Finite Element Brain Model Research Articles

Investigation of Head Kinematics and Brain Strain Response During Soccer Heading Using a Custom-Fit Instrumented Mouthguard

Association football, also known as soccer in some regions, is unique in encouraging its participants to intentionally use their head to gain a competitive advantage, including scoring a goal. Repetitive head impacts are now being increasingly linked to an inflated risk of developing long-term neurodegenerative disease. This study investigated the effect of heading passes from different distances, using head acceleration data and finite element modelling to estimate brain injury risk. Seven university-level participants wore a custom-fitted instrumented mouthguard to capture linear and angular acceleration-time data. They performed 10 headers within a laboratory environment, from a combination of short, medium, and long passes. Kinematic data was then used to calculate peak linear acceleration, peak angular velocity, and peak angular acceleration as well as two brain injury metrics: head injury criterion and rotational injury criterion. Six degrees of freedom acceleration-time data were also inputted into a widely accepted finite element brain model to estimate strain-response using mean peak strain and cumulative strain damage measure values. Five headers were considered to have a 25% concussion risk. Mean peak linear acceleration equalled 26 ± 7.9 g, mean peak angular velocity 7.20 ± 2.18 rad/s, mean peak angular acceleration 1730 ± 611 rad/s2, and 95th percentile mean peak strain 0.0962 ± 0.252. Some of these data were similar to brain injury metrics reported from American football, which supports the need for further investigation into soccer heading.

Strain Response ofan Anatomically Accurate Nonhuman Primate Finite Element Brain Model Under Sagittal Loading.

Prevention and treatment of traumatic brain injuries is critical to preserving soldier brain health. Laboratory studies are commonly used to reproduce injuries, understand injury mechanisms, and develop tolerance limits; however, this approach has limitations for studying brain injury, which requires a physiological response. The nonhuman primate (NHP) has been used as an effective model for investigating brain injury for many years. Prior research using the NHP provides a valuable resource to leverage using modern analysis and modeling techniques to improve our understanding of brain injury. The objectives of the present study are to develop an anatomically accurate finite element model of the NHP and determine regional brain responses using previously collected NHP data. The finite element model was developed using a neuroimaging-based anatomical atlas of the rhesus macaque that includes both cortical and subcortical structures. Head kinematic data from 10 sagittal NHP experiments, four +Gx (rearward) and six -Gx (frontal), were used to test model stability and obtain brain strain responses from multiple severities and vectors. For +Gx tests, the whole-brain cumulative strain damage measure exceeding a strain threshold of 0.15 (CSDM15) ranged from 0.28 to 0.89, and 95th percentile of the whole-brain maximum principal strain (MPS95) ranged from 0.21 to 0.59. For -Gx tests, whole-brain CSDM15 ranged from 0.02 to 0.66, and whole-brain MPS95 ranged from 0.08 to 0.39. Recognizing that NHPs are the closest surrogate to humans combined with the limitations of conducting brain injury research in the laboratory, a detailed anatomically accurate finite element model of an NHP was developed and exercised using previously collected data from the Naval Biodynamics Laboratory. The presently developed model can be used to conduct additional analyses to act as pilot data for the design of newer experiments with statistical power because of the sensitivity and resources needed to conduct experiments with NHPs.

Regional brain strain dependance on direction of head rotation

Brain injuries in automated vehicles during crash events are likely to include mechanisms of head impact in non-standard positions and postures (i.e., occupants not facing forward in an upright position). Federal regulations currently focus on impact conditions in primary planes of motion, such as frontal or rear impacts (sagittal plane of motion) or side impact (coronal plane of motion) and do not account for out of position occupants or non-standard postures. The objective of the present study was to develop and use the anatomically accurate brain finite element model to parametrically determine the injury metrics under different vectors with head rotation. A custom developed brain finite element model with anatomical accuracy and several anatomical regions defined was used to evaluate whole-brain strain as well as regional brain strain. Cumulative Strain Damage Measure (CSDM) at a threshold of 20% strain and the 95th percentile of the maximum principal strain (MPS95) were calculated for the whole brain and each brain region under multiple rotational directions. The model was exposed to a sinusoidal angular acceleration pulse of 5000 rad per second squared (rad/s2–) over 12.5 ms. The same pulse was used in the primary axes of motion and (lateral bending, flexion, extension, axial rotation) and combined axes representing oblique flexion and oblique extension. Whole brain CSDM20 was highest for lateral bending. Whole brain MPS95 was highest for axial rotation. The rCSDM20 was more susceptible to impact direction, with several brain regions having substantial accumulation of strain for oblique flexion and lateral bending. Comparatively, rMPS95 was more consistent across all rotation directions. The present study quantified the regional brain strain response under multiple rotational vectors identifying a high amount of variability in the accumulation of strain (i.e., CSDM20) in the hypothalamus, hippocampus, and midbrain specifically. While there was a high amount of variability in the accumulation of strain for multiple regions, the maximum strain measured (i.e., MPS95) in the regions was more consistent.

Modal analysis of computational human brain dynamics during helmeted impacts

Sports-related mild traumatic brain injury (mTBI) is a growing public health concern, affecting millions in the U.S., annually. Current helmets are primarily designed to mitigate head kinematics, despite the importance of the brain substructures mechanics in mTBI mechanism. Therefore, it is crucial to consider the dynamical behavior of brain substructures, which has been shown in prior studies to be associated with strain concentration. Here, we studied the modal behavior and strain patterns of the substructures of the brain finite element (FE) model through Dynamic Mode Decomposition. We conducted side and front impact pendulum tests on a dummy headform equipped with hockey, football, ski, and bicycle helmets. After simulating the impact tests using a brain FE model, we calculated the dynamic modes of this computational model for the whole brain, corpus callosum, brainstem, and cerebellum. The main mode of oscillation in all regions for all helmet types occurred around the frequency regime of 7–15 Hz. Also, in cerebellum, a second harmonic was observed at 40–50 Hz in front impact, and 38 and 62 Hz in side impact in bicycle and ski helmets, respectively. Furthermore, we analyzed the correlation between the modal response and peak maximum principal strain (MPS). These analyses mostly showed a direct association between the computational modal behavior and MPS, where helmet tests with closely spaced modes and high-frequency modal amplitudes led to higher MPS values. This association between the computational modal behavior and strain patterns demonstrated a potential for improving helmet designs through a novel design objective.Statement of significance: Sport-related mild traumatic brain injury (mTBI), which is one of the leading cause of death, can be reduced in severity by using headgears including helmets. Despite the recent innovations and technologies in helmet design, there are important factors that still have been missed. While it’s been shown that the brain substructures mechanics play an important role in mTBI mechanism, current helmets are designed to only mitigate the head kinematics. Moreover, dynamical behavior of these substructures, and existence of multimodal behavior in the brain are factors that have not been addressed in designing helmets. This paper shows the effect of different helmet types on modal behavior of the brain substructures and how the dynamical modes of these regions can be affected by using various helmet types.

Mechanical loading of the ventricular wall as a spatial indicator for periventricular white matter degeneration

Progressive white matter degeneration in periventricular and deep white matter regions appears as white matter hyperintensities (WMH) on MRI scans. To date, periventricular WMHs are often associated with vascular dysfunction. Here, we demonstrate that ventricular inflation resulting from cerebral atrophy and hemodynamic pulsation with every heartbeat leads to a mechanical loading state of periventricular tissues that significantly affects the ventricular wall. Specifically, we present a physics-based modeling approach that provides a rationale for ependymal cell involvement in periventricular WMH formation. Building on eight previously created 2D finite element brain models, we introduce novel mechanomarkers for ependymal cell loading and geometric measures that characterize lateral ventricular shape. We show that our novel mechanomarkers, such as maximum ependymal cell deformations and maximum curvature of the ventricular wall, spatially overlap with periventricular WMH locations and are sensitive predictors for WMH formation. We also explore the role of the septum pellucidum in mitigating mechanical loading of the ventricular wall by constraining the radial expansion of the lateral ventricles during loading. Our models consistently show that ependymal cells are stretched thin only in the horns of the ventricles irrespective of ventricular shape. We therefore pose that periventricular WMH etiology is strongly linked to the deterioration of the over-stretched ventricular wall resulting in CSF leakage into periventricular white matter. Subsequent secondary damage mechanisms, including vascular degeneration, exacerbate lesion formation and lead to progressive growth into deep white matter regions.

Deep learning method to assess brain injury risk

Model-based brain injury criteria can present higher potential to predict injury than global head kinematic parameters. Numerical head injury prediction tools are time consuming and require Finite Element (FE) skilled users. To address these difficulties, a deep learning technique was applied to an existing previously developed brain FE model for which an injury risk curve has been proposed in terms of maximum Von Mises stress to predict moderate diffuse axonal injury. A total of 4492 experimental head impacts coming from experimental helmet testing were considered as input for the analysis. Each input was expressed in terms of three linear accelerations and three angular velocities versus time, when the target metric was the time history Von Mises Stress () curve computed within the brain via the FE analysis. The architecture used for the Deep Learning (DL) model is the U-Net and four models based on it were evaluated. The dataset was split into three datasets dedicated for learning and testing. The quality of the DL models were assessed via the Maximum Absolute Error between FE and DL models computed brain maximum Further, a regression analysis of brain response with both methods was conducted. The results demonstrated that deep learning methods can be applied in the context of brain response estimation when helmet assessment is considered without any FE computation, only by considering the 6 D head kinematic vs time demonstrating that the deep learning approach should be further developed for research and industrial applications.

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

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

Head Impact Kinematics and Brain Deformation in Paired Opposing Youth Football Players.

Head impact exposure is often quantified using peak resultant kinematics. While kinematics describes the inertial response of the brain to impact, they do not fully capture the dynamic brain response. Strain, a measure of the tissue-level response of the brain, may be a better predictor of injury. In this study, kinematic and strain metrics were compared to contact characteristics in youth football. Players on 2 opposing teams were instrumented with head impact sensors to record impact kinematics. Video was collected to identify contact scenarios involving opposing instrumented players (ie,paired contact scenarios) and code contact characteristics (eg,player role, impact location). A previously validated, high-resolution brain finite element model, the atlas-based brain model, was used to simulate head impacts and calculate strain metrics. Fifty-two paired contact scenarios (n = 105 impacts) were evaluated. Lighter players tended to have greater biomechanical metrics compared to heavier players. Impacts to the top of the helmet were associated with lower strain metrics. Overall, strain was better correlated with rotational kinematics, suggesting these metrics may be better predictors of the tissue-level brain response than linear kinematics. Understanding the effect of contact characteristics on brain strain will inform future efforts to improve sport safety.

The Effect of Three-Dimensional Whole, Major, and Small Vasculature on Mouse Brain Strain Under Both Diffuse and Focal Brain Injury Loading.

Blood vessels are much stiffer than brain parenchyma and their effects in finite element (FE) brain models need to be investigated. Despite the publication of some comprehensive three-dimensional (3D) brain vasculature models, no mechanical model exists for the mouse brain vasculature. Moreover, how the vasculature affects the mechanical behavior of brain tissue remains controversial. Therefore, we developed FE mouse brain models with detailed 3D vasculature to investigate the effect of the vasculature on brain strains under both diffuse (closed-head impact) and focal injury (controlled cortical impact (CCI)) loading, two commonly laboratory models of traumatic brain injury. The effect of the vasculature was examined by comparing maximum principal strain in mouse brain FE models with and without the vasculature. On average, modeling comprehensive vasculature under diffuse injury loading reduced average brain strain predictions by 32% with nonlinear elastic properties. Nearly three-fourths of the 32% strain reduction was attributable to the effects of the major branches of the vasculature. Meanwhile, during focal open-skull CCI injury loading, the contribution of the vasculature was limited, producing a less than 5% reduction in all cases. Overall, the vasculature, especially the major branches, increased the load-bearing capacity of the brain FE model and thus reduced brain strain predictions.

Header biomechanics in youth and collegiate female soccer

Concerns about the effects of intentional heading in soccer have led to regulatory restrictions on headers for youth players. However, there is limited data describing how header exposure varies across age levels, and few studies have attempted to compare head impact exposure across different levels of play with the same sensor. Additionally, little is known about the biomechanical response of the brain to header impacts. The objective of this study was to evaluate head kinematics and the resulting tissue-level brain strain associated with intentional headers among youth and collegiate female soccer players. Six youth and 13 collegiate participants were instrumented with custom mouthpiece-based sensors measuring six-degree-of-freedom head kinematics of headers during practices and games. Kinematics of film-verified headers were used to drive impact simulations with a detailed brain finite element model to estimate tissue-level strain. Linear and rotational head kinematics and strain metrics, specifically 95th percentile maximum principal strain (ε1,95) and the area under the cumulative strain damage measure curve (VSM1), were compared across levels of play (i.e., youth vs. collegiate) while adjusting for session type and ball delivery method. A total of 483 headers (n = 227 youth, n = 256 collegiate) were analyzed. Level of play was significantly associated with linear acceleration, rotational acceleration, rotational velocity, ε1,95, and VSM1. Headers performed by collegiate players had significantly greater mean head kinematics and strain metrics compared to those performed by youth players (all p < .001). Targeted interventions aiming to reduce head impact magnitude in soccer should consider factors associated with the level of play.

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach.

Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20–40 rad/s), durations (30–60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

Human brain FE modeling including incompressible fluid dynamics of intraventricular cerebrospinal fluid

Elucidating the mechanisms of mild traumatic brain injuries (mild TBIs), including concussions, is important for developing brain injury criteria and designing head protection devices. Using a finite element (FE) model of the human brain to predict the deformation of the brain parenchyma during a head impact could provide mechanical insights on mild TBIs. However, most conventional brain FE models do not consider how fluid behavior and the perfusion pressure of the cerebrospinal fluid (CSF) will affect brain deformation. This study proposes a novel brain FE model that uses incompressible fluid dynamics (ICFD) to represent the fluid behavior of CSF in the ventricle. In the model with ICFD, the validation accuracy scores on the brain strain during a head impact with a rotational acceleration were significantly higher than those in the model without ICFD. Reconstruction simulations based on two reported mild TBI cases from a rear-end collision and an American football game were conducted using the model with and without ICFD. We found that the maximum principal strain values in the subcortical region and corpus callosum of the model with ICFD were higher and lasted longer than those of the model without ICFD, and this tendency was further enhanced when perfusion pressure was applied. These findings suggested that the fluid behavior and perfusion pressure of the intraventricular CSF could significantly affect the deformation of the brain parenchyma during head impacts. The proposed brain multiphysical FE model could enhance the understanding of mild TBI mechanisms. Statement of SignificanceMild TBIs or concussions can result from brain deformations caused by the rapid acceleration of the head in the situations such as falls, vehicular accidents, and collisions in sports-related activities. A FE analysis is an effective tool for simulating head impact scenarios associated with mild TBIs and estimating the brain strain. Accurate prediction of mild TBIs requires a brain FE model with high biofidelity. Here, we firstly revealed that the validation accuracy of the model on the brain strain can be improved by considering the fluid behavior of intraventricular CSF. By analyzing existing mild TBI cases using the proposed model, the fluid behavior and perfusion pressure of the CSF were found to significantly affect the brain strain history, resulting in an outcome similar to the clinical symptom. The proposed multiphysical brain model could potentially provide new mechanical insights and further understanding of mild TBIs. Additionally, these findings in this study could be useful in developing brain injury criteria and designing protective equipment.

Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain

Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95th percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions. Statement of SignificanceThe study presents a method to calibrate and evaluate subject-specific finite element brain models using a combination of advanced magnetic resonance imaging (MRI) data. The imaging data is acquired in human volunteers and includes anatomical MRI, magnetic resonance elastography (MRE), and tagged MRI to generate subject-specific geometry, calibrate subject-specific material properties, and evaluate simulation response using subject-specific brain deformation. This dataset of MRE and tagged MRI allows for a unique evaluation of whether material properties from MRE can be used to create biofidelic computational models of the human brain. The study develops a calibration procedure to readily calculate linear viscoelastic material parameters from MRE data and then provides a sensitivity study of the effect of mechanical heterogeneity of the brain on simulation response. The calibrated computational models are used to simulate each subject's tagged MRI experiment; the results show good agreement between the simulated and experimental strain fields. The presented study and results will be informative in guiding the calibration of subject-specific computational brain model from experimental MRE data. The processed MRI, MRE, and tagged MRI data are publicly available at https://www.nitrc.org/projects/bbir/.

Multi-Scale White Matter Tract Embedded Brain Finite Element Model Predicts the Location of Traumatic Diffuse Axonal Injury.

Finite element models (FEMs) are used increasingly in the traumatic brain injury (TBI) field to provide an estimation of tissue responses and predict the probability of sustaining TBI after a biomechanical event. However, FEM studies have mainly focused on predicting the absence/presence of TBI rather than estimating the location of injury. In this study, we created a multi-scale FEM of the pig brain with embedded axonal tracts to estimate the sites of acute (≤6 h) traumatic axonal injury (TAI) after rapid head rotation. We examined three finite element (FE)-derived metrics related to the axonal bundle deformation and three FE-derived metrics based on brain tissue deformation for prediction of acute TAI location. Rapid head rotations were performed in pigs, and TAI neuropathological maps were generated and colocalized to the FEM. The distributions of the FEM-derived brain/axonal deformations spatially correlate with the locations of acute TAI. For each of the six metric candidates, we examined a matrix of different injury thresholds (thx) and distance to actual TAI sites (ds) to maximize the average of two optimization criteria. Three axonal deformation-related TAI candidates predicted the sites of acute TAI within 2.5 mm, but no brain tissue metric succeeded. The optimal range of thresholds for maximum axonal strain, maximum axonal strain rate, and maximum product of axonal strain and strain rate were 0.08-0.14, 40-90, and 2.0-7.5 s-1, respectively. The upper bounds of these thresholds resulted in higher true-positive prediction rate. In summary, this study confirmed the hypothesis that the large axonal-bundle deformations occur on/close to the areas that sustained TAI.

Biomechanical modelling and computer aided simulation of deep brain retraction in neurosurgery

Background and Objectives: Surgical simulators are widely used to promote faster and safer surgical training. They not only provide a platform for enhancing surgical skills but also minimize risks to the patient’s safety, operation theatre usage, and financial expenditure. Retracting the soft brain tissue is an unavoidable procedure during any surgery to access the lesioned tissue deep within the brain. Excessive retraction often results in damaging the brain tissue, thus requiring advanced skills and prior training using virtual platforms. Such surgical simulation platforms require an anatomically correct computational model that can accurately predict the brain deformation in real-time.Methods: In this study, we present a 3D finite element brain model reconstructed from MRI dataset. The model incorporates precisely the anatomy and geometrical features of the canine brain. The brain model has been used to formulate and solve a quasi-static boundary value problem for brain deformation during brain retraction. The visco-hyperelastic framework within the theory of non-linear elasticity has been used to set up the boundary value problem. Consequently, the derived non-linear field equations have been solved using finite element solver ABAQUS.Results: The retraction simulations have been performed for two scenarios: retraction pressure in the brain and forces required to perform the surgery. The brain was retracted by 5 mm and retained at that position for 30 minutes, during which the retraction pressure attenuates to 36% of its peak value. Both the model predictions as well as experimental observations on canine brain indicate that brain retraction up to 30 minutes did not cause any significant risk of induced damage. Also, the peak retraction pressure level indicates that intermittent retraction is a safer procedure as compared to the continuous retraction, for the same extent of retraction.Conclusions: The results of the present study indicate the potential of a visco-hyperelastic framework for simulating deep brain retraction effectively. The simulations were able to capture the dominant characteristics of brain tissue undergoing retraction. The developed platform could serve as a basis for the development of a detailed model in the future that can eventually be used for effective preoperative planning and training purposes.

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models.

Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subject-specific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subject-specific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinaryresearch between thebiomechanics and neuroimagingfields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis.

Investigation of dynamic deformation of the midbrain in rear-end collision using human brain FE model

Mild traumatic brain injury (TBI), including concussions, can cause symptoms affecting physical or cognitive domains in the acute and chronic phases. In this study, we investigated the dynamic deformation of the brain stem, which might be important for these symptoms, using a human brain finite element model through reconstruction simulations of rear-end collisions in three different velocities. In all simulations, high maximum principal strain values were observed at the midbrain that were higher than those in the corpus callosum. These findings could provide some mechanical insights into brain disorders associated with mild TBI.

Three-dimensional nonlinear finite element model to estimate backflow during flow-controlled infusions into the brain.

Convection-enhanced delivery is a technique to bypass the blood-brain barrier and deliver therapeutic drugs into the brain tissue. However, animal investigations and preliminary clinical trials have reported reduced efficacy to transport the infused drug in specific zones, attributed mainly to backflow, in which an annular gap is formed outside the catheter and the fluid preferentially flows toward the surface of the brain rather than through the tissue in front of the cannula tip. In this study, a three-dimensional human brain finite element model of backflow was developed to study the influence of anatomical structures during flow-controlled infusions. Predictions of backflow length were compared under the influence of ventricular pressure and the distance between the cannula and the ventricles. Simulations with zero relative ventricle pressure displayed similar backflow length predictions for larger cannula-ventricle distances. In addition, infusions near the ventricles revealed smaller backflow length and the liquid was observed to escape to the longitudinal fissure and ventricular cavities. Simulations with larger cannula-ventricle distances and nonzero relative ventricular pressure showed an increase of fluid flow through the tissue and away from the ventricles. These results reveal the importance of considering both the subject-specific anatomical details and the nonlinear effects in models focused on analyzing current and potential treatment options associated with convection-enhanced delivery optimization for future clinical trials.

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis.

Concussion is a significant public health problem affecting 1.6–2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

Development of a Subhuman Primate Brain Finite Element Model to Investigate Brain Injury Thresholds Induced by Head Rotation.

An anatomically detailed rhesus monkey brain FE model was developed to simulate in vivo responses of the brain of sub-human primates subjected to rotational accelerations resulting in diffuse axonal injury (DAI). The material properties used in the monkey model are those in the GHBMC 50th percentile male head model (Global Human Body Model Consortium). The angular loading simulations consisted of coronal, oblique and sagittal plane rotations with the center of rotation in neck to duplicate experimental conditions. Maximum principal strain (MPS) and Cumulative strain damage measure (CSDM) were analyzed for various white matter structures such as the cerebrum subcortical white matter, corpus callosum and brainstem. The MPS in coronal rotation were 45% to 54% higher in the brainstem, 8% to 48% higher in the corpus callosum, 13% to 22% higher in the white matter when compared to those in oblique and sagittal rotations, suggesting that more severe DAI was expected from coronal and oblique rotations as compared to that from sagittal rotation. The level 1+ DAI was associated with 1.3 to 1.42 MPS and 50% CSDM (0.5) responses in the brainstem, corpus callosum and cerebral white matter. The mass scaling method, sometimes referred to as Holbourn's inverse 2/3 power law, used for development of human brain injury criterion was evaluated to understand the effect of geometrical and anatomical differences between human and animal head. Based on simulations conducted with the animal and human models in three different planes - sagittal, coronal and horizontal - the scaling from animal to human models are not supported due to lack of geometrical similitude between the animal and human brains. Thus, the scaling method used in the development of brain injury criterion for rotational acceleration/velocity is unreliable.