A study of the response of the human cadaver head to impact

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.

Dynamic strain fields of the mouse brain during rotation

Mouse models are used to better understand brain injury mechanisms in humans, yet there is a limited understanding of biomechanical relevance, beginning with how the murine brain deforms when the head undergoes rapid rotation from blunt impact. This problem makes it difficult to translate some aspects of diffuse axonal injury from mouse to human. To address this gap, we present the two-dimensional strain field of the mouse brain undergoing dynamic rotation in the sagittal plane. Using a high-speed camera with digital image correlation measurements of the exposed mid-sagittal brain surface, we found that pure rotations (no direct impact to the skull) of 100-200 rad/s are capable of producing complex strain fields that evolve over time with respect to rotational acceleration and deceleration. At the highest rotational velocity tested, the largest tensile strains (≥ 21% elongation) in selected regions of the mouse brain approach strain thresholds previously associated with axonal injury in prior work. These findings provide a benchmark to validate the mechanical response in biomechanical computational models predicting diffuse axonal injury, but much work remains in correlating tissue deformation patterns from computational models with underlying neuropathology.

The Influence of Headform Friction and Inertial Properties on Oblique Impact Helmet Testing

Helmet-testing headforms replicate the human head impact response, allowing the assessment of helmet protection and injury risk. However, the industry uses three different headforms with varying inertial and friction properties making study comparisons difficult because these headforms have different inertial and friction properties that may affect their impact response. This study aimed to quantify the influence of headform coefficient of friction (COF) and inertial properties on oblique impact response. The static COF of each headform condition (EN960, Hybrid III, NOCSAE, Hybrid III with a skull cap, NOCSAE with a skull cap) was measured against the helmet lining material used in a KASK prototype helmet. Each headform condition was tested with the same helmet model at two speeds (4.8 & 7.3 m/s) and two primary orientations (y-axis and x-axis rotation) with 5 repetitions, totaling 100 tests. The influence of impact location, inertial properties, and friction on linear and rotational impact kinematics was investigated using a MANOVA, and type II sums of squares were used to determine how much variance in dependent variables friction and inertia accounted for. Our results show significant differences in impact response between headforms, with rotational head kinematics being more sensitive to differences in inertial rather than frictional properties. However, at high-speed impacts, linear head kinematics are more affected by changes in frictional properties rather than inertial properties. Helmet testing protocols should consider differences between headforms' inertial and frictional properties during interpretation. These results provide a framework for cross-comparative analysis between studies that use different headforms and headform modifiers.

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

INTRODUCTION

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

Evaluation of Brain Rotational Injury Criteria (BrIC) in vehicle frontal crashes

OBJECTIVE

The objective of this study was to estimate strains in the human brain in regulatory, research, and due care frontal crashes by simulating those impacts. In addition, brain strain simulations were estimated for belted human volunteer tests and in impacts between two players in National Football League (NFL), some with no injury and some with mild Traumatic Brain Injuries (mTBI).

METHODS

The brain strain responses were determined using version 5 of the Global Human Body Modeling Consortium (GHBMC) 50th percentile human brain model. One hundred and sixty simulations with the brain model were conducted using rotational velocities and accelerations of Anthropomorphic Test Devices (ATD's) or those of human volunteers in sled or crash tests, as inputs to the model and strain related responses like Maximum Principal Strains (MPS) and Cumulative Strain Damage Measure (CSDM) in various regions of the brain were monitored. The simulated vehicle tests ranged from sled tests at 24 and 32 kph delta-V with three-point belts without airbags to full scale crash and sled tests at 56 kph and a series of Research Mobile Deformable Barrier (RMDB) tests described in Prasad et al.

RESULTS

The severity of rotational input into the model as represented by BrIC, averaged between 0.5 and 1.2 for the various test conditions, and as high as 1.5 for an individual case. The MPS responses for the various test conditions averaged between 0.28 and 0.86 and as high as 1.3 in one test condition. The MPS responses in the brain for volunteers, low velocity sled, and NCAP tests were similar to those in the no-mTBI group in the NFL cases and consistent with real world accident data. The MPS responses of the brain in angular crash and sled tests were similar to those in the mTBI group.

CONCLUSIONS

The brain strain estimations do not indicate the likelihood of severe-to-fatal brain injuries in the crash environments studied in this paper. However, using the risk functions associated with BrIC, severe-to-fatal brain injuries (AIS4+) are predicted in several environments in which they are not observed or expected.

Evaluating the Intracranial Pressure Biofidelity and Response Repeatability of a Physical Head-Brain Model in Frontal Impacts

Headforms are widely used in head injury research and headgear assessment. Common headforms are limited to replicating global head kinematics, although intracranial responses are crucial to understanding brain injuries. This study aimed to evaluate the biofidelity of intracranial pressure (ICP) and the repeatability of head kinematics and ICP of an advanced headform subjected to frontal impacts. Pendulum impacts were performed on the headform using various impact velocities (1-5 m/s) and impactor surfaces (vinyl nitrile 600 foam, PCM746 urethane, and steel) to simulate a previous cadaveric experiment. Head linear accelerations and angular rates in three axes, cerebrospinal fluid ICP (CSFP), and intraparenchymal ICP (IPP) at the front, side, and back of the head were measured. The head kinematics, CSFP, and IPP demonstrated acceptable repeatability with coefficients of variation generally being less than 10%. The BIPED front CSFP peaks and back negative peaks were within the range of the scaled cadaver data (between the minimum and maximum values reported by Nahum et al.), while side CSFPs were 30.9-92.1% greater than the cadaver data. CORrelation and Analysis (CORA) ratings evaluating the closeness of two time histories demonstrated good biofidelity of the front CSFP (0.68-0.72), while the ratings for the side (0.44-0.70) and back CSFP (0.27-0.66) showed a large variation. The BIPED CSFP at each side was linearly related to head linear accelerations with coefficients of determination greater than 0.96. The slopes for the BIPED front and back CSFP-acceleration linear trendlines were not significantly different from cadaver data, whereas the slope for the side CSFP was significantly greater than cadaver data. This study informs future applications and improvements of a novel head surrogate.

Consensus Head Acceleration Measurement Practices (CHAMP): Study Design and Statistical Analysis

Head impact measurement devices enable opportunities to collect impact data directly from humans to study topics like concussion biomechanics, head impact exposure and its effects, and concussion risk reduction techniques in sports when paired with other relevant data. With recent advances in head impact measurement devices and cost-effective price points, more and more investigators are using them to study brain health questions. However, as the field's literature grows, the variance in study quality is apparent. This brief paper aims to provide a high-level set of key considerations for the design and analysis of head impact measurement studies that can help avoid flaws introduced by sampling biases, false data, missing data, and confounding factors. We discuss key points through four overarching themes: study design, operational management, data quality, and data analysis.

Football Shoulder Pad Design and Its Effect on Head Kinematics in Shoulder-to-Helmet Impacts

Shoulder-to-helmet (STH) impacts have been shown to cause approximately twenty percent of concussions in football yet little research has investigated shoulder pad design and STH impacts. This study aimed to characterize STH impacts and identify the effect of shoulder pad design on the struck head kinematics. Additional padding was added to a shoulder pad, and was then compared to an unmodified control shoulder pad. Participants performed a series of tests where they struck a helmeted Hybrid III dummy with both shoulder pad variations to compare struck head linear and rotational kinematics. The study found the modified shoulder pad reduced peak linear acceleration by 31% (Δµ = - 9.13 g's (- ∞, - 7.25), (p = 4.10e-08)), rotational acceleration by 28% (Δµ = - 565 rad s^-2(- ∞, - 435), (p = 2.10e-07)), peak rotational velocity by 10% (Δµ = - 2.42 rad s^-1 (- ∞, - 1.54), (p = 6.9e-05)), and increased impact duration by 40% (Δµ = 9.96 ms (8.06, ∞), (p = 1.142e-08)). Impact response corridors were developed for both shoulder pad conditions and can be used to establish a controlled lab test setup that replicates STH impacts. Our findings suggest that shoulder pads have the potential to reduce head injury in football and warrant further research.

Assessment of brain injury characterization and influence of modeling approaches

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

Pilot characterization of head kinematics in grassroots dirt track racing

OBJECTIVE

The objective of this study was to utilize an instrumented mouthpiece sensor to characterize head kinematics experienced by grassroots dirt track race car drivers.

METHODS

Four dirt track race car drivers (ages 16-19) were instrumented with custom mouthpiece sensors capable of accurately measuring head motion during racing. Sensors were deployed before races and recorded tri-axial linear acceleration and rotational velocity for approximately 10 min at 200 Hz. Film review was performed to identify data associated with racing laps. For each lap, moving average kinematics were computed and subtracted from the head motion signals to obtain 'adjusted' head motion accounting for lower frequency variance due to periodic motion around the track. From adjusted data, linear and angular head perturbations (i.e., deviations from moving average) were extracted using a custom algorithm.

RESULTS

Data was collected during 400 driver-races. A total of 2438 laps were segmented from mouthpiece recordings. The median (95th percentile) peak linear acceleration, rotational velocity, and rotational acceleration of all laps were 5.33 (8.28) g, 2.89 (4.60) rad/s, and 179 (310) rad/s², respectively. Angular perturbations occurred most frequently about the anterior-posterior axis (median lap frequency = 6.39 Hz); whereas linear perturbations occurred most frequently in the inferior-superior direction (7.96 Hz). Nine crash events were recorded by the mouthpiece sensors. The median (95^th percentile) peak head kinematics of these events were 13.4 (36.6) g, 9.67 (21.9) rad/s, and 630 (1330) rad/s².

CONCLUSIONS

Mouthpiece sensors can be used to measure head kinematics during active racing. Laps, head perturbations, and crashes may be useful units of observation to describe typical head kinematic exposure experienced by drivers while racing. Subsequent research is needed to understand the associations between repetitive racing exposure and neurological function. Higher magnitude events (i.e., crashes) are not uncommon and may result in concussion or more severe injury. Results represent novel characterizations of head kinematic exposure experienced in a dirt track racing environment. This information may inform evidence-based strategies (e.g., vehicle/seat design) to improve driver safety.

A Finite Element Model of Cerebral Vascular Injury for Predicting Microbleeds Location

Finite Element (FE) models of brain mechanics have improved our understanding of the brain response to rapid mechanical loads that produce traumatic brain injuries. However, these models have rarely incorporated vasculature, which limits their ability to predict the response of vessels to head impacts. To address this shortcoming, here we used high-resolution MRI scans to map the venous system anatomy at a submillimetre resolution. We then used this map to develop an FE model of veins and incorporated it in an anatomically detailed FE model of the brain. The model prediction of brain displacement at different locations was compared to controlled experiments on post-mortem human subject heads, yielding over 3,100 displacement curve comparisons, which showed fair to excellent correlation between them. We then used the model to predict the distribution of axial strains and strain rates in the veins of a rugby player who had small blood deposits in his white matter, known as microbleeds, after sustaining a head collision. We hypothesised that the distribution of axial strain and strain rate in veins can predict the pattern of microbleeds. We reconstructed the head collision using video footage and multi-body dynamics modelling and used the predicted head accelerations to load the FE model of vascular injury. The model predicted large axial strains in veins where microbleeds were detected. A region of interest analysis using white matter tracts showed that the tract group with microbleeds had 95th percentile peak axial strain and strain rate of 0.197 and 64.9 s⁻¹ respectively, which were significantly larger than those of the group of tracts without microbleeds (0.163 and 57.0 s⁻¹). This study does not derive a threshold for the onset of microbleeds as it investigated a single case, but it provides evidence for a link between strain and strain rate applied to veins during head impacts and structural damage and allows for future work to generate threshold values. Moreover, our results suggest that the FE model has the potential to be used to predict intracranial vascular injuries after TBI, providing a more objective tool for TBI assessment and improving protection against it.

Development and Validation of a New Anisotropic Visco-Hyperelastic Human Head Finite Element Model Capable of Predicting Multiple Brain Injuries

This paper reports on the latest refinement of the Finite Element Global Human Body Models Consortium 50th percentile (GHBMC M50) adult male head model by the development and incorporation of a new material model into the white matter tissue of the brain. The white matter is represented by an anisotropic visco-hyperelastic material model capable of simulating direction-dependent response of the brain tissue to further improve the bio-fidelity and injury predictive capability of the model. The parameters representing the material were optimized by comparing model responses to seven experimentally reported strain responses of brains of postmortem human subjects (PMHS) subjected to head impact. The head model was subjected to rigorous validation against experimental data on force–deflection responses in the skull and face, intracranial pressure, and brain strain responses from over 34 PMHS head impact experiments. Crash-induced injury indices (CIIs) for facial bone fracture, skull fracture, cerebral contusion, acute subdural hematomas (ASDHs), and diffuse brain injury were developed by reconstructing 32 PMHS and real-world injury cases with the model. Model predicted maximum principal strain (MPS) and stress were determined as fracture CIIs for compact bone and spongy bones, respectively, in the skull and face. Brain responses in terms of MPS, MPS rates, and pressure distribution in injury producing experimental impacts were determined using the model and analyzed with logistic regression and survival analysis to develop CIIs for brain contusions, diffuse brain injuries, and ASDH. The statistical models using logistic regression and survival analysis showed high accuracy with area under the receiver operating curve greater than 0.8. Because of lack of sufficient moderate diffuse brain injury data, a statistical model was not created, but all indications are that the MPS rate is an essential brain response that discriminates between moderate and severe brain injuries. The authors stated that the current GHBMC M50 v.6.0 is an advanced tool for injury prediction and mitigation of injuries in automotive crashes, sports, recreational, and military environments.

The Presence of the Temporal Horn Exacerbates the Vulnerability of Hippocampus During Head Impacts

Hippocampal injury is common in traumatic brain injury (TBI) patients, but the underlying pathogenesis remains elusive. In this study, we hypothesize that the presence of the adjacent fluid-containing temporal horn exacerbates the biomechanical vulnerability of the hippocampus. Two finite element models of the human head were used to investigate this hypothesis, one with and one without the temporal horn, and both including a detailed hippocampal subfield delineation. A fluid-structure interaction coupling approach was used to simulate the brain-ventricle interface, in which the intraventricular cerebrospinal fluid was represented by an arbitrary Lagrangian-Eulerian multi-material formation to account for its fluid behavior. By comparing the response of these two models under identical loadings, the model that included the temporal horn predicted increased magnitudes of strain and strain rate in the hippocampus with respect to its counterpart without the temporal horn. This specifically affected cornu ammonis (CA) 1 (CA1), CA2/3, hippocampal tail, subiculum, and the adjacent amygdala and ventral diencephalon. These computational results suggest that the presence of the temporal horn exacerbate the vulnerability of the hippocampus, highlighting the mechanobiological dependency of the hippocampus on the temporal horn.

Instantaneous Brain Strain Estimation for Automotive Head Impacts via Deep Learning

Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (v_rot) and the corresponding rotational acceleration (a_rot) profiles were concatenated as static images to serve as CNN input. Three training strategies were evaluated: 1) "baseline", using random initial weights; 2) "transfer learning", using weight transfer from a previous CNN model trained on head impacts drawn from contact sports; and 3) "combined training", combining previous training data from contact sports (N=5661) for training. The combined training achieved the best performances. For peak MPS, the CNN achieved a coefficient of determination (R²) of 0.932 and root mean squared error (RMSE) of 0.031 for the real-world testing dataset. It also achieved a success rate of 60.5% and 94.8% for elementwise MPS, where the linear regression slope, k, and correlation coefficient, r, between estimated and simulated MPS did not deviate from 1.0 (when identical) by more than 0.1 and 0.2, respectively. Cumulative strain damage measure (CSDM) from the CNN estimation was also highly accurate compared to those from direct simulation across a range of thresholds (R² of 0.899-0.943 with RMSE of 0.054-0.069). Finally, the CNN achieved an average k and r of 0.98±0.12 and 0.90±0.07, respectively, for six reconstructed car crash impacts drawn from two other sources independent of the training dataset. Importantly, the CNN is able to efficiently estimate elementwise MPS with sufficient accuracy while conventional kinematic injury metrics cannot. Therefore, the CNN has the potential to supersede current kinematic injury metrics that can only approximate a global peak MPS or CSDM. The CNN technique developed here may offer enhanced utility in the design and development of head protective countermeasures, including in the automotive industry. This is the first study aimed at instantly estimating spatially detailed brain strains for automotive head impacts, which employs >8.8 thousand impact simulations generated from ~1.5 years of nonstop computations on a high-performance computing platform.

Computational modeling investigation of pulsed high peak power microwaves and the potential for traumatic brain injury

When considering safety standards for human exposure to radiofrequency (RF) and microwave energy, the dominant concerns pertain to a thermal effect. However, in the case of high-power pulsed RF/microwave energy, a rapid thermal expansion can lead to stress waves within the body. In this study, a computational model is used to estimate the temperature profile in the human brain resulting from exposure to various RF/microwave incident field parameters. The temperatures are subsequently used to simulate the resulting mechanical response of the brain. Our simulations show that, for certain extremely high-power microwave exposures (permissible by current safety standards), very high stresses may occur within the brain that may have implications for neuropathological effects. Although the required power densities are orders of magnitude larger than most real-world exposure conditions, they can be achieved with devices meant to emit high-power electromagnetic pulses in military and research applications.

An Overview of the Effectiveness of Bicycle Helmet Designs in Impact Testing

Cycling accidents are the leading cause of sports-related head injuries in the US. Conventional bicycle helmets typically consist of polycarbonate shell over Expanded Polystyrene (EPS) foam and are tested with drop tests to evaluate a helmet’s ability to reduce head kinematics. Within the last decade, novel helmet technologies have been proposed to mitigate brain injuries during bicycle accidents, which necessitates the evaluation of their effectiveness in impact testing as compared to conventional helmets. In this paper, we reviewed the literature to collect and analyze the kinematic data of drop test experiments carried out on helmets with different technologies. In order to provide a fair comparison across different types of tests, we clustered the datasets with respect to their normal impact velocities, impact angular momentum, and the type of neck apparatus. When we analyzed the data based on impact velocity and angular momentum clusters, we found that the bicycle helmets that used rotation damping based technology, namely MIPS, had significantly lower peak rotational acceleration (PRA) and Generalized Acceleration Model for Brain Injury Threshold (GAMBIT) as compared to the conventional EPS liner helmets (p < 0.01). SPIN helmets had a superior performance in PRA compared to conventional helmets (p < 0.05) in the impact angular momentum clustered group, but not in the impact-velocity clustered comparisons. We also analyzed other recently developed helmets that primarily use collapsible structures in their liners, such as WaveCel and Koroyd. In both of the impact velocity and angular momentum groups, helmets based on the WaveCel technology had significantly lower peak linear acceleration (PLA), PRA, and GAMBIT at low impact velocities as compared to the conventional helmets, respectively (p < 0.05). The protective gear with the airbag technology, namely Hövding, also performed significantly better compared to the conventional helmets in the analyzed kinematic-based injury metrics (p < 0.001), possibly due to its advantage in helmet size and stiffness. We also observed that the differences in the kinematic datasets strongly depend on the type of neck apparatus. Our findings highlight the importance and benefits of developing new technologies and impact testing standards for bicycle helmet designs for better prevention of traumatic brain injury (TBI).

An anatomically detailed and personalizable head injury model: Significance of brain and white matter tract morphological variability on strain

Finite element head (FE) models are important numerical tools to study head injuries and develop protection systems. The generation of anatomically accurate and subject-specific head models with conforming hexahedral meshes remains a significant challenge. The focus of this study is to present two developmental works: first, an anatomically detailed FE head model with conforming hexahedral meshes that has smooth interfaces between the brain and the cerebrospinal fluid, embedded with white matter (WM) fiber tracts; second, a morphing approach for subject-specific head model generation via a new hierarchical image registration pipeline integrating Demons and Dramms deformable registration algorithms. The performance of the head model is evaluated by comparing model predictions with experimental data of brain-skull relative motion, brain strain, and intracranial pressure. To demonstrate the applicability of the head model and the pipeline, six subject-specific head models of largely varying intracranial volume and shape are generated, incorporated with subject-specific WM fiber tracts. DICE similarity coefficients for cranial, brain mask, local brain regions, and lateral ventricles are calculated to evaluate personalization accuracy, demonstrating the efficiency of the pipeline in generating detailed subject-specific head models achieving satisfactory element quality without further mesh repairing. The six head models are then subjected to the same concussive loading to study the sensitivity of brain strain to inter-subject variability of the brain and WM fiber morphology. The simulation results show significant differences in maximum principal strain and axonal strain in local brain regions (one-way ANOVA test, p < 0.001), as well as their locations also vary among the subjects, demonstrating the need to further investigate the significance of subject-specific models. The techniques developed in this study may contribute to better evaluation of individual brain injury and the development of individualized head protection systems in the future. This study also contains general aspects the research community may find useful: on the use of experimental brain strain close to or at injury level for head model validation; the hierarchical image registration pipeline can be used to morph other head models, such as smoothed-voxel models.

Design of 3D Additively Manufactured Hybrid Structures for Cranioplasty

A wide range of materials has been considered to repair cranial defects. In the field of cranioplasty, poly(methyl methacrylate) (PMMA)-based bone cements and modifications through the inclusion of copper doped tricalcium phosphate (Cu-TCP) particles have been already investigated. On the other hand, aliphatic polyesters such as poly(ε-caprolactone) (PCL) and polylactic acid (PLA) have been frequently investigated to make scaffolds for cranial bone regeneration. Accordingly, the aim of the current research was to design and fabricate customized hybrid devices for the repair of large cranial defects integrating the reverse engineering approach with additive manufacturing, The hybrid device consisted of a 3D additive manufactured polyester porous structures infiltrated with PMMA/Cu-TCP (97.5/2.5 w/w) bone cement. Temperature profiles were first evaluated for 3D hybrid devices (PCL/PMMA, PLA/PMMA, PCL/PMMA/Cu-TCP and PLA/PMMA/Cu-TCP). Peak temperatures recorded for hybrid PCL/PMMA and PCL/PMMA/Cu-TCP were significantly lower than those found for the PLA-based ones. Virtual and physical models of customized devices for large cranial defect were developed to assess the feasibility of the proposed technical solutions. A theoretical analysis was preliminarily performed on the entire head model trying to simulate severe impact conditions for people with the customized hybrid device (PCL/PMMA/Cu-TCP) (i.e., a rigid sphere impacting the implant region of the head). Results from finite element analysis (FEA) provided information on the different components of the model.

Brain Strain from Motion of Sparse Markers

Brain strain secondary to head impact or inertial loading is closely associated with pathologic observations in the brain. The only experimental brain strain dataset under loadings close to traumatic levels was calculated by imposing the experimentally measured motion of markers embedded in the brain to an auxiliary model formed by triad elements (Hardy et al., 2007). However, fidelity of the calculated strain as well as the suitability of using triad elements for three-dimensional (3D) strain estimation remains to be verified. Therefore, this study proposes to use tetrahedron elements as a new approach to estimate the brain strain. Fidelity of this newly-proposed approach along with the previous triad-based approach is evaluated with the aid of three independently-developed finite element (FE) head models by numerically replicating the experimental impacts and strain estimation procedures. Strain in the preselected brain elements obtained from the whole head simulation exhibits good correlation with its tetra estimation and exceeds its triad estimation, indicating that the tetra approach more accurately estimates the strain in the preselected region. The newly calculated brain strain curves using tetra elements provide better approximations for the 3D experimental brain deformation and can be used for strain validation of FE models of human head.

Feasibility of using a novel instrumented human head surrogate to measure helmet, head and brain kinematics and intracranial pressure during multidirectional impact tests

OBJECTIVES

Aim of the work is to present the feasibility of using an Instrumented Human Head Surrogate (IHHS-1) during multidirectional impacts while wearing a modern ski helmet. The IHHS-1 is intended to provide reliable and repeatable data for the experimental validation of FE models and for the experimental evaluation of modern helmets designed to enhance the degree of protection against multidirectional impacts.

DESIGN

The new IHHS-1 includes 9 triaxial MEMS accelerometers embedded in a silicone rubber brain, independently molded and presenting lobes separation and cerebellum, placed into an ABS skull filled with surrogate cerebrospinal fluid. A triaxial MEMS gyroscope is placed at the brain center of mass. Intracranial pressure can be detected by eight pressure sensors applied to the skull internal surface along a transversal plane located at the brain center of mass and two at the apex. Additional MEMS sensors positioned over the skull and the helmet allow comparison between outer and inner structure kinematics and surrogate CSF pressure behavior.

METHODS

The IHHS-1 was mounted through a Hybrid III neck on a force platform and impacted with a striker connected to a pendulum tower, with the impact energies reaching 24J. Impact locations were aligned with the brain center of mass and located in the back (sagittal axis), right (90° from sagittal axis), back/right (45°), and front right (135°) locations. Following dynamic data were collected: values of the linear accelerations and angular velocities of the brain, skull and helmet; intracranial pressures inside the skull.

RESULTS

Despite the relatively low intensity of impacts (HIC at skull max value 46), the skull rotational actions reached BrIC values of 0.33 and angular accelerations of 5216rad/s², whereas brain angular acceleration resulted between 1,44 and 2,1 times lower with similar values of BrIC.

CONCLUSIONS

The IHHS-1 is a physical head surrogate that can produce repeatable data for the interpretation of inner structures behavior during multidirectional impacts with or without helmets of different characteristics.

Impact of traumatic brain injury on amyotrophic lateral sclerosis: from bedside to bench

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the loss of upper and lower motor neurons, which manifests clinically as progressive weakness. Although several epidemiological studies have found an association between traumatic brain injury (TBI) and ALS, there is not a consensus on whether TBI is an ALS risk factor. It may be that it can cause ALS in a subset of susceptible patients, based on a history of repetitive mild TBI and genetic predisposition. This cannot be determined based on clinical observational studies alone. Better preclinical models are necessary to evaluate the effects of TBI on ALS onset and progression. To date, only a small number of preclinical studies have been performed, mainly in the superoxide dismutase 1 transgenic rodents, which, taken together, have mixed results and notable methodological limitations. The more recent incorporation of additional animal models such as Drosophila flies, as well as patient-induced pluripotent stem cell-derived neurons, should facilitate a better understanding of a potential functional interaction between TBI and ALS.

A Reanalysis of Experimental Brain Strain Data: Implication for Finite Element Head Model Validation

Relative motion between the brain and skull and brain deformation are biomechanics aspects associated with many types of traumatic brain injury (TBI). Thus far, there is only one experimental endeavor (Hardy et al., 2007) reported brain strain under loading conditions commensurate with levels that were capable of producing injury. Most of the existing finite element (FE) head models are validated against brain-skull relative motion and then used for TBI prediction based on strain metrics. However, the suitability of using a model validated against brain-skull relative motion for strain prediction remains to be determined. To partially address the deficiency of experimental brain deformation data, this study revisits the only existing dynamic experimental brain strain data and updates the original calculations, which reflect incremental strain changes. The brain strain is recomputed by imposing the measured motion of neutral density target (NDT) to the NDT triad model. The revised brain strain and the brain-skull relative motion data are then used to test the hypothesis that an FE head model validated against brainskull relative motion does not guarantee its accuracy in terms of brain strain prediction. To this end, responses of brain strain and brain-skull relative motion of a previously developed FE head model (Kleiven, 2007) are compared with available experimental data. CORrelation and Analysis (CORA) and Normalized Integral Square Error (NISE) are employed to evaluate model validation performance for both brain strain and brain-skull relative motion. Correlation analyses (Pearson coefficient) are conducted between average cluster peak strain and average cluster peak brain-skull relative motion, and also between brain strain validation scores and brain-skull relative motion validation scores. The results show no significant correlations, neither between experimentally acquired peaks nor between computationally determined validation scores. These findings indicate that a head model validated against brain-skull relative motion may not be sufficient to assure its strain prediction accuracy. It is suggested that a FE head model with intended use for strain prediction should be validated against the experimental brain deformation data and not just the brain-skull relative motion.

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

In this study, we investigate the effects of modelling choices for the brain-skull interface (layers of tissues between the brain and skull that determine boundary conditions for the brain) and the constitutive model of brain parenchyma on the brain responses under violent impact as predicted using computational biomechanics model. We used the head/brain model from Total HUman Model for Safety (THUMS)-extensively validated finite element model of the human body that has been applied in numerous injury biomechanics studies. The computations were conducted using a well-established nonlinear explicit dynamics finite element code LS-DYNA. We employed four approaches for modelling the brain-skull interface and four constitutive models for the brain tissue in the numerical simulations of the experiments on post-mortem human subjects exposed to violent impacts reported in the literature. The brain-skull interface models included direct representation of the brain meninges and cerebrospinal fluid, outer brain surface rigidly attached to the skull, frictionless sliding contact between the brain and skull, and a layer of spring-type cohesive elements between the brain and skull. We considered Ogden hyperviscoelastic, Mooney-Rivlin hyperviscoelastic, neo-Hookean hyperviscoelastic and linear viscoelastic constitutive models of the brain tissue. Our study indicates that the predicted deformations within the brain and related brain injury criteria are strongly affected by both the approach of modelling the brain-skull interface and the constitutive model of the brain parenchyma tissues. The results suggest that accurate prediction of deformations within the brain and risk of brain injury due to violent impact using computational biomechanics models may require representation of the meninges and subarachnoidal space with cerebrospinal fluid in the model and application of hyperviscoelastic (preferably Ogden-type) constitutive model for the brain tissue.

Do blast induced skull flexures result in axonal deformation?

Subject-specific computer models (male and female) of the human head were used to investigate the possible axonal deformation resulting from the primary phase blast-induced skull flexures. The corresponding axonal tractography was explicitly incorporated into these finite element models using a recently developed technique based on the embedded finite element method. These models were subjected to extensive verification against experimental studies which examined their pressure and displacement response under a wide range of loading conditions. Once verified, a parametric study was developed to investigate the axonal deformation for a wide range of loading overpressures and directions as well as varying cerebrospinal fluid (CSF) material models. This study focuses on early times during a blast event, just as the shock transverses the skull (< 5 milliseconds). Corresponding boundary conditions were applied to eliminate the rotation effects and the resulting axonal deformation. A total of 138 simulations were developed– 128 simulations for studying the different loading scenarios and 10 simulations for studying the effects of CSF material model variance–leading to a total of 10,702 simulation core hours. Extreme strains and strain rates along each of the fiber tracts in each of these scenarios were documented and presented here. The results suggest that the blast-induced skull flexures result in strain rates as high as 150–378 s^-1. These high-strain rates of the axonal fiber tracts, caused by flexural displacement of the skull, could lead to a rate dependent micro-structural axonal damage, as pointed by other researchers.

Validation performance comparison for finite element models of the human brain

The objective of this study was to compare the performance of six validated brain finite element (FE) models to localized brain motion validation data in five experimental configurations. Model performance was measured using the objective metric CORA (CORrelation and Analysis), where higher ratings represent better correlation. The KTH model achieved the highest average CORA rating, and the ABM received the highest average rating among models robustly validated against five cadaver impacts in three directions. This technique can be more frequently employed to build better models and, when associated limitations are well understood, to compare inter-model performance under similar conditions.

Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability

Computational models incorporating anisotropic features of brain tissue have become a valuable tool for studying the occurrence of traumatic brain injury. The tissue deformation in the direction of white matter tracts (axonal strain) was repeatedly shown to be an appropriate mechanical parameter to predict injury. However, when assessing the reliability of axonal strain to predict injury in a population, it is important to consider the predictor sensitivity to the biological inter-subject variability of the human brain. The present study investigated the axonal strain response of 485 white matter subject-specific anisotropic finite element models of the head subjected to the same loading conditions. It was observed that the biological variability affected the orientation of the preferential directions (coefficient of variation of 39.41% for the elevation angle—coefficient of variation of 29.31% for the azimuth angle) and the determination of the mechanical fiber alignment parameter in the model (gray matter volume 55.55–70.75%). The magnitude of the maximum axonal strain showed coefficients of variation of 11.91%. On the contrary, the localization of the maximum axonal strain was consistent: the peak of strain was typically located in a 2 cm³ volume of the brain. For a sport concussive event, the predictor was capable of discerning between non-injurious and concussed populations in several areas of the brain. It was concluded that, despite its sensitivity to biological variability, axonal strain is an appropriate mechanical parameter to predict traumatic brain injury.

Development of an Unbiased Validation Protocol to Assess the Biofidelity of Finite Element Head Models used in Prediction of Traumatic Brain Injury

This study describes a method to identify laboratory test procedures and impact response requirements suitable for assessing the biofidelity of finite element head models used in prediction of traumatic brain injury. The selection of the experimental data and the response requirements were result of a critical evaluation based on the accuracy, reproducibility and relevance of the available experimental data. A weighted averaging procedure was chosen in order to consider different contributions from the various test conditions and target measurements based on experimental error. According to the quality criteria, 40 experimental cases were selected to be a representative dataset for validation. Based on the evaluation of response curves from four head finite element models, CORA was chosen as a quantitative method to compare the predicted time history response to the measured data. Optimization of the CORA global settings led to the recommendation of performing curve comparison on a fixed time interval of 0-30 ms for intracranial pressure and at least 0-40 ms for brain motion and deformation. The allowable maximum time shift was adjusted depending on the shape of the experimental curves (D_MAX = 0.12 for intracranial pressure, D_MAX = 0.40 for brain motion and D_MAX = 0.25 for brain deformation). Finally, bigger penalization of ratings was assigned to curves with fundamentally incorrect shape compared to those having inaccuracies in amplitude or time shift (cubic vs linear). This rigorous approach is necessary to ensure confidence in the model results and progress in the usage of finite element head models for traumatic brain injury prediction.

Response of an Impact Test Apparatus for Fall Protective Headgear Testing Using a Hybrid-III Head/Neck Assembly

A test method based upon a Hybrid-III head and neck assembly that includes measurement of both linear and angular acceleration is investigated for potential use in impact testing of protective headgear. The test apparatus is based upon a twin wire drop test system modified with the head/neck assembly and associated flyarm components. This study represents a preliminary assessment of the test apparatus for use in the development of protective headgear designed to prevent injury due to falls. By including angular acceleration in the test protocol it becomes possible to assess and intentionally reduce this component of acceleration. Comparisons of standard and reduced durometer necks, various anvils, front, rear, and side drop orientations, and response data on performance of the apparatus are provided. Injury measures summarized for an unprotected drop include maximum linear and angular acceleration, head injury criteria (HIC), rotational injury criteria (RIC), and power rotational head injury criteria (PRHIC). Coefficient of variation for multiple drops ranged from 0.4 to 6.7% for linear acceleration. Angular acceleration recorded in a side drop orientation resulted in highest coefficient of variation of 16.3%. The drop test apparatus results in a reasonably repeatable test method that has potential to be used in studies of headgear designed to reduce head impact injury.

Development and validation of an atlas-based finite element brain model

Traumatic brain injury is a leading cause of disability and injury-related death. To enhance our ability to prevent such injuries, brain response can be studied using validated finite element (FE) models. In the current study, a high-resolution, anatomically accurate FE model was developed from the International Consortium for Brain Mapping brain atlas. Due to wide variation in published brain material parameters, optimal brain properties were identified using a technique called Latin hypercube sampling, which optimized material properties against three experimental cadaver tests to achieve ideal biomechanics. Additionally, falx pretension and thickness were varied in a lateral impact variation. The atlas-based brain model (ABM) was subjected to the boundary conditions from three high-rate experimental cadaver tests with different material parameter combinations. Local displacements, determined experimentally using neutral density targets, were compared to displacements predicted by the ABM at the same locations. Error between the observed and predicted displacements was quantified using CORrelation and Analysis (CORA), an objective signal rating method that evaluates the correlation of two curves. An average CORA score was computed for each variation and maximized to identify the optimal combination of parameters. The strongest relationships between CORA and material parameters were observed for the shear parameters. Using properties obtained through the described multiobjective optimization, the ABM was validated in three impact configurations and shows good agreement with experimental data. The final model developed in this study consists of optimized brain material properties and was validated in three cadaver impacts against local brain displacement data.

Development and Validation of the Total HUman Model for Safety (THUMS) Version 5 Containing Multiple 1D Muscles for Estimating Occupant Motions with Muscle Activation During Side Impacts

Accurate prediction of occupant head kinematics is critical for better understanding of head/face injury mechanisms in side impacts, especially far-side occupants. In light of the fact that researchers have demonstrated that muscle activations, especially in neck muscles, can affect occupant head kinematics, a human body finite element (FE) model that considers muscle activation is useful for predicting occupant head kinematics in real-world automotive accidents. In this study, we developed a human body FE model called the THUMS (Total HUman Model for Safety) Version 5 that contains 262 one-dimensional (1D) Hill-type muscle models over the entire body. The THUMS was validated against 36 series of PMHS (Post Mortem Human Surrogate) and volunteer test data in this study, and 16 series of PMHS and volunteer test data on side impacts are presented. Validation results with force-time curves were also evaluated quantitatively using the CORA (CORrelation and Analysis) method. The validation results suggest that the THUMS has good biofidelity in the responses of the regional or full body for side impacts, but relatively poor biofidelity in its local level of responses such as brain displacements. Occupant kinematics predicted by the THUMS with a muscle controller using 22 PID (Proportional-Integral- Derivative) controllers were compared with those of volunteer test data on low-speed lateral impacts. The THUMS with muscle controller reproduced the head kinematics of the volunteer data more accurately than that without muscle activation, although further studies on validation of torso kinematics are needed for more accurate predictions of occupant head kinematics.

Evaluation of Axonal Strain as a Predictor for Mild Traumatic Brain Injuries Using Finite Element Modeling

Finite element (FE) models are often used to study the biomechanical effects of traumatic brain injury (TBI). Measures based on mechanical responses, such as principal strain or invariants of the strain tensor, are used as a metric to predict the risk of injury. However, the reliability of inferences drawn from these models depends on the correspondence between the mechanical measures and injury data, as well as the establishment of accurate thresholds of tissue injury. In the current study, a validated anisotropic FE model of the human head is used to evaluate the hypothesis that strain in the direction of fibers (axonal strain) is a better predictor of TBI than maximum principal strain (MPS), anisotropic equivalent strain (AESM) and cumulative strain damage measure (CSDM). An analysis of head kinematics-based metrics, such as head injury criterion (HIC) and brain injury criterion (BrIC), is also provided. Logistic regression analysis is employed to compare binary injury data (concussion/no concussion) with continuous strain/kinematics data. The threshold corresponding to 50% of injury probability is determined for each parameter. The predictive power (area under the ROC curve, AUC) is calculated from receiver operating characteristic (ROC) curve analysis. The measure with the highest AUC is considered to be the best predictor of mTBI. Logistic regression shows a statistical correlation between all the mechanical predictors and injury data for different regions of the brain. Peaks of axonal strain have the highest AUC and determine a strain threshold of 0.07 for corpus callosum and 0.15 for the brainstem, in agreement with previously experimentally derived injury thresholds for reversible axonal injury. For a data set of mild TBI from the national football league, the strain in the axonal direction is found to be a better injury predictor than MPS, AESM, CSDM, BrIC and HIC.

Functional tolerance to mechanical deformation developed from organotypic hippocampal slice cultures

In this study, we measured changes in electrophysiological activity after mechanical deformation of living organotypic hippocampal brain slice cultures at tissue strains and strain rates relevant to traumatic brain injury (TBI). Electrophysiological activity was measured throughout the hippocampus with a 60-electrode microelectrode array. Electrophysiological parameters associated with unstimulated spontaneous activity (neural event firing rate, duration, and magnitude), stimulated evoked responses (the maximum response [Formula: see text], the stimulus current necessary for a half-maximal response [Formula: see text], and the electrophysiological parameter m that is representative of firing uniformity), and paired-pulse responses (paired-pulse ratio at varying interstimulus intervals) were quantified for each hippocampal region (CA1, CA3, and DG). We present functional tolerance criteria for the hippocampus in the form of mathematical relationships between the input tissue-level injury parameters (strain and strain rate) and altered neuronal network function. Most changes in electrophysiology were dependent on strain and strain rate in a complex fashion, independent of hippocampal anatomy, with the notable exception of [Formula: see text]. Until it becomes possible to directly measure brain tissue deformation in vivo, finite element (FE) models will be necessary to simulate and predict the in vivo consequences of TBI. One application of our study is to provide functional relationships that can be incorporated into these FE models to enhance their biofidelity of accident and collision reconstructions by predicting biological outcomes in addition to mechanical responses.

Parametric comparisons of intracranial mechanical responses from three validated finite element models of the human head

A number of human head finite element (FE) models have been developed from different research groups over the years to study the mechanisms of traumatic brain injury. These models can vary substantially in model features and parameters, making it important to evaluate whether simulation results from one model are readily comparable with another, and whether response-based injury thresholds established from a specific model can be generalized when a different model is employed. The purpose of this study is to parametrically compare regional brain mechanical responses from three validated head FE models to test the hypothesis that regional brain responses are dependent on the specific head model employed as well as the region of interest (ROI). The Dartmouth Scaled and Normalized Model (DSNM), the Simulated Injury Monitor (SIMon), and the Wayne State University Head Injury Model (WSUHIM) were selected for comparisons. For model input, 144 unique kinematic conditions were created to represent the range of head impacts sustained by male collegiate hockey players during play. These impacts encompass the 50th, 95th, and 99th percentile peak linear and rotational accelerations at 16 impact locations around the head. Five mechanical variables (strain, strain rate, strain × strain rate, stress, and pressure) in seven ROIs reported from the FE models were compared using Generalized Estimating Equation statistical models. Highly significant differences existed among FE models for nearly all output variables and ROIs. The WSUHIM produced substantially higher peak values for almost all output variables regardless of the ROI compared to the DSNM and SIMon models (p < 0.05). DSNM also produced significantly different stress and pressure compared with SIMon for all ROIs (p < 0.05), but such differences were not consistent across ROIs for other variables. Regardless of FE model, most output variables were highly correlated with linear and rotational peak accelerations. The significant disparities in regional brain responses across head models regardless of the output variables strongly suggest that model-predicted brain responses from one study should not be extended to other studies in which a different model is utilized. Consequently, response-based injury tolerance thresholds from a specific model should not be generalized to other studies either in which a different model is used. However, the similar relationships between regional responses and the linear/rotational peak accelerations suggest that each FE model can be used independently to assess regional brain responses to impact simulations in order to perform statistical correlations with medical images and/or well-selected experiments with documented injury findings.

Head impact accelerations for brain strain-related responses in contact sports: a model-based investigation

Both linear [Formula: see text] and rotational [Formula: see text] accelerations contribute to head impacts on the field in contact sports; however, they are often isolated in injury studies. It is critical to evaluate the feasibility of estimating brain responses using isolated instead of full degrees-of-freedom (DOFs) accelerations. In this study, we investigated the sensitivities of regional brain strain-related responses to resultant [Formula: see text] and [Formula: see text] as well as the relative contributions of these acceleration components to the responses via random sampling and linear regression using parameterized, triangulated head impacts with kinematic variable values based on on-field measurements. Two independently established and validated finite element models of the human head were employed to evaluate model-consistency and dependency in results: the Dartmouth Head Injury Model and Simulated Injury Monitor. For the majority of the brain, volume-weighted regional peak strain, strain rate, and von Mises stress accumulated from the simulation significantly correlated with the product of the magnitude and duration of [Formula: see text], or effectively, the rotational velocity, but not to [Formula: see text]. Responses from [Formula: see text]-only were comparable to the full-DOF counterparts especially when normalized by injury-causing thresholds (e.g., volume fractions of large differences virtually diminished (i.e., [Formula: see text]1 %) at typical difference percentage levels of 1-4 % on average). These model-consistent results support the inclusion of both rotational acceleration magnitude and duration into kinematics-based injury metrics and demonstrate the feasibility of estimating strain-related responses from isolated [Formula: see text] for analyses of strain-induced injury relevant to contact sports without significant loss of accuracy, especially for the cerebrum.

Development and validation of two subject-specific finite element models of human head against three cadaveric experiments

Head injury, being one of the main causes of death or permanent disability, continues to remain a major health problem with significant socioeconomic costs. Numerical simulations using the FEM offer a cost-effective method and alternative to experimental methods in the biomechanical studies of head injury. The present study aimed to develop two realistic subject-specific FEMs of the human head with detailed anatomical features from medical images (Model 1: without soft tissue and Model 2: with soft tissue and differentiation of white and gray matters) and to validate them against the intracranial pressure (ICP) and relative intracranial motion data of the three cadaver experimental tests. In general, both the simulated results were in reasonably good agreement with the experimental measured ICP and relative displacements, despite slight discrepancy in a few neutral density targets markers. Sensitivity analysis showed some variations in the brain's relative motion to the material properties or marker's location. The addition of soft tissue in Model 2 helped to damp out the oscillations of the model response. It was also found that, despite the fundamental anatomical differences between the two models, there existed little evident differences in the predicted ICP and relative displacements of the two models. This indicated that the advancements on the details of the extracranial features would not improve the model's predicting capabilities of brain injury.

Development of brain injury criteria (BrIC)

Rotational motion of the head as a mechanism for brain injury was proposed back in the 1940s. Since then a multitude of research studies by various institutions were conducted to confirm/reject this hypothesis. Most of the studies were conducted on animals and concluded that rotational kinematics experienced by the animal's head may cause axonal deformations large enough to induce their functional deficit. Other studies utilized physical and mathematical models of human and animal heads to derive brain injury criteria based on deformation/pressure histories computed from their models. This study differs from the previous research in the following ways: first, it uses two different detailed mathematical models of human head (SIMon and GHBMC), each validated against various human brain response datasets; then establishes physical (strain and stress based) injury criteria for various types of brain injury based on scaled animal injury data; and finally, uses Anthropomorphic Test Devices (ATDs) (Hybrid III 50th Male, Hybrid III 5th Female, THOR 50th Male, ES-2re, SID-IIs, WorldSID 50th Male, and WorldSID 5th Female) test data (NCAP, pendulum, and frontal offset tests) to establish a kinematically based brain injury criterion (BrIC) for all ATDs. Similar procedures were applied to college football data where thousands of head impacts were recorded using a six degrees of freedom (6 DOF) instrumented helmet system. Since animal injury data used in derivation of BrIC were predominantly for diffuse axonal injury (DAI) type, which is currently an AIS 4+ injury, cumulative strain damage measure (CSDM) and maximum principal strain (MPS) were used to derive risk curves for AIS 4+ anatomic brain injuries. The AIS 1+, 2+, 3+, and 5+ risk curves for CSDM and MPS were then computed using the ratios between corresponding risk curves for head injury criterion (HIC) at a 50% risk. The risk curves for BrIC were then obtained from CSDM and MPS risk curves using the linear relationship between CSDM - BrIC and MPS - BrIC respectively. AIS 3+, 4+ and 5+ field risk of anatomic brain injuries was also estimated using the National Automotive Sampling System - Crashworthiness Data System (NASS-CDS) database for crash conditions similar to the frontal NCAP and side impact conditions that the ATDs were tested in. This was done to assess the risk curve ratios derived from HIC risk curves. The results of the study indicated that: (1) the two available human head models - SIMon and GHBMC - were found to be highly correlated when CSDMs and max principal strains were compared; (2) BrIC correlates best to both - CSDM and MPS, and rotational velocity (not rotational acceleration) is the mechanism for brain injuries; and (3) the critical values for angular velocity are directionally dependent, and are independent of the ATD used for measuring them. The newly developed brain injury criterion is a complement to the existing HIC, which is based on translational accelerations. Together, the two criteria may be able to capture most brain injuries and skull fractures occurring in automotive or any other impact environment. One of the main limitations for any brain injury criterion, including BrIC, is the lack of human injury data to validate the criteria against, although some approximation for AIS 2+ injury is given based on the angular velocities calculated at 50% probability of concussion in college football players instrumented with 5 DOF helmet system. Despite the limitations, a new kinematic rotational brain injury criterion - BrIC - may offer a way to capture brain injuries in situations when using translational accelerations based HIC alone may not be sufficient.

Angular Impact Mitigation system for bicycle helmets to reduce head acceleration and risk of traumatic brain injury

Angular acceleration of the head is a known cause of traumatic brain injury (TBI), but contemporary bicycle helmets lack dedicated mechanisms to mitigate angular acceleration. A novel Angular Impact Mitigation (AIM) system for bicycle helmets has been developed that employs an elastically suspended aluminum honeycomb liner to absorb linear acceleration in normal impacts as well as angular acceleration in oblique impacts. This study tested bicycle helmets with and without AIM technology to comparatively assess impact mitigation. Normal impact tests were performed to measure linear head acceleration. Oblique impact tests were performed to measure angular head acceleration and neck loading. Furthermore, acceleration histories of oblique impacts were analyzed in a computational head model to predict the resulting risk of TBI in the form of concussion and diffuse axonal injury (DAI). Compared to standard helmets, AIM helmets resulted in a 14% reduction in peak linear acceleration (p<0.001), a 34% reduction in peak angular acceleration (p<0.001), and a 22-32% reduction in neck loading (p<0.001). Computational results predicted that AIM helmets reduced the risk of concussion and DAI by 27% and 44%, respectively. In conclusion, these results demonstrated that AIM technology could effectively improve impact mitigation compared to a contemporary expanded polystyrene-based bicycle helmet, and may enhance prevention of bicycle-related TBI. Further research is required.

Kinematics of the thoracoabdominal contents under various loading scenarios

High-speed biplane x-ray was used to investigate relative kinematics of the thoracoabdominal organs in response to blunt loading. Four post-mortem human surrogates instrumented with radiopaque markers were subjected to eight crash- specific loading scenarios, including frontal chest and abdominal impacts, as well as driver-shoulder seatbelt loading. Testing was conducted with each surrogate perfused, ventilated, and positioned in an inverted, fixed-back configuration. Displacement of radiopaque markers recorded with high-speed x-ray in two perspectives was tracked using motion analysis software and projected into calibrated three-dimensional coordinates. Internal organ kinematics in response to blunt impact were quantified for the pericardium, lungs, diaphragm, liver, spleen, stomach, mesentery, and bony structures. These data can be used to better understand the interaction of anatomical structures during impact and the associated injury mechanisms, and for the development or validation of human body finite element models.

Effects of neck damping properties on brain response underimpact loading

In this paper, head-neck boundary conditions and modeling of the head are studied circumspectly. The neck is modeled using discrete elements and the head model is three-dimensional. In the study presented here, a viscoelastic foundation (i.e., foundation defined by both springs and dampers) concept is introduced to simulate the head-neck boundary conditions during the impact load to the head. Time histories of the brain response in finite element head models with a viscoelastic neck are compared with the corresponding solutions of finite element head models with an elastic neck, and without a neck. It is observed that the magnitude of peaks in the brain's response time histories, at a later stage (i.e., 6 to 15 ms) of the simulation, decreases when dampers are induced to the elastic neck. A parametric study is also conducted to examine the brain response while varying different damping coefficient values for the neck. The magnitude of peaks in the brain's response time histories for models with different neck damping coefficients is observed to maintain some form of proportionality. In other words, the magnitude of peaks in the brain's response time histories decreases with an increased damping coefficient of the neck at the later stage of the simulation (i.e., 6 to 15 ms). From the outcomes of this study, it can be determined that the head-neck boundary conditions during head impact loading are important for studying the brain's response at the later stages of the head impact.

Biomechanical assessment of brain dynamic responses due to blast pressure waves

A mechanized and integrated computational scheme is introduced to determine the human brain responses in an environment where the human head is exposed to explosions from trinitrotoluene (TNT), or other high-yield explosives, in military applications. The procedure is based on a three-dimensional (3-D) non-linear finite element method (FEM) that implements a simultaneous conduction of explosive detonation, shock wave propagation, blast-head interactions, and the confronting human head. The processes of blast propagation in the air and blast interaction with the head are modeled by an Arbitrary Lagrangian-Eulerian (ALE) multi-material FEM formulation, together with a penalty-based fluid/structure interaction (FSI) algorithm. Such a model has already been successfully validated against experimental data regarding air-free blast and plate-blast interactions. The human head model is a 3-D geometrically realistic configuration that has been previously validated against the brain intracranial pressure (ICP), as well as shear and principal strains under different impact loadings of cadaveric experimental tests of Hardy et al. [Hardy W. N., C. Foster, M. Mason, S. Chirag, J. Bishop, M. Bey, W. Anderst, and S. Tashman. A study of the response of the human cadaver head to impact. Proc. 51 ( st ) Stapp. Car Crash J. 17-80, 2007]. Different scenarios have been assumed to capture an appropriate picture of the brain response at a constant stand-off distance of nearly 80 cm from the core of the explosion, but exposed to different amounts of a highly explosive (HE) material such as TNT. The over-pressures at the vicinity of the head are in the range of about 2.4-8.7 atmosphere (atm), considering the reflected pressure from the head. The methodology provides brain ICP, maximum shear stresses and maximum principal strain within the milli-scale time frame of this highly dynamic phenomenon. While focusing on the two mechanical parameters of pressure, and also on the maximum shear stress and maximum principal strain to predict the brain injury, the research provides an assessment of the brain responses to different amounts of over-pressure. The research also demonstrates the ability to predict the ICP, as well as the stress and strain within the brain, due to such an event. The research cannot identify, however, the specific levels of ICP, stress and strain that necessarily lead to traumatic brain injury (TBI) because there is no access to experimental data regarding head-blast interactions.

Mechanisms of traumatic rupture of the aorta and associated peri-isthmic motion and deformation

This study investigated the mechanisms of traumatic rupture of the aorta (TRA). Eight unembalmed human cadavers were tested using various dynamic blunt loading modes. Impacts were conducted using a 32-kg impactor with a 152-mm face, and high-speed seatbelt pretensioners. High-speed biplane x-ray was used to visualize aortic motion within the mediastinum, and to measure deformation of the aorta. An axillary thoracotomy approach was used to access the peri-isthmic region to place radiopaque markers on the aorta. The cadavers were inverted for testing. Clinically relevant TRA was observed in seven of the tests. Peak average longitudinal Lagrange strain was 0.644, with the average peak for all tests being 0.208 +/- 0.216. Peak intraluminal pressure of 165 kPa was recorded. Longitudinal stretch of the aorta was found to be a principal component of injury causation. Stretch of the aorta was generated by thoracic deformation, which is required for injury to occur. The presence of atherosclerosis was demonstrated to promote injury. The isthmus of the aorta moved dorsocranially during frontal impact and submarining loading modes. The aortic isthmus moved medially and anteriorly during impact to the left side. The results of this study provide a better understanding of the mechanisms associated with TRA, and can be used for the validation of finite element models developed for the examination and prediction of TRA.