Quantitative morphological analysis framework of infant cranial sutures and fontanelles based on CT images.

Characterizing the suture morphological variation is a crucial step to investigate the influence of sutures on infant head biomechanics. This study aimed to establish a comprehensive quantitative framework for accurately capturing the cranial suture and fontanelle morphologies in infants. A total of 69 CT scans of 2-4 month-old infant heads were segmented to identify semilandmarks at the borders of cranial sutures and fontanelles. Morphological characteristics, including length, width, sinuosity index (SI), and surface area, were measured. For this, an automatic method was developed to determine the junction points between sutures and fontanelles, and thin-plate-spline (TPS) was utilized for area calculation. Different dimensionality reduction methods were compared, including nonlinear and linear principal component analysis (PCA), as well as deep-learning-based variational autoencoder (VAE). Finally, the significance of various covariates was analyzed, and regression analysis was performed to establish a statistical model relating morphological parameters with global parameters. This study successfully developed a quantitative morphological framework and demonstrate its application in quantifying morphologies of infant sutures and fontanelles, which were shown to significantly relate to global parameters of cranial size, suture SI, and surface area for infants aged 2-4 months. The developed framework proved to be reliable and applicable in extracting infant suture morphology features from CT scans. The demonstrated application highlighted its potential to provide valuable insights into the morphologies of infant cranial sutures and fontanelles, aiding in the diagnosis of suture-related skull fractures. Infant suture, Infant fontanelle, Morphological variation, Morphology analysis framework, Statistical model.

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.

Designing electrode configuration of electroosmosis based edema treatment as a complement to hyperosmotic therapy.

BACKGROUND: Hyperosmotic therapy is a mainstay treatment for cerebral edema. Although often effective, its disadvantages include mainly acting on the normal brain region with limited effectiveness in eliminating excess fluid in the edema region. This study investigates how to configure our previously proposed novel electroosmosis based edema treatment as a complement to hyperosmotic therapy. METHODS: Three electrode configurations are designed to drive the excess fluid out of the edema region, including 2-electrode, 3-electrode, and 5-electrode designs. The focality and directionality of the induced electroosmotic flow (EOF) are then investigated using the same patient-specific head model with localized edema. RESULTS: The 5-electrode design shows improved EOF focality with reduced effect on the normal brain region than the other two designs. Importantly, this design also achieves better directionality driving excess edema tissue fluid to a larger region of surrounding normal brain where hyperosmotic therapy functions better. Thus, the 5-electrode design is suggested to treat edema more efficiently via a synergic effect: the excess fluid is first driven out from the edema to surrounding normal brain via EOF, where it can then be treated with hyperosmotic therapy. Meanwhile, the 5-electrode design drives 2.22 mL excess fluid from the edema region in an hour comparable to the other designs, indicating a similar efficiency of EOF. CONCLUSIONS: The results show that the promise of our previously proposed novel electroosmosis based edema treatment can be designed to achieve better focality and directionality towards a complement to hyperosmotic therapy.

Axons Embedded in a Tissue May Withstand Larger Deformations Than Isolated Axons Before Mechanoporation Occurs.

Diffuse axonal injury (DAI) is the pathological consequence of traumatic brain injury (TBI) that most of all requires a multiscale approach in order to be, first, understood and then possibly prevented. While in fact the mechanical insult usually happens at the head (or macro) level, the consequences affect structures at the cellular (or microlevel). The quest for axonal injury tolerances has so far been addressed both with experimental and computational approaches. On one hand, the experimental approach presents challenges connected to both temporal and spatial resolution in the identification of a clear axonal injury trigger after the application of a mechanical load. On the other hand, computational approaches usually consider axons as homogeneous entities and therefore are unable to make inferences about their viability, which is thought to depend on subcellular damages. Here, we propose a computational multiscale approach to investigate the onset of axonal injury in two typical experimental scenarios. We simulated single-cell and tissue stretch injury using a composite finite element axonal model in isolation and embedded in a matrix, respectively. Inferences on axonal damage are based on the comparison between axolemma strains and previously established mechanoporation thresholds. Our results show that, axons embedded in a tissue could withstand higher deformations than isolated axons before mechanoporation occurred and this is exacerbated by the increase in strain rate from 1/s to 10/s.

Mechanistic Insights into Human Brain Impact Dynamics through Modal Analysis.

Although concussion is one of the greatest health challenges today, our physical understanding of the cause of injury is limited. In this Letter, we simulated football head impacts in a finite element model and extracted the most dominant modal behavior of the brain's deformation. We showed that the brain's deformation is most sensitive in low frequency regimes close to 30 Hz, and discovered that for most subconcussive head impacts, the dynamics of brain deformation is dominated by a single global mode. In this Letter, we show the existence of localized modes and multimodal behavior in the brain as a hyperviscoelastic medium. This dynamical phenomenon leads to strain concentration patterns, particularly in deep brain regions, which is consistent with reported concussion pathology.

A National Survey of Traumatic Brain Injuries Admitted to Hospitals in Sweden from 1987 to 2010.

BACKGROUND: With an increasing and aging population, there is a global demand for improving the primary prevention strategies aimed at reducing traumatic brain injuries (TBIs). The objective of the present epidemiological study was to evaluate the pattern of TBI in Sweden over a 24 years period (1987-2010). METHODS: The Swedish Hospital Discharge Register was used, where in-patient care with a main diagnosis of TBI according to ICD9/10 was included. External factors, age and gender distribution was evaluated. RESULTS: A decreasing number of annual incidence was observed, that is, from 230 to 156 per 100,000 inhabitants. A steady decrease of concussion was observed while other intracranial injuries increased especially traumatic subdural hemorrhage and subarachnoid hemorrhage. The study identified 3 groups of patients - young, adults and elderly. The highest incidence and the largest increase of incidence were seen in the oldest age group (85+ years) while the population under 65 years had a decreasing incidence of TBI. The most frequent etiology was fall accidents (57%) with a relative constant trend over the study period. CONCLUSIONS: More effort should be focused on different strategies for different age groups, especially the elderly group. A well-planned strategy for primary prevention guidelines for different age groups will have the chance to further reduce not only the health-care costs but also complications among elderly care.

The Biomechanical Determinants of Concussion: Finite Element Simulations to Investigate Tissue-Level Predictors of Injury During Sporting Impacts to the Unprotected Head.

Biomechanical studies of concussions have progressed from qualitative observations of head impacts to physical and numerical reconstructions, direct impact measurements, and finite element analyses. Supplementary to a previous study, which investigated maximum principal strain, the current study used a detailed finite element head model to simulate unhelmeted concussion and no-injury head impacts and evaluate the effectiveness of various tissue-level brain injury predictors: strain rate, product of strain and strain rate, cumulative strain damage measure, von Mises stress, and intracranial pressure. Von Mises stress was found to be the most effective predictor of concussion. It was also found that the thalamus and corpus callosum were brain regions with strong associations with concussion. Tentative tolerance limits for tissue-level predictors were proposed in an attempt to broaden the understanding of unhelmeted concussions. For the thalamus, tolerance limits were proposed for a 50% likelihood of concussion: 2.24 kPa, 24.0 s-1, and 2.49 s-1 for von Mises stress, strain rate, and the product of strain and strain rate, respectively. For the corpus callosum, tolerance limits were proposed for a 50% likelihood of concussion: 3.51 kPa, 25.1 s-1, and 2.76 s-1 for von Mises stress, strain rate, and the product of strain and strain rate, respectively.

Development of personalizable female and male pedestrian SAFER human body models.

OBJECTIVES: Vulnerable road users are globally overrepresented as victims of road traffic injuries. Developing biofidelic male and female pedestrian human body models (HBMs) that represent diverse anthropometries is essential to enhance road safety and propose intervention strategies. METHODS: In this study, 50th percentile male and female pedestrians of the SAFER HBM were developed via a newly developed image registration-based mesh morphing framework. The performance of the HBMs was evaluated by means of a set of cadaver experiments, involving subjects struck laterally by a generic sedan buck. RESULTS: In simulated whole-body pedestrian collisions, the personalized HBMs effectively replicate trajectories of the head and lower body regions, as well as head kinematics, in lateral impacts. The results also demonstrate the personalization framework's capacity to generate personalized HBMs with reliable mesh quality, ensuring robust simulations. CONCLUSIONS: The presented pedestrian HBMs and personalization framework provide robust means to reconstruct and evaluate head impacts in pedestrian-to-vehicle collisions thoroughly and accurately.

A method for generating case-specific vehicle models from a single-view vehicle image for accurate pedestrian injury reconstructions.

Developing vehicle finite element (FE) models that match real accident-involved vehicles is challenging. This is related to the intricate variety of geometric features and components. The current study proposes a novel method to efficiently and accurately generate case-specific buck models for car-to-pedestrian simulations. To achieve this, we implemented the vehicle side-view images to detect the horizontal position and roundness of two wheels to rectify distortions and deviations and then extracted the mid-section profiles for comparative calculations against baseline vehicle models to obtain the transformation matrices. Based on the generic buck model which consists of six key components and corresponding matrices, the case-specific buck model was generated semi-automatically based on the transformation metrics. Utilizing this image-based method, a total of 12 vehicle models representing four vehicle categories including family car (FCR), Roadster (RDS), small Sport Utility Vehicle (SUV), and large SUV were generated for car-to-pedestrian collision FE simulations in this study. The pedestrian head trajectories, total contact forces, head injury criterion (HIC), and brain injury criterion (BrIC) were analyzed comparatively. We found that, even within the same vehicle category and initial conditions, the variation in wrap around distance (WAD) spans 84-165 mm, in HIC ranges from 98 to 336, and in BrIC fluctuates between 1.25 and 1.46. These findings highlight the significant influence of vehicle frontal shape and underscore the necessity of using case-specific vehicle models in crash simulations. The proposed method provides a new approach for further vehicle structure optimization aiming at reducing pedestrian head injury and increasing traffic safety.

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.

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, 22.3 g; PAV, 10.4 rad/s; MPS95, 0.05) were lower than for high school male lacrosse (PLA, 24.2 g; PAV, 15.4 rad/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.

The biomechanical determinants of concussion: finite element simulations to investigate brain tissue deformations during sporting impacts to the unprotected head.

Concussion is an injury of specific interest in collision and contact sports, resulting in a need to develop effective preventive strategies. A detailed finite element model of the human head was used to approximate the regional distribution of tissue deformations in the brain by simulating reconstructions of unhelmeted concussion and no-injury head impacts. The results were evaluated using logistic regression analysis and it was found that angular kinematics, in the coronal plane, and maximum principal strains, in all regions of the brain, were significantly associated with concussion. The results suggested that impacts to the temporal region of the head cause coronal rotations, which render injurious strain levels in the brain. Tentative strain tolerance levels of 0.13, 0.15, and 0.26 in the thalamus, corpus callosum, and white matter, respectively, for a 50% likelihood of concussion were determined by logistic regression. The tentative strain tolerance levels compared well with previously reported results from reconstruction studies of American football and single axon, optic nerve, and brain slice culture model studies. The methods used in the current study provide an opportunity to collect unique kinematic data of sporting impacts to the unprotected head, which can be employed in various ways to broaden the understanding of concussion.

Injury risk functions for the four primary knee ligaments.

The purpose of this study was to develop injury risk functions (IRFs) for the anterior and posterior cruciate ligaments (ACL and PCL, respectively) and the medial and lateral collateral ligaments (MCL and LCL, respectively) in the knee joint. The IRFs were based on post-mortem human subjects (PMHSs). Available specimen-specific failure strains were supplemented with statistically generated failure strains (virtual values) to accommodate for unprovided detailed experimental data in the literature. The virtual values were derived from the reported mean and standard deviation in the experimental studies. All virtual and specimen-specific values were thereafter categorized into groups of static and dynamic rates, respectively, and tested for the best fitting theoretical distribution to derive a ligament-specific IRF. A total of 10 IRFs were derived (three for ACL, two for PCL, two for MCL, and three for LCL). ACL, MCL, and LCL received IRFs in both dynamic and static tensile rates, while a sufficient dataset was achieved only for dynamic rates of the PCL. The log-logistic and Weibull distributions had the best fit (p-values: >0.9, RMSE: 2.3%-4.7%) to the empirical datasets for all the ligaments. These IRFs are, to the best of the authors' knowledge, the first attempt to generate injury prediction tools based on PMHS data for the four knee ligaments. The study has summarized all the relevant literature on PHMS experimental tensile tests on the knee ligaments and utilized the available empirical data to create the IRFs. Future improvements require upcoming experiments to provide comparable testing and strain measurements. Furthermore, emphasis on a clear definition of failure and transparent reporting of each specimen-specific result is necessary.

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.

Personalization of human body models and beyond via image registration.

Finite element human body models (HBMs) are becoming increasingly important numerical tools for traffic safety. Developing a validated and reliable HBM from the start requires integrated efforts and continues to be a challenging task. Mesh morphing is an efficient technique to generate personalized HBMs accounting for individual anatomy once a baseline model has been developed. This study presents a new image registration-based mesh morphing method to generate personalized HBMs. The method is demonstrated by morphing four baseline HBMs (SAFER, THUMS, and VIVA+ in both seated and standing postures) into ten subjects with varying heights, body mass indices (BMIs), and sex. The resulting personalized HBMs show comparable element quality to the baseline models. This method enables the comparison of HBMs by morphing them into the same subject, eliminating geometric differences. The method also shows superior geometry correction capabilities, which facilitates converting a seated HBM to a standing one, combined with additional positioning tools. Furthermore, this method can be extended to personalize other models, and the feasibility of morphing vehicle models has been illustrated. In conclusion, this new image registration-based mesh morphing method allows rapid and robust personalization of HBMs, facilitating personalized simulations.

Increased strain levels and water content in brain tissue after decompressive craniotomy.

BACKGROUND: At present there is a debate on the effectiveness of the decompressive craniotomy (DC). Stretching of axons was speculated to contribute to the unfavourable outcome for the patients. The quantification of strain level could provide more insight into the potential damage to the axons. The aim of the present study was to evaluate the strain level and water content (WC) of the brain tissue for both the pre- and post-craniotomy period. METHODS: The stretching of brain tissue was quantified retrospectively based on the computerised tomography (CT) images of six patients before and after DC by a non-linear image registration method. WC was related to specific gravity (SG), which in turn was related to the Hounsfield unit (HU) value in the CT images by a photoelectric correction according to the chemical composition of brain tissue. RESULTS: For all the six patients, the strain level showed a substantial increase in the brain tissue close to the treated side of DC compared with that found at the pre-craniotomy period and ranged from 24 to 55 % at the post-craniotomy period. Increase of strain level was also observed at the brain tissue opposite to the treated side, however, to a much lesser extent. The mean area of craniotomy was found to be 91.1 ± 12.7 cm(2). The brain tissue volume increased from 27 to 127 ml, corresponding to 1.65 % and 8.13 % after DC in all six patients. Also, the increased volume seemed to correlate with increased strain level. Specifically, the overall WC of brain tissue for two patients evaluated presented a significant increase after the treatment compared with the condition seen before the treatment. Furthermore, the Glasgow Coma Scale (GCS) improved in four patients after the craniotomy, while two patients died. The GCS did not seem to correlate with the strain level. CONCLUSIONS: We present a new numerical method to quantify the stretching or strain level of brain tissue and WC following DC. The significant increase in strain level and WC in the post-craniotomy period may cause electrophysiological changes in the axons, resulting in loss of neuronal function. Hence, this new numerical method provides more insight of the consequences following DC and may be used to better define the most optimal size and area of the craniotomy in reducing the strain level development.

Upright trunk and lateral or slight anterior rotation of the pelvis cause the highest proximal femur forces during sideways falls.

Whole-body models are historically developed for traffic injury prevention, and they are positioned accordingly in the standing or sitting configuration representing pedestrian or occupant postures. Those configurations are appropriate for vehicle accidents or pedestrian-vehicle accidents; however, they are uncommon body posture during a fall accident to the ground. This study aims to investigate the influence of trunk and pelvis angles on the proximal femur forces during sideways falls. For this purpose, a previously developed whole-body model was positioned into different fall configurations varying the trunk and pelvis angles. The trunk angle was varied in steps of 10° from 10 to 80°, and the pelvis rotation was changed every 5° from -20° (rotation toward posterior) to +20° (rotation toward anterior). The simulations were performed on a medium-size male (177 cm, 76 kg) and a small-size female (156 cm, 55 kg), representative for elderly men and women, respectively. The results demonstrated that the highest proximal femur force measured on the femoral head was reached when either male or female model had a 10-degree trunk angle and +10° anterior pelvis rotation.

Influence of Strain post-processing on Brain Injury Prediction.

Finite element head models are a tool to better understand brain injury mechanisms. Many of the models use strain as output but with different percentile values such as 100th, 95th, 90th, and 50th percentiles. Some use the element value, whereas other use the nodal average value for the element. Little is known how strain post-processing is affecting the injury predictions and evaluation of different prevention systems. The objective of this study was to evaluate the influence of strain output on injury prediction and ranking. Two models with different mesh densities were evaluated (KTH Royal Institute of Technology head model and the Total Human Models for Safety (THUMS)). Pulses from reconstructions of American football impacts with and without a diagnosis of mild traumatic brain injury were applied to the models. The value for 100th, 99th, 95th, 90th, and 50th percentile for element and nodal averaged element strain was evaluated based on peak values, injury risk functions, injury predictability, correlation in ranking, and linear correlation. The injury risk functions were affected by the post-processing of the strain, especially the 100th percentile element value stood out. Meanwhile, the area under the curve (AUC) value was less affected, as well as the correlation in ranking (Kendall's tau 0.71-1.00) and the linear correlation (Pearson's r2 0.72-1.00). With the results presented in this study, it is important to stress that the same post-processed strain should be used for injury predictions as the one used to develop the risk function.

A rubberized impact absorbing pavement can reduce the head injury risk in vulnerable road users: A bicycle and a pedestrian accident case study.

OBJECTIVE: Vulnerable Road Users (VRU), including pedestrians and cyclists, are generally the least protected road users and are frequently missed in the planning process of preventive measures. Rubberized asphalt mixtures were originally developed as a possible environmentally friendly solution to recycle the End-of-Life Tires while making the pavements more durable. The objective of the current study was to explore the effects of increasing the rubber content of the common rubberized asphalt mixtures in reducing the head injuries risk for VRUs. METHOD: To achieve this purpose, four different sample series with 0, 14, 28, and 33 weight percent rubber in each were tested. A compressive test without permanent deformation and one with failure were performed on each sample series. The mechanical behavior of each set was modeled using a MAT_SIMPLIFIED_RUBBER material model in LS-Dyna and validated against a standard Head Injury Criterion (HIC) drop test. Ultimately, previously low-speed accident reconstructed cases, a bicycle and a pedestrian one, were used to assess the effect of varying the rubber content on reducing the head injury risk. RESULTS: In the bicycle accident case, the risk of skull fracture was reduced from 0.99 to 0.29 when comparing the non-rubberized asphalt mixture with the 33% rubber mixture. In the same accident case, the risk of concussion, evaluated using the logistic regression method, was reduced from 0.97 in the non-rubberized mixture to 0.81 in the 33% rubber mixture. The initial conditions, linear and rotational velocities, were lower for the pedestrian case compared to the bicycle case (the bicycle case was more severe compared to the pedestrian case), which led to lower strains in the pedestrian case. In the pedestrian accident case, the risk of skull fracture was reduced from 1.00 in the non-rubberized mixture to 0.63 in the 33% rubber mixture, while the risk of concussion was reduced from 0.64 to 0.07. CONCLUSION: The rubberized asphalt mixtures could reduce the head injury risk for the studied cases when the rubber content in the asphalt mixture increases.

Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports.

Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes.