Prediction of brain deformations and risk of traumatic brain injury due to closed-head impact: quantitative analysis of the effects of boundary conditions and brain tissue constitutive model

The relationship between header type and head acceleration during heading in male youth football players

OBJECTIVE

To explore head impact magnitude (linear head acceleration and rotational velocity) during heading for four different header types in youth male footballers. A secondary objective was to explore whether head impact magnitude was influenced by player-related factors (i.e. age, neck strength).

DESIGN

An observational field-based research design was utilised for this research.

PARTICIPANTS

27 youth male footballers (mean age14.81, SD1.88) from one football club.

MAIN OUTCOME MEASURES

Peak linear acceleration and peak angular velocity were measured during four different header types (two from 15-m and two from 5-m). Maximal isometric neck strength of each player was also assessed (flexors, extensors, left and right-side flexors).

RESULTS

Statistically significant differences were observed between header type and both peak linear acceleration (F(3,78) = 60.90, R2 = 0.76, p=<0.001) and peak angular velocity (F(3,78) = 43.63, R2 = 0.75, p=<0.001). In terms of players related factors, for linear acceleration, 6% (P = 0.008) of the variance was predicted by age, and for angular velocity, 12% (P = 0.003) was predicted by age, with neck strength accounting for <9% of the variance.

CONCLUSIONS

In this youth cohort, head impact magnitude during heading is influenced by the type of header being performed.

Recent insights from non-mammalian models of brain injuries: an emerging literature

Traumatic brain injury (TBI) is a major global health concern and is increasingly recognized as a risk factor for neurodegenerative diseases including Alzheimer's disease (AD) and chronic traumatic encephalopathy (CTE). Repetitive TBIs (rTBIs), commonly observed in contact sports, military service, and intimate partner violence (IPV), pose a significant risk for long-term sequelae. To study the long-term consequences of TBI and rTBI, researchers have typically used mammalian models to recapitulate brain injury and neurodegenerative phenotypes. However, there are several limitations to these models, including: (1) lengthy observation periods, (2) high cost, (3) difficult genetic manipulations, and (4) ethical concerns regarding prolonged and repeated injury of a large number of mammals. Aquatic vertebrate model organisms, including Petromyzon marinus (sea lampreys), zebrafish (Danio rerio), and invertebrates, Caenorhabditis elegans (C. elegans), and Drosophila melanogaster (Drosophila), are emerging as valuable tools for investigating the mechanisms of rTBI and tauopathy. These non-mammalian models offer unique advantages, including genetic tractability, simpler nervous systems, cost-effectiveness, and quick discovery-based approaches and high-throughput screens for therapeutics, which facilitate the study of rTBI-induced neurodegeneration and tau-related pathology. Here, we explore the use of non-vertebrate and aquatic vertebrate models to study TBI and neurodegeneration. Drosophila, in particular, provides an opportunity to explore the longitudinal effects of mild rTBI and its impact on endogenous tau, thereby offering valuable insights into the complex interplay between rTBI, tauopathy, and neurodegeneration. These models provide a platform for mechanistic studies and therapeutic interventions, ultimately advancing our understanding of the long-term consequences associated with rTBI and potential avenues for intervention.

Development of a head-weighted injury criterion for evaluation of multiple types of AIS 4+ injuries for vulnerable road users

Vulnerable Road users (VRUs) often suffer multiple fatal head injury types simultaneously in road accidents. In this study, a head-weighted injury criterion (HWIC4) was proposed for assessing the risk of head AIS 4+ injuries considering multiple injury types. Firstly, the kinematic characteristics of VRUs in the 50 in-depth accidents were reconstructed by using multi-body system models, and head injuries were reconstructed using eight head kinematic-based injury criteria and eight brain tissue injury criteria via the THUMS (Ver. 4.0.2) head finite element model. The predictive capability of each injury criterion to predict head AIS 4+ injuries was assessed and four better predictors (HIC15, angular acceleration, coup pressure, and maximum principal strain) were selected. The different head injury types and the weighting parameters for each injury type were taken into account in the development of HWIC4. Finally, the effectiveness and evaluation of HWIC4 for head AIS 4+ injury was validated based on the area under of receiver operating characteristic (AUROC) curve and reconstruction results from 10 additional selected accident cases. The results showed that HWIC4 has a good predictive capability for head AIS 4+ injuries with an AUROC of 0.983, which means that HWIC4 is superior and more reliable than a single head injury criterion. This knowledge further improves the capability of head injury criteria to predict head AIS 4+ injuries.

Why Intracranial Compliance Is Not Utilized as a Common Practical Tool in Clinical Practice

Intracranial compliance (ICC) holds significant potential in neuromonitoring, serving as a diagnostic tool and contributing to the evaluation of treatment outcomes. Despite its comprehensive concept, which allows consideration of changes in both volume and intracranial pressure (ICP), ICC monitoring has not yet established itself as a standard component of medical care, unlike ICP monitoring. This review highlighted that the first challenge is the assessment of ICC values, because of the invasive nature of direct measurement, the time-consuming aspect of non-invasive calculation through computer simulations, and the inability to quantify ICC values in estimation methods. Addressing these challenges is crucial, and the development of a rapid, non-invasive computer simulation method could alleviate obstacles in quantifying ICC. Additionally, this review indicated the second challenge in the clinical application of ICC, which involves the dynamic and time-dependent nature of ICC. This was considered by introducing the concept of time elapsed (TE) in measuring the changes in volume or ICP in the ICC equation (volume change/ICP change). The choice of TE, whether short or long, directly influences the ICC values that must be considered in the clinical application of the ICC. Compensatory responses of the brain exhibit non-monotonic and variable changes in long TE assessments for certain disorders, contrasting with the mono-exponential pattern observed in short TE assessments. Furthermore, the recovery behavior of the brain undergoes changes during the treatment process of various brain disorders when exposed to short and long TE conditions. The review also highlighted differences in ICC values across brain disorders with various strain rates and loading durations on the brain, further emphasizing the dynamic nature of ICC for clinical application. The insight provided in this review may prove valuable to professionals in neurocritical care, neurology, and neurosurgery for standardizing ICC monitoring in practical application related to the diagnosis and evaluation of treatment outcomes in brain disorders.

Numerical Reconstruction of Cyclist Impact Accidents: Can Helmets Protect the Head-Neck of Cyclists?

Cyclists are vulnerable road users and often suffer head-neck injuries in car-cyclist accidents. Wearing a helmet is currently the most prevalent protection method against such injuries. Today, there is an ongoing debate about the ability of helmets to protect the cyclists' head-neck from injury. In the current study, we numerically reconstructed five real-world car-cyclist impact accidents, incorporating previously developed finite element models of four cyclist helmets to evaluate their protective performances. We made comparative head-neck injury predictions for unhelmeted and helmeted cyclists. The results show that helmets could clearly lower the risk of severe (AIS 4+) brain injury and skull fracture, as assessed by the predicted head injury criterion (HIC), while a relatively limited decrease in AIS 4+ brain injury risk can be achieved in terms of the analysis of CSDM0.25. Assessment using the maximum principal strain (MPS0.98) and head impact power (HIP) criteria suggests that helmets could lower the risk of diffuse axonal injury and subdural hematoma of the cyclist. The helmet efficacy in neck protection depends on the impact scenario. Therefore, wearing a helmet does not seem to cause a significant neck injury risk level increase to the cyclist. Our work presents important insights into the helmet's efficacy in protecting the head-neck of cyclists and motivates further optimization of protective equipment.

In vivo measurement of human brain material properties under quasi-static loading

Computational modelling of the brain requires accurate representation of the tissues concerned. Mechanical testing has numerous challenges, in particular for low strain rates, like neurosurgery, where redistribution of fluid is biomechanically important. A finite-element (FE) model was generated in FEBio, incorporating a spring element/fluid-structure interaction representation of the pia-arachnoid complex (PAC). The model was loaded to represent gravity in prone and supine positions. Material parameter identification and sensitivity analysis were performed using statistical software, comparing the FE results to human in vivo measurements. Results for the brain Ogden parameters µ, α and k yielded values of 670 Pa, -19 and 148 kPa, supporting values reported in the literature. Values of the order of 1.2 MPa and 7.7 kPa were obtained for stiffness of the pia mater and out-of-plane tensile stiffness of the PAC, respectively. Positional brain shift was found to be non-rigid and largely driven by redistribution of fluid within the tissue. To the best of our knowledge, this is the first study using in vivo human data and gravitational loading in order to estimate the material properties of intracranial tissues. This model could now be applied to reduce the impact of positional brain shift in stereotactic neurosurgery.

Numerical investigation of the rider's head injury in typical single-electric self-balancing scooter accident scenarios

As the use of electric self-balancing scooters (ESSs) increases, so does the number of related traffic accidents. Because of the special control method, mechanical structure and driving posture, ESSs are prone to various single-vehicle accidents, such as collisions with fixed obstacles and falls due to mechanical failures. In various ESS accident scenarios, the rider's head injury is the most frequent injury type. In this study, several typical single-ESS accident scenarios are reconstructed via computational methods, and the risk of riders' head/brain injury is assessed in depth using various injury criteria. Results showed that two types of ESSs (solo- and two-wheeler) do not have clear differences in head kinematics and head injury risks; the head kinematics (or falling posture) and ESS accident scenario exhibit a distinct effect on the head injury responses; half of the analysed ESS riders have a 50% probability of skull fracture, a few riders have a 50% risk of abbreviated injury scale (AIS) 4+ brain injury, and none has a diffuse axonal injury; the ESS speed plays an important role in producing the head/brain injury in ESS riders, and generally, higher ESS speed generates higher level of predicted head injury parameters. These findings will provide theoretical support for preventing head injury among ESS riders and data support for developing and legislating ESSs.

Assessment of brain injury characterization and influence of modeling approaches

In this study, using computational biomechanics models, we investigated influence of the skull-brain interface modeling approach and the material property of cerebrum on the kinetic, kinematic and injury outputs. Live animal head impact tests of different severities were reconstructed in finite element simulations and DAI and ASDH injury results were compared. We used the head/brain models of Total HUman Model for Safety (THUMS) and Global Human Body Models Consortium (GHBMC), which had been validated under several loading conditions. Four modeling approaches of the skull-brain interface in the head/brain models were evaluated. They were the original models from THUMS and GHBMC, the THUMS model with skull-brain interface changed to sliding contact, and the THUMS model with increased shear modulus of cerebrum, respectively. The results have shown that the definition of skull-brain interface would significantly influence the magnitude and distribution of the load transmitted to the brain. With sliding brain-skull interface, the brain had lower maximum principal stress compared to that with strong connected interface, while the maximum principal strain slightly increased. In addition, greater shear modulus resulted in slightly higher the maximum principal stress and significantly lower the maximum principal strain. This study has revealed that using models with different modeling approaches, the same value of injury metric may correspond to different injury severity.

Should anthropometric differences between the commonly used pedestrian computational biomechanics models and Chinese population be taken into account when predicting pedestrian head kinematics and injury in vehicle collisions in China?

Computational biomechanics models play a key role in predicting/evaluating pedestrian head kinematics and injury risk in car-to-pedestrian collisions. The human multibody models most commonly used in car-to-pedestrian collision reconstruction, such as pedestrian model by The Netherlands Organisation for Applied Scientific Research TNO, are built using the anthropometry of Western European population as defined in TNO (2013) human multibody model database. In this study, we investigate the effects of the anthropometric differences between the Western European and Chinese populations on the pedestrian head kinematics and injury responses predicted using multibody models. The comparison was conducted through car-to-pedestrian collision simulations using pedestrian multibody models representing anthropometric characteristics of Western European and Chinese populations, three typical vehicle shapes (sedan, SUV and minivan), five initial vehicle impact speeds (30, 35, 40, 45, 50 km/h), and six pedestrian walking postures. The results indicate that the change of pedestrian model anthropometry (from Western European to Chinese) exerts appreciable effects on both the predicted initial boundary conditions of the head-to-windscreen impact (in particular the head-to-windscreen impact angle) and the head injury indices in the impact with the road surface (secondary impact).

Tissue-Scale Biomechanical Testing of Brain Tissue for the Calibration of Nonlinear Material Models

Brain tissue is one of the most complex and softest tissues in the human body. Due to its ultrasoft and biphasic nature, it is difficult to control the deformation state during biomechanical testing and to quantify the highly nonlinear, time-dependent tissue response. In numerous experimental studies that have investigated the mechanical properties of brain tissue over the last decades, stiffness values have varied significantly. One reason for the observed discrepancies is the lack of standardized testing protocols and corresponding data analyses. The tissue properties have been tested on different length and time scales depending on the testing technique, and the corresponding data have been analyzed based on simplifying assumptions. In this review, we highlight the advantage of using nonlinear continuum mechanics based modeling and finite element simulations to carefully design experimental setups and protocols as well as to comprehensively analyze the corresponding experimental data. We review testing techniques and protocols that have been used to calibrate material model parameters and discuss artifacts that might falsify the measured properties. The aim of this work is to provide standardized procedures to reliably quantify the mechanical properties of brain tissue and to more accurately calibrate appropriate constitutive models for computational simulations of brain development, injury and disease. Computational models can not only be used to predictively understand brain tissue behavior, but can also serve as valuable tools to assist diagnosis and treatment of diseases or to plan neurosurgical procedures. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.

Effective Viscoplastic-Softening Model Suitable for Brain Impact Modelling

In this paper, we address the numerical aspects and implementation of a nonlinear viscoplastic model of the mechanical behaviour of brain tissue to simulate the dynamic responses related to impact loads which may cause traumatic injury. Among the various viscoelastic models available, we deliberately considered modifying the Norton–Hoff model in order to introduce non-typical viscoplastic softening behaviour that imitates a brain’s response just several milliseconds after a rapid impact. We describe the discretisation and three dimensional implementation of the model, with the aim of obtaining accurate numerical results in a reasonable computational time. Due to the large scale and complexity of the problem, a parallel computation technique, using a space–time finite element method, was used to facilitate the computation boost. It is proven that, after calibrating, the introduced viscoplastic-softening model is better suited for modelling brain tissue behaviour for the specific case of rapid impact loading rather than the commonly used viscoelastic models.

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Increased intracranial pressure is the source of most critical symptoms in patients with glioma, and often the main cause of death. Clinical interventions could benefit from non-invasive estimates of the pressure distribution in the patient's parenchyma provided by computational models. However, existing glioma models do not simulate the pressure distribution and they rely on a large number of model parameters, which complicates their calibration from available patient data. Here we present a novel model for glioma growth, pressure distribution and corresponding brain deformation. The distinct feature of our approach is that the pressure is directly derived from tumour dynamics and patient-specific anatomy, providing non-invasive insights into the patient's state. The model predictions allow estimation of critical conditions such as intracranial hypertension, brain midline shift or neurological and cognitive impairments. A diffuse-domain formalism is employed to allow for efficient numerical implementation of the model in the patient-specific brain anatomy. The model is tested on synthetic and clinical cases. To facilitate clinical deployment, a high-performance computing implementation of the model has been publicly released.

Assessment of standing passenger traumatic brain injury caused by ground impact in subway collisions

Human head is the most vulnerable region in subway collisions. To design a safer subway, the head impact biomechanical response should be studied first. This paper aims to investigate the standing passenger head-ground impact dynamic response and traumatic brain injury (TBI) in subway collisions. A standing passenger-subway interior dynamic model was numerically developed by using our previous validated finite element (FE)-multibody (MB) coupled human body model, which was integrated by the Total Human Model for Safety (THUMS) head-neck FE model and the extracted remaining body segments pedestrian MB model of TNO. A parametric study considering the handrail type, standing angle, and friction coefficient between the shoes and ground was performed. Results show that the passenger dynamic response could be divided into two categories according to whether the passenger hit handrails. Passenger TBIs severity could be efficiently alleviated by the passenger body (excluding the head) hitting the handrail first before head-ground impact. The probabilities of DAI in the cerebellum and brain stem were low. A statistical analysis of TBIs demonstrated that the risks of TBIs were sensitive to the handrail type in subway collisions, but did not to the standing angle and friction coefficient. This study provides practical help for improving the interior crashworthiness performance of subways.

Evaluation of the head protection effectiveness of cyclist helmets using full-scale computational biomechanics modelling of cycling accidents

INTRODUCTION

Cycling is a popular choice for urban transportation. Helmets are important and the most popular means of head protection for cyclists. However, a debate about the effectiveness of helmets in protecting a cyclist's head from injury continues.

METHOD

We employed computational biomechanics methods to analyze the head protection effectiveness of nine off-the-shelf-helmets for two typical impact scenarios that occur in cycling accidents: cyclist's head impacting a kerb (kerb-impact) and cyclist skidding (skidding impact) on the road surface. We conducted drop tests for all nine analyzed helmets, and used the test data for validation of the corresponding helmet finite element (FE) models created in this study. The validated helmet models were then used in the full-scale computer simulations (FE analysis for the skull, brain and helmet, and multibody dynamics for the remaining segments of the cyclist's body) of the cycling accidents for cyclists wearing a helmet and without a helmet.

RESULTS

The results indicate that helmets can reduce both the peak linear acceleration of the cyclist head center of gravity (COG) and the risk of cyclist skull fracture. However, higher rotational acceleration of the head COG was predicted for cyclists wearing helmets. The results obtained using the injury criteria that rely on the brain deformations (maximum shear strain MPS and cumulative strain damage measure CSDM) suggest that helmets may offer protection in all the analyzed cyclist impact scenarios. However, the predicted level of protection varies for different helmets and impact scenarios with appreciable variations in the predictions obtained using different injury criteria. Reduction in the maximum principal strain (MPS_0.98) for helmeted cyclists was predicted for both impact scenarios. In contrast, wearing the helmet reduced the CSDM only for the skidding impact scenario. For the kerb-impact scenario, no clear influence of the helmet on the predicted CSDM was observed.

The Effect of the FIFA 11 + with Added Neck Exercises on Maximal Isometric Neck Strength and Peak Head Impact Magnitude During Heading: A Pilot Study

BACKGROUND

Higher neck strength has been postulated to reduce head impact magnitude during purposeful heading in football.

OBJECTIVES

This pilot trial explored the effect of a neck exercise programme on (1) neck strength and (2) head impact magnitude during heading in male and female adolescent football players.

METHODS

Boys and girls (aged 12-17 years) were randomised by team to the intervention (5 weeks of supervised neuromuscular neck exercises integrated into part 2 of the FIFA 11 + , completed three times per week) or the control group (usual part 2 of the FIFA 11 + , no neck exercises). Outcomes included isometric neck strength and head impact magnitude (peak linear acceleration and peak angular velocity) during standardised heading from a throw-in (at baseline and 6 weeks) plus completion of an evaluation survey by intervention players and coaches.

RESULTS

In total, 52 players (n = 31 intervention; n = 21 control) completed the study. Mixed-model analysis of variance (ANOVA) revealed significant differences in neck strength variables (p < 0.001), peak linear acceleration (p = 0.04) and peak angular velocity (p = 0.04) between the intervention and control groups over time. Intervention players demonstrated increases in mean composite neck strength (53.8% intervention vs 15.6% control) as well as decreases in mean peak linear head acceleration during heading (- 11.8% vs - 5.0%) from baseline to follow-up. Reduction in peak angular velocity was more pronounced in girls (- 27.7%) than boys (- 11.5%) in the intervention group. The addition of neck exercises into part 2 of the FIFA 11 + was feasible and accepted by players and coaches.

CONCLUSION

On average, players who completed neck exercises demonstrated an increase in isometric neck strength and a decrease in head impact magnitude during heading. These exercises were easily incorporated into usual training. Australian New Zealand Clinical Trials Registry (no: ACTRN12619001375145).

Mesoscale Simulation-based Parametric Study of Damage Potential in Brain Tissue Using Hyperelastic and Internal State Variable Models

Two-dimensional mesoscale finite element analysis (FEA) of a multi-layered brain tissue was performed to calculate the damage related average stress triaxiality and local maximum von Mises strain in the brain. The FEA was integrated with rate dependent hyperelastic and internal state variable (ISV) models respectively describing the behaviors of wet and dry brain tissues. Using the finite element results, a statistical method of design of experiments (DOE) was utilized to independently screen the relative influences of seven parameters related to brain morphology (sulcal width/depth, gray matter (GM) thickness, cerebrospinal fluid (CSF) thickness and brain lobe) and loading/environment conditions (strain rate and humidity) with respect to the potential damage growth/coalescence in the brain tissue. The results of the parametric study illustrated that the GM thickness and humidity were the two most crucial parameters affecting average stress triaxiality. For the local maximum von Mises strain at the depth of brain sulci, the brain lobe/region was the most influential factor. The conclusion of this investigation gives insight for the future development and refinement of a macroscale brain damage model incorporating information from lower length scale.

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

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

Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents

This study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated through the computational biomechanics method; head injuries observed in the analyzed accidents were reconstructed by using a finite element (FE)-multibody (MB) coupled pedestrian model [including the Total Human Model for Safety (THUMS) head-neck FE model and the remaining body segments of TNO MB pedestrian model], which was developed and validated in our previous study. Various typical HICs were used to predict head injuries in all accident cases. Pearson's correlation coefficient analysis method was adopted to investigate the correlation between head kinematics-based injury criteria and the actual head injury of VRU; the effectiveness of brain deformation-based injury criteria in predicting typical brain injuries [such as diffuse axonal injury diffuse axonal injury (DAI) and contusion] was assessed by using head injury risk curves reported in the literature. Results showed that for head kinematics-based injury criteria, the most widely used HICs and head impact power (HIP) can accurately and effectively predict head injury, whereas for brain deformation-based injury criteria, the maximum principal strain (MPS) behaves better than cumulative strain damage measure (CSDM0.15 and CSDM0.25) in predicting the possibility of DAI. In comparison with the dilatation damage measure (DDM), MPS seems to better predict the risk of brain contusion.

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.

Smoothed particle hydrodynamic modelling of the cerebrospinal fluid for brain biomechanics: Accuracy and stability

The Cerebrospinal Fluid (CSF) can undergo shear deformations under head motions. Finite Element (FE) models, which are commonly used to simulate biomechanics of the brain, including traumatic brain injury, employ solid elements to represent the CSF. However, the limited number of elements paired with shear deformations in CSF can decrease the accuracy of their predictions. Large deformation problems can be accurately modelled using the mesh-free Smoothed Particle Hydrodynamics (SPH) method, but there is limited previous work on using this method for modelling the CSF. Here we explored the stability and accuracy of key modelling parameters of an SPH model of the CSF when predicting relative brain/skull displacements in a simulation of an in vivo mild head impact in human. The Moving Least Squares (MLS) SPH formulation and Ogden rubber material model were found to be the most accurate and stable. The strain and strain rate in the brain differed across the SPH and FE models of CSF. The FE mesh anchored the gyri, preventing them from experiencing the level of strains seen in the in vivo brain experiments and predicted by the SPH model. Additionally, SPH showed higher levels of strains in the sulci compared to the FE model. However, tensile instability was found to be a key challenge of the SPH method, which needs to be addressed in future. Our study provides a detailed investigation of the use of SPH and shows its potential for improving the accuracy of computational models of brain biomechanics.

A new approach for uncertainty analysis of ETW Rider Head Injury Reconstruction via Coupling Response Surface and Monte Carlo methodologies

Traditional vehicle accident reconstructions do not take into account all existing uncertainties and may over- or under-estimate the injury risk. The objective of this study was to introduce a new uncertainty analysis method by applying Response Surface-Monte Carlo Methods (RS-MCM) to predict head injury risk in real electric two wheelers (ETW) to vehicle accidents. Vehicle impact velocity ranges in three detailed ETWs accidents (including video records and injury reports) were estimated using direct linear transformation (DLT) or video frame (VF) methods. A response surface methodology (RSM) was used to obtain an approximate model of the each real ETW accident, and a vehicle impact velocity distribution was estimated by applying the Monte Carlo Method (MCM) to the resulting model. If the velocity distribution was in agreement with the initial estimated velocity, the reconstruction quality was deemed acceptable. The injury severity was then assessed using the initial conditions resulting from the range of potential head impact conditions identified in the reconstruction activities. The identified head linear and angular impact velocities were input to finite element analyses to the THUMS Ver4.02 pedestrian head model and resulting in head injury criteria (HIC). The HIC values were further explored using the same RSM method used earlier to establish impact conditions. The distribution of reconstructed AIS levels show good agreement with the injury results from forensic reports. The results illustrated that the RS-MCM enriches the information for head trauma injury mechanisms caused by the vehicle collisions or ground impact.

Influence of Skull Fracture on Traumatic Brain Injury Risk Induced by Blunt Impact

This study is aimed at investigating the influence of skull fractures on traumatic brain injury induced by blunt impact via numerous studies of head-ground impacts. First, finite element (FE) damage modeling was implemented in the skull of the Total HUman Model for Safety (THUMS), and the skull fracture prediction performance was validated against a head-ground impact experiment. Then, the original head model of the THUMS was assigned as the control model without skull element damage modeling. Eighteen (18) head-ground impact models were established using these two FE head models, with three head impact locations (frontal, parietal, and occipital regions) and three impact velocities (25, 35, and 45 km/h). The predicted maximum principal strain and cumulative strain damage measure of the brain tissue were employed to evaluate the effect of skull fracture on the cerebral contusion and diffuse brain injury risks, respectively. Simulation results showed that the skull fracture could reduce the risk of diffuse brain injury risk under medium and high velocities significantly, while it could increase the risk of brain contusion under high-impact velocity.

Evaluation of injury thresholds for predicting severe head injuries in vulnerable road users resulting from ground impact via detailed accident reconstructions

The aim of this study was to evaluate the effectiveness of various head injury criteria and associated risk functions in prediction of vulnerable road users (VRUs) severe head injuries caused by ground impact during vehicle collisions. Ten VRU accidents with video information were reconstructed by using Chalmers Pedestrian Model, vehicle multi-body system models and the THUMS (Ver. 4.0.2) finite element model. The head kinematics were used to calculate injury risks for seven head kinematics-based criteria: head angular velocity and acceleration, linear acceleration, head injury criterion (HIC), head impact power (HIP) and two versions of brain injury criterion (i.e., BRIC and BrIC). In addition, the intracranial responses were used to estimate seven tissue injury criteria, Von Mises stress, shear stress, coup pressure (C.P.) and countercoup pressure (CC.P.), maximum principal strain (MPS), cumulative strain damage measure (CSDM), and dilatation damage measure (DDM). A review of the medical reports for all cases indicated that each individual suffered severe head injuries and died. The injury risks predicted through simulations were compared to the head injuries recorded in the medical or forensic reports. The results indicated that 75-100% of the reconstructed ground impact accidents injuries were correctly predicted by angular acceleration, linear acceleration, HIC, C.P., MPS and CSDM_0.15. Shear stress, CC.P. and CSDM_0.25 correctly predicted 50-75% of the reconstructed accidents injuries. For angular velocity, HIP, BRIC and BrIC, the injuries were correctly predicted for less than 50% of the reconstructed accidents. The Von Mises stress and DDM did not correctly predict any reconstructed accidents injuries. The results could help to understand the effectiveness of the brain injury criteria for future head injury evaluation.

Biomechanical modeling and computer simulation of the brain during neurosurgery

Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and its collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach and describe the brain deformation in mechanical terms (such as displacements, strains, and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading, and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady-state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.

A Computationally Efficient Finite Element Pedestrian Model for Head Safety: Development and Validation

Head injuries are often fatal or of sufficient severity to pedestrians in vehicle crashes. Finite element (FE) simulation provides an effective approach to understand pedestrian head injury mechanisms in vehicle crashes. However, studies of pedestrian head safety considering full human body response and a broad range of impact scenarios are still scarce due to the long computing time of the current FE human body models in expensive simulations. Therefore, the purpose of this study is to develop and validate a computationally efficient FE pedestrian model for future studies of pedestrian head safety. Firstly, a FE pedestrian model with a relatively small number of elements (432,694 elements) was developed in the current study. This pedestrian model was then validated at both segment and full body levels against cadaver test data. The simulation results suggest that the responses of the knee, pelvis, thorax, and shoulder in the pedestrian model are generally within the boundaries of cadaver test corridors under lateral impact loading. The upper body (head, T1, and T8) trajectories show good agreements with the cadaver data in vehicle-to-pedestrian impact configuration. Overall, the FE pedestrian model developed in the current study could be useful as a valuable tool for a pedestrian head safety study.

Ballistic Head Protection in the Light of Injury Criteria in the Case of the Wz.93 Combat Helmet

This paper discusses the general conditions relating to ballistic head protection, analyzing the risks that may occur on contemporary battlefields. A thorough literature review has enabled us to present development trends for helmets used in the largest armies in the world. The authors have focused on impacts to the helmet shell, overloading the entire helmet-protected head–neck system. The main objective of this study is to investigate the protective capability of a helmet shell when subjected to projectile–helmet contact, with contact curvature taken as being an indicator of the impact energy concentration. Blunt head trauma was estimated using backface deformation (BFD). The Wz.93 combat helmet was used for testing. Analytically, dependencies were derived to determine the scope of BFD. A five-parameter model of the helmet piercing process was adopted, thus obtaining the optimal BFD range. Verification of theoretical considerations was carried out on a specially developed research stand. In the ballistic tests, dynamic deflection of the helmet’s body was registered using a speed camera. On the impact testing stand, a fragment of the helmet was pierced, producing results in the low impact velocity range. Data have been presented on the appropriate graph in order to compare them with values specified in the relevant standard and existing literature. Our results correlate well with the norm and literature values.