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

Average Biomechanical Responses of the Human Brain Grouped by Age and Sex

Traumatic brain injuries (TBIs) occur from rapid head motion that results in brain deformation. Computational models are typically used to estimate brain deformation to predict risk of injury and evaluate the effectiveness of safety countermeasures. The accuracy of these models relies on validation to experimental brain deformation data. In this study, we create the first group-average biomechanical responses of the brain, including structure, material properties, and deformation response, by age and sex from 157 subjects. Subjects were sorted intro three age groups-young, mid-age, and older-and by sex to create group-average neuroanatomy, material properties, and brain deformation response to non-injurious loading using structural and specialized magnetic resonance imaging data. Computational models were also built using the group-average geometry and material properties for each of the six groups. The material properties did not depend on sex, but showed a decrease in shear stiffness in the older adult group. The brain deformation response also showed differences in the distribution of strain and a decrease in the magnitude of maximum strain in the older adult group. The computational models were simulated using the same non-injurious loading conditions as the subject data. While the models' strain response showed differences among the models, there were no clear relationships with age. Further studies, both modeling and experimental, with more data from subjects in each age group, are needed to clarify the mechanisms underlying the observed changes in strain response with age, and for computational models to better match the trends observed across the group-average responses.

A fast-modeling framework for personalized human body models based on a single image

Finite element human body models (HBMs) are the primary method for predicting human biological responses in vehicle collisions, especially personalized HBMs that allow accounting for diverse populations. Yet, creating personalized HBMs from a single image is a challenging task. This study addresses this challenge by providing a framework for HBM personalization, starting from a single image used to estimate the subject's skin point cloud, the skeletal point cloud, and the relative positions of the skeletons. Personalized HBMs were created by morphing the baseline HBM accounting skin and skeleton point clouds using a point cloud registration-based mesh morphing method. Using this framework, eight personalized HBMs with various biological characteristics (e.g., sex, height, and weight) were created, with comparable element quality to the baseline HBM. The mean geometric errors of the personalized FEMs generated by the framework are less than 7 mm, which was found to be acceptable based on biomechanical response evaluations conducted in this study.

Role of player-specific white matter parcellation and scaling in impact-induced strain responses

Head finite element models (hFEMs) are valuable in understanding injury mechanisms in head impacts. Personalizing hFEMs is important for capturing individualized brain responses, with brain volume scaling proving effective. However, the role of refined white matter (WM) parcellation in hFEMs for evaluating brain strain responses, particularly important in the context of subconcussive head impacts (SHIs) often assessed through changes in WM integrity, remains relatively underexplored. This study evaluated the effect of refined subject-specific WM parcellation in 34 WM segments on responses variability due to brain volume variations, using peak maximum principal strain (95MPS) and strain rate (95MPSr) as injury predictive metrics. Data from diffusion-weighted imaging of 21 Canadian varsity football players were utilized to personalize 21 hFEMs. Simulating four different head impacts, representing 50th and 99th percentile resultant accelerations in frontal and angled-top-right directions, refined player-specific WM parcellation better captured variability of strain responses compared to baseline parcellation. Up to 75.71% of 95MPS and 77.14% of 95MPSr responses were deemed different across refined WM segments for players, compared to a maximum of 16.19% of responses with baseline parcellation. These results suggest that player-specific refined WM parcellation improves the ability to capture player-specific responses. Both impact direction and intensity influenced variations in strain response, with angled-top head impacts combined with high intensity showing greater player-specificity compared to lower intensity and frontal head impacts. These findings highlight the potential benefit of model scaling along with player-specific refined WM parcellation in hFEMs for comprehensively evaluating strain responses. Detailed WM parcellation in hFEMs is important for comprehensive injury assessment, enhancing the alignment of hFEMs with imaging studies evaluating changes in WM integrity across segments. The simple and straightforward method presented herein to achieve player-specific strain response is promising for future SHI studies.

The White Matter Fiber Tract Deforms Most in the Perpendicular Direction During In Vivo Volunteer Impacts

White matter (WM) tract-related strains are increasingly used to quantify brain mechanical responses, but their dynamics in live human brains during in vivo impact conditions remain largely unknown. Existing research primarily looked into the normal strain along the WM fiber tracts (i.e., tract-oriented normal strain), but it is rarely the case that the fiber tract only endures tract-oriented normal strain during impacts. In this study, we aim to extend the in vivo measurement of WM fiber deformation by quantifying the normal strain perpendicular to the fiber tract (i.e., tract-perpendicular normal strain) and the shear strain along and perpendicular to the fiber tract (i.e., tract-oriented shear strain and tract-perpendicular shear strain, respectively). To achieve this, we combine the three-dimensional strain tensor from the tagged magnetic resonance imaging with the diffusion tensor imaging (DTI) from an open-access dataset, including 44 volunteer impacts under two head loading modes, i.e., neck rotations (N = 30) and neck extensions (N = 14). The strain tensor is rotated to the coordinate system with one axis aligned with DTI-revealed fiber orientation, and then four tract-related strain measures are calculated. The results show that tract-perpendicular normal strain peaks are the largest among the four strain types (p < 0.05, Friedman's test). The distribution of tract-related strains is affected by the head loading mode, of which laterally symmetric patterns with respect to the midsagittal plane are noted under neck extensions, but not under neck rotations. Our study presents a comprehensive in vivo strain quantification toward a multifaceted understanding of WM dynamics. We find that the WM fiber tract deforms most in the perpendicular direction, illuminating new fundamentals of brain mechanics. The reported strain images can be used to evaluate the fidelity of computational head models, especially those intended to predict fiber deformation under noninjurious conditions.

Patient-specific surrogate model to predict pelvic floor dynamics during vaginal delivery

Childbirth is a challenging event that can lead to long-term consequences such as prolapse or incontinence. While computational models are widely used to mimic vaginal delivery, their integration into clinical practice is hindered by time constraints. The primary goal of this study is to introduce an artificial intelligence pipeline that leverages patient-specific surrogate modeling to predict pelvic floor injuries during vaginal delivery. A finite element-based machine learning approach was implemented to generate a dataset with information from finite element simulations. Thousands of childbirth simulations were conducted, varying the dimensions of the pelvic floor muscles and the mechanical properties used for their characterization. Additionally, a mesh morphing algorithm was developed to obtain patient-specific models. Machine learning models, specifically tree-based algorithms such as Random Forest (RF) and Extreme Gradient Boosting, as well as Artificial Neural Networks, were trained to predict the nodal coordinates of nodes within the pelvic floor, aiming to predict the muscle stretch during a critical interval. The results indicate that the RF model performs best, with a mean absolute error (MAE) of 0.086 mm and a mean absolute percentage error of 0.38%. Overall, more than 80% of the nodes have an error smaller than 0.1 mm. The MAE for the calculated stretch is equal to 0.0011. The implemented pipeline allows loading the trained model and making predictions in less than 11 s. This work demonstrates the feasibility of implementing a machine learning framework in clinical practice to predict potential maternal injuries and assist in medical-decision making.

Concussion biomechanics, head acceleration exposure and brain injury criteria in sport: a review

There are mounting concerns surrounding the risk of neurodegenerative diseases and complications associated with concussion incidence and repetitive head acceleration events (HAE) in sport. The aim of this review is to provide an overview of concussion biomechanics, head acceleration exposure and brain injury criteria in sport. Rotational head motion appears to be the primary contributor to brain injury risk due to the unique mechanical properties of the brain and its location within the body. There is a growing evidence base of different biomechanical brain injury mechanisms, including those involving repetitive HAE. Historically, many studies on concussion biomechanics, head acceleration exposure and brain injury criteria in sport have been limited by validity of the biomechanical approaches undertaken. Biomechanical approaches such as instrumented mouthguards and subject-specific finite element (FE) brain models provide a unique opportunity to develop greater brain injury criteria and aid in on-field athlete removal. Implementing these approaches on a large-scale can gain insight into potential risk factors within sports and certain athletes/cohorts who sustain a greater number and/or severity of HAE throughout their playing career. These findings could play a key role in the development of concussion prevention strategies and techniques that mitigate the severity of HAE in sport.

A Spatial Interpolation Approach to Assign Magnetic Resonance Imaging-Derived Material Properties for Finite Element Models of Adeno-Associated Virus Infusion Into a Recurrent Brain Tumor

Adeno-associated virus (AAV) is a clinically useful gene delivery vehicle for treating neurological diseases. To deliver AAV to focal targets, direct infusion into brain tissue by convection-enhanced delivery (CED) is often needed due to AAV's limited penetration across the blood-brain-barrier and its low diffusivity in tissue. In this study, computational models that predict the spatial distribution of AAV in brain tissue during CED were developed to guide future placement of infusion catheters in recurrent brain tumors following primary tumor resection. The brain was modeled as a porous medium, and material property fields that account for magnetic resonance imaging (MRI)-derived anatomical regions were interpolated and directly assigned to an unstructured finite element mesh. By eliminating the need to mesh complex surfaces between fluid regions and tissue, mesh preparation was expedited, increasing the model's clinical feasibility. The infusion model predicted preferential fluid diversion into open fluid regions such as the ventricles and subarachnoid space (SAS). Additionally, a sensitivity analysis of AAV delivery demonstrated that improved AAV distribution in the tumor was achieved at higher tumor hydraulic conductivity or lower tumor porosity. Depending on the tumor infusion site, the AAV distribution covered 3.67-70.25% of the tumor volume (using a 10% AAV concentration threshold), demonstrating the model's potential to inform the selection of infusion sites for maximal tumor coverage.

Traumatic axonal injury: Clinic, forensic and biomechanics perspectives

Identification of Traumatic axonal injury (TAI) is critical in clinical practice, particularly in terms of long-term prognosis, but also for medico-legal issues, to verify whether the death or the after-effects were attributable to trauma. Multidisciplinary approaches are an undeniable asset when it comes to solving these problems. The aim of this work is therefore to list the different techniques needed to identify axonal lesions and to understand the lesion mechanisms involved in their formation. Imaging can be used to assess the consequences of trauma, to identify indirect signs of TAI, to explain the patient's initial symptoms and even to assess the patient's prognosis. Three-dimensional reconstructions of the skull can highlight fractures suggestive of trauma. Microscopic and immunohistochemical techniques are currently considered as the most reliable tools for the early identification of TAI following trauma. Finite element models use mechanical equations to predict biomechanical parameters, such as tissue stresses and strains in the brain, when subjected to external forces, such as violent impacts to the head. These parameters, which are difficult to measure experimentally, are then used to predict the risk of injury. The integration of imaging data with finite element models allows researchers to create realistic and personalized computational models by incorporating actual geometry and properties obtained from imaging techniques. The personalization of these models makes their forensic approach particularly interesting.

A Morphologically Individualized Deep Learning Brain Injury Model

The brain injury modeling community has recommended improving model subject specificity and simulation efficiency. Here, we extend an instantaneous (< 1 sec) convolutional neural network (CNN) brain model based on the anisotropic Worcester Head Injury Model (WHIM) V1.0 to account for strain differences due to individual morphological variations. Linear scaling factors relative to the generic WHIM along the three anatomical axes are used as additional CNN inputs. To generate training samples, the WHIM is randomly scaled to pair with augmented head impacts randomly generated from real-world data for simulation. An estimation of voxelized peak maximum principal strain of the whole-brain is said to be successful when the linear regression slope and Pearson's correlation coefficient relative to directly simulated do not deviate from 1.0 (when identical) by more than 0.1. Despite a modest training dataset (N = 1363 vs. ∼5.7 k previously), the individualized CNN achieves a success rate of 86.2% in cross-validation for scaled model responses, and 92.1% for independent generic model testing for impacts considered as complete capture of kinematic events. Using 11 scaled subject-specific models (with scaling factors determined from pre-established regression models based on head dimensions and sex and age information, and notably, without neuroimages), the morphologically individualized CNN remains accurate for impacts that also yield successful estimations for the generic WHIM. The individualized CNN instantly estimates subject-specific and spatially detailed peak strains of the entire brain and thus, supersedes others that report a scalar peak strain value incapable of informing the location of occurrence. This tool could be especially useful for youths and females due to their anticipated greater morphological differences relative to the generic model, even without the need for individual neuroimages. It has potential for a wide range of applications for injury mitigation purposes and the design of head protective gears. The voxelized strains also allow for convenient data sharing and promote collaboration among research groups.

Personalization of human body models and beyond via image registration

Finite element human body models (HBMs) are becoming increasingly important numerical tools for traffic safety. Developing a validated and reliable HBM from the start requires integrated efforts and continues to be a challenging task. Mesh morphing is an efficient technique to generate personalized HBMs accounting for individual anatomy once a baseline model has been developed. This study presents a new image registration-based mesh morphing method to generate personalized HBMs. The method is demonstrated by morphing four baseline HBMs (SAFER, THUMS, and VIVA+ in both seated and standing postures) into ten subjects with varying heights, body mass indices (BMIs), and sex. The resulting personalized HBMs show comparable element quality to the baseline models. This method enables the comparison of HBMs by morphing them into the same subject, eliminating geometric differences. The method also shows superior geometry correction capabilities, which facilitates converting a seated HBM to a standing one, combined with additional positioning tools. Furthermore, this method can be extended to personalize other models, and the feasibility of morphing vehicle models has been illustrated. In conclusion, this new image registration-based mesh morphing method allows rapid and robust personalization of HBMs, facilitating personalized simulations.

A sensor-enabled cloud-based computing platform for computational brain biomechanics

BACKGROUND AND OBJECTIVES

Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as "instrumented" or "smart" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion.

METHODS

We have built a brain modeling platform using several of Amazon's Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline.

RESULTS

The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D.

CONCLUSION

We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.

Approximating subject-specific brain injury models via scaling based on head-brain morphological relationships

Most human head/brain models represent a generic adult male head/brain. They may suffer in accuracy when investigating traumatic brain injury (TBI) on a subject-specific basis. Subject-specific models can be developed from neuroimages; however, neuroimages are not typically available in practice. In this study, we establish simple and elegant regression models between brain outer surface morphology and head dimensions measured from neuroimages along with age and sex information (N = 191; 141 males and 50 females with age ranging 14-25 years). The regression models are then used to approximate subject-specific brain models by scaling a generic counterpart, without using neuroimages. Model geometrical accuracy is assessed using adjusted [Formula: see text] and absolute percentage error (e.g., 0.720 and 3.09 ± 2.38%, respectively, for brain volume when incorporating tragion-to-top). For a subset of 11 subjects (from smallest to largest in brain volume), impact-induced brain strains are compared with those from "morphed models" derived from neuroimage-based mesh warping. We find that regional peak strains from the scaled subject-specific models are comparable to those of the morphed counterparts but could be considerably different from those of the generic model (e.g., linear regression slope of 1.01-1.03 for gray and white matter regions versus 1.16-1.19, or up to ~ 20% overestimation for the smallest brain studied). These results highlight the importance of incorporating brain morphological variations in impact simulation and demonstrate the feasibility of approximating subject-specific brain models without neuroimages using age, sex, and easily measurable head dimensions. The scaled models may improve subject specificity for future TBI investigations.

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

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

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

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

Brain architecture-based vulnerability to traumatic injury

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

Cumulative strain-based metrics for predicting subconcussive head impact exposure-related imaging changes in a cohort of American youth football players

OBJECTIVE

Youth football athletes are exposed to repetitive subconcussive head impacts during normal participation in the sport, and there is increasing concern about the long-term effects of these impacts. The objective of the current study was to determine if strain-based cumulative exposure measures are superior to kinematic-based exposure measures for predicting imaging changes in the brain.

METHODS

This prospective, longitudinal cohort study was conducted from 2012 to 2017 and assessed youth, male football athletes. Kinematic data were collected at all practices and games from enrolled athletes participating in local youth football organizations in Winston-Salem, North Carolina, and were used to calculate multiple risk-weighted cumulative exposure (RWE) kinematic metrics and 36 strain-based exposure metrics. Pre- and postseason imaging was performed at Wake Forest School of Medicine, and diffusion tensor imaging (DTI) measures, including fractional anisotropy (FA), and its components (CL, CP, and CS), and mean diffusivity (MD), were investigated. Included participants were youth football players ranging in age from 9 to 13 years. Exclusion criteria included any history of previous neurological illness, psychiatric illness, brain tumor, concussion within the past 6 months, and/or contraindication to MRI.

RESULTS

A total of 95 male athletes (mean age 11.9 years [SD 1.0 years]) participated between 2012 and 2017, with some participating for multiple seasons, resulting in 116 unique athlete-seasons. Regression analysis revealed statistically significant linear relationships between the FA, linear coefficient (CL), and spherical coefficient (CS) and all strain exposure measures, and well as the planar coefficient (CP) and 8 strain measures. For the kinematic exposure measures, there were statistically significant relationships between FA and RWE linear (RWEL) and RWE combined probability (RWECP) as well as CS and RWEL. According to area under the receiver operating characteristic (ROC) curve (AUC) analysis, the best-performing metrics were all strain measures, and included metrics based on tensile, compressive, and shear strain.

CONCLUSIONS

Using ROC curves and AUC analysis, all exposure metrics were ranked in order of performance, and the results demonstrated that all the strain-based metrics performed better than any of the kinematic metrics, indicating that strain-based metrics are better discriminators of imaging changes than kinematic-based measures. Studies relating the biomechanics of head impacts with brain imaging and cognitive function may allow equipment designers, care providers, and organizations to prevent, identify, and treat injuries in order to make football a safer activity.

Integrating Human and Non-Human Primate Data to Estimate Human Tolerances for Traumatic Brain Injury

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

Subject-Specific Head Model Generation by Mesh Morphing: A Personalization Framework and Its Applications

Finite element (FE) head models have become powerful tools in many fields within neuroscience, especially for studying the biomechanics of traumatic brain injury (TBI). Subject-specific head models accounting for geometric variations among subjects are needed for more reliable predictions. However, the generation of such models suitable for studying TBIs remains a significant challenge and has been a bottleneck hindering personalized simulations. This study presents a personalization framework for generating subject-specific models across the lifespan and for pathological brains with significant anatomical changes by morphing a baseline model. The framework consists of hierarchical multiple feature and multimodality imaging registrations, mesh morphing, and mesh grouping, which is shown to be efficient with a heterogeneous dataset including a newborn, 1-year-old (1Y), 2Y, adult, 92Y, and a hydrocephalus brain. The generated models of the six subjects show competitive personalization accuracy, demonstrating the capacity of the framework for generating subject-specific models with significant anatomical differences. The family of the generated head models allows studying age-dependent and groupwise brain injury mechanisms. The framework for efficient generation of subject-specific FE head models helps to facilitate personalized simulations in many fields of neuroscience.

Toward a Comprehensive Delineation of White Matter Tract-Related Deformation

Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) recently has been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly proposed strain peaks along with the previously used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.

Toward subject-specific evaluation: methods of evaluating finite element brain models using experimental high-rate rotational brain motion

Computational models of the brain have become the gold standard in biomechanics to understand, predict, and mitigate traumatic brain injuries. Many models have been created and evaluated with limited experimental data and without accounting for subject-specific morphometry of the specimens in the dataset. Recent advancements in the measurement of brain motion using sonomicrometry allow for a comprehensive evaluation of brain model biofidelity using a high-rate, rotational brain motion dataset. In this study, four methods were used to determine the best technique to compare nodal displacement to experimental brain motion, including a new morphing method to match subject-specific inner skull geometry. Three finite element brain models were evaluated in this study: the isotropic GHBMC and SIMon models, as well as an anisotropic model with explicitly embedded axons (UVA-EAM). Using a weighted cross-correlation score (between 0 and 1), the anisotropic model yielded the highest average scores across specimens and loading conditions ranging from 0.53 to 0.63, followed by the isotropic GHBMC with average scores ranging from 0.46 to 0.58, and then the SIMon model with average scores ranging from 0.36 to 0.51. The choice of comparison method did not significantly affect the cross-correlation score, and differences of global strain up to 0.1 were found for the morphed geometry relative to baseline models. The morphed or scaled geometry is recommended when evaluating computational brain models to capture the subject-specific skull geometry of the experimental specimens.

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.

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 95^th 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.