callosum. The CNN is further tested using an independent impact dataset (N = 314) measured in American football. Based on 2592 training samples, it achieves a testing R² of 0.916 and root mean squared error (RMSE) of 0.014 for MPS of the whole brain. Combining all impact-strain response data available (N = 3069), the CNN achieves an R² of 0.966 and RMSE of 0.013 in a 10-fold cross-validation. This technique may enable a clinical diagnostic capability to a sophisticated head injury model, such as facilitating head impact sensors in concussion detection via a mobile device. In addition, it may transform current acceleration-based injury studies into focusing on regional brain strains. The trained CNN is publicly available along with associated code and examples at https://github.com/Jilab-biomechanics/CNN-brain-strains. They will be updated as needed in the future.

Analysis of Swedish and Dutch accident data on cyclist injuries in cyclist-car collisions

Objective: To reduce the number of severe injuries sustained by cyclists in crashes with vehicles, it is important to understand which kinds of injuries are occurring to identify what should be assessed by means of virtual testing.Method: A detailed analysis of injuries was made based on Swedish and Dutch accident data. The most frequently injured body regions and the most frequent single injuries of these body regions were analysed.Results: Cyclists most frequently injured their heads, upper and lower extremities, and bone fractures as well as brain injuries were identified as one of the most important injuries.Conclusions: For the virtual assessment of cyclist protection, injury predictors for long bone, skull and pelvic fractures as well as brain injuries are required in Human Body Models.

Suitability of enhanced head injury criteria for vehicle rating

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

Development and Multi-Scale Validation of a Finite Element Football Helmet Model

Head injury is a growing concern within contact sports, including American football. Computational tools such as finite element (FE) models provide an avenue for researchers to study, and potentially optimize safety tools, such as helmets. The goal of this study was to develop an accurate representative helmet model that could be used in further study of head injury to mitigate the toll of concussions in contact sports. An FE model of a Schutt Air XP Pro football helmet was developed through three major steps: geometry development, material characterization, and model validation. The fully assembled helmet model was fit onto a Hybrid III dummy head–neck model and National Operating Committee on Standards for Athletic Equipment (NOCSAE) head model and validated through a series of 67 representative impacts similar to those experienced by a football player. The kinematic and kinetic response of the model was compared to the response of the physical experiments, which included force, head linear acceleration, head angular velocity, and carriage acceleration. The outputs between the model and the physical tests were quantitatively evaluated using CORelation and Analysis (CORA), amounting to an overall averaged score of 0.76. The model described in this study has been extensively validated and can function as a building block for innovation in player safety.

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 Open-Source Dummy and Impactor Models for the Assessment of American Football Helmet Finite Element Models

The objective of this study was to develop and validate a set of Hybrid-III head and neck (HIII-HN) and impactor models that can be used to assess American football design modifications with established dummy-based injury metrics. The model was validated in two bare-head impact test configurations used by the National Football League and research groups to rank and assess helmet performance. The first configuration was a rigid pendulum impact to three locations on the HIII head (front, rear, side) at 3 m/s. The second configuration was a set of eight 5.5 m/s impacts to the HIII head at different locations using a linear impactor with a compliant end cap. The ISO/TS 18571 objective rating metric was used to quantify the correlation between the experimental and model head kinematics (linear and rotational velocity and acceleration) and neck kinetics (neck force and moment). The HIII-HN model demonstrated good correlation with overall mean ISO scores of 0.69-0.78 in the pendulum impacts and 0.65-0.81 in the linear impacts. These publically available models will serve as an in silico design platform that will be useful for investigating novel football helmet designs and studying human head impact biomechanics, in general.