Finite Element Brain Model Research Articles

Study on Behind Helmet Blunt Trauma Caused by High-Speed Bullet.

The mechanism of Behind Helmet Blunt Trauma (BHBT) caused by a high-speed bullet is difficult to understand. At present, there is still a lack of corresponding parameters and test methods to evaluate this damage effectively. The purpose of the current study is therefore to investigate the response of the human skull and brain tissue under the loading of a bullet impacting a bullet-proof helmet, with the effects of impact direction, impact speed, and impactor structure being considered. A human brain finite element model which can accurately reconstruct the anatomical structures of the scalp, skull, brain tissue, etc., and can realistically reflect the biomechanical response of the brain under high impact speed was employed in this study. The responses of Back Face Deformation (BFD), brain displacement, skull stress, and dura mater pressure were extracted from simulations as the parameters reflecting BHBT risk, and the relationships between BHBT and bullet-proof equipment structure and performance were also investigated. The simulation results show that the frontal impact of the skull produces the largest amount of BFD, and when the impact directions are from the side, the skull stress is about twice higher than other directions. As the impact velocity increases, BFD, brain displacement, skull stress, and dura mater pressure increase. The brain damage caused by different structural bullet bodies is different under the condition of the same kinetic energy. The skull stress caused by the handgun bullet is the largest. The findings indicate that when a bullet impacts on the bullet-proof helmet, it has a higher probability of causing brain displacement and intracranial high pressure. The research results can provide a reference value for helmet optimization design and antielasticity evaluation and provide the theoretical basis for protection and rescue.

Multi-Directional Dynamic Model for Traumatic Brain Injury Detection.

Given the worldwide adverse impact of traumatic brain injury (TBI) on the human population, its diagnosis and prediction are of utmost importance. Historically, many studies have focused on associating head kinematics to brain injury risk. Recently, there has been a push toward using computationally expensive finite element (FE) models of the brain to create tissue deformation metrics of brain injury. Here, we develop a new brain injury metric, the brain angle metric (BAM), based on the dynamics of a 3 degree-of-freedom lumped parameter brain model. The brain model is built based on the measured natural frequencies of an FE brain model simulated with live human impact data. We show that it can be used to rapidly estimate peak brain strains experienced during head rotational accelerations that cause mild TBI. In our data set, the simplified model correlates with peak principal FE strain (R2 = 0.82). Further, coronal and axial brain model displacement correlated with fiber-oriented peak strain in the corpus callosum (R2 = 0.77). Our proposed injury metric BAM uses the maximum angle predicted by our brain model and is compared against a number of existing rotational and translational kinematic injury metrics on a data set of head kinematics from 27 clinically diagnosed injuries and 887 non-injuries. We found that BAM performed comparably to peak angular acceleration, translational acceleration, and angular velocity in classifying injury and non-injury events. Metrics that separated time traces into their directional components had improved model deviance compare with those that combined components into a single time trace magnitude. Our brain model can be used in future work to rapidly approximate the peak strain resulting from mild to moderate head impacts and to quickly assess brain injury risk.

Accident reconstructions of falls, collisions, and punches in sports

Objective Impacts to the head are the primary cause of concussive injuries in sport and can occur in a multitude of different environments. Each event is composed of combinations of impact characteristics (striking velocity, impact mass, and surface compliance) that present unique loading conditions on the head and brain. The purpose of this study was to compare falls, collisions, and punches from accident reconstructions of sports-related head impacts using linear, rotational accelerations and maximal principal strain of brain tissue from finite element simulation. Methods This study compared four types of head impact events through reconstruction. Seventy-two head impacts were taken from medical reports of accidental falls and game video of ice hockey, American football, and mixed-martial arts. These were reconstructed using physical impact systems to represent helmeted and unhelmeted falls, player-to-player collisions, and punches to the head. Head accelerations were collected using a Hybrid III headform and were input into a finite element brain model used to approximate strain in the cerebrum associated with the external loading conditions. Results Significant differences ( p < 0.01) were found for peak linear and rotational accelerations magnitudes (30–300 g and 3.2–7.8 krad/s2) and pulse durations between all impact event types characterized by unique impact parameters. The only exception was found where punch impacts and helmeted falls had similar rotational durations. Regression analysis demonstrated that increases to strain from unhelmeted falls were significantly influenced by both linear and rotational accelerations, meanwhile helmeted falls, punches, and collisions were influenced by rotational accelerations alone. Conclusion This report illustrates that the four distinct impact events created unique peak head kinematics and brain tissue strain values. These distinct patterns of head acceleration characteristics suggest that it is important to keep in mind that head injury can occur from a range of low to high acceleration magnitudes and that impact parameters (surface compliance, striking velocity, and impact mass) play an important role on the duration-dependent tolerance to impact loading.

Explicit Modeling of White Matter Axonal Fiber Tracts in a Finite Element Brain Model.

Many human brain finite element (FE) models lack mesoscopic (~ 1mm) white matter structures, which may limit their capability in predicting TBI and assessing tissue-based injury metrics such as axonal strain. This study investigated an embedded method to explicitly incorporate white matter axonal fibers into an existing 50th percentile male brain model. The white matter was decomposed into myelinated axon tracts and an isotropic ground substance that had similar material properties to gray matter. The axon tract bundles were derived from a population-based tractography template explicitly modeled using 1-D cable elements. The axonal fibers and ground substance material were implemented using hyper-viscoelastic constitutive models, which were calibrated using white and gray matter brain tissue material testing data available in the literature. Finally, the new axon-based model was extensively validated for brain-skull relative deformation under various loading conditions (n = 17) and showed good biofidelity compared to other brain models. Through these analyses, we demonstrated the applicability of this method for incorporating axonal fiber tracts into an existing FE brain model. The axon-based model will be a useful tool for understanding the mechanisms of TBI, evaluating tissue-based injury metrics, and developing injury mitigation systems.

Interaction of external head impact parameters on region and volume of strain for collisions in sport

Collisions with the head are the primary cause of concussion in contact sports. Head impacts can be further characterized by velocity, striking mass, compliance, and location (direction). The purpose of this study was to describe the interaction effects of these parameters on peak strain in four brain regions and the volume of strain for collision impacts. A pendulum test set-up was used to deliver impacts to an adult Hybrid III headform according to four levels of mass (3, 9, 15, and 21 kg), four velocities (1.5, 3.0, 4.5, and 6.0 m/s), two impact locations (through the centre of gravity and a non-centre of gravity), and three levels of compliance simulating unprotected, helmeted, and well-padded conditions in sport. Headform accelerations were input into a brain finite element model to obtain peak strain in the frontal, temporal, parietal, and occipital lobes and the volume of the brain experiencing 0.10, 0.15, 0.20, and 0.25 strains. Centre-of-gravity impacts created the highest strains (peak and volume) under low compliance and non-centre-of-gravity impacts produced greater strain responses under medium and high compliance conditions. The temporal lobe was the region that consistently displayed the highest peak strains, which may be due to the proximity of the impact locations to this region. Interactions between mass and velocity displayed effects where the 9-kg mass had higher peak and volumes of strain than the 15-kg mass at velocities of 3.0 and 4.5 m/s. This study demonstrates the important role of interacting impact parameters on increasing strain responses that are relevant to the spectrum of diffuse brain injuries, including concussion.

A reanalysis of football impact reconstructions for head kinematics and finite element modeling

BackgroundHead kinematics generated by laboratory reconstructions of professional football helmet impacts have been applied to computational models to study the biomechanics of concussion. Since the original publication of this data, techniques for evaluating accelerometer consistency and error correction have been developed. This study applies these techniques to the original reconstruction data and reanalyzes the results given the current state of concussion biomechanics. MethodsConsistency checks were applied to the sensor data collected in the head of each test dummy. Inconsistent data were corrected using analytical techniques, and head kinematics were recalculated from the corrected data. Reconstruction videos were reviewed to identify artefactual impacts during the reconstruction to establish the region of applicability for simulations. Corrected head kinematics were input into finite element brain models to investigate strain response to the corrected dataset. FindingsMultiple reconstruction cases had inconsistent sensor arrays caused by a problematic sensor; corrections to the arrays caused changes in calculated rotational head motion. These corrections increased median peak angular velocity for the concussion cases from 35.6 to 41.5 rad/s. Using the original kinematics resulted in an average error of 20% in maximum principal strain results for each case. Simulations of the reconstructions also demonstrated that simulation lengths less than 40 ms did not capture the entire brain strain response and under-predicted strain. InterpretationThis study corrects data that were used to determine concussion risk, and indicates altered head angular motion and brain strain response for many reconstructions. Conclusions based on the original data should be re-examined based on this new study.

Development and validation of a new finite element human head model

PurposeCurrently, there are some finite element head models developed by research groups all around the world. Nevertheless, the majority are not geometrically accurate. One of the problems is the brain geometry, which usually resembles a sphere. This may raise problems when reconstructing any event that involves brain kinematics, such as accidents, affecting the correct evaluation of resulting injuries. Thus, the purpose of this study is to develop a new finite element head model more accurate than the existing ones.Design/methodology/approachIn this work, a new and geometrically detailed finite element brain model is proposed. Special attention was given to sulci and gyri modelling, making this model more geometrically accurate than currently available ones. In addition, these brain features are important to predict specific injuries such as brain contusions, which usually involve the crowns of gyri.FindingsThe model was validated against experimental data from impact tests on cadavers, comparing the intracranial pressure at frontal, parietal, occipital and posterior fossa regions.Originality/valueAs this model is validated, it can be now used in accident reconstruction and injury evaluation and even as a design tool for protective head gear.

Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction.

Impairments of executive brain function after traumatic brain injury (TBI) due to head impacts in traffic accidents need to be obviated. Finite element (FE) analyses with a human brain model facilitate understanding of the TBI mechanisms. However, conventional brain FE models do not suitably describe the anatomical structure in the deep brain, which is a critical region for executive brain function, and the material properties of brain parenchyma. In this study, for better TBI prediction, a novel brain FE model with anatomical structure in the deep brain was developed. The developed model comprises a constitutive model of brain parenchyma considering anisotropy and strain rate dependency. Validation was performed against postmortem human subject test data associated with brain deformation during head impact. Brain injury analyses were performed using head acceleration curves obtained from reconstruction analysis of rear-end collision with a human whole-body FE model. The difference in structure was found to affect the regions of strain concentration, while the difference in material model contributed to the peak strain value. The injury prediction result by the proposed model was consistent with the characteristics in the neuroimaging data of TBI patients due to traffic accidents.

Predicting Cumulative and Maximum Brain Strain Measures From HybridIII Head Kinematics: A Combined Laboratory Study and Post-Hoc Regression Analysis

Due to growing concern on brain injury in sport, and the role that helmets could play in preventing brain injury caused by impact, biomechanics researchers and helmet certification organizations are discussing how helmet assessment methods might change to assess helmets based on impact parameters relevant to brain injury. To understand the relationship between kinematic measures and brain strain, we completed hundreds of impacts using a 50th percentile Hybrid III head-neck wearing an ice hockey helmet and input three-dimensional impact kinematics to a finite element brain model called the Simulated Injury Monitor (SIMon) (n = 267). Impacts to the helmet front, back and side included impact speeds from 1.2 to 5.8 ms−1. Linear regression models, compared through multiple regression techniques, calculating adjusted R2 and the F-statistic, determined the most efficient set of kinematics capable of predicting SIMon-computed brain strain, including the cumulative strain damage measure (specifically CSDM-15) and maximum principal strain (MPS). Resultant change in angular velocity, ΔωR, better predicted CSDM-15 and MPS than the current helmet certification metric, peak g, and was the most efficient model for predicting strain, regardless of impact location. In nearly all cases, the best two-variable model included peak resultant angular acceleration, αR, and ΔωR.

Biofidelic white matter heterogeneity decreases computational model predictions of white matter strains during rapid head rotations

The finite element (FE) brain model is used increasingly as a design tool for developing technology to mitigate traumatic brain injury. We developed an ultra high-definition FE brain model (>4 million elements) from CT and MRI scans of a 2-month-old pre-adolescent piglet brain, and simulated rapid head rotations. Strain distributions in the thalamus, coronal radiata, corpus callosum, cerebral cortex gray matter, brainstem and cerebellum were evaluated to determine the influence of employing homogeneous brain moduli, or distinct experimentally derived gray and white matter property representations, where some white matter regions are stiffer and others less stiff than gray matter. We find that constitutive heterogeneity significantly lowers white matter deformations in all regions compared with homogeneous properties, and should be incorporated in FE model injury prediction.

Brain injury tolerance limit based on computation of axonal strain

Traumatic brain injury (TBI) is the leading cause of death and permanent impairment over the last decades. In both the severe and mild TBIs, diffuse axonal injury (DAI) is the most common pathology and leads to axonal degeneration. Computation of axonal strain by using finite element head model in numerical simulation can enlighten the DAI mechanism and help to establish advanced head injury criteria. The main objective of this study is to develop a brain injury criterion based on computation of axonal strain. To achieve the objective a state-of-the-art finite element head model with enhanced brain and skull material laws, was used for numerical computation of real world head trauma. The implementation of new medical imaging data such as, fractional anisotropy and axonal fiber orientation from Diffusion Tensor Imaging (DTI) of 12 healthy patients into the finite element brain model was performed to improve the brain constitutive material law with more efficient heterogeneous anisotropic visco hyper-elastic material law. The brain behavior has been validated in terms of brain deformation against Hardy et al. (2001), Hardy et al. (2007), and in terms of brain pressure against Nahum et al. (1977) and Trosseille et al. (1992) experiments. Verification of model stability has been conducted as well. Further, 109 well-documented TBI cases were simulated and axonal strain computed to derive brain injury tolerance curve. Based on an in-depth statistical analysis of different intra-cerebral parameters (brain axonal strain rate, axonal strain, first principal strain, Von Mises strain, first principal stress, Von Mises stress, CSDM (0.10), CSDM (0.15) and CSDM (0.25)), it was shown that axonal strain was the most appropriate candidate parameter to predict DAI. The proposed brain injury tolerance limit for a 50% risk of DAI has been established at 14.65% of axonal strain. This study provides a key step for a realistic novel injury metric for DAI.

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.

Finite Element Model for Hydrocephalus and Idiopathic Intracranial Hypertension.

Hydrocephalus and idiopathic intracranial hypertension (IIH) are neuropathies associated with disturbed cerebrospinal fluid dynamics. Several finite element (FE) brain models were suggested to simulate the pathological changes in hydrocephalus, but with overly simplified assumptions regarding the properties of the brain parenchyma. This study proposes a two-dimensional FE brain model, capable of simulating both hydrocephalus and IIH by incorporating poro-hyperelasticity of the brain and detailed structural information (i.e., sulci).

Validation of Local Brain Kinematics of a Novel Rat Brain Finite Element Model under Rotational Acceleration

Validation of Local Brain Kinematics of a Novel Rat Brain Finite Element Model under Rotational Acceleration

Mechanical characterization of brain tissue in tension at dynamic strain rates

Mechanical characterization of brain tissue at high loading velocities is crucial for modeling Traumatic Brain Injury (TBI). During severe impact conditions, brain tissue experiences compression, tension and shear. Limited experimental data is available for brain tissue in extension at dynamic strain rates. In this research, a High Rate Tension Device (HRTD) was developed to obtain dynamic properties of brain tissue in extension at strain rates of ≤90/s. In vitro tensile tests were performed to obtain properties of brain tissue at strain rates of 30, 60 and 90/s up to 30% strain. The brain tissue showed a stiffer response with increasing strain rates, showing that hyperelastic models are not adequate. Specifically, the tensile engineering stress at 30% strain was 3.1±0.49kPa, 4.3±0.86kPa, 6.5±0.76kPa (mean±SD) at strain rates of 30, 60 and 90/s, respectively. Force relaxation tests in tension were also conducted at different strain magnitudes (10–60% strain) with the average rise time of 24ms, which were used to derive time dependent parameters. One-term Ogden, Fung and Gent models were used to obtain material parameters from the experimental data. Numerical simulations were performed using a one-term Ogden model to analyze hyperelastic behavior of brain tissue up to 30% strain. The material parameters obtained in this study will help to develop biofidelic human brain finite element models, which can subsequently be used to predict brain injuries under impact conditions and as a reconstruction and simulation tool for forensic investigations.

Modeling Brain Injury Response for Rotational Velocities of Varying Directions and Magnitudes

An estimated 1.7 million people in the United States sustain a traumatic brain injury (TBI) annually. To investigate the effects of rotational motions on TBI risk and location, this study modeled rotational velocities of five magnitudes and 26 directions of rotation using the Simulated Injury Monitor finite element brain model. The volume fraction of the total brain exceeding a predetermined strain threshold, the Cumulative Strain Damage Measure (CSDM), was investigated to evaluate global model response. To evaluate regional response, this metric was computed relative to individual brain structures and termed the Structure Cumulative Strain Damage Measure (SCSDM). CSDM increased as input magnitude increased and varied with the direction of rotation. CSDM was 0.55-1.7 times larger in simulations with transverse plane rotation compared to those without transverse plane rotation. The largest SCSDM in the cerebrum and brainstem occurred with rotations in the transverse and sagittal planes, respectively. Velocities causing medial rotation of the cerebellum resulted in the largest SCSDM in this structure. For velocities of the same magnitude, injury risk calculated from CSDM varied from 0 to 97% with variations in the direction of rotation. These findings demonstrate injury risk, as estimated by CSDM and SCSDM, is affected by the direction of rotation and input magnitude, and these may be important considerations for injury prediction.

Mechanical characterization of brain tissue in compression at dynamic strain rates

Traumatic brain injury (TBI) occurs when local mechanical load exceeds certain tolerance levels for brain tissue. Extensive research has been done previously for brain matter experiencing compression at quasistatic loading; however, limited data is available to model TBI under dynamic impact conditions. In this research, an experimental setup was developed to perform unconfined compression tests and stress relaxation tests at strain rates ≤90/s. The brain tissue showed a stiffer response with increasing strain rates, showing that hyperelastic models are not adequate. Specifically, the compressive nominal stress at 30% strain was 8.83 ± 1.94, 12.8 ± 3.10 and 16.0 ± 1.41 kPa (mean ± SD) at strain rates of 30, 60 and 90/s, respectively. Relaxation tests were also conducted at 10%–50% strain with the average rise time of 10 ms, which can be used to derive time dependent parameters. Numerical simulations were performed using one-term Ogden model with initial shear modulus μo=6.06±1.44, 9.44 ± 2.427 and 12.64 ± 1.227 kPa (mean ± SD) at strain rates of 30, 60 and 90/s, respectively. A separate set of bonded and lubricated tests were also performed under the same test conditions to estimate the friction coefficient μ, by adopting combined experimental–computational approach. The values of μ were 0.1 ± 0.03 and 0.15 ± 0.07 (mean ± SD) at 30 and 90/s strain rates, respectively, indicating that pure slip conditions cannot be achieved in unconfined compression tests even under fully lubricated test conditions. The material parameters obtained in this study will help to develop biofidelic human brain finite element models, which can subsequently be used to predict brain injuries under impact conditions.

Development of high-quality hexahedral human brain meshes using feature-based multi-block approach

The finite element (FE) method is a powerful tool to study brain injury that remains to be a critical health concern. Subject/patient-specific FE brain models have the potential to accurately predict a specific subject/patient's brain responses during computer-assisted surgery or to design subject-specific helmets to prevent brain injury. Unfortunately, efforts required in the development of high-quality hexahedral FE meshes for brain, which consists of complex intracranial surfaces and varying internal structures, are daunting. Using multi-block techniques, an efficient meshing process to develop all-hexahedral FE brain models for an adult and a paediatric brain (3-year old) was demonstrated in this study. Furthermore, the mesh densities could be adjusted at ease using block techniques. Such an advantage can facilitate a mesh convergence study and allows more freedom for choosing an appropriate brain mesh density by balancing available computation power and prediction accuracy. The multi-block meshing approach is recommended to efficiently develop 3D all-hexahedral high-quality models in biomedical community to enhance the acceptance and application of numerical simulations.

Mechanical properties of bovine pia–arachnoid complex in shear

Traumatic brain injury (TBI) has become a major public health and socioeconomic problem that affects 1.5 million Americans annually. Finite element methods have been widely used to investigate TBI mechanisms. The pia–arachnoid complex (PAC) covering the brain plays an important role in the mechanical response of the brain during impact or inertial loading. Existing finite element brain models have tended to oversimplify the response of the PAC due to a lack of accurately defined material properties of this structure, possibly resulting in a loss of accuracy in the model predictions. The objectives of this study were to experimentally determine the material properties of the PAC under shear loading. Bovine PAC was selected in the current study in view of its availability and comparability with previous studies. Tangential shear tests were conducted at 0.8, 7.3, and 72 s −1. The mean shear moduli were 11.73, 20.04, and 22.37 kPa at the three strain rates tested. The ultimate stress, at the three strain rates, was 9.21, 17.01, and 22.26 kPa, while the ultimate strain was 1.52, 1.58, and 1.81. Results from the current study provide essential information to properly model the PAC membrane, an important component in the skull/brain interface, in a computational model of the human/animal head. Such an improved representation of the in vivo skull/brain interface will enhance future studies investigating brain injury mechanisms under various loading conditions.

Numerical calculation of the eddy current problem in magnetic induction tomography for brain edema

Magnetic induction tomography is considered to be valuable in medical application of testing brain edema. In this paper, we use the edge finite element method with electric fiel d intensity E as the vector variable to simulate a multi-channel measuring system based on a real human brain finite element mo del. In simulation, we change the conductivity of brain edema and the frequency of exciting current to research the performance of the multi-channel measuring system in testing the brain edema.