White matter tract-oriented deformation predicts traumatic axonal brain injury and reveals rotational direction-specific vulnerabilities

A comparative study on the acute-phase behavioral and pathological responses of closed head injury induced by cranial vertex and temporal lobe impacts in male rats

The outcomes of traumatic brain injury (TBI) are closely linked to the strength of mechanical loads applied to the head. However, the same mechanical load can lead to significant variations in injury outcomes depending on the location of impact. To compare the acute-phase behavioral and pathological effects of different impact locations on TBI outcomes, we conducted a closed head injury experimental study using male rats subjected to cranial vertex and temporal lobe impacts. The rats were injured by an impactor according to the experimental protocol established using the L4 (23) orthogonal table, and the behavioral and pathological outcomes were assessed. The contribution rates of impact location and strength to TBI were quantified using Analysis of Variance. The results indicated that impact strength played a dominant role in TBI and showed a positive correlation, while the role of impact location in TBI cannot be ignored. Behaviorally, cranial vertex impacts led to more severe coma, motor, memory, and anxiety deficits. Pathologically, cranial vertex impacts caused more severe diffuse axonal injury in the corpus callosum and brainstem. In the left hippocampus and amygdala, cell loss due to cranial vertex impacts was more pronounced than that caused by temporal lobe impacts, whereas the opposite was true on the right side. Notably, the pathological changes observed in the left (non-impact) hippocampus and amygdala due to temporal lobe impacts showed a stronger linear correlation with behavioral outcomes, suggesting that damage to the left side has greater predictive power for behavioral deficits. This suggests that the impact location is an important factor affecting TBI and should be considered in the study.

Modeling, experiment, and validation of a piglet head

INTRODUCTION

Traumatic brain injury (TBI) is a prevalent type of disabling and fatal injury in infants/toddlers, which is usually caused by falls or traffic accidents. Considering that it is difficult to collect realistic material properties and validation data of child heads due to ethical reasons, experiments on the piglet heads and the finite element (FE) models are generally used as a substitute for the investigations of child TBI.

METHODS

In this study, first, a high-quality FE model of a 4-week-old piglet head, including brain (cerebrum, cerebellum, brainstem), skull, soft tissue, cerebrospinal fluid, dura matter, pia matter and mandible, is developed. Then, test for the material properties of the piglet head and that for the global validation data are conducted. For the former, the mechanical properties of the brain, overlying soft tissue and skull of the 4-week-old piglet head are tested, and the constitutive models and corresponding parameters are further defined. For the latter, the quasi-static compression test and dynamic impact test (free-fall drop impact test, drop-hammer impact test) are performed on the piglet head. Finally, the piglet head FE model was validated against tests in terms of the contact force and intracranial pressure (ICP) under eight conditions (one for the compression condition, four for the free-fall impact condition, and three for the drop-hammer impact condition).

RESULTS

The trends of simulated curves are consistent with the experimental results under all conditions. For the contact force, the average error of the peak values between simulations and tests is about 12.9 %, and the average error of time durations is about 6.8 %. For the ICP, the average errors of peak values and time durations between simulations and tests are about 8.9 % and 9.9 %.

CONCLUSIONS

The results show that the piglet head model has high bio-fidelity, which can be used to predict the head global response and the ICP, and further to assist the investigation of child TBI. The model provides another effective way to evaluate the modeling strategies and material constitute models suitable for child head FE model, and can better to understand the inducement and mechanism of child TBI under different external loading conditions.

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.

Validation of a computational biomechanical mouse brain model for rotational head acceleration

Recent mouse brain injury experiments examine diffuse axonal injury resulting from accelerative head rotations. Evaluating brain deformation during these events would provide valuable information on tissue level thresholds for brain injury, but there are many challenges to imaging the brain's mechanical response during dynamic loading events, such as a blunt head impact. To address this shortcoming, we present an experimentally validated computational biomechanics model of the mouse brain that predicts tissue deformation, given the motion of the mouse head during laboratory experiments. First, we developed a finite element model of the mouse brain that computes tissue strains, given the same head rotations as previously conducted in situ hemicephalic mouse brain experiments. Second, we calibrated the model using a single brain segment, and then validated the model based on the spatial and temporal strain responses of other regions. The result is a computational tool that will provide researchers with the ability to predict brain tissue strains that occur during mouse laboratory experiments, and to link the experiments to the resulting neuropathology, such as diffuse axonal injury.

Finite element brain deformation in adolescent soccer heading

Finite element (FE) modeling provides a means to examine how global kinematics of repetitive head loading in sports influences tissue level injury metrics. FE simulations of controlled soccer headers in two directions were completed using a human head FE model to estimate biomechanical loading on the brain by direction. Overall, headers were associated with 95th percentile peak maximum principal strains up to 0.07 and von Mises stresses up to 1450 Pa, and oblique headers trended toward higher values than frontal headers but below typical injury levels. These quantitative data provide insight into repetitive loading effects on the brain.

Dynamic strain fields of the mouse brain during rotation

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

Pupillary Light Response Deficits in 4-Week-Old Piglets and Adolescent Children after Low-Velocity Head Rotations and Sports-Related Concussions

Neurological disorders and traumatic brain injury (TBI) are among the leading causes of death and disability. The pupillary light reflex (PLR) is an emerging diagnostic tool for concussion in humans. We compared PLR obtained with a commercially available pupillometer in the 4 week old piglet model of the adolescent brain subject to rapid nonimpact head rotation (RNR), and in human adolescents with and without sports-related concussion (SRC). The 95% PLR reference ranges (RR, for maximum and minimum pupil diameter, latency, and average and peak constriction velocities) were established in healthy piglets (N = 13), and response reliability was validated in nine additional healthy piglets. PLR assessments were obtained in female piglets allocated to anesthetized sham (N = 10), single (sRNR, N = 13), and repeated (rRNR, N = 14) sagittal low-velocity RNR at pre-injury, as well as days 1, 4, and 7 post injury, and evaluated against RRs. In parallel, we established human PLR RRs in healthy adolescents (both sexes, N = 167) and compared healthy PLR to values obtained <28 days from a SRC (N = 177). In piglets, maximum and minimum diameter deficits were greater in rRNR than sRNR. Alterations peaked on day 1 post sRNR and rRNR, and remained altered at day 4 and 7. In SRC adolescents, the proportion of adolescents within the RR was significantly lower for maximum pupil diameter only (85.8%). We show that PLR deficits may persist in humans and piglets after low-velocity head rotations. Differences in timing of assessment after injury, developmental response to injury, and the number and magnitude of impacts may contribute to the differences observed between species. We conclude that PLR is a feasible, quantifiable involuntary physiological metric of neurological dysfunction in pigs, as well as humans. Healthy PLR porcine and human reference ranges established can be used for neurofunctional assessments after TBI or hypoxic exposures (e.g., stroke, apnea, or cardiac arrest).

Consensus Head Acceleration Measurement Practices (CHAMP): Laboratory Validation of Wearable Head Kinematic Devices

Wearable devices are increasingly used to measure real-world head impacts and study brain injury mechanisms. These devices must undergo validation testing to ensure they provide reliable and accurate information for head impact sensing, and controlled laboratory testing should be the first step of validation. Past validation studies have applied varying methodologies, and some devices have been deployed for on-field use without validation. This paper presents best practices recommendations for validating wearable head kinematic devices in the laboratory, with the goal of standardizing validation test methods and data reporting. Key considerations, recommended approaches, and specific considerations were developed for four main aspects of laboratory validation, including surrogate selection, test conditions, data collection, and data analysis. Recommendations were generated by a group with expertise in head kinematic sensing and laboratory validation methods and reviewed by a larger group to achieve consensus on best practices. We recommend that these best practices are followed by manufacturers, users, and reviewers to conduct and/or review laboratory validation of wearable devices, which is a minimum initial step prior to on-field validation and deployment. We anticipate that the best practices recommendations will lead to more rigorous validation of wearable head kinematic devices and higher accuracy in head impact data, which can subsequently advance brain injury research and management.

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.

Tractography methods and findings in brain tumors and traumatic brain injury

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.

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.

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.

MR Imaging of Human Brain Mechanics In Vivo: New Measurements to Facilitate the Development of Computational Models of Brain Injury

Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of which differ from the living human brain. Here we describe efforts to noninvasively measure the biomechanical response of the human brain with MRI-at non-injurious strain levels-and generate data that can be used to develop, calibrate, and evaluate computational brain biomechanics models. Specifically, this paper reports on a project supported by the National Institute of Neurological Disorders and Stroke to comprehensively image brain anatomy and geometry, mechanical properties, and brain deformations that arise from impulsive and harmonic skull loadings. The outcome of this work will be a publicly available dataset ( http://www.nitrc.org/projects/bbir ) that includes measurements on both males and females across an age range from adolescence to older adulthood. This article describes the rationale and approach for this study, the data available, and how these data may be used to develop new computational models and augment existing approaches; it will serve as a reference to researchers interested in using these data.

Displacement Error Propagation From Embedded Markers to Brain Strain

Head injury model validation has evolved from against pressure to relative brain-skull displacement, and more recently, against marker-based strain. However, there are concerns on strain data quality. In this study, we parametrically investigate how displacement random errors and synchronization errors propagate into strain. Embedded markers from four representative configurations are used to form unique and nonoverlapping tetrahedrons, triangles, and linear elements. Marker displacements are then separately subjected to up to ±10% random displacement errors and up to ±2 ms synchronization errors. Based on 100 random trials in each perturbation test, we find that smaller strain errors relative to the baseline peak strains are significantly associated with larger element sizes (volume, area, or length; p < 0.05). When displacement errors are capped at the two extreme levels, the earlier "column" and "cluster" configurations provide few usable elements with relative strain error under an empirical threshold of 20%, while about 30-80% of elements in recent "repeatable" and "uniform" configurations are considered otherwise usable. Overall, denser markers are desired to provide exhaustive pairwise linear elements with a range of sizes to balance the need for larger elements to minimize strain error but smaller elements to increase the spatial resolution in strain sampling. Their signed strains also provide unique and unambiguous information on tissue tension and compression. This study may provide useful insights into the scrutinization of existing experimental data for head injury model strain validation and to inform how best to design new experiments in the future.

Orientation of neurites influences severity of mechanically induced tau pathology

Chronic traumatic encephalopathy is a neurodegenerative disease associated with repeated traumatic brain injury (TBI). Chronic traumatic encephalopathy is a tauopathy, in which cognitive decline is accompanied by the accumulation of neurofibrillary tangles of the protein tau in patients' brains. We recently found that mechanical force alone can induce tau mislocalization to dendritic spines and loss of synaptic function in in vitro neuronal cultures with random cell organization. However, in the brain, neurons are highly aligned, so here we aimed to determine how neuronal organization influences early-stage tauopathy caused by mechanical injury. Using microfabricated cell culture constructs to control the growth of neurites and an in vitro simulated TBI device to apply controlled mechanical deformation, we found that neuronal orientation with respect to the direction of a uniaxial high-strain-rate stretch injury influences the degree of tau pathology in injured neurons. We found that a mechanical stretch applied parallel to the neurite alignment induces greater mislocalization of tau proteins to dendritic spines than does a stretch with the same strain applied perpendicular to the neurites. Synaptic function, characterized by the amplitude of miniature excitatory postsynaptic currents, was similarly decreased in neurons with neurites aligned parallel to stretch, whereas in neurons aligned perpendicular to stretch, it had little to no functional loss. Experimental injury parameters (strain, strain rate, direction of stretch) were combined with a standard viscoelastic solid model to show that in our in vitro model, neurite work density during stretch correlates with tau mislocalization. These findings suggest that in a TBI, the magnitude of brain deformation is not wholly predictive of neurodegenerative consequences of TBI but that deformation relative to local neuronal architecture and the neurite mechanical energy during injury are better metrics for predicting trauma-induced tauopathy.

Reproducibility and Characterization of Head Kinematics During a Large Animal Acceleration Model of Traumatic Brain Injury

Acceleration parameters have been utilized for the last six decades to investigate pathology in both human and animal models of traumatic brain injury (TBI), design safety equipment, and develop injury thresholds. Previous large animal models have quantified acceleration from impulsive loading forces (i.e., machine/object kinematics) rather than directly measuring head kinematics. No study has evaluated the reproducibility of head kinematics in large animal models. Nine (five males) sexually mature Yucatan swine were exposed to head rotation at a targeted peak angular velocity of 250 rad/s in the coronal plane. The results indicated that the measured peak angular velocity of the skull was 51% of the impulsive load, was experienced over 91% longer duration, and was multi- rather than uni-planar. These findings were replicated in a second experiment with a smaller cohort (N = 4). The reproducibility of skull kinematics data was mostly within acceptable ranges based on published industry standards, although the coefficients of variation (8.9% for peak angular velocity or 12.3% for duration) were higher than the impulsive loading parameters produced by the machine (1.1 vs. 2.5%, respectively). Immunohistochemical markers of diffuse axonal injury and blood-brain barrier breach were not associated with variation in either skull or machine kinematics, suggesting that the observed levels of variance in skull kinematics may not be biologically meaningful with the current sample sizes. The findings highlight the reproducibility of a large animal acceleration model of TBI and the importance of direct measurements of skull kinematics to determine the magnitude of angular velocity, refine injury criteria, and determine critical thresholds.

Evaluation of Tissue-Level Brain Injury Metrics Using Species-Specific Simulations

Traumatic brain injury (TBI) is a significant public health burden, and the development of advanced countermeasures to mitigate and prevent these injuries during automotive, sports, and military impact events requires an understanding of the intracranial mechanisms related to TBI. In this study, the efficacy of tissue-level injury metrics for predicting TBI was evaluated using finite element reconstructions from a comprehensive, multi-species TBI database. The database consisted of human volunteer tests, laboratory-reconstructed head impacts from sports, in vivo non-human primate (NHP) tests, and in vivo pig tests. Eight tissue-level metrics related to brain tissue strain, axonal strain, and strain-rate were evaluated using survival analysis for predicting mild and severe TBI risk. The correlation between TBI risk and most of the assessed metrics were statistically significant, but when injury data was analyzed by species, the best metric was often inconclusive and limited by the small datasets. When the human and animal datasets were combined, the injury analysis was able to delineate maximum axonal strain as the best predictor of injury for all species and TBI severities, with maximum principal strain as a suitable alternative metric. The current study is the first to provide evidence to support the assumption that brain strain response between human, pig, and NHP result in similar injury outcomes through a multi-species analysis. This assumption is the biomechanical foundation for translating animal brain injury findings to humans. The findings in the study provide fundamental guidelines for developing injury criteria that would contribute towards the innovation of more effective safety countermeasures.

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

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

White Matter Tract-Oriented Deformation Is Dependent on Real-Time Axonal Fiber Orientation

Traumatic axonal injury (TAI) is a critical public health issue with its pathogenesis remaining largely elusive. Finite element (FE) head models are promising tools to bridge the gap between mechanical insult, localized brain response, and resultant injury. In particular, the FE-derived deformation along the direction of white matter (WM) tracts (i.e., tract-oriented strain) has been shown to be an appropriate predictor for TAI. The evolution of fiber orientation in time during the impact and its potential influence on the tract-oriented strain remains unknown, however. To address this question, the present study leveraged an embedded element approach to track real-time fiber orientation during impacts. A new scheme to calculate the tract-oriented strain was proposed by projecting the strain tensors from pre-computed simulations along the temporal fiber direction instead of its static counterpart directly obtained from diffuse tensor imaging. The results revealed that incorporating the real-time fiber orientation not only altered the direction but also amplified the magnitude of the tract-oriented strain, resulting in a generally more extended distribution and a larger volume ratio of WM exposed to high deformation along fiber tracts. These effects were exacerbated with the impact severities characterized by the acceleration magnitudes. Results of this study provide insights into how best to incorporate fiber orientation in head injury models and derive the WM tract-oriented deformation from computational simulations, which is important for furthering our understanding of the underlying mechanisms of TAI.

Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator

Head motion induced by impacts has been deemed as one of the most important measures in brain injury prediction, given that the vast majority of brain injury metrics use head kinematics as input. Recently, researchers have focused on using fast approaches, such as machine learning, to approximate brain deformation in real time for early brain injury diagnosis. However, training such models requires large number of kinematic measurements, and therefore data augmentation is required given the limited on-field measured data available. In this study we present a principal component analysis-based method that emulates an empirical low-rank substitution for head impact kinematics, while requiring low computational cost. In characterizing our existing data set of 537 head impacts, each consisting of 6 degrees of freedom measurements, we found that only a few modes, e.g., 15 in the case of angular velocity, is sufficient for accurate reconstruction of the entire data set. Furthermore, these modes are predominantly low frequency since over 70% of the angular velocity response can be captured by modes that have frequencies under 40 Hz. We compared our proposed method against existing impact parametrization methods and showed significantly better performance in injury prediction using a range of kinematic-based metrics—such as head injury criterion (HIC), rotational injury criterion (RIC), and brain injury metric (BrIC)—and brain tissue deformation-based metrics—such as brain angle metric (BAM), maximum principal strain (MPS), and axonal fiber strains (FS). In all cases, our approach reproduced injury metrics similar to the ground truth measurements with no significant difference, whereas the existing methods obtained significantly different (p < 0.01) values as well as substantial differences in injury classification sensitivity and specificity. This emulator will enable us to provide the necessary data augmentation to build a head impact kinematic data set of any size. The emulator and corresponding examples are available on our website¹.

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

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

Repeated mild traumatic brain injuries impair visual discrimination learning in adolescent mice

Cognitive deficits following a mild traumatic brain injury (mTBI) are common and are associated with learning deficits in school-age children. Some of these deficits include problems with long-term memory, working memory, processing speeds, attention, mental fatigue, and executive function. Processing speed deficits have been associated with alterations in white matter, but the underlying mechanisms of many of the other deficits are unclear. Without a clear understanding of the underlying mechanisms we cannot effectively treat these injuries. The goal of these studies is to validate a translatable touchscreen discrimination/reversal task to identify deficits in executive function following a single or repeated mTBIs. Using a mild closed skull injury model in adolescent mice we were able to identify clear deficits in discrimination learning following repeated injuries that were not present from a single mTBI. The repeated injuries were not associated with any deficits in motor-based behavior but did induce a robust increase in astrocyte activation. These studies provide an essential platform to interrogate the underlying neurological dysfunction associated with these injuries.

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 (th_x) and distance to actual TAI sites (d_s) 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.

A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact

Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost while maintaining a computational accuracy comparable with that of the high-fidelity model is desired. In this study, we constructed magnetic resonance (MR) image-based finite element (FE) models of a mouse brain for simulations of controlled cortical impact. The anatomical details were kept by mapping each image voxel to a corresponding FE mesh element. We constructed a super-resolution neural network that could produce computational results of a refined FE model with a mesh size of 70 μm from a coarse FE model with a mesh size of 280 μm. The peak signal-to-noise ratio of the reconstructed results was 33.26 dB, while the computational speed was increased by 50-fold. This proof-of-concept study showed that using machine learning techniques, MR image-based computational modeling could be applied and evaluated in a timely fashion. This paved ways for fast FE modeling and computation based on MR images. Results also support the potential clinical applications of MR image-based computational modeling of the human brain in a variety of scenarios such as brain impact and intervention.Graphical abstract MR image-based FE models with different mesh sizes were generated for CCI. The training and testing data sets were computed with 5 different impact locations and 3 different impact velocities. High-resolution strain maps were estimated using a SR neural network with greatly reduced computational cost.

In vivo estimates of axonal stretch and 3D brain deformation during mild head impact

The rapid deformation of brain tissue in response to head impact can lead to traumatic brain injury. In vivo measurements of brain deformation during non-injurious head impacts are necessary to understand the underlying mechanisms of traumatic brain injury and compare to computational models of brain biomechanics. Using tagged magnetic resonance imaging (MRI), we obtained measurements of three-dimensional strain tensors that resulted from a mild head impact after neck rotation or neck extension. Measurements of maximum principal strain (MPS) peaked shortly after impact, with maximal values of 0.019-0.053 that correlated strongly with peak angular velocity. Subject-specific patterns of MPS were spatially heterogeneous and consistent across subjects for the same motion, though regions of high deformation differed between motions. The largest MPS values were seen in the cortical gray matter and cerebral white matter for neck rotation and the brainstem and cerebellum for neck extension. Axonal fiber strain (Ef) was estimated by combining the strain tensor with diffusion tensor imaging data. As with MPS, patterns of Ef varied spatially within subjects, were similar across subjects within each motion, and showed group differences between motions. Values were highest and most strongly correlated with peak angular velocity in the corpus callosum for neck rotation and in the brainstem for neck extension. The different patterns of brain deformation between head motions highlight potential areas of greater risk of injury between motions at higher loading conditions. Additionally, these experimental measurements can be directly compared to predictions of generic or subject-specific computational models of traumatic brain injury.

A Nonlinear Reduced-Order Model of the Corpus Callosum Under Planar Coronal Excitation

Traumatic brain injury (TBI) is often associated with microstructural tissue damage in the brain, which results from its complex biomechanical behavior. Recent studies have shown that the deep white matter (WM) region of the human brain is susceptible to being damaged due to strain localization in that region. Motivated by these studies, in this paper, we propose a geometrically nonlinear dynamical reduced order model (ROM) to model and study the dynamics of the deep WM region of the human brain under coronal excitation. In this model, the brain hemispheres were modeled as lumped masses connected via viscoelastic links, resembling the geometry of the corpus callosum (CC). Employing system identification techniques, we determined the unknown parameters of the ROM, and ensured the accuracy of the ROM by comparing its response against the response of an advanced finite element (FE) model. Next, utilizing modal analysis techniques, we determined the energy distribution among the governing modes of vibration of the ROM and concluded that the demonstrated nonlinear behavior of the FE model might be predominantly due to the special geometry of the brain deep WM region. Furthermore, we observed that, for sufficiently high input energies, high frequency harmonics at approximately 45 Hz, were generated in the response of the CC, which, in turn, are associated with high-frequency oscillations of the CC. Such harmonics might potentially lead to strain localization in the CC. This work is a step toward understanding the brain dynamics during traumatic injury.

A new model of repeat mTBI in adolescent rats

Sports-related injury is frequently associated with repeated diffuse and mild traumatic brain injury (mTBI). We combined two existing models for inducing TBI in rats, the Impact Acceleration and Controlled Cortical Impact models, to create a new method relevant to the study of cognitive sequelae of repeat mTBI in adolescent athletes. Repeated mTBI, such as those incurred in sports, can result in a wide range of outcomes, with many individuals experiencing no chronic sequela while others develop profound cognitive and behavioral impairments, typically in the absence of lasting motor symptoms or gross tissue loss appreciable antemortem. It is critical to develop models of mTBI and repeat mTBI that have the flexibility to assess multiple parameters related to injury (e.g. number and magnitude of impacts, inter-injury interval, etc) that are associated with brain vulnerability compared to normal recovery. We designed a 3D-printed plastic implant to permanently secure a metal disc to the skull of adolescent rats in order to induce multiple injuries without performing multiple survival surgeries and also to minimize pre-injury anesthesia time. Rats were randomly assigned to sham injury (n = 12), single injury (n = 12; injury on P41), or repeat injury (n = 14; injuries on P35, P38, and P41) groups. Compared to single injury and sham injury, repeat injuries caused increased toe pinch reflex latency (F(2,34) = 4.126, p < .05) and diminished weight gain (F(2, 34) = 4.767, p < .05). Spatial navigation was tested using Morris water maze, beginning one week after the final injury (P48). While there were no differences between groups during acquisition, both single and repeat injuries resulted in deficits on probe trial performance (p < .01 and p < .05 respectively). Single injury animals also exhibited a deficit in working memory deficit across three days of testing (p < .05). Neither injury group had neuronal loss in the hilus or CA3, according to stereological quantification of NeuN. Therefore, by implanting a helmet we have created a relevant model of sports-related injury and repeated mTBI that results in subtle but significant changes in cognitive outcome in the absence of significant hippocampal cell death.

The closed-head impact model of engineered rotational acceleration (CHIMERA) as an application for traumatic brain injury pre-clinical research: A status report

Closed-head traumatic brain injury (TBI) is a worldwide concern with increasing prevalence and cost to society. Rotational acceleration is a primary mechanism in TBI that results from tissue strains that give rise to diffuse axonal injury. The Closed-Head Impact Model of Engineered Rotational Acceleration (CHIMERA) was recently introduced as a method for the study of impact acceleration effects in pre-clinical TBI research. This review provides a survey of the published literature implementing the CHIMERA device and describes pathological, imaging, neurophysiological, and behavioral findings. Findings show CHIMERA inflicts damage in white matter tracts as a key area of injury. Behaviorally, repeated studies have shown motor deficits and more chronic cognitive effects after CHIMERA injury. Good progress with model application has been accomplished by investigators attending to what is required for model validation. However, the majority of CHIMERA studies only utilize adult male mice. To further establish this model, more work with female animals and various age groups need to be performed, as well as studies to further establish and standardize methodologies for validation of the models for clinical relevance. Common data elements to standardize the reporting methodology for the CHIMERA literature are suggested.

Embedded axonal fiber tracts improve finite element model predictions of traumatic brain injury

With the growing rate of traumatic brain injury (TBI), there is an increasing interest in validated tools to predict and prevent brain injuries. Finite element models (FEM) are valuable tools to estimate tissue responses, predict probability of TBI, and guide the development of safety equipment. In this study, we developed and validated an anisotropic pig brain multi-scale FEM by explicitly embedding the axonal tract structures and utilized the model to simulate experimental TBI in piglets undergoing dynamic head rotations. Binary logistic regression, survival analysis with Weibull distribution, and receiver operating characteristic curve analysis, coupled with repeated k-fold cross-validation technique, were used to examine 12 FEM-derived metrics related to axonal/brain tissue strain and strain rate for predicting the presence or absence of traumatic axonal injury (TAI). All 12 metrics performed well in predicting of TAI with prediction accuracy rate of 73-90%. The axonal-based metrics outperformed their rival brain tissue-based metrics in predicting TAI. The best predictors of TAI were maximum axonal strain times strain rate (MASxSR) and its corresponding optimal fraction-based metric (AF-MASxSR7.5) that represents the fraction of axonal fibers exceeding MASxSR of 7.5 s-1. The thresholds compare favorably with tissue tolerances found in in-vitro/in-vivo measurements in the literature. In addition, the damaged volume fractions (DVF) predicted using the axonal-based metrics, especially MASxSR (DVF = 0.05-4.5%), were closer to the actual DVF obtained from histopathology (AIV = 0.02-1.65%) in comparison with the DVF predicted using the brain-related metrics (DVF = 0.11-41.2%). The methods and the results from this study can be used to improve model prediction of TBI in humans.

A patient-specific fluid–structure interaction model of the cerebrovascular damage in relation to traumatic brain injury

Background

There is a lack of knowledge on the magnitudes of the biomechanical stresses and deformations occurring in the cerebral arterial wall after traumatic brain injury (TBI). Experimental techniques are unable to calculate the stresses and deformations in the cerebral arterial wall after TBI; therefore, the application of numerical simulations, such as finite element modeling, is preferred.

Methods

This study was aimed to calculate the stresses and deformations as well as the alteration in the pressure and velocity of the blood in the cerebrovascular artery using a fluid–structure interaction model.

Results

The results revealed considerable increase in the pressure and velocity of the blood which might lead to cerebrovascular damage followed by hemorrhage. The arterial wall showed the highest deformation of 0.047 mm in the X direction which was higher than that in the Y (0.035–0.050 mm) and Z (0.019–0.030 mm) directions.

Conclusions

These results have implications not only for the understanding of the stresses and deformations in the cerebral artery because of TBI, but also for providing a comprehensive knowledge for biomechanical and medical experts in regard to thresholds of cerebrovascular damage for use in establishing preventive and/or treatment methods.

Multi-Directional Dynamic Model for Traumatic Brain Injury Detection

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

Predictions of neonatal porcine bridging vein rupture and extra-axial hemorrhage during rapid head rotations

When the head is rotated rapidly, the movement of the brain lags that of the skull. Intracranial contents between the brain and skull include meninges, cerebrospinal fluid (CSF), and cerebral vasculature. Among the cerebral vasculature in this space are the parasagittal bridging veins (BVs), which drain blood from the brain into the superior sagittal sinus (SSS), which is housed within the falx cerebri, adhered to the inner surface of the skull. Differential motion between the brain and skull that may occur during a traumatic event is thought to stretch BVs, causing damage and producing extra-axial hemorrhage (EAH). Finite element (FE) modeling is a technique often used to aid in the understanding and prediction of traumatic brain injury (TBI), and estimation of tissue deformation during traumatic events provides insight into kinematic injury thresholds. Using a FE model of the newborn porcine head with neonatal porcine brain and BV properties, single and cyclic rapid head rotations without impact were simulated. Measured BV failure properties were used to predict BV rupture. By comparing simulation outputs to observations of EAH in a development group of in vivo studies of rapid non-impact head rotations in the piglet, it was determined that failure of 16.7% of BV elements was associated with a 50% risk of EAH, and showed in a separate validation group that this threshold predicted the occurrence of EAH with 100% sensitivity and 100% specificity for single rapid non-impact rotations. This threshold for failed BV elements performed with 90% overall correct prediction in simulations of cyclic rotational head injuries. A 50% risk of EAH was associated with head angular velocities of 94.74 rad/s and angular accelerations of 29.60 krad/s² in the newborn piglet. Future studies may build on these findings for BV failure in the piglet to develop predictive models for BV failure in human infants.

Spatial distribution of human arachnoid trabeculae

Traumatic brain injury (TBI) is a common injury modality affecting a diverse patient population. Axonal injury occurs when the brain experiences excessive deformation as a result of head impact. Previous studies have shown that the arachnoid trabeculae (AT) in the subarachnoid space significantly influence the magnitude and distribution of brain deformation during impact. However, the quantity and spatial distribution of cranial AT in humans is unknown. Quantification of these microstructural features will improve understanding of force transfer during TBI, and may be a valuable dataset for microneurosurgical procedures. In this study, we quantify the spatial distribution of cranial AT in seven post-mortem human subjects. Optical coherence tomography (OCT) was used to conduct in situ imaging of AT microstructure across the surface of the human brain. OCT images were segmented to quantify the relative amounts of trabecular structures through a volume fraction (VF) measurement. The average VF for each brain ranged from 22.0% to 29.2%. Across all brains, there was a positive spatial correlation, with VF significantly greater by 12% near the superior aspect of the brain (p < .005), and significantly greater by 5%-10% in the frontal lobes (p < .005). These findings suggest that the distribution of AT between the brain and skull is heterogeneous, region-dependent, and likely contributes to brain deformation patterns. This study is the first to image and quantify human AT across the cerebrum and identify region-dependencies. Incorporation of this spatial heterogeneity may improve the accuracy of computational models of human TBI and enhance understanding of brain dynamics.

Head Rotational Kinematics, Tissue Deformations, and Their Relationships to the Acute Traumatic Axonal Injury

Head rotational kinematics and tissue deformation metrics obtained from finite element models (FEM) have the potential to be used as traumatic axonal injury (TAI) assessment criteria and headgear evaluation standards. These metrics have been used to predict the likelihood of TAI occurrence; however, their ability in the assessment of the extent of TAI has not been explored. In this study, a pig model of TAI was used to examine a wide range of head loading conditions in two directions. The extent of TAI was quantified through histopathology and correlated to the FEM-derived tissue deformations and the head rotational kinematics. Peak angular acceleration and maximum strain rate of axonal fiber and brain tissue showed relatively good correlation to the volume of axonal injury, with similar correlation trends for both directions separately or combined. These rotational kinematics and tissue deformations can estimate the extent of acute TAI. The relationships between the head kinematics and the tissue strain, strain rate, and strain times strain rate were determined over the experimental range examined herein, and beyond that through parametric simulations. These relationships demonstrate that peak angular velocity and acceleration affect the underlying tissue deformations and the knowledge of both help to predict TAI risk. These relationships were combined with the injury thresholds, extracted from the TAI risk curves, and the kinematic-based risk curves representing overall axonal and brain tissue strain and strain rate were determined for predicting TAI. After scaling to humans, these curves can be used for real-time TAI assessment.

Thresholds for the assessment of inflicted head injury by shaking trauma in infants: a systematic review

In order to investigate potential causal relations between the shaking of infants and injuries, biomechanical studies compare brain and skull dynamic behavior during shaking to injury thresholds. However, performing shaking tolerance research on infants, either in vivo or ex vivo, is extremely difficult, if not impossible. Therefore, infant injury thresholds are usually estimated by scaling or extrapolating adult or animal data obtained from crash tests or whiplash experiments. However, it is doubtful whether such data accurately matches the biomechanics of shaking in an infant. Hence some thresholds may be inappropriate to be used for the assessment of inflicted head injury by shaking trauma in infants. A systematic literature review was conducted to 1) provide an overview of existing thresholds for head- and neck injuries related to violent shaking, and 2) to identify and discuss which thresholds have been used or could be used for the assessment of inflicted head injury by shaking trauma in infants. Key findings: The majority of studies establishing or proposing injury thresholds were found to be based on loading cycle durations and loading cycle repetitions that did not resemble those occurring during shaking, or had experimental conditions that were insufficiently documented in order to evaluate the applicability of such thresholds. Injury thresholds that were applied in studies aimed at assessing whether an injury could occur under certain shaking conditions were all based on experiments that did not properly replicate the loading characteristics of shaking. Somewhat validated threshold scaling methods only exist for scaling concussive injury thresholds from adult primate to adult human. Scaling methods that have been used for scaling other injuries, or for scaling adult injury thresholds to infants were not validated. There is a clear and urgent need for new injury thresholds established by accurately replicating the loading characteristics of shaking.

Statistical Characterization of Human Brain Deformation During Mild Angular Acceleration Measured In Vivo by Tagged Magnetic Resonance Imaging

Understanding of in vivo brain biomechanical behavior is critical in the study of traumatic brain injury (TBI) mechanisms and prevention. Using tagged magnetic resonance imaging, we measured spatiotemporal brain deformations in 34 healthy human volunteers under mild angular accelerations of the head. Two-dimensional (2D) Lagrangian strains were examined throughout the brain in each subject. Strain metrics peaked shortly after contact with a padded stop, corresponding to the inertial response of the brain after head deceleration. Maximum shear strain of at least 3% was experienced at peak deformation by an area fraction (median±standard error) of 23.5±1.8% of cortical gray matter, 15.9±1.4% of white matter, and 4.0±1.5% of deep gray matter. Cortical gray matter strains were greater in the temporal cortex on the side of the initial contact with the padded stop and also in the contralateral temporal, frontal, and parietal cortex. These tissue-level deformations from a population of healthy volunteers provide the first in vivo measurements of full-volume brain deformation in response to known kinematics. Although strains differed in different tissue type and cortical lobes, no significant differences between male and female head accelerations or strain metrics were found. These cumulative results highlight important kinematic features of the brain's mechanical response and can be used to facilitate the evaluation of computational simulations of TBI.

Development of a Single-Degree-of-Freedom Mechanical Model for Predicting Strain-Based Brain Injury Responses

Linking head kinematics to injury risk has been the focus of numerous brain injury criteria. Although many early forms were developed using mechanics principles, recent criteria have been developed using empirical methods based on subsets of head impact data. In this study, a single-degree-of-freedom (sDOF) mechanical analog was developed to parametrically investigate the link between rotational head kinematics and brain deformation. Model efficacy was assessed by comparing the maximum magnitude of displacement to strain-based brain injury predictors from finite element (FE) human head models. A series of idealized rotational pulses covering a broad range of acceleration and velocity magnitudes (0.1-15 krad/s2 and 1-100 rad/s) with durations between 1 and 3000 ms were applied to the mechanical models about each axis of the head. Results show that brain deformation magnitude is governed by three categories of rotational head motion each distinguished by the duration of the pulse relative to the brain's natural period: for short-duration pulses, maximum brain deformation depended primarily on angular velocity magnitude; for long-duration pulses, brain deformation depended primarily on angular acceleration magnitude; and for pulses relatively close to the natural period, brain deformation depended on both velocity and acceleration magnitudes. These results suggest that brain deformation mechanics can be adequately explained by simple mechanical systems, since FE model responses and experimental brain injury tolerances exhibited similar patterns to the sDOF model. Finally, the sDOF model was the best correlate to strain-based responses and highlighted fundamental limitations with existing rotational-based brain injury metrics.

Risk of rupture of the cerebral aneurysm in relation to traumatic brain injury using a patient-specific fluid-structure interaction model

BACKGROUND AND OBJECTIVE

Cerebral aneurysm, which is defined as one of the weakened area in the wall of an artery in the brain, ruptures when wall tension exceeds its mechanical strength. Traumatic brain injury (TBI) by exerting a sudden impact load to the brain can lead to mechanical failure of the cerebral blood vessels followed by an alteration in not only the structure but also the function of the cerebrovascular. TBI also alters the hemodynamics of the blood flow in the cerebrovascular, while it has been shown that hemodynamics has a key asset in the progression and rupture of the cerebral aneurysms. So far, there is a lack of knowledge on the risk of rupture of the cerebral aneurysm in relation to TBI. Therefore, this study aimed to calculate the mechanical stresses and deformations in the arterial wall as well as the pressure and velocity of the blood using a fluid-structure interaction (FSI) model of the cerebral aneurysm located in the anterior circulation region of the circle of Willis.

METHOD

A patient-specific FSI model of the human skull, brain, and cerebral aneurysm, was established using human computed tomography (CT)/ magnetic resonance imaging (MRI) data and subjected to a frontal TBI.

RESULTS

The results revealed considerable increasing of ∼ 8 kPa (60 mmHg) and 0.40 m/s in the pressure and velocity of the blood in the intraluminal of the cerebral artery after TBI. The von Mises stress, shear stress, and deformation of the cerebral aneurysm wall also showed the increasing of 56.03 kPa, 15.66 Pa, and 0.072 mm after TBI, respectively.

CONCLUSIONS

Although the injury to the aneurysm wall after TBI is lower than that of the aneurysm wall mechanical strength, it still can alter the stress pattern in the wall and disrupt the hemodynamics of the blood. These results have implications in understanding the rupture risk of the cerebral aneurysm due to TBI, which may contribute in establishing preventive and/or treatment methods.

Changes in event-related potential functional networks predict traumatic brain injury in piglets

BACKGROUND

Traumatic brain injury is a leading cause of cognitive and behavioral deficits in children in the US each year. None of the current diagnostic tools, such as quantitative cognitive and balance tests, have been validated to identify mild traumatic brain injury in infants, adults and animals. In this preliminary study, we report a novel, quantitative tool that has the potential to quickly and reliably diagnose traumatic brain injury and which can track the state of the brain during recovery across multiple ages and species.

METHODS

Using 32 scalp electrodes, we recorded involuntary auditory event-related potentials from 22 awake four-week-old piglets one day before and one, four, and seven days after two different injury types (diffuse and focal) or sham. From these recordings, we generated event-related potential functional networks and assessed whether the patterns of the observed changes in these networks could distinguish brain-injured piglets from non-injured.

FINDINGS

Piglet brains exhibited significant changes after injury, as evaluated by five network metrics. The injury prediction algorithm developed from our analysis of the changes in the event-related potentials functional networks ultimately produced a tool with 82% predictive accuracy.

INTERPRETATION

This novel approach is the first application of auditory event-related potential functional networks to the prediction of traumatic brain injury. The resulting tool is a robust, objective and predictive method that offers promise for detecting mild traumatic brain injury, in particular because collecting event-related potentials data is noninvasive and inexpensive.

Lateral impacts correlate with falx cerebri displacement and corpus callosum trauma in sports-related concussions

Corpus callosum trauma has long been implicated in mild traumatic brain injury (mTBI), yet the mechanism by which forces penetrate this structure is unknown. We investigated the hypothesis that coronal and horizontal rotations produce motion of the falx cerebri that damages the corpus callosum. We analyzed previously published head kinematics of 115 sports impacts (2 diagnosed mTBI) measured with instrumented mouthguards and used finite element (FE) simulations to correlate falx displacement with corpus callosum deformation. Peak coronal accelerations were larger in impacts with mTBI (8592 rad/s² avg.) than those without (1412 rad/s² avg.). From FE simulations, coronal acceleration was strongly correlated with deep lateral motion of the falx center (r = 0.85), while horizontal acceleration was correlated with deep lateral motion of the falx periphery (r > 0.78). Larger lateral displacement at the falx center and periphery was correlated with higher tract-oriented strains in the corpus callosum body (r = 0.91) and genu/splenium (r > 0.72), respectively. The relationship between the corpus callosum and falx was unique: removing the falx from the FE model halved peak strains in the corpus callosum from 35% to 17%. Consistent with model results, we found indications of corpus callosum trauma in diffusion tensor imaging of the mTBI athletes. For a measured alteration of consciousness, depressed fractional anisotropy and increased mean diffusivity indicated possible damage to the mid-posterior corpus callosum. Our results suggest that the corpus callosum may be sensitive to coronal and horizontal rotations because they drive lateral motion of a relatively stiff membrane, the falx, in the direction of commissural fibers below.

Development of a Second-Order System for Rapid Estimation of Maximum Brain Strain

Diffuse brain injuries are assessed with deformation-based criteria that utilize metrics based on rotational head kinematics to estimate brain injury severity. Although numerous metrics have been proposed, many are based on empirically-derived models that use peak kinematics, which often limit their applicability to a narrow range of head impact conditions. However, over a broad range of impact conditions, brain deformation response to rotational head motion behaves similarly to a second-order mechanical system, which utilizes the full kinematic time history of a head impact. This study describes a new brain injury metric called Diffuse Axonal Multi-Axis General Evaluation (DAMAGE). DAMAGE is based on the equations of motion of a three-degree-of-freedom, coupled 2nd-order system, and predicts maximum brain strain using the directionally dependent angular acceleration time-histories from a head impact. Parameters for the effective mass, stiffness, and damping were determined using simplified rotational pulses which were applied multiaxially to a 50th percentile adult human male finite element model. DAMAGE was then validated with a separate database of 1747 head impacts including helmet, crash, and sled tests and human volunteer responses. Relative to existing rotational brain injury metrics that were evaluated in this study, DAMAGE was found to be the best predictor of maximum brain strain.

Mesh Convergence Behavior and the Effect of Element Integration of a Human Head Injury Model

Numerous head injury models exist that vary in mesh density by orders of magnitude. A careful study of the mesh convergence behavior is necessary, especially in terms of strain most relevant to brain injury. To this end, as well as to investigate the effect of element integration scheme on simulated strains, we re-meshed the Worcester Head Injury Model at five mesh densities (~ 7.2-1000 k high-quality hexahedral elements of the brain). Results from explicit dynamic simulations of three cadaveric impacts and an in vivo head rotation were compared. First, scalar metrics of the whole brain only considering magnitude were used, including peak maximum principal strain and population-based median strain. They were further extended to deep white matter regions and the entire brain elements, respectively, to form two "response vectors" to account for both magnitude and distribution. Using benchmark enhanced full-integration elements (C3D8I), a minimum of 202.8 k brain elements were necessary to converge for response vectors of the deep white matter regions. This model was further used to simulate with reduced integration (C3D8R). We found that hourglass energy higher than the common rule of thumb (e.g., up to 44.38% vs. < 10% of internal energy) was necessary to maintain comparable strain relative to C3D8I. Based on these results, it is recommended that a human head injury model should have a minimum number of 202.8 k elements, or an average element size of no larger than 1.8 mm, for the brain. C3D8R formulation with relax stiffness hourglass control using a high scaling factor is also recommended to achieve sufficient accuracy without substantial computational cost.

White Matter Anisotropy for Impact Simulation and Response Sampling in Traumatic Brain Injury

Advanced neuroimaging provides new opportunities to enhance head injury models, including the incorporation of white matter (WM) structural anisotropy. Information from high-resolution neuroimaging, however, usually has to be "down-sampled" to match a typically coarse brain mesh. To understand how this mesh-image resolution mismatch affects impact simulation and subsequent response sampling, we compared three competing anisotropy implementations (using either voxels, tractography, or a multiscale submodeling) and two response sampling strategies (element-wise or tractography-based, using brain mesh or neuroimaging for region segmentation, respectively). Using the combination of high resolution options as a baseline, we studied how the choice in each individual category affected the resulting injury metrics. By simulating a recorded loss of consciousness head impact, we found that injury metrics including peak strain and injury susceptibility in the deep WM regions based on fiber strain, but not on maximum principal strain, were sensitive to the anisotropy implementation, response sampling, and region segmentation. Overall, it was recommended to use tractography for anisotropy implementation and response sampling, and to employ neuroimaging for region segmentation, because they led to more accurate injury metrics. Further refining mesh locally via submodeling was unnecessary. Brain strain responses were also parametrically found to be closer to that from minimum fiber reinforcement, consistent with the fact that the majority of WM had a rather high degree of fiber dispersion. Finally, the upgraded Worcester Head Injury Model incorporating WM anisotropy was successfully re-validated against cadaveric impacts and an in vivo head rotation ("good" to "excellent" validation with an average Correlation Analysis score of 0.437 and 0.509, respectively). These investigations may facilitate further continual development of head injury models to better study traumatic brain injury.