Proposing a Radial Basis Function and CSDM Indices to Predict the Traumatic Brain Injury Risk

BackgroundMany head injury indices and finite element (FE) head models have been proposed to predict traumatic brain injury (TBI). Although FE head models are suitable methods with high accuracy, they are computationally intensive. Head motion-based brain injury criteria are usually fast tools with lower accuracy. So, the objective of this study is to propose new criteria along with an artificial neural network model to predict TBI risks, which can be fast and accurate.MethodsFor this purpose, 250 FE head simulations have been carried out at 5 magnitudes and 50 rotational impact directions using the SIMon model. The effects of directions and magnitudes of rotational impacts were assessed for cumulative strain damage measure (CSDM) values. Next, statistical analysis and neural network were applied to predict CSDM values.ResultsThe results of the present research showed that the direction of rotation in the sagittal and frontal planes had a considerable effect on the CSDM values. Furthermore, new brain injury indices and a radial basis function neural network have been proposed to predict CSDM values which having high correlation coefficients with SIMon responses.ConclusionsThe results of this research demonstrated that rotational impact directions should be used to develop new head injury criteria being able to predict CSDM values. However, findings of present research proved that head motion-based brain injury criteria and RBF network can be used to predict FE head model responses with high speed and accuracy.     

Computational neurotrauma—design, simulation, and analysis of controlled cortical impact model

The controlled cortical impact (CCI) model is widely used in many laboratories to study traumatic brain injury (TBI). Although external impact parameters during CCI tests could be clearly defined, little is known about the internal tissue-level mechanical responses of the rat brain. Furthermore, the external impact parameters tend to vary considerably among different labs making the comparison of research findings difficult if not impossible. In this study, a design of computer experiments was performed with typical external impact parameters commonly found in the literature. An anatomically detailed finite element (FE) rat brain model was used to simulate the CCI experiments to correlate external mechanical parameters (impact depth, impact velocity, impactor shape, impactor size, and craniotomy pattern) with rat brain internal responses, as predicted by the FE model. Systematic analysis of the results revealed that impact depth was the leading factor affecting the predicted brain internal responses. Interestingly, impactor shape ranked as the second most important factor, surpassing impactor diameter and velocity which were commonly reported in the literature as indicators of injury severity along with impact depth. The differences in whole brain response due to a unilateral or a bilateral craniotomy were small, but those of regional intracranial tissue stretches were large. The interaction effects of any two external parameters were not significant. This study demonstrates the potential of using numerical FE modeling to engineer better experimental TBI models in the future.     

Robust human body model injury prediction in simulated side impact crashes

This study developed a parametric methodology to robustly predict occupant injuries sustained in real-world crashes using a finite element (FE) human body model (HBM). One hundred and twenty near-side impact motor vehicle crashes were simulated over a range of parameters using a Toyota RAV4 (bullet vehicle), Ford Taurus (struck vehicle) FE models and a validated human body model (HBM) Total HUman Model for Safety (THUMS). Three bullet vehicle crash parameters (speed, location and angle) and two occupant parameters (seat position and age) were varied using a Latin hypercube design of Experiments. Four injury metrics (head injury criterion, half deflection, thoracic trauma index and pelvic force) were used to calculate injury risk. Rib fracture prediction and lung strain metrics were also analysed. As hypothesized, bullet speed had the greatest effect on each injury measure. Injury risk was reduced when bullet location was further from the B-pillar or when the bullet angle was more oblique. Age had strong correlation to rib fractures frequency and lung strain severity. The injuries from a real-world crash were predicted using two different methods by (1) subsampling the injury predictors from the 12 best crush profile matching simulations and (2) using regression models. Both injury prediction methods successfully predicted the case occupant's low risk for pelvic injury, high risk for thoracic injury, rib fractures and high lung strains with tight confidence intervals. This parametric methodology was successfully used to explore crash parameter interactions and to robustly predict real-world injuries.     

Utilizing multiple scale models to improve predictions of extra-axial hemorrhage in the immature piglet

Traumatic brain injury (TBI) is a leading cause of death and disability in the USA. To help understand and better predict TBI, researchers have developed complex finite element (FE) models of the head which incorporate many biological structures such as scalp, skull, meninges, brain (with gray/white matter differentiation), and vasculature. However, most models drastically simplify the membranes and substructures between the pia and arachnoid membranes. We hypothesize that substructures in the pia–arachnoid complex (PAC) contribute substantially to brain deformation following head rotation, and that when included in FE models accuracy of extra-axial hemorrhage prediction improves. To test these hypotheses, microscale FE models of the PAC were developed to span the variability of PAC substructure anatomy and regional density. The constitutive response of these models were then integrated into an existing macroscale FE model of the immature piglet brain to identify changes in cortical stress distribution and predictions of extra-axial hemorrhage (EAH). Incorporating regional variability of PAC substructures substantially altered the distribution of principal stress on the cortical surface of the brain compared to a uniform representation of the PAC. Simulations of 24 non-impact rapid head rotations in an immature piglet animal model resulted in improved accuracy of EAH prediction (to 94 % sensitivity, 100 % specificity), as well as a high accuracy in regional hemorrhage prediction (to 82–100 % sensitivity, 100 % specificity). We conclude that including a biofidelic PAC substructure variability in FE models of the head is essential for improved predictions of hemorrhage at the brain/skull interface.     

A multi-body dynamics study on a weight-drop test of rat brain injury

Traumatic brain injury (TBI), induced by impact of an object with the head, is a major health problem worldwide. Rats are a well-established animal analogue for study of TBI and the weight-drop impact-acceleration (WDIA) method is a well-established model in rats for creating diffuse TBI, the most common form of TBI seen in humans. However, little is known of the biomechanics of the WDIA method and, to address this, we have developed a four-degrees-of-freedom multi-body mass-spring-damper model for the WDIA test in rats. An analytical expression of the maximum skull acceleration, one of the important head injury predictor, was derived and it shows that the maximum skull acceleration is proportional to the impact velocity but independent of the impactor mass. Furthermore, a dimensional analysis disclosed that the maximum force on the brain and maximum relative displacement between brain and skull are also linearly proportional to impact velocity. Additionally, the effects of the impactor mass were examined through a parametric study from the developed multi-body dynamics model. It was found that increasing impactor mass increased these two brain injury predictors.     

Finite element modeling of human brain response to football helmet impacts

The football helmet is used to help mitigate the occurrence of impact-related traumatic (TBI) and minor traumatic brain injuries (mTBI) in the game of American football. While the current helmet design methodology may be adequate for reducing linear acceleration of the head and minimizing TBI, it however has had less effect in minimizing mTBI. The objectives of this study are (a) to develop and validate a coupled finite element (FE) model of a football helmet and the human body, and (b) to assess responses of different regions of the brain to two different impact conditions – frontal oblique and crown impact conditions. The FE helmet model was validated using experimental results of drop tests. Subsequently, the integrated helmet–human body FE model was used to assess the responses of different regions of the brain to impact loads. Strain-rate, strain, and stress measures in the corpus callosum, midbrain, and brain stem were assessed. Results show that maximum strain-rates of 27 and 19 s^?1 are observed in the brain-stem and mid-brain, respectively. This could potentially lead to axonal injuries and neuronal cell death during crown impact conditions. The developed experimental-numerical framework can be used in the study of other helmet-related impact conditions.     

Simulation-based assessment for construction helmets

In recent years, there has been a concerted effort for greater job safety in all industries. Personnel protective equipment (PPE) has been developed to help mitigate the risk of injury to humans that might be exposed to hazardous situations. The human head is the most vulnerable to impact as a moderate magnitude can cause serious injury or death. That is why industries have required the use of an industrial hard hat or helmet. There have only been a few articles published to date that are focused on the risk of head injury when wearing an industrial helmet. A full understanding of the effectiveness of construction helmets on reducing injury is lacking. This paper presents a simulation-based method to determine the threshold at which a human will sustain injury when wearing a construction helmet and assesses the risk of injury for wearers of construction helmets or hard hats. Advanced finite element, or FE, models were developed to study the impact on construction helmets. The FE model consists of two parts: the helmet and the human models. The human model consists of a brain, enclosed by a skull and an outer layer of skin. The level and probability of injury to the head was determined using both the head injury criterion (HIC) and tolerance limits set by Deck and Willinger. The HIC has been widely used to assess the likelihood of head injury in vehicles. The tolerance levels proposed by Deck and Willinger are more suited for finite element models but lack wide-scale validation. Different cases of impact were studied using LSTC's LS-DYNA.     

A Study on the Finite Element Model for Head Injury in Facial Collision Accident

In order to predict and evaluate injury mechanism and biomechanical response of the facial impact on head injury in a crash accident. With the combined modern medical imaging technologies, namely computed tomography (CT) and magnetic resonance imaging (MRI), both geometric and finite element (FE) models for human head-neck with detailed cranio-facial structure were developed. The cadaveric head impact tests were conducted to validate the headneck finite element model. The intracranial pressure, skull dynamic response and skull-brain relative displacement of the whole head-neck model were compared with experimental data. Nine typical cases of facial traffic accidents were simulated, with the individual stress wave propagation paths to the intracranial contents through the facial and cranial skeleton being discussed thoroughly. Intracranial pressure, von Mises stress and shear stress distribution were achieved. It is proved that facial structure dissipates a large amount of impact energy to protect the brain in its most natural way. The propagation path and distribution of stress wave in the skull and brain determine the mechanism of brain impact injury, which provides a theoretic basis for the diagnosis, treatment and protection of craniocerebral injury caused by facial impact.     

Controlled Cortical Impact Model for Traumatic Brain Injury

Every year over a million Americans suffer a traumatic brain injury (TBI). Combined with the incidence of TBIs worldwide, the physical, emotional, social, and economical effects are staggering. Therefore, further research into the effects of TBI and effective treatments is necessary. The controlled cortical impact (CCI) model induces traumatic brain injuries ranging from mild to severe. This method uses a rigid impactor to deliver mechanical energy to an intact dura exposed following a craniectomy. Impact is made under precise parameters at a set velocity to achieve a pre-determined deformation depth. Although other TBI models, such as weight drop and fluid percussion, exist, CCI is more accurate, easier to control, and most importantly, produces traumatic brain injuries similar to those seen in humans. However, no TBI model is currently able to reproduce pathological changes identical to those seen in human patients. The CCI model allows investigation into the short-term and long-term effects of TBI, such as neuronal death, memory deficits, and cerebral edema, as well as potential therapeutic treatments for TBI.     

Development of a finite element model of the tibia for short-duration high-force axial impact loading

Finite element (FE) models can allow computer simulations of impact loading, providing a useful companion to cadaveric testing. These models allow injury evaluations to be conducted under a variety of conditions, but must be validated against experimental data. An FE model of a cadaveric tibia was developed using geometry from CT scans, and the quality of the mesh was evaluated. Loading and boundary conditions from experimental tests were simulated, and the model was optimised to best represent the response of natural bone to impacts. The model was shown to have good agreement for impact force, duration, impulse and strain during simulation of three non-injurious and one injurious axial impact when compared with experimental test data for the specimen. Failure criteria were evaluated for their ability to predict fracture. This model of the tibia can be used for future injury prediction assessment studies.     

A memory diffusion model for molecular anisotropic diffusion in siliceous β-zeolite

A memory diffusion model of molecules on β-zeolite is proposed. In the model, molecular diffusion in β-zeolites is treated as jumping from one adsorption site to its neighbors and the jumping probability is a compound probability which includes that provided by the transitional state theory as well as that derived from the information about which direction the target molecule comes from. The proposed approach reveals that the diffusivities along two crystal axes on β-zeolite are correlated. The model is tested by molecular dynamics simulations on diffusion of benzene and other simple molecules in β-zeolites. The results show that the molecules with larger diameters fit the prediction much better and that the “memory effects” are important in all cases.     

Altered lipid metabolism in post-traumatic epileptic rat model: one proposed pathway

Molecular Biology Reports - Post-traumatic epilepsy (PTE) is a common long-term risk associated with traumatic brain injury (TBI). PTE rat model, proposed by Willmore et al., is a well known model...     

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.     

Overexpression of long noncoding RNA Malat1 ameliorates traumatic brain injury induced brain edema by inhibiting AQP4 and the NF-κB/IL-6 pathway

Brain edema is a major traumatic brain injury (TBI)-related neurological complication. In the initiation stage of TBI, brain edema is characterized by astrocyte swelling (cytotoxic edema). We studied the impact of a long noncoding RNA, Malat1, on the TBI-induced astrocyte swelling and brain edema. Our results showed that Malat1 was downregulated in both the TBI rat model and the astrocyte fluid percussion injury (FPI) model, which concurred with brain edema and astrocyte swelling. Overexpression of Malat1 significantly inhibited rat brain edema, meanwhile reducing interleukin-6 (IL-6), nuclear factor-κB (NF-κB), and aquaporin 4 (AQP4) expression after TBI. In addition, overexpression of Malat1 ameliorated FPI-induced astrocyte swelling and reduced IL-6 release. Quantitative real-time polymerase chain reaction and Western blot analysis also corroborated the inhibitory effects of Malat1 on NF-κB and AQP4 expression after FPI. Our results highlighted the protective effects of Malat1 on the TBI-induced brain edema, which were mediated through regulating IL-6, NF-κB, and AQP4 expression. Our study could provide a novel approach for TBI treatment.     

A Dynamic Finite Element Analysis of Human Foot Complex in the Sagittal Plane during Level Walking

The objective of this study is to develop a computational framework for investigating the dynamic behavior and the internal loading conditions of the human foot complex during locomotion. A subject-specific dynamic finite element model in the sagittal plane was constructed based on anatomical structures segmented from medical CT scan images. Three-dimensional gait measurements were conducted to support and validate the model. Ankle joint forces and moment derived from gait measurements were used to drive the model. Explicit finite element simulations were conducted, covering the entire stance phase from heel-strike impact to toe-off. The predicted ground reaction forces, center of pressure, foot bone motions and plantar surface pressure showed reasonably good agreement with the gait measurement data over most of the stance phase. The prediction discrepancies can be explained by the assumptions and limitations of the model. Our analysis showed that a dynamic FE simulation can improve the prediction accuracy in the peak plantar pressures at some parts of the foot complex by 10%–33% compared to a quasi-static FE simulation. However, to simplify the costly explicit FE simulation, the proposed model is confined only to the sagittal plane and has a simplified representation of foot structure. The dynamic finite element foot model proposed in this study would provide a useful tool for future extension to a fully muscle-driven dynamic three-dimensional model with detailed representation of all major anatomical structures, in order to investigate the structural dynamics of the human foot musculoskeletal system during normal or even pathological functioning.     

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.     

Fish oil improves motor function,limits blood–brain barrier disruption,and reduces Mmp9 gene expression in a rat model of juvenile traumatic brain injury

The effects of an oral fish oil treatment regimen on sensorimotor, blood–brain barrier, and biochemical outcomes of traumatic brain injury (TBI) were investigated in a juvenile rat model. Seventeen-day old Long-Evans rats were given a 15 mL/kg fish oil (2.01 g/kg EPA, 1.34 g/kg DHA) or soybean oil dose via oral gavage 30 min prior to being subjected to a controlled cortical impact injury or sham surgery, followed by daily doses for seven days. Fish oil treatment resulted in less severe hindlimb deficits after TBI as assessed with the beam walk test, decreased cerebral IgG infiltration, and decreased TBI-induced expression of the Mmp9 gene one day after injury. These results indicate that fish oil improved functional outcome after TBI resulting, at least in part from decreased disruption of the blood–brain barrier through a mechanism that includes attenuation of TBI-induced expression of Mmp9.     

Reactive Oxygen Species-Activated Nanoprodrug of Ibuprofen for Targeting Traumatic Brain Injury in Mice

Traumatic brain injury (TBI) is an enormous public health problem, with 1.7 million new cases of TBI recorded annually by the Centers for Disease Control. However, TBI has proven to be an extremely challenging condition to treat. Here, we apply a nanoprodrug strategy in a mouse model of TBI. The novel nanoprodrug contains a derivative of the nonsteroidal anti-inflammatory drug (NSAID) ibuprofen in an emulsion with the antioxidant α-tocopherol. The ibuprofen derivative, Ibu₂TEG, contains a tetra ethylene glycol (TEG) spacer consisting of biodegradable ester bonds. The biodegradable ester bonds ensure that the prodrug molecules break down hydrolytically or enzymatically. The drug is labeled with the fluorescent reporter Cy5.5 using nonbiodegradable bonds to 1-octadecanethiol, allowing us to reliably track its accumulation in the brain after TBI. We delivered a moderate injury using a highly reproducible mouse model of closed-skull controlled cortical impact to the parietal region of the cortex, followed by an injection of the nanoprodrug at a dose of 0.2 mg per mouse. The blood brain barrier is known to exhibit increased permeability at the site of injury. We tested for accumulation of the fluorescent drug particles at the site of injury using confocal and bioluminescence imaging of whole brains and brain slices 36 hours after administration. We demonstrated that the drug does accumulate preferentially in the region of injured tissue, likely due to an enhanced permeability and retention (EPR) phenomenon. The use of a nanoprodrug approach to deliver therapeutics in TBI represents a promising potential therapeutic modality.     

Can sulci protect the brain from traumatic injury?

The influence of sulci in dynamic finite element simulations of the human head has been investigated. First, a detailed 3D FE model was constructed based on an MRI scan of a human head. A second model with a smoothed brain surface was created based on the same MRI scan as the first FE model. These models were validated against experimental data to confirm their human-like dynamic responses during impact. The validated FE models were subjected to several acceleration impulses and the maximum principle strain and strain rate in the brain were analyzed. The results suggested that the inclusion of sulci should be considered for future FE head models as it alters the strain and strain distribution in an FE model.     

Distinct temporal and anatomical distributions of amyloid-β and tau abnormalities following controlled cortical impact in transgenic mice

Traumatic brain injury (TBI) is a major environmental risk factor for Alzheimer's disease. Intracellular accumulations of amyloid-β and tau proteins have been observed within hours following severe TBI in humans. Similar abnormalities have been recapitulated in young 3xTg-AD mice subjected to the controlled cortical impact model (CCI) of TBI and sacrificed at 24 h and 7 days post injury. This study investigated the temporal and anatomical distributions of amyloid-β and tau abnormalities from 1 h to 24 h post injury in the same model. Intra-axonal amyloid-β accumulation in the fimbria was detected as early as 1 hour and increased monotonically over 24 hours following injury. Tau immunoreactivity in the fimbria and amygdala had a biphasic time course with peaks at 1 hour and 24 hours, while tau immunoreactivity in the contralateral CA1 rose in a delayed fashion starting at 12 hours after injury. Furthermore, rapid intra-axonal amyloid-β accumulation was similarly observed post controlled cortical injury in APP/PS1 mice, another transgenic Alzheimer's disease mouse model. Acute increases in total and phospho-tau immunoreactivity were also evident in single transgenic Tau(P301L) mice subjected to controlled cortical injury. These data provide further evidence for the causal effects of moderately severe contusional TBI on acceleration of acute Alzheimer-related abnormalities and the independent relationship between amyloid-β and tau in this setting.