An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks.

The brain is a complex network consisting of neuron cell bodies in the gray matter and their axonal projections, forming the white matter tracts. These neurons are supported by an equally complex vascular network as well as glial cells. Traumatic brain injury (TBI) can lead to the disruption of the structural and functional brain networks due to disruption of both neuronal cell bodies in the gray matter as well as their projections and supporting cells. To explore how an impact can alter the function of brain networks, we integrated a finite element (FE) brain mechanics model with linked models of brain dynamics (Kuramoto oscillator) and vascular perfusion (Balloon-Windkessel) in this study. We used empirical resting-state functional magnetic resonance imaging (MRI) data to optimize the fit of our brain dynamics and perfusion models to clinical data. Results from the FE model were used to mimic injury in these optimized brain dynamics models: injury to the nodes (gray matter) led to a decrease in the nodal oscillation frequency, while damage to the edges (axonal connections/white matter) progressively decreased coupling among connected nodes. A total of 53 cases, including 33 non-injurious and 20 concussive head impacts experienced by professional American football players were simulated using this integrated model. We examined the correlation of injury outcomes with global measures of structural connectivity, neural dynamics, and functional connectivity of the brain networks when using different lesion methods. Results show that injurious head impacts cause significant alterations in global network topology regardless of lesion methods. Changes between the disrupted and healthy functional connectivity (measured by Pearson correlation) consistently correlated well with injury outcomes (AUC≥0.75), although the predictive performance is not significantly different (p>0.05) to that of traditional kinematic measures (angular acceleration). Intriguingly, our lesion model for gray matter damage predicted increases in global efficiency and clustering coefficient with increases in injury risk, while disrupting axonal connections led to lower network efficiency and clustering. When both injury mechanisms were combined into a single injury prediction model, the injury prediction performance depended on the thresholds used to determine neurodegeneration and mechanical tolerance for axonal injury. Together, these results point towards complex effects of mechanical trauma to the brain and provide a new framework for understanding brain injury at a causal mechanistic level and developing more effective diagnostic methods and therapeutic interventions.

Generating Human Arm Kinematics Using Reinforcement Learning to Train Active Muscle Behavior in Automotive Research.

Computational human body models (HBMs) are important tools for predicting human biomechanical responses under automotive crash environments. In many scenarios, the prediction of the occupant response will be improved by incorporating active muscle control into the HBMs to generate biofidelic kinematics during different vehicle maneuvers. In this study, we have proposed an approach to develop an active muscle controller based on reinforcement learning (RL). The RL muscle activation control (RL-MAC) approach is a shift from using traditional closed-loop feedback controllers, which can mimic accurate active muscle behavior under a limited range of loading conditions for which the controller has been tuned. Conversely, the RL-MAC uses an iterative training approach to generate active muscle forces for desired joint motion and is analogous to how a child develops gross motor skills. In this study, the ability of a deep deterministic policy gradient (DDPG) RL controller to generate accurate human kinematics is demonstrated using a multibody model of the human arm. The arm model was trained to perform goal-directed elbow rotation by activating the responsible muscles and investigated using two recruitment schemes: as independent muscles or as antagonistic muscle groups. Simulations with the trained controller show that the arm can move to the target position in the presence or absence of externally applied loads. The RL-MAC trained under constant external loads was able to maintain the desired elbow joint angle under a simplified automotive impact scenario, implying the robustness of the motor control approach.

Integrating Human and Nonhuman Primate Data to Estimate Human Tolerances for Traumatic Brain Injury.

Traumatic brain injury (TBI) contributes to a significant portion of the injuries resulting from motor vehicle crashes, falls, and sports collisions. The development of advanced countermeasures to mitigate these injuries requires a complete understanding of the tolerance of the human brain to injury. In this study, we developed a new method to establish human injury tolerance levels using an integrated database of reconstructed football impacts, subinjurious human volunteer data, and nonhuman primate data. The human tolerance levels were analyzed using tissue-level metrics determined using harmonized species-specific finite element (FE) brain models. Kinematics-based metrics involving complete characterization of angular motion (e.g., diffuse axonal multi-axial general evaluation (DAMAGE)) showed better power of predicting tissue-level deformation in a variety of impact conditions and were subsequently used to characterize injury tolerance. The proposed human brain tolerances for mild and severe TBI were estimated and presented in the form of injury risk curves based on selected tissue-level and kinematics-based injury metrics. The application of the estimated injury tolerances was finally demonstrated using real-world automotive crash data.

Test-retest reliability and long-term stability of three-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data.

PURPOSE: Several recent studies have used a three-tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion-weighted MRI data. The three tissue compartments, consisting of white matter, gray matter, and CSF-like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long-term stability of these metrics have not yet been evaluated. METHODS: This study examined estimates of whole-brain microstructure for the three tissue compartments, in three separate test-retest cohorts. Each cohort had different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to 3 months in the longest interval. Each cohort was also collected with different acquisition parameters. RESULTS: The CSF-like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter-like and gray matter-like compartments both demonstrated very high reliability in the immediate cohort (both ICC > 0.90); however, this declined in the 3-month interval cohort to both compartments having ICC > 0.80. Regional CSF-like signal fraction was examined in bilateral hippocampus and had an ICC > 0.80 in each cohort. CONCLUSION: The three-tissue constrained spherical deconvolution techniques provide reliable and stable estimates of tissue-microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations using three-tissue constrained spherical deconvolution techniques to track changes in microstructure across a variety of brain pathologies.

A Multibody Model for Predicting Spatial Distribution of Human Brain Deformation Following Impact Loading.

With an increasing focus on long-term consequences of concussive brain injuries, there is a new emphasis on developing tools that can accurately predict the mechanical response of the brain to impact loading. Although finite element models (FEM) estimate the brain response under dynamic loading, these models are not capable of delivering rapid (∼seconds) estimates of the brain's mechanical response. In this study, we develop a multibody spring-mass-damper model that estimates the regional motion of the brain to rotational accelerations delivered either about one anatomic axis or across three orthogonal axes simultaneously. In total, we estimated the deformation across 120 locations within a 50th percentile human brain. We found the multibody model (MBM) correlated, but did not precisely predict, the computed finite element response (average relative error: 18.4 ± 13.1%). We used machine learning (ML) to combine the prediction from the MBM and the loading kinematics (peak rotational acceleration, peak rotational velocity) and significantly reduced the discrepancy between the MBM and FEM (average relative error: 9.8 ± 7.7%). Using an independent sports injury testing set, we found the hybrid ML model also correlated well with predictions from a FEM (average relative error: 16.4 ± 10.2%). Finally, we used this hybrid MBM-ML approach to predict strains appearing in different locations throughout the brain, with average relative error estimates ranging from 8.6% to 25.2% for complex, multi-axial acceleration loading. Together, these results show a rapid and reasonably accurate method for predicting the mechanical response of the brain for single and multiplanar inputs, and provide a new tool for quickly assessing the consequences of impact loading throughout the brain.

Development of a Single-Degree-of-Freedom Mechanical Model for Predicting Strain-Based Brain Injury Responses.

Linking head kinematics to injury risk has been the focus of numerous brain injury criteria. Although many early forms were developed using mechanics principles, recent criteria have been developed using empirical methods based on subsets of head impact data. In this study, a single-degree-of-freedom (sDOF) mechanical analog was developed to parametrically investigate the link between rotational head kinematics and brain deformation. Model efficacy was assessed by comparing the maximum magnitude of displacement to strain-based brain injury predictors from finite element (FE) human head models. A series of idealized rotational pulses covering a broad range of acceleration and velocity magnitudes (0.1-15 krad/s2 and 1-100 rad/s) with durations between 1 and 3000 ms were applied to the mechanical models about each axis of the head. Results show that brain deformation magnitude is governed by three categories of rotational head motion each distinguished by the duration of the pulse relative to the brain's natural period: for short-duration pulses, maximum brain deformation depended primarily on angular velocity magnitude; for long-duration pulses, brain deformation depended primarily on angular acceleration magnitude; and for pulses relatively close to the natural period, brain deformation depended on both velocity and acceleration magnitudes. These results suggest that brain deformation mechanics can be adequately explained by simple mechanical systems, since FE model responses and experimental brain injury tolerances exhibited similar patterns to the sDOF model. Finally, the sDOF model was the best correlate to strain-based responses and highlighted fundamental limitations with existing rotational-based brain injury metrics.

Significant head accelerations can influence immediate neurological impairments in a murine model of blast-induced traumatic brain injury.

Although blast-induced traumatic brain injury (bTBI) is well recognized for its significance in the military population, the unique mechanisms of primary bTBI remain undefined. Animate models of primary bTBI are critical for determining these potentially unique mechanisms, but the biomechanical characteristics of many bTBI models are poorly understood. In this study, we examine some common shock tube configurations used to study blast-induced brain injury in the laboratory and define the optimal configuration to minimize the effect of torso overpressure and blast-induced head accelerations. Pressure transducers indicated that a customized animal holder successfully reduced peak torso overpressures to safe levels across all tested configurations. However, high speed video imaging acquired during the blast showed significant head accelerations occurred when animals were oriented perpendicular to the shock tube axis. These findings of complex head motions during blast are similar to previous reports [Goldstein et al., 2012, "Chronic Traumatic Encephalopathy in Blast-Exposed Military Veterans and a Blast Neurotrauma Mouse Model," Sci. Transl. Med., 4(134), 134ra160; Sundaramurthy et al., 2012, "Blast-Induced Biomechanical Loading of the Rat: An Experimental and Anatomically Accurate Computational Blast Injury Model," J. Neurotrauma, 29(13), pp. 2352-2364; Svetlov et al., 2010, "Morphologic and Biochemical Characterization of Brain Injury in a Model of Controlled Blast Overpressure Exposure," J. Trauma, 69(4), pp. 795-804]. Under the same blast input conditions, minimizing head acceleration led to a corresponding elimination of righting time deficits. However, we could still achieve righting time deficits under minimal acceleration conditions by significantly increasing the peak blast overpressure. Together, these data show the importance of characterizing the effect of blast overpressure on head kinematics, with the goal of producing models focused on understanding the effects of blast overpressure on the brain without the complicating factor of superimposed head accelerations.

Development of a biofidelic computational model of human pelvis for predicting biomechanical responses and pelvic fractures.

BACKGROUND AND OBJECTIVE: The pelvis, a crucial structure for human locomotion, is susceptible to injuries resulting in significant morbidity and disability. This study aims to introduce and validate a biofidelic computational pelvis model, enhancing our understanding of pelvis injury mechanisms under lateral loading conditions. METHODS: The Finite Element (FE) pelvic model, representing a mid-sized male, was developed with variable cortical thickness in pelvis bones. Material properties were determined through a synthesis of existing constitutive models, parametric studies, and multiple validations. Comprehensive validation included various tests, such as load-displacement assessments of sacroiliac joints, quasi-static and dynamic lateral compression on the acetabulum, dynamic side impacts on the acetabulum and iliac wing using defleshed pelvis, and lateral impacts by a rigid plate on the full body's pelvis region. RESULTS: Simulation results demonstrated a reasonable correlation between the pelvis model's overall response and cadaveric testing data. Predicted fracture patterns of the isolated pelvis exhibited fair agreement with experimental results. CONCLUSIONS: This study introduces a credible computational model, providing valuable biomechanical insights into the pelvis' response under diverse lateral loading conditions and fracture patterns. The work establishes a robust framework for developing and enhancing the biofidelity of pelvis FE models through a multi-level validation approach, stimulating further research in modeling, validation, and experimental studies related to pelvic injuries. The findings are expected to offer critical perspectives for predicting, preventing, and mitigating pelvic injuries from vehicular accidents, contributing to advancements in clinical research on medical treatments for pelvic fractures.

Cervical vertebral and spinal cord injuries in rollover occupants.

BACKGROUND: Rollover crashes continue to be a substantial public health issue in North America. Previous research has shown that the cervical spine is the most injured spine segment in rollovers, but much of the past research has focused on risk factors rather than the actual cervical spine injuries. We sought to examine how different types of cervical spine injuries (vertebral and/or cord injury) vary with different occupant-related factors in rollovers and to compare these with non-rollovers. METHODS: We obtained crash and injury information from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) for 2005-2015 and Crash Investigation Sampling System (CISS) for 2017-2022. Based on weighted data, we calculated relative risks to assess how occupant sex, seat belt use, ejection status, and fatal outcome relate to the rate of different cervical spine injuries in rollovers and non-rollovers. RESULTS: In NASS-CDS occupants with cervical spine injuries (N = 111,040 weighted cases), about 91.5% experienced at least one vertebral injury whereas only 11.3% experienced a spinal cord injury (most of which had a concomitant vertebral fracture). All types of cervical spine injuries we examined were 3.4-5.2 times more likely to occur in rollovers compared to non-rollovers. These relative risks were similar for both sexes, belted and unbelted, non-ejected, and non-fatal occupants. The number of weighted CISS occupants with cervical spine injuries (N = 42,003) was smaller than in the NASS analysis, but cervical spine injuries remained 6.25 to 6.36 times more likely in rollovers compared to non-rollovers despite a more modern vehicle fleet. CONCLUSIONS: These findings underscore the continued need for rollover-specific safety countermeasures, especially those focused on cervical spine injury prevention, and elucidate the frequency, severity and other characteristics of the specific vertebral and spinal cord injuries being sustained in rollovers. Our findings suggest that countermeasures focused on preventing cervical vertebral fractures will also effectively prevent most cervical spinal cord injuries.

Attenuation of blast pressure behind ballistic protective vests.

BACKGROUND: Clinical studies increasingly report brain injury and not pulmonary injury following blast exposures, despite the increased frequency of exposure to explosive devices. The goal of this study was to determine the effect of personal body armour use on the potential for primary blast injury and to determine the risk of brain and pulmonary injury following a blast and its impact on the clinical care of patients with a history of blast exposure. METHODS: A shock tube was used to generate blast overpressures on soft ballistic protective vests (NIJ Level-2) and hard protective vests (NIJ Level-4) while overpressure was recorded behind the vest. RESULTS: Both types of vest were found to significantly decrease pulmonary injury risk following a blast for a wide range of conditions. At the highest tested blast overpressure, the soft vest decreased the behind armour overpressure by a factor of 14.2, and the hard vest decreased behind armour overpressure by a factor of 56.8. Addition of body armour increased the 50th percentile pulmonary death tolerance of both vests to higher levels than the 50th percentile for brain injury. CONCLUSIONS: These results suggest that ballistic protective body armour vests, especially hard body armour plates, provide substantial chest protection in primary blasts and explain the increased frequency of head injuries, without the presence of pulmonary injuries, in protected subjects reporting a history of blast exposure. These results suggest increased clinical suspicion for mild to severe brain injury is warranted in persons wearing body armour exposed to a blast with or without pulmonary injury.

Investigating the Effect of Brain Size on Deformation Magnitude Using Subject-Specific Finite Element Models.

Abstract In the last decade, computational models of the brain have become the gold standard tool for investigating traumatic brain injury (TBI) mechanisms and developing novel protective equipment and other safety countermeasures. However, most studies utilizing finite element (FE) models of the brain have been conducted using models developed to represent the average neuroanatomy of a target demographic, such as the 50th percentile male. Although this is an efficient strategy, it neglects normal anatomical variations present within the population and their contributions on the brain's deformation response. As a result, the contributions of structural characteristics of the brain, such as brain volume, on brain deformation are not well understood. The objective of this study was to develop a set of statistical regression models relating measures of the size and shape of the brain to the resulting brain deformation. This was performed using a database of 125 subject-specific models, simulated under six independent head kinematic boundary conditions, spanning a range of impact modes (frontal, oblique, side), severity (non-injurious and injurious), and environments (volunteer, automotive, and American football). Two statistical regression techniques were utilized. First, simple linear regression (SLR) models were trained to relate intracranial volume (ICV) and the 95th percentile of maximum principal strain (MPS-95) for each of the impact cases. Second, a partial least squares regression model was constructed to predict MPS-95 based on the affine transformation parameters from each subject, representing the size and shape of their brain, considering the six impact conditions collectively. Both techniques indicated a strong linear relationship between ICV and MPS-95, with MPS-95 varying by approximately 5% between the smallest and largest brains. This difference represented up to 40% of the mean strain across all subjects. This study represents a comprehensive assessment of the relationships between brain anatomy and deformation, which is crucial for the development of personalized protective equipment, identifying individuals at higher risk of injury, and using computational models to aid clinical diagnostics of TBI.

Quantifying the Effect of Sex and Neuroanatomical Biomechanical Features on Brain Deformation Response in Finite Element Brain Models.

Recent automotive epidemiology studies have concluded that females have significantly higher odds of sustaining a moderate brain injury or concussion than males in a frontal crash after controlling for multiple crash and occupant variables. Differences in neuroanatomical features, such as intracranial volume (ICV), have been shown between male and female subjects, but how these sex-specific neuroanatomical differences affect brain deformation is unknown. This study used subject-specific finite element brain models, generated via registration-based morphing using both male and female magnetic resonance imaging scans, to investigate sex differences of a variety of neuroanatomical features and their effect on brain deformation; additionally, this study aimed to determine the relative importance of these neuroanatomical features and sex on brain deformation metrics for a single automotive loading environment. Based on the Bayesian linear mixed models, sex had a significant effect on ICV, white matter volume and gray matter volume, as well as a section of cortical gray matter regions' thicknesses and volumes; however, after these neuroanatomical features were accounted for in the statistical model, sex was not a significant factor in predicting brain deformation. ICV had the highest relative effect on the brain deformation metrics assessed. Therefore, ICV should be considered when investigating both brain injury biomechanics and injury risk.

Brain architecture-based vulnerability to traumatic injury.

The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups' dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.

Concussion Prone Scenarios: A Multi-Dimensional Exploration in Impact Directions, Brain Morphology, and Network Architectures Using Computational Models.

While individual susceptibility to traumatic brain injury (TBI) has been speculated, past work does not provide an analysis considering how physical features of an individual's brain (e.g., brain size, shape), impact direction, and brain network features can holistically contribute to the risk of suffering a TBI from an impact. This work investigated each of these features simultaneously using computational modeling and analyses of simulated functional connectivity. Unlike the past studies that assess the severity of TBI based on the quantification of brain tissue damage (e.g., principal strain), we approached the brain as a complex network in which neuronal oscillations orchestrate to produce normal brain function (estimated by functional connectivity) and, to this end, both the anatomical damage location and its topological characteristics within the brain network contribute to the severity of brain function disruption and injury. To represent the variations in the population, we analyzed a publicly available database of brain imaging data and selected five distinct network architectures, seven different brain sizes, and three uniaxial head rotational conditions to study the consequences of 74 virtual impact scenarios. Results show impact direction produces the most significant change in connections across brain areas (structural connectome) and the functional coupling of activity across these brain areas (functional connectivity). Axial rotations were more injurious than those with sagittal and coronal rotations when the head kinematics were the same for each condition. When the impact direction was held constant, brain network architecture showed a significantly different vulnerability across axial and sagittal, but not coronal rotations. As expected, brain size significantly affected the expected change in structural and functional connectivity after impact. Together, these results provided groupings of predicted vulnerability to impact-a subgroup of male brain architectures exposed to axial impacts were most vulnerable, while a subgroup of female brain architectures was the most tolerant to the sagittal impacts studied. These findings lay essential groundwork for subject-specific analyses of concussion and provide invaluable guidance for designing personalized protection equipment.

Helmet Technology, Head Impact Exposure, and Cortical Thinning Following a Season of High School Football.

The purpose of this study was to compare the effects of wearing older, lower-ranked football helmets (LRank) to wearing newer, higher-ranked football helmets (HRank) on pre- to post-season changes in cortical thickness in response to repetitive head impacts and assess whether changes in cortical thickness are associated with head impact exposure for either helmet type. 105 male high-school athletes (NHRank = 52, NLRank = 53) wore accelerometers affixed behind the left mastoid during all practices and games for one regular season of American football to monitor head impact exposure. Pre- and post-season magnetic resonance imaging (MRI) were completed to assess longitudinal changes in cortical thickness. Significant reductions in cortical thickness (i.e., cortical thinning) were observed pre- to post-season for each group, but these longitudinal alterations were not significantly different between the LRank and HRank groups. Further, significant group-by-head impact exposure interactions were observed when predicting changes in cortical thickness. Specifically, a greater frequency of high magnitude head impacts during the football season resulted in greater cortical thinning for the LRank group, but not for the HRank group. These data provide preliminary in vivo evidence that HRank helmets may provide a buffer between the specific effect of high magnitude head impacts on regional thinning by dissipating forces more evenly throughout the cortex. However, future research with larger sample sizes, increased longitudinal measures and additional helmet technologies is warranted to both expand upon and further validate the present study findings.

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.

Evaluation and Validation of Thorax Model Responses: A Hierarchical Approach to Achieve High Biofidelity for Thoracic Musculoskeletal System.

As one of the most frequently occurring injuries, thoracic trauma is a significant public health burden occurring in road traffic crashes, sports accidents, and military events. The biomechanics of the human thorax under impact loading can be investigated by computational finite element (FE) models, which are capable of predicting complex thoracic responses and injury outcomes quantitatively. One of the key challenges for developing a biofidelic FE model involves model evaluation and validation. In this work, the biofidelity of a mid-sized male thorax model has been evaluated and enhanced by a multi-level, hierarchical strategy of validation, focusing on injury characteristics, and model improvement of the thoracic musculoskeletal system. At the component level, the biomechanical responses of several major thoracic load-bearing structures were validated against different relevant experimental cases in the literature, including the thoracic intervertebral joints, costovertebral joints, clavicle, sternum, and costal cartilages. As an example, the thoracic spine was improved by accurate representation of the components, material properties, and ligament failure features at tissue level then validated based on the quasi-static response at the segment level, flexion bending response at the functional spinal unit level, and extension angle of the whole thoracic spine. At ribcage and full thorax levels, the thorax model with validated bony components was evaluated by a series of experimental testing cases. The validation responses were rated above 0.76, as assessed by the CORA evaluation system, indicating the model exhibited overall good biofidelity. At both component and full thorax levels, the model showed good computational stability, and reasonable agreement with the experimental data both qualitatively and quantitatively. It is expected that our validated thorax model can predict thorax behavior with high biofidelity to assess injury risk and investigate injury mechanisms of the thoracic musculoskeletal system in various impact scenarios. The relevant validation cases established in this study shall be directly used for future evaluation of other thorax models, and the validation approach and process presented here may provide an insightful framework toward multi-level validating of human body models.

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 Tissue-Level Brain Injury Metrics Using Species-Specific Simulations.

Traumatic brain injury (TBI) is a significant public health burden, and the development of advanced countermeasures to mitigate and prevent these injuries during automotive, sports, and military impact events requires an understanding of the intracranial mechanisms related to TBI. In this study, the efficacy of tissue-level injury metrics for predicting TBI was evaluated using finite element reconstructions from a comprehensive, multi-species TBI database. The database consisted of human volunteer tests, laboratory-reconstructed head impacts from sports, in vivo non-human primate (NHP) tests, and in vivo pig tests. Eight tissue-level metrics related to brain tissue strain, axonal strain, and strain-rate were evaluated using survival analysis for predicting mild and severe TBI risk. The correlation between TBI risk and most of the assessed metrics were statistically significant, but when injury data was analyzed by species, the best metric was often inconclusive and limited by the small datasets. When the human and animal datasets were combined, the injury analysis was able to delineate maximum axonal strain as the best predictor of injury for all species and TBI severities, with maximum principal strain as a suitable alternative metric. The current study is the first to provide evidence to support the assumption that brain strain response between human, pig, and NHP result in similar injury outcomes through a multi-species analysis. This assumption is the biomechanical foundation for translating animal brain injury findings to humans. The findings in the study provide fundamental guidelines for developing injury criteria that would contribute towards the innovation of more effective safety countermeasures.