Finite Element Head Model Research Articles

An Objective Injury Threshold for the Maximum Principal Strain Criterion for Brain Tissue in the Finite Element Head Model and Its Application.

Although the finite element head model (FEHM) has been widely utilized to analyze injury locations and patterns in traumatic brain injury, significant controversy persists regarding the selection of a mechanical injury variable and its corresponding threshold. This paper aims to determine an objective injury threshold for maximum principal strain (MPS) through a novel data-driven method, and to validate and apply it. We extract the peak responses from all elements across 100 head impact simulations to form a dataset, and then determine the objective injury threshold by analyzing the relationship between the combined injury degree and the threshold according to the stationary value principle. Using an occipital impact case from a clinical report as an example, we evaluate the accuracy of the injury prediction based on the new threshold. The results show that the injury area predicted by finite element analysis closely matches the main injury area observed in CT images, without the issue of over- or underestimating the injury due to an unreasonable threshold. Furthermore, by applying this threshold to the finite element analysis of designed occipital impacts, we observe, for the first time, supra-tentorium cerebelli injury, which is related to visual memory impairment. This discovery may indicate the biomechanical mechanism of visual memory impairment after occipital impacts reported in clinical cases.

Accuracy and efficiency of finite element head models: The role of finite element formulation and material laws.

Traumatic brain injury is a significant problem worldwide. In the United States of America, around 1.7 million cases are documented annually, displaying the need for a deeper understanding of the effects on the human brain. The tests required for this assessment are very complex. Tests on cadavers may raise serious ethical questions, and in vivo crash tests are not viable. In this context, there is a great need to developing finite element head models (FEHM) to study the biomechanics of the tissues when submitted to a certain impact or acceleration/deceleration scenario. An excellent compromise between accuracy and CPU efficiency is always desirable for a FEHM, For this reason, this work focuses on the improvement of an existing head model, including the study of the behavior of the brain using distinct finite element types. The finite element type and formulation is of utmost importance for the general accuracy and efficiency of the models. Several validations were performed, comparing the simulation results against experimental data. The simulations with hexahedral elements, under specific conditions, obtained more accurate results with a lower computational cost. Using hexahedrals, a comparison was also performed using two material characterizations with more than 10 years apart, using the latest finite element head model validation experiment. Overall, the newer material model displays a less stiff response, although its implementation must always depend on the overall purpose of the model it is being applied to.

American Football On-Field Head Impact Kinematics: Influence of Acceleration Signal Characteristics on Peak Maximal Principal Strain.

Recorded head kinematics from head-impact measurement devices (HIMd) are pivotal for evaluating brain stress and strain through head finite element models (hFEM). The variability in kinematic recording windows across HIMd presents challenges as they yield inconsistent hFEM responses. Despite establishing an ideal recording window for maximum principal strain (MPS) in brain tissue, uncertainties persist about the impact characteristics influencing vulnerability when this window is shortened. This study aimed to scrutinize factors within impact kinematics affecting the reliability of different recording windows on whole-brain peak MPS using a validated hFEM. Utilizing 53 on-field head impacts recorded via an instrumented mouthguard during a Canadian varsity football game, 10 recording windows were investigated with varying pre- and post-impact-trigger durations. Tukey pair-wise comparisons revealed no statistically significant differences in MPS responses for the different recording windows. However, specific impacts showed marked variability up to 40%. It was found, through correlation analyses, that impacts with lower peak linear acceleration exhibited greater response variability across different pre-trigger durations. Signal shape, analyzed through spectral analysis, influenced the time required for MPS development, resulting in specific impacts requiring a prolonged post-trigger duration. This study adds to the existing consensus on standardizing HIMd acquisition time windows and sheds light on impact characteristics leading to peak MPS variation across different head impact kinematic recording windows. Considering impact characteristics in research assessments is crucial, as certain impacts, affected by recording duration, may lead to significant errors in peak MPS responses during cumulative longitudinal exposure assessments.

A data-mining study on the prediction of head injury in traffic accidents among vulnerable road users with varying body sizes and head anatomical characteristics.

Body sizes and head anatomical characteristics play the major role in the head injuries sustained by vulnerable road users (VRU) in traffic accidents. In this study, in order to study the influence mechanism of body sizes and head anatomical characteristics on head injury, we used age, gender, height, and Body Mass Index (BMI) as characteristic parameters to develop the personalized human body multi-rigid body (MB) models and head finite element (FE) models. Next, using simulation calculations, we developed the VRU head injury dataset based on the personalized models. In the dataset, the dependent variables were the degree of head injury and the brain tissue von Mises value, while the independent variables were height, BMI, age, gender, traffic participation status, and vehicle speed. The statistical results of the dataset show that the von Mises value of VRU brain tissue during collision ranges from 4.4 kPa to 46.9kPa at speeds between 20 and 60km/h. The effects of anatomical characteristics on head injury include: the risk of a more serious head injury of VRU rises with age; VRU with higher BMIs has less head injury in collision accidents; height has very erratic and nonlinear impacts on the von Mises values of the VRU's brain tissue; and the severity of head injury is not significantly influenced by VRU's gender. Furthermore, we developed the classification prediction models of head injury degree and the regression prediction models of head injury response parameter by applying eight different data mining algorithms to this dataset. The classification prediction models have the best accuracy of 0.89 and the best R2 value of 0.85 for the regression prediction models.

Head Impact Kinematics and Brain Tissue Strains in High School Lacrosse.

Male lacrosse and female lacrosse have differences in history, rules, and equipment. There is current debate regarding the need for enhanced protective headwear in female lacrosse like that worn by male lacrosse players. To inform this discussion, 17 high school lacrosse players (6 female and 11 male) wore the Stanford Instrumented Mouthguard during 26 competitive games over the 2021 season. Time-windowing and video review were used to remove false-positive recordings and verify head acceleration events (HAEs). The HAE rate in high school female lacrosse (0.21 per athlete exposure and 0.24 per player hour) was approximately 35% lower than the HAE rate in high school male lacrosse (0.33 per athlete exposure and 0.36 per player hour). Previously collected kinematics data from the 2019 high school male and female lacrosse season were combined with the newly collected 2021 kinematics data, which were used to drive a finite element head model and simulate 42 HAEs. Peak linear acceleration (PLA), peak angular velocity (PAV), and 95th percentile maximum principal strain (MPS95) of brain tissue were compared between HAEs in high school female and male lacrosse. Median values for peak kinematics and MPS95 of HAEs in high school female lacrosse (PLA, 22.3g; PAV, 10.4rad/s; MPS95, 0.05) were lower than for high school male lacrosse (PLA, 24.2g; PAV, 15.4rad/s; MPS95, 0.07), but the differences were not statistically significant. Quantifying a lower HAE rate in high school female lacrosse compared to high school male lacrosse, but similar HAE magnitudes, provides insight into the debate regarding helmets in female lacrosse. However, due to the small sample size, additional video-verified data from instrumented mouthguards are required.

Revealing the role of material properties in impact-related injuries: Investigating the influence of brain and skull density variations on head injury severity

Traumatic brain injuries (TBI) resulting from head impacts are a major public health concern, which prompted our research to investigate the complex relationship between the material properties of brain tissue and the severity of TBI. The goal of this research is to investigate how variations in brain and skull density influence the vulnerability of brain tissue to traumatic injury, thereby enhancing our understanding of injury mechanism.To achieve this goal, we employed a well-validated finite element head model (FEHM). The current investigation was divided into two phases: in the first one, three distinct brain viscoelastic materials that had been utilized in prior studies were analyzed. The review of the properties of these three materials has been meticulous, encompassing both the spectrum of mechanical properties and the behaviors that are relevant to the way in which brain tissue reacts to traumatic loading conditions. In the second phase, the material properties of both the brain and skull tissue, alongside the impact conditions, were held constant. After this step, the focus was directed towards the variation of density in the brain and skull, which was consistent with the results obtained from previous experimental investigations, in order to determine the precise impact of these variations in density. This approach allowed a more profound comprehension of the impacts that density had on the simulation results.In the first phase, Material No. 2 exhibited the highest maximum first principal strain value in the frontal region (εmax=15.41%), indicating lower stiffness to instantaneous deformation. This characteristic suggests that Material No. 2 may deform more extensively upon impact, potentially increasing the risk of injury due to its viscoelastic behavior. In contrast, Material No. 1, with a lower maximum first principal strain in the frontal region (εmax=7.87%), displayed greater stiffness to instantaneous deformation, potentially reducing the risk of brain injury upon head impact. The second phase provided quantitative findings revealing a proportional relationship between brain tissue density and the pressures experienced by the brain. A 2 % increase in brain tissue density corresponded to approximately a 1 % increase in pressure on the brain tissue. Similarly, changes in skull density exhibited a similar quantitative relationship, with a 6 % increase in skull density leading to a 2.5 % increase in brain pressure. This preliminary approximate ratio of 2 to 1 between brain and skull density variations provides an initial quantitative framework for assessing the impact of density changes on brain vulnerability. These findings have several implications for the development of protective measures and injury prevention strategies, particularly in contexts where head trauma is a major issue.

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.

Subject-specific finite element head models for skull fracture evaluation-a new tool in forensic pathology.

Post-mortem computed tomography (PMCT) enables the creation of subject-specific 3D head models suitable for quantitative analysis such as finite element analysis (FEA). FEA of proposed traumatic events is an objective and repeatable numerical method for assessing whether an event could cause a skull fracture such as seen at autopsy. FEA of blunt force skull fracture in adults with subject-specific 3D models in forensic pathology remains uninvestigated. This study aimed to assess the feasibility of FEA for skull fracture analysis in routine forensic pathology. Five cases with blunt force skull fracture and sufficient information on the kinematics of the traumatic event to enable numerical reconstruction were chosen. Subject-specific finite element (FE) head models were constructed by mesh morphing based on PMCT 3D models and A Detailed and Personalizable Head Model with Axons for Injury Prediction (ADAPT) FE model. Morphing was successful in maintaining subject-specific 3D geometry and quality of the FE mesh in all cases. In three cases, the simulated fracture patterns were comparable in location and pattern to the fractures seen at autopsy/PMCT. In one case, the simulated fracture was in the parietal bone whereas the fracture seen at autopsy/PMCT was in the occipital bone. In another case, the simulated fracture was a spider-web fracture in the frontal bone, whereas a much smaller fracture was seen at autopsy/PMCT; however, the fracture in the early time steps of the simulation was comparable to autopsy/PMCT. FEA might be feasible in forensic pathology in cases with a single blunt force impact and well-described event circumstances.

Brain Material Properties and Integration of Arachnoid Complex for Biofidelic Impact Response for Human Head Finite Element Model.

Finite element head models offer great potential to study brain-related injuries; however, at present may be limited by geometric and material property simplifications required for continuum-level human body models. Specifically, the mechanical properties of the brain tissues are often represented with simplified linear viscoelastic models, or the material properties have been optimized to specific impact cases. In addition, anatomical structures such as the arachnoid complex have been omitted or implemented in a simple lumped manner. Recent material test data for four brain regions at three strain rates in three modes of loading (tension, compression, and shear) was used to fit material parameters for a hyper-viscoelastic constitutive model. The material model was implemented in a contemporary detailed head finite element model. A detailed representation of the arachnoid trabeculae was implemented with mechanical properties based on experimental data. The enhanced head model was assessed by re-creating 11 ex vivo head impact scenarios and comparing the simulation results with experimental data. The hyper-viscoelastic model faithfully captured mechanical properties of the brain tissue in three modes of loading and multiple strain rates. The enhanced head model showed a high level of biofidelity in all re-created impacts in part due to the improved brain-skull interface associated with implementation of the arachnoid trabeculae. The enhanced head model provides an improved predictive capability with material properties based on tissue level data and is positioned to investigate head injury and tissue damage in the future.

Impact of the ear canal motion on the impedance boundary conditions in models of the occlusion effect.

The occlusion effect (OE) denotes the increased low-frequency perception of bone-conducted sounds when the ear canal (EC) is occluded. Circuit and finite element (FE) models are commonly used to investigate the OE and improve its prediction, often applying acoustic impedances at the EC entrance and tympanic membrane (TM). This study investigates the sound generation caused by the structural motion of the EC. In addition to the EC wall vibration, it accounts for the motions of the EC entrance and TM, resulting from nondeforming motion of the surrounding structures. A model extension including these motions with the impedances is proposed. Related mechanisms are illustrated based on a circuit model. Implications are discussed by using an EC motion extracted from a FE model of a human head. The results demonstrate that the motions of the EC entrance and TM, addressed by the proposed extension, affects the TM sound pressure and may lead to a reduction of the OE at lower frequencies compared to solely considering the EC wall vibration. Accordingly, this phenomenon potentially reconciles differences between experimental data and OE simulations at frequencies below about 250 Hz, highlighting the importance to discern between multiple contributing mechanisms to the TM sound pressure.

Normative tDCS over V5 and FEF reveals practice-induced modulation of extraretinal smooth pursuit mechanisms, but no specific stimulation effect

The neural networks subserving smooth pursuit eye movements (SPEM) provide an ideal model for investigating the interaction of sensory processing and motor control during ongoing movements. To better understand core plasticity aspects of sensorimotor processing for SPEM, normative sham, anodal or cathodal transcranial direct current stimulation (tDCS) was applied over visual area V5 and frontal eye fields (FEF) in sixty healthy participants. The identical within-subject paradigm was used to assess SPEM modulations by practice. While no specific tDCS effects were revealed, within- and between-session practice effects indicate plasticity of top-down extraretinal mechanisms that mainly affect SPEM in the absence of visual input and during SPEM initiation. To explore the potential of tDCS effects, individual electric field simulations were computed based on calibrated finite element head models and individual functional localization of V5 and FEF location (using functional MRI) and orientation (using combined EEG/MEG) was conducted. Simulations revealed only limited electric field target intensities induced by the applied normative tDCS montages but indicate the potential efficacy of personalized tDCS for the modulation of SPEM. In sum, results indicate the potential susceptibility of extraretinal SPEM control to targeted external neuromodulation (e.g., personalized tDCS) and intrinsic learning protocols.

Regional Strain Response of an Anatomically Accurate Human Finite Element Head Model Under Frontal Versus Lateral Loading.

Because brain regions are responsible for specific functions, regional damage may cause specific, predictable symptoms. However, the existing brain injury criteria focus on whole brain response. This study developed and validated a detailed human brain computational model with sufficient fidelity to include regional components and demonstrate its feasibility to obtain region-specific brain strains under selected loading. Model development used the Simulated Injury Monitor (SIMon) model as a baseline. Each SIMon solid element was split into 8, with each shell element split into 4. Anatomical regions were identified from FreeSurfer fsaverage neuroimaging template. Material properties were obtained from literature. The model was validated against experimental intracranial pressure, brain-skull displacement, and brain strain data. Model simulations used data from laboratory experiments with a rigid arm pendulum striking a helmeted head-neck system. Data from impact tests (6 m/s) at 2 helmet sites (front and left) were used. Model validation showed good agreement with intracranial pressure response, fair to good agreement with brain-skull displacement, and good agreement for brain strain. CORrelation Analysis scores were between 0.72 and 0.93 for both maximum principal strain (MPS) and shear strain. For frontal impacts, regional MPS was between 0.14 and 0.36 (average of left and right hemispheres). For lateral impacts, MPS was between 0.20 and 0.48 (left hemisphere) and between 0.22 and 0.51 (right hemisphere). For frontal impacts, regional cumulative strain damage measure (CSDM20) was between 0.01 and 0.87. For lateral impacts, CSDM20 was between 0.36 and 0.99 (left hemisphere) and between 0.09 and 0.93 (right hemisphere). Recognizing that neural functions are related to anatomical structures and most model-based injury metrics focus on whole brain response, this study developed an anatomically accurate human brain model to capture regional responses. Model validation was comparable with current models. The model provided sufficient anatomical detail to describe brain regional responses under different impact conditions.

Determination of optimal injection current pattern for multichannel transcranial electrical stimulation without individual MRI using multiple head models

Background and ObjectiveMultichannel transcranial electrical stimulation (tES) is widely used to achieve improved stimulation focality. In the multichannel tES, the injection current pattern is generally determined through an optimization process with a finite element (FE) head model extracted from individual magnetic resonance images (MRIs). Although using an individual head model ensures the best outcome, acquiring MRIs of individual subjects in many practical applications is often difficult. Alternatively, a standard head model can be used to determine the optimal injection current pattern to stimulate a specific target; however, this may result in a relatively inaccurate delivery of stimulation current owing to the difference in individual anatomical structures. To address this issue, we propose a new approach for determining the injection current pattern using multiple head models, which can improve the stimulation focality compared to that achieved with a single standard head model. MethodsTwenty FE head models were used to optimize the injection current patterns to stimulate three cortical regions that are widely considered targets for tES. The individual injection current patterns were then averaged to obtain each target's mean injection current pattern. The stimulation focality for each target was then calculated by applying different current patterns (the mean current, individual current, and current from a standard model). ResultsOur results showed that the stimulation focality obtained using the mean injection current pattern was significantly higher than that obtained using the injection current pattern from a standard head model. Additionally, our results demonstrated that a minimum of 13 head models are required to determine mean current pattern, allowing for a higher stimulation focality than when using the current from a standard head model. ConclusionsHence, using multiple head models can provide a viable solution for improving the stimulation efficacy of multichannel tES when individual MRIs are not available.

Prediction of skull fractures in blunt force head traumas using finite element head models.

Traumatic head injuries remain a leading cause of death and disability worldwide. Although skull fractures are one of the most common head injuries, the fundamental mechanics of cranial bone and its impact tolerance are still uncertain. In the present study, a strain-rate-dependent material model for cranial bone has been proposed and implemented in subject-specific Finite Element (FE) head models in order to predict skull fractures in five real-world fall accidents. The subject-specific head models were developed following an established image-registration-based personalization pipeline. Head impact boundary conditions were derived from accident reconstructions using personalized human body models. The simulated fracture lines were compared to those visible in post-mortem CT scans of each subject. In result, the FE models did predict the actual occurrence and extent of skull fractures in all cases. In at least four out of five cases, predicted fracture patterns were comparable to ones from CT scans and autopsy reports. The tensile material model, which was tuned to represent rate-dependent tensile data of cortical skull bone from literature, was able to capture observed linear fractures in blunt indentation loading of a skullcap specimen. The FE model showed to be sensitive to modeling parameters, in particular to the constitutive parameters of the cortical tables. Nevertheless, this study provides a currently lacking strain-rate dependent material model of cranial bone that has the capacity to accurately predict linear fracture patterns. For the first time, a procedure to reconstruct occurrences of skull fractures using computational engineering techniques, capturing the all-in-all fracture initiation, propagation and final pattern, is presented.

Experimental and computational approach to human brain modelling – aHEAD

The human head is a highly complex structure, with a combination of hard and soft tissues and a variety of materials and interactions. Many researchers have used computational approaches to model the head, and several human finite element head models can be found in the literature. However, most of them are not geometrically accurate – for instance, the brain is simplified to a smooth spherical volume, which poses some concerns regarding boundary conditions and geometrical accuracy. Therefore, an advanced head model of a 28-year-old, designated as aHEAD 28 yo (aHEAD: advanced Head models for safety Enhancement And medical Development), has been developed. The model consists entirely of hexahedral elements for 3D structures of the head such as the cerebellum, skull and cerebrum, with detailed geometry of the gyri and sulci. Additionally, it is one of the first human head approaches published in the literature that includes cerebrospinal fluid simulated by Smoothed Particle Hydrodynamics (SPH) and a detailed model of pressurized bridging veins. To support the model’s credibility, this study is focused on physical material testing. A novel comprehensive experimental-computational approach is presented, which involves the brain tissue’s response to induced vibrations. The experiment successfully aimed to validate the material models used in the numerical analysis. Additionally, the authors present a kinematical model validation based on the Hardy experimental cadaver test. The developed model, along with its verification, aims to establish a further benchmark in finite element head modelling and can potentially provide new insights into injury mechanisms.

Finite element brain deformation in adolescent soccer heading

Finite element (FE) modeling provides a means to examine how global kinematics of repetitive head loading in sports influences tissue level injury metrics. FE simulations of controlled soccer headers in two directions were completed using a human head FE model to estimate biomechanical loading on the brain by direction. Overall, headers were associated with 95th percentile peak maximum principal strains up to 0.07 and von Mises stresses up to 1450 Pa, and oblique headers trended toward higher values than frontal headers but below typical injury levels. These quantitative data provide insight into repetitive loading effects on the brain.

Evaluation of full-face, open-face, and airbag-equipped helmets for facial impact protection

ObjectiveTwo-wheeler riders frequently sustain injuries to the head and face in real-world crashes, including traumatic brain injury, basilar skull fracture, and facial fracture. Different types of helmets exist today, which are recognized as preventing head injuries in general; however, their efficacy and limitations in facial impact protection are underexplored. Biofidelic surrogate test devices and assessment criteria are lacking in current helmet standards. This study addresses these gaps by applying a new, more biofidelic test method to evaluate conventional full-face helmets and a novel airbag-equipped helmet design. Ultimately, this study aims to contribute to better helmet design and testing standards. MethodsFacial impact tests at two locations, mid-face and lower face, were conducted with a complete THOR dummy. Forces applied to the face and at the junction of the head and neck were measured. Brain strain was predicted by a finite element head model taking both linear and rotational head kinematics as input. Four helmet types were evaluated: full-face motorcycle and bike helmets, a novel design called a face airbag (an inflatable structure integrated into an open-face motorcycle helmet), and an open-face motorcycle helmet. The unpaired, two-sided student’s t-test was performed between the open-face helmet and the others, which featured face-protective designs. ResultsA substantial reduction in brain strain and facial forces was found with the full-face motorcycle helmet and face airbag. Upper neck tensile forces increased slightly with both full-face motorcycle (14.4%, p >.05) and bike helmets (21.7%, p =.039). The full-face bike helmet reduced the brain strain and facial forces for lower-face impacts, but not for mid-face impacts. The motorcycle helmet reduced mid-face impact forces while slightly increasing forces in the lower face. Significance of resultsThe chin guards of full-face helmets and the face airbag protect by reducing facial load and brain strain for lower face impact; however, the full-face helmets’ influence on neck tension and increased risk for basilar skull fracture need further investigation. The motorcycle helmet’s visor re-directed mid-face impact forces to the forehead and lower face via the helmet’s upper rim and chin guard: a thus-far undescribed protective mechanism. Given the significance of the visor for facial protection, an impact test procedure should be included in helmet standards, and the use of helmet visors promoted. A simplified, yet biofidelic, facial impact test method should be included in future helmet standards to ensure a minimum level of protection performance.

A finite rotation, small strain 2D elastic head model, with applications in mild traumatic brain injury

Rotational head motions have been shown to play a key role in traumatic brain injury. There is great interest in developing methods to rapidly predict brain tissue strains and strain rates resulting from rotational head motions to estimate brain injury risk and to guide the design of protective equipment. Idealized continuum mechanics based head models provide an attractive approach for rapidly estimating brain strains and strain rates. These models are capable of capturing the wave dynamics and transient response of the brain while being significantly easier and faster to apply compared to more sophisticated and detailed finite element head models. In this work, we present a new idealized continuum mechanics based head model that accounts for the head’s finite rotations, which is an improvement upon prior models that have been based on a small rotation assumption. Despite the simplicity of the model, we show that the proposed 2D elastic finite rotations head model predicts comparable strains to a more detailed finite element head model, demonstrating the potential usefulness of the model in rapidly estimating brain injury risk. This newly proposed model can serve as a basis for introducing finite rotations into more sophisticated head models in the future.

Development, validation and a case study: The female finite element head model (FeFEHM)

Background and objective: Traumatic brain injuries are one of the leading causes of death and disability in the world. To better understand the interactions and forces applied in different constituents of the human head, several finite element head models have been developed throughout the years, for offering a good cost-effective and ethical approach compared to experimental tests. Once validated, the female finite element head model (FeFEHM) will allow a better understanding of injury mechanisms resulting in neuronal damage, which can later evolve into neurodegenerative diseases.Methods: This work encompasses the approached methodology starting from medical images and finite element modelling until the validation process using novel experimental data of brain displacements conducted on human cadavers. The material modelling of the brain is performed using an age-specific characterization of the brain using microindentation at dynamic rates and under large deformation, with a similar age to the patient used to model the FeFEHM.Results: The numerical displacement curves are in good accordance with the experimental data, displaying similar peak times and values, in all three anatomical planes. The case study result shows a similarity between the pressure fields of the FeFEHM compared to another model, highlighting the future potential of the model.Conclusions: The initial objective was met, and a new female finite element head model has been developed with biofidelic brain motion. This model will be used for the assessment of repetitive impact scenarios and its repercussions on the female brain.

Biomechanics of Traumatic Head and Neck Injuries on Women: A State-of-the-Art Review and Future Directions.

The biomechanics of traumatic injuries of the human body as a consequence of road crashes, falling, contact sports, and military environments have been studied for decades. In particular, traumatic brain injury (TBI), the so-called "silent epidemic", is the traumatic insult responsible for the greatest percentage of death and disability, justifying the relevance of this research topic. Despite its great importance, only recently have research groups started to seriously consider the sex differences regarding the morphology and physiology of women, which differs from men and may result in a specific outcome for a given traumatic event. This work aims to provide a summary of the contributions given in this field so far, from clinical reports to numerical models, covering not only the direct injuries from inertial loading scenarios but also the role sex plays in the conditions that precede an accident, and post-traumatic events, with an emphasis on neuroendocrine dysfunctions and chronic traumatic encephalopathy. A review on finite element head models and finite element neck models for the study of specific traumatic events is also performed, discussing whether sex was a factor in validating them. Based on the information collected, improvement perspectives and future directions are discussed.