Development of a Finite Element Model for Blast Brain Injury and the Effects of CSF Cavitation

In Silico Investigation of Biomechanical Response of a Human Brain Subjected to Primary Blast

The brain response to the explosion-induced primary blast waves is actively sought. Over the past decade, reasonable progress has been made in the fundamental understanding of blast traumatic brain injury (bTBI) using head surrogates and animal models. Yet, the current understanding of how blast waves interact with human is in nascent stages, primarily due to the lack of data in human. The biomechanical response in human is critically required to faithfully establish the connection to the aforementioned bTBI models. In this work, the biomechanical cascade of the brain under a primary blast has been elucidated using a detailed, full-body human model. The full-body model allowed us to holistically probe short- (<5 ms) and long-term (200 ms) brain responses. The full-body model has been extensively validated against impact loading in the past. We have further validated the head model against blast loading. We have also incorporated the structural anisotropy of the brain white matter. The blast wave transmission, and linear and rotational motion of the head were dominant pathways for the loading of the brain, and these loading paradigms generated distinct biomechanical fields within the brain. Blast transmission and linear motion of the head governed the volumetric response, whereas the rotational motion of the head governed the deviatoric response. Blast induced head rotation alone produced diffuse injury pattern in white matter fiber tracts. The biomechanical response under blast was comparable to the impact event. These insights will augment laboratory and clinical investigations of bTBI and help devise better blast mitigation strategies.

Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types

OBJECTIVE

The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM.

METHODS

To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR).

RESULTS

The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than [Formula: see text] in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models.

CONCLUSION

The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate.

SIGNIFICANCE

This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.

Investigating neuropathological changes and underlying neurobiological mechanisms in the early stages of primary blast-induced traumatic brain injury: Insights from a rat model

The utilization of explosives and chemicals has resulted in a rise in blast-induced traumatic brain injury (bTBI) in recent times. However, there is a dearth of diagnostic biomarkers and therapeutic targets for bTBI due to a limited understanding of biological mechanisms, particularly in the early stages. The objective of this study was to examine the early neuropathological characteristics and underlying biological mechanisms of primary bTBI. A total of 83 Sprague Dawley rats were employed, with their heads subjected to a blast shockwave of peak overpressure ranging from 172 to 421 kPa in the GI, GII, and GIII groups within a closed shock tube, while the body was shielded. Neuromotor dysfunctions, morphological changes, and neuropathological alterations were detected through modified neurologic severity scores, brain water content analysis, MRI scans, histological, TUNEL, and caspase-3 immunohistochemical staining. In addition, label-free quantitative (LFQ)-proteomics was utilized to investigate the biological mechanisms associated with the observed neuropathology. Notably, no evident damage was discernible in the GII and GI groups, whereas mild brain injury was observed in the GIII group. Neuropathological features of bTBI were characterized by morphologic changes, including neuronal injury and apoptosis, cerebral edema, and cerebrovascular injury in the shockwave's path. Subsequently, 3153 proteins were identified and quantified in the GIII group, with subsequent enriched neurological responses consistent with pathological findings. Further analysis revealed that signaling pathways such as relaxin signaling, hippo signaling, gap junction, chemokine signaling, and sphingolipid signaling, as well as hub proteins including Prkacb, Adcy5, and various G-protein subunits (Gnai2, Gnai3, Gnao1, Gnb1, Gnb2, Gnb4, and Gnb5), were closely associated with the observed neuropathology. The expression of hub proteins was confirmed via Western blotting. Accordingly, this study proposes signaling pathways and key proteins that exhibit sensitivity to brain injury and are correlated with the early pathologies of bTBI. Furthermore, it highlights the significance of G-protein subunits in bTBI pathophysiology, thereby establishing a theoretical foundation for early diagnosis and treatment strategies for primary bTBI.

Twenty Years of Blast-Induced Neurotrauma: Current State of Knowledge

Blast-induced neurotrauma (BINT) is an important injury paradigm of neurotrauma research. This short communication summarizes the current knowledge of BINT. We divide the BINT research into several broad categories-blast wave generation in laboratory, biomechanics, pathology, behavioral outcomes, repetitive blast in animal models, and clinical and neuroimaging investigations in humans. Publications from 2000 to 2023 in each subdomain were considered. The analysis of the literature has brought out salient aspects. Primary blast waves can be simulated reasonably in a laboratory using carefully designed shock tubes. Various biomechanics-based theories of BINT have been proposed; each of these theories may contribute to BINT by generating a unique biomechanical signature. The injury thresholds for BINT are in the nascent stages. Thresholds for rodents are reasonably established, but such thresholds (guided by primary blast data) are unavailable in humans. Single blast exposure animal studies suggest dose-dependent neuronal pathologies predominantly initiated by blood-brain barrier permeability and oxidative stress. The pathologies were typically reversible, with dose-dependent recovery times. Behavioral changes in animals include anxiety, auditory and recognition memory deficits, and fear conditioning. The repetitive blast exposure manifests similar pathologies in animals, however, at lower blast overpressures. White matter irregularities and cortical volume and thickness alterations have been observed in neuroimaging investigations of military personnel exposed to blast. Behavioral changes in human cohorts include sleep disorders, poor motor skills, cognitive dysfunction, depression, and anxiety. Overall, this article provides a concise synopsis of current understanding, consensus, controversies, and potential future directions.

Predicting shock-induced cavitation using machine learning: implications for blast-injury models

While cavitation has been suspected as a mechanism of blast-induced traumatic brain injury (bTBI) for a number of years, this phenomenon remains difficult to study due to the current inability to measure cavitation in vivo. Therefore, numerical simulations are often implemented to study cavitation in the brain and surrounding fluids after blast exposure. However, these simulations need to be validated with the results from cavitation experiments. Machine learning algorithms have not generally been applied to study blast injury or biological cavitation models. However, such algorithms have concrete measures for optimization using fewer parameters than those of finite element or fluid dynamics models. Thus, machine learning algorithms are a viable option for predicting cavitation behavior from experiments and numerical simulations. This paper compares the ability of two machine learning algorithms, k-nearest neighbor (kNN) and support vector machine (SVM), to predict shock-induced cavitation behavior. The machine learning models were trained and validated with experimental data from a three-dimensional shock tube model, and it has been shown that the algorithms could predict the number of cavitation bubbles produced at a given temperature with good accuracy. This study demonstrates the potential utility of machine learning in studying shock-induced cavitation for applications in blast injury research.

Simultaneous High-Frame-Rate Acoustic Plane-Wave and Optical Imaging of Intracranial Cavitation in Polyacrylamide Brain Phantoms during Blunt Force Impact

Blunt and blast impacts occur in civilian and military personnel, resulting in traumatic brain injuries necessitating a complete understanding of damage mechanisms and protective equipment design. However, the inability to monitor in vivo brain deformation and potential harmful cavitation events during collisions limits the investigation of injury mechanisms. To study the cavitation potential, we developed a full-scale human head phantom with features that allow a direct optical and acoustic observation at high frame rates during blunt impacts. The phantom consists of a transparent polyacrylamide material sealed with fluid in a 3D-printed skull where windows are integrated for data acquisition. The model has similar mechanical properties to brain tissue and includes simplified yet key anatomical features. Optical imaging indicated reproducible cavitation events above a threshold impact energy and localized cavitation to the fluid of the central sulcus, which appeared as high-intensity regions in acoustic images. An acoustic spectral analysis detected cavitation as harmonic and broadband signals that were mapped onto a reconstructed acoustic frame. Small bubbles trapped during phantom fabrication resulted in cavitation artifacts, which remain the largest challenge of the study. Ultimately, acoustic imaging demonstrated the potential to be a stand-alone tool, allowing observations at depth, where optical techniques are limited.

A technique for in situ intracranial strain measurement within a helmeted deformable headform

Despite the broad use of helmets, incidence of concussion remains high. Current methods for helmet evaluation focus on the measurement of head kinematics as the primary tool for quantifying risk of brain injury. Though the primary cause of mild Traumatic Brain Injury (mTBI) is thought to be intracranial strain, helmet testing methodologies are not able to directly resolve these parameters. Computational injury models and impact severity measures are currently used to approximate intracranial strains from head kinematics and predict injury outcomes. Advancing new methodologies that enable experimental intracranial strain measurements in a physical model would be useful in the evaluation of helmet performance. This study presents a proof-of-concept head surrogate and novel helmet evaluation platform that allows for the measurement of intracranial strain using high-speed X-ray digital image correlation (XDIC). In the present work, the head surrogate was subjected to a series of bare and helmeted impacts using a pneumatically-driven linear impactor. Impacts were captured at 5,000 fps using a high-speed X-ray cineradiography system, and strain fields were computed using digital image correlation. This test platform, once validated, will open the door to using brain tissue-level measurements to evaluate helmet performance, providing a tool that can be translated to represent mTBI injury mechanisms, benefiting the helmet design processes.

Rate- and Region-Dependent Mechanical Properties of Göttingen Minipig Brain Tissue in Simple Shear and Unconfined Compression

Traumatic brain injury (TBI), particularly from explosive blasts, is a major cause of casualties in modern military conflicts. Computational models are an important tool in understanding the underlying biomechanics of TBI but are highly dependent on the mechanical properties of soft tissue to produce accurate results. Reported material properties of brain tissue can vary by several orders of magnitude between studies, and no published set of material parameters exists for porcine brain tissue at strain rates relevant to blast. In this work, brain tissue from the brainstem, cerebellum, and cerebrum of freshly euthanized adolescent male Göttingen minipigs was tested in simple shear and unconfined compression at strain rates ranging from quasi-static (QS) to 300 s-1. Brain tissue showed significant strain rate stiffening in both shear and compression. Minimal differences were seen between different regions of the brain. Both hyperelastic and hyper-viscoelastic constitutive models were fit to experimental stress, considering data from either a single loading mode (unidirectional) or two loading modes together (bidirectional). The unidirectional hyper-viscoelastic models with an Ogden hyperelastic representation and a one-term Prony series best captured the response of brain tissue in all regions and rates. The bidirectional models were generally able to capture the response of the tissue in high-rate shear and all compression modes, but not the QS shear. Our constitutive models describe the first set of material parameters for porcine brain tissue relevant to loading modes and rates seen in blast injury.

Modeling of active skeletal muscles: a 3D continuum approach incorporating multiple muscle interactions

Skeletal muscles have a highly organized hierarchical structure, whose main function is to generate forces for movement and stability. To understand the complex heterogeneous behaviors of muscles, computational modeling has advanced as a non-invasive approach to evaluate relevant mechanical quantities. Aiming to improve musculoskeletal predictions, this paper presents a framework for modeling 3D deformable muscles that includes continuum constitutive representation, parametric determination, model validation, fiber distribution estimation, and integration of multiple muscles into a system level for joint motion simulation. The passive and active muscle properties were modeled based on the strain energy approach with Hill-type hyperelastic constitutive laws. A parametric study was conducted to validate the model using experimental datasets of passive and active rabbit leg muscles. The active muscle model with calibrated material parameters was then implemented to simulate knee bending during a squat with multiple quadriceps muscles. A computational fluid dynamics (CFD) fiber simulation approach was utilized to estimate the fiber arrangements for each muscle, and a cohesive contact approach was applied to simulate the interactions among muscles. The single muscle simulation results showed that both passive and active muscle elongation responses matched the range of the testing data. The dynamic simulation of knee flexion and extension showed the predictive capability of the model for estimating the active quadriceps responses, which indicates that the presented modeling pipeline is effective and stable for simulating multiple muscle configurations. This work provided an effective framework of a 3D continuum muscle model for complex muscle behavior simulation, which will facilitate additional computational and experimental studies of skeletal muscle mechanics. This study will offer valuable insight into the future development of multiscale neuromuscular models and applications of these models to a wide variety of relevant areas such as biomechanics and clinical research.

Mathematical model of mechanobiology of acute and repeated synaptic injury and systemic biomarker kinetics

Background

Blast induced Traumatic Brain Injury (bTBI) has become a signature casualty of military operations. Recently, military medics observed neurocognitive deficits in servicemen exposed to repeated low level blast (LLB) waves during military heavy weapons training. In spite of significant clinical and preclinical TBI research, current understanding of injury mechanisms and short- and long-term outcomes is limited. Mathematical models of bTBI biomechanics and mechanobiology of sensitive neuro-structures such as synapses may help in better understanding of injury mechanisms and in the development of improved diagnostics and neuroprotective strategies.

Methods and results

In this work, we formulated a model of a single synaptic structure integrating the dynamics of the synaptic cell adhesion molecules (CAMs) with the deformation mechanics of the synaptic cleft. The model can resolve time scales ranging from milliseconds during the hyperacute phase of mechanical loading to minutes-hours acute/chronic phase of injury progression/repair. The model was used to simulate the synaptic injury responses caused by repeated blast loads.

Conclusion

Our simulations demonstrated the importance of the number of exposures compared to the duration of recovery period between repeated loads on the synaptic injury responses. The paper recognizes current limitations of the model and identifies potential improvements.

Design and characterization of 3-D printed hydrogel lattices with anisotropic mechanical properties

The goal of this study was to design, fabricate, and characterize hydrogel lattice structures with consistent, controllable, anisotropic mechanical properties. Lattices, based on three unit-cell types (cubic, diamond, and vintile), were printed using stereolithography (SLA) of polyethylene glycol diacrylate (PEGDA). To create structural anisotropy in the lattices, unit cell design files were scaled by a factor of two in one direction in each layer and then printed. The mechanical properties of the scaled lattices were measured in shear and compression and compared to those of the unscaled lattices. Two apparent shear moduli of each lattice were measured by dynamic shear tests in two planes: (1) parallel and (2) perpendicular to the scaling direction, or cell symmetry axis. Three apparent Young's moduli of each lattice were measured by compression in three different directions: (1) the "build" direction or direction of added layers, (2) the scaling direction, and (3) the unscaled direction perpendicular to both scaling and build directions. For shear deformation in unscaled lattices, the apparent shear moduli were similar in the two perpendicular directions. In contrast, scaled lattices exhibit clear differences in apparent shear moduli. In compression of unscaled lattices, apparent Young's moduli were independent of direction in cubic and vintile lattices; in diamond lattices Young's moduli differed in the build direction, but were similar in the other two directions. Scaled lattices in compression exhibited additional differences in apparent Young's moduli in the scaled and unscaled directions. Notably, the effects of scaling on apparent modulus differed between each lattice type (cubic, diamond, or vintile) and deformation mode (shear or compression). Scaling of 3D-printed, hydrogel lattices may be harnessed to create tunable, structures of desired shape, stiffness, and mechanical anisotropy, in both shear and compression.

An investigation of the effect of brain atrophy on brain injury in multiple sclerosis

Multiple sclerosis (MS) is a disease of the central nervous system (CNS) that affects the brain and spinal cord. It is estimated that the average prevalence of MS is 35.9 cases per 100,000 and a total of 2.8 million people worldwide have MS. Brain atrophy is usually seen in the early stages of MS, and its progress is faster than healthy people. The present study was a numerical study that uses the Fluid-structure interaction (FSI) model to investigate the effect of brain atrophy on brain injury in MS. Firstly, a healthy model was constructed from MRI images and validated by experimental data. Then three models with different degrees of brain atrophy, which showed the rate of brain atrophy in different years in MS patients, were developed to model the brain atrophy in MS. The models were subjected to two different types of impact conditions. Type I, which only produced a translational motion and the HIC value of 744, was applied to each model. Type II produced both translational and rotational motion. In this type of impact, the experimental kinematics, with peaks of 450 g for the translational acceleration and 26.2 krad/s2 for the rotational acceleration, were applied to the nodes that located in the center of gravity of the head models and the results were extracted from each one. According to the results of impact type I, the pressure of the frontal lobe of the brain is 149,647 Pa in the health model and 137,690 Pa in the model with severe atrophy.

Effects of cervical rotatory manipulation on the cervical spinal cord complex with ossification of the posterior longitudinal ligament in the vertebral canal: A finite element study

Background: There are few studies focusing on biomechanism of spinal cord injury according to the ossification of the posterior longitudinal ligament (OPLL) during cervical rotatory manipulation (CRM). This study aimed to explore the biomechanical effects of CRM on the spinal cord, dura matter and nerve roots with OPLL in the cervical vertebral canal. Methods: Three validated FE models of the craniocervical spine and spinal cord complex were constructed by adding mild, moderate, and severe OPLL to the healthy FE model, respectively. We simulated the static compression of the spinal cord by OPLL and the dynamic compression during CRM in the flexion position. The stress distribution of the spinal cord complex was investigated. Results: The cervical spinal cord experienced higher von Mises stress under static compression by the severe OPLL. A higher von Mises stress was observed on the spinal cord in the moderate and severe OPLL models during CRM. The dura matter and nerve roots had a higher von Mises stress in all three models during CRM. Conclusion: The results show a high risk in performing CRM in the flexion position on patients with OPLL, in that different occupying ratios in the vertebral canal due to OPLL could significantly increase the stress on the spinal cord complex.

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.

Non-Lethal Blasts can Generate Cavitation in Cerebrospinal Fluid While Severe Helmeted Impacts Cannot: A Novel Mechanism for Blast Brain Injury

Cerebrospinal fluid (CSF) cavitation is a likely physical mechanism for producing traumatic brain injury (TBI) under mechanical loading. In this study, we investigated CSF cavitation under blasts and helmeted impacts which represented loadings in battlefield and road traffic/sports collisions. We first predicted the human head response under the blasts and impacts using computational modelling and found that the blasts can produce much lower negative pressure at the contrecoup CSF region than the impacts. Further analysis showed that the pressure waves transmitting through the skull and soft tissue are responsible for producing the negative pressure at the contrecoup region. Based on this mechanism, we hypothesised that blast, and not impact, can produce CSF cavitation. To test this hypothesis, we developed a one-dimensional simplified surrogate model of the head and exposed it to both blasts and impacts. The test results confirmed the hypothesis and computational modelling of the tests validated the proposed mechanism. These findings have important implications for prevention and diagnosis of blast TBI.

Revealing the Effect of Skull Deformation on Intracranial Pressure Variation During the Direct Interaction Between Blast Wave and Surrogate Head

Intracranial pressure (ICP) during the interaction between blast wave and the head is a crucial evaluation criterion for blast-induced traumatic brain injury (bTBI). ICP variation is mainly induced by the blast wave transmission and skull deformation. However, how the skull deformation influences the ICP remains unclear, which is meaningful for mitigating bTBI. In this study, both experimental and numerical models are developed to elucidate the effect of skull deformation on ICP variation. Firstly, we performed the shock tube experiment of the high-fidelity surrogate head to measure the ICP, the blast overpressure, and the skull surface strain of specific positions. The results show that the ICP profiles of all measured points show oscillations with positive and negative change, and the variation is consistent with the skull surface strain. Further numerical analysis reveals that when the blast wave reaches the measured point, the peak overpressure transmits directly through the skull to the brain, forming the local positive ICP peak, and the impulse induces the local inward deformation of the skull. As the peak overpressure passes through, the blast impulse impacts the nearby skull supported by the soft and incompressible brain tissue and extrudes the skull outward in the initial position. The inward and outward skull deformation leads to the oscillation of ICP. These numerical analyses agree with experimental results, which explain the appearance of negative and positive ICP peaks and the synchronization of negative ICP with surface strain. The study has implications for medical injury diagnosis and protective equipment design.

A Finite Element Model of Cerebral Vascular Injury for Predicting Microbleeds Location

Finite Element (FE) models of brain mechanics have improved our understanding of the brain response to rapid mechanical loads that produce traumatic brain injuries. However, these models have rarely incorporated vasculature, which limits their ability to predict the response of vessels to head impacts. To address this shortcoming, here we used high-resolution MRI scans to map the venous system anatomy at a submillimetre resolution. We then used this map to develop an FE model of veins and incorporated it in an anatomically detailed FE model of the brain. The model prediction of brain displacement at different locations was compared to controlled experiments on post-mortem human subject heads, yielding over 3,100 displacement curve comparisons, which showed fair to excellent correlation between them. We then used the model to predict the distribution of axial strains and strain rates in the veins of a rugby player who had small blood deposits in his white matter, known as microbleeds, after sustaining a head collision. We hypothesised that the distribution of axial strain and strain rate in veins can predict the pattern of microbleeds. We reconstructed the head collision using video footage and multi-body dynamics modelling and used the predicted head accelerations to load the FE model of vascular injury. The model predicted large axial strains in veins where microbleeds were detected. A region of interest analysis using white matter tracts showed that the tract group with microbleeds had 95th percentile peak axial strain and strain rate of 0.197 and 64.9 s⁻¹ respectively, which were significantly larger than those of the group of tracts without microbleeds (0.163 and 57.0 s⁻¹). This study does not derive a threshold for the onset of microbleeds as it investigated a single case, but it provides evidence for a link between strain and strain rate applied to veins during head impacts and structural damage and allows for future work to generate threshold values. Moreover, our results suggest that the FE model has the potential to be used to predict intracranial vascular injuries after TBI, providing a more objective tool for TBI assessment and improving protection against it.

Cavitation nucleation and its ductile-to-brittle shape transition in soft gels under translational mechanical impact

Cavitation bubbles in the human body, when subjected to impact, are being increasingly considered as a possible brain injury mechanism. However, the onset of cavitation and its complex dynamics in biological materials remain unclear. Our experimental results using soft gels as a tissue simulant show that the critical acceleration (a_cr) at cavitation nucleation monotonically increases with increasing stiffness of gelatin A/B, while a_cr for agarose and agar initially increases but is followed by a plateau or even decrease after stiffness reach to ∼100 kPa. Our image analyses of cavitation bubbles and theoretical work reveal that the observed trends in a_cr are directly linked to how bubbles grow in each gel. Gelatin A/B, regardless of their stiffness, form a localized damaged zone (tens of nanometers) at the gel-bubble interface during bubble growth. In contrary, the damaged zone in agar/agarose becomes significantly larger (> 100 times) with increasing shear modulus, which triggers the transition from formation of a small, damaged zone to activation of crack propagation. STATEMENT OF SIGNIFICANCE: We have studied cavitation nucleation and bubble growth in four different types of soft gels (i.e., tissue simulants) under translational impact. The critical linear acceleration for cavitation nucleation has been measured in the simulants by utilizing a recently developed method that mimics acceleration profiles of typical head blunt events. Each gel type exhibits significantly different trends in the critical acceleration and bubble shape (e.g., A gel-specific sphere-to-saucer transition) with increasing gel stiffness. Our theoretical framework, based on the concepts of a damaged zone and crack propagation in each gel, explains underlying mechanisms of the experimental observations. Our in-depth studies shed light on potential links between traumatic brain injuries and cavitation bubbles induced by translational acceleration, the overlooked mechanism in the literature.

The Presence of the Temporal Horn Exacerbates the Vulnerability of Hippocampus During Head Impacts

Hippocampal injury is common in traumatic brain injury (TBI) patients, but the underlying pathogenesis remains elusive. In this study, we hypothesize that the presence of the adjacent fluid-containing temporal horn exacerbates the biomechanical vulnerability of the hippocampus. Two finite element models of the human head were used to investigate this hypothesis, one with and one without the temporal horn, and both including a detailed hippocampal subfield delineation. A fluid-structure interaction coupling approach was used to simulate the brain-ventricle interface, in which the intraventricular cerebrospinal fluid was represented by an arbitrary Lagrangian-Eulerian multi-material formation to account for its fluid behavior. By comparing the response of these two models under identical loadings, the model that included the temporal horn predicted increased magnitudes of strain and strain rate in the hippocampus with respect to its counterpart without the temporal horn. This specifically affected cornu ammonis (CA) 1 (CA1), CA2/3, hippocampal tail, subiculum, and the adjacent amygdala and ventral diencephalon. These computational results suggest that the presence of the temporal horn exacerbate the vulnerability of the hippocampus, highlighting the mechanobiological dependency of the hippocampus on the temporal horn.

Protective Performance of Helmets and Goggles in Mitigating Brain Biomechanical Response to Primary Blast Exposure

The current combat helmets are primarily designed to mitigate blunt impacts and ballistic loadings. Their protection against primary blast wave is not well studied. In this paper, we comprehensively assessed the protective capabilities of the advanced combat helmet and goggles against blast waves with different intensity and directions. Using a high-fidelity human head model, we compared the intracranial pressure (ICP), cerebrospinal fluid (CSF) cavitation, and brain strain and strain rate predicted from bare head, helmet-head and helmet-goggles-head simulations. The helmet was found to be effective in mitigating the positive ICP (24–57%) and strain rate (5–34%) in all blast scenarios. Goggles were found to be effective in mitigating the positive ICP in frontal (6–16%) and lateral (5–7%) blast exposures. However, the helmet and goggles had minimal effects on mitigating CSF cavitation and even increased brain strain. Further investigation showed that wearing a helmet leads to higher risk of cavitation. In addition, their presence increased the head kinetic energy, leading to larger strains in the brain. Our findings can improve our understanding of the protective effects of helmets and goggles and guide the design of helmet pads to mitigate brain responses to blast.

Sulcal Cavitation in Linear Head Acceleration: Possible Correlation With Chronic Traumatic Encephalopathy

Traumatic Brain Injury (TBI) is a significant public health and financial concern that is affecting tens of thousands of people in the United States annually. There were over a million hospital visits related to TBI in 2017. Along with immediate and short-term morbidity from TBI, chronic traumatic encephalopathy (CTE) can have life-altering, chronic morbidity, yet the direct linkage of how head impacts lead to this pathology remains unknown. A possible clue is that chronic traumatic encephalopathy appears to initiate in the depths of the sulci. The purpose of this study was to isolate the injury mechanism/s associated with blunt force impact events. To this end, drop tower experiments were performed on a human head phantom. Our phantom was fabricated into a three-dimensional extruded ellipsoid geometry made out of Polyacrylamide gelatin that incorporated gyri-sulci interaction. The phantom was assembled into a polylactic acid 3D-printed skull, surrounded with deionized water, and enclosed between two optical windows. The phantom received repetitive low-force impacts on the order of magnitude of an average boxing punch. Intracranial pressure profiles were recorded in conjunction with high-speed imaging, 25 k frames-per-second. Cavitation was observed in all trials. Cavitation is the spontaneous formation of vapor bubbles in the liquid phase resulting from a pressure drop that reaches the vapor pressure of the liquid. The observed cavitation was predominately located in the contrecoup during negative pressure phases of local intracranial pressure. To further investigate the cavitation interaction with the brain tissue phantom, a 2D plane strain computational model was built to simulate the deformation of gyrated tissue as a result from the initiation of cavitation bubbles seen in the phantom experiments. These computational experiments demonstrated a focusing of strain at the depths of the sulci from bubble expansion. Our results add further evidence that mechanical interactions could contribute to the development of chronic traumatic encephalopathy and also that fluid cavitation may play a role in this interaction.

Low-intensity blast induces acute glutamatergic hyperexcitability in mouse hippocampus leading to long-term learning deficits and altered expression of proteins involved in synaptic plasticity and serine protease inhibitors

Neurocognitive consequences of blast-induced traumatic brain injury (bTBI) pose significant concerns for military service members and veterans with the majority of "invisible injury." However, the underlying mechanism of such mild bTBI by low-intensity blast (LIB) exposure for long-term cognitive and mental deficits remains elusive. Our previous studies have shown that mice exposed to LIB result in nanoscale ultrastructural abnormalities in the absence of gross or apparent cellular damage in the brain. Here we tested the hypothesis that glutamatergic hyperexcitability may contribute to long-term learning deficits. Using brain slice electrophysiological recordings, we found an increase in averaged frequencies with a burst pattern of miniature excitatory postsynaptic currents (mEPSCs) in hippocampal CA3 neurons in LIB-exposed mice at 1- and 7-days post injury, which was blocked by a specific NMDA receptor antagonist AP5. In addition, cognitive function assessed at 3-months post LIB exposure by automated home-cage monitoring showed deficits in dynamic patterns of discrimination learning and cognitive flexibility in LIB-exposed mice. Collected hippocampal tissue was further processed for quantitative global-proteomic analysis. Advanced data-independent acquisition for quantitative tandem mass spectrometry analysis identified altered expression of proteins involved in synaptic plasticity and serine protease inhibitors in LIB-exposed mice. Some were correlated with the ability of discrimination learning and cognitive flexibility. These findings show that acute glutamatergic hyperexcitability in the hippocampus induced by LIB may contribute to long-term cognitive dysfunction and protein alterations. Studies using this military-relevant mouse model of mild bTBI provide valuable insights into developing a potential therapeutic strategy to ameliorate hyperexcitability-modulated LIB injuries.

Effects of cervical rotatory manipulation on the cervical spinal cord: a finite element study

BACKGROUND

Little information is available concerning the biomechanism involved in the spinal cord injury after cervical rotatory manipulation (CRM). The primary purpose of this study was to explore the biomechanical and kinematic effects of CRM on a healthy spinal cord.

METHODS

A finite element (FE) model of the basilaris cranii, C1-C7 vertebral bodies, nerve root complex and vertebral canal contents was constructed and validated against in vivo and in vitro published data. The FE model simulated CRM in the flexion, extension and neutral positions. The stress distribution, forma and relative position of the spinal cord were observed.

RESULTS

Lower von Mises stress was observed on the spinal cord after CRM in the flexion position. The spinal cord in CRM in the flexion and neutral positions had a lower sagittal diameter and cross-sectional area. In addition, the spinal cord was anteriorly positioned after CRM in the flexion position, while the spinal cord was posteriorly positioned after CRM in the extension and neutral positions.

CONCLUSION

CRM in the flexion position is less likely to injure the spinal cord, but caution is warranted when posterior vertebral osteophytes or disc herniations exist.

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.

Computational models of cortical folding: A review of common approaches

The process of gyrification, by which the brain develops the intricate pattern of gyral hills and sulcal valleys, is the result of interactions between biological and mechanical processes during brain development. Researchers have developed a vast array of computational models in order to investigate cortical folding. This review aims to summarize these studies, focusing on five essential elements of the brain that affect development and gyrification and how they are represented in computational models: (i) the constraints of skull, meninges, and cerebrospinal fluid; (ii) heterogeneity of cortical layers and regions; (iii) anisotropic behavior of subcortical fiber tracts; (iv) material properties of brain tissue; and (v) the complex geometry of the brain. Finally, we highlight areas of need for future simulations of brain development.

Cerebral Vasculature Influences Blast-Induced Biomechanical Responses of Human Brain Tissue

Multiple finite-element (FE) models to predict the biomechanical responses in the human brain resulting from the interaction with blast waves have established the importance of including the brain-surface convolutions, the major cerebral veins, and using non-linear brain-tissue properties to improve model accuracy. We hypothesize that inclusion of a more detailed network of cerebral veins and arteries can further enhance the model-predicted biomechanical responses and help identify correlates of blast-induced brain injury. To more comprehensively capture the biomechanical responses of human brain tissues to blast-wave exposure, we coupled a three-dimensional (3-D) detailed-vasculature human-head FE model, previously validated for blunt impact, with a 3-D shock-tube FE model. Using the coupled model, we computed the biomechanical responses of a human head facing an incoming blast wave for blast overpressures (BOPs) equivalent to 68, 83, and 104 kPa. We validated our FE model, which includes the detailed network of cerebral veins and arteries, the gyri and the sulci, and hyper-viscoelastic brain-tissue properties, by comparing the model-predicted intracranial pressure (ICP) values with previously collected data from shock-tube experiments performed on cadaver heads. In addition, to quantify the influence of including a more comprehensive network of brain vessels, we compared the biomechanical responses of our detailed-vasculature model with those of a reduced-vasculature model and a no-vasculature model for the same blast-loading conditions. For the three BOPs, the predicted ICP values matched well with the experimental results in the frontal lobe, with peak-pressure differences of 4-11% and phase-shift differences of 9-13%. As expected, incorporating the detailed cerebral vasculature did not influence the ICP, however, it redistributed the peak brain-tissue strains by as much as 30% and yielded peak strain differences of up to 7%. When compared to existing reduced-vasculature FE models that only include the major cerebral veins, our high-fidelity model redistributed the brain-tissue strains in most of the brain, highlighting the importance of including a detailed cerebral vessel network in human-head FE models to more comprehensively account for the biomechanical responses induced by blast exposure.

Effect of random fiber networks on bubble growth in gelatin hydrogels

In hydrodynamics, the event of dynamic bubble growth in a pure liquid under tensile pressure is known as cavitation. The same event can also be observed in soft materials (e.g., elastomers and hydrogels). However, for soft materials, bubble/cavity growth is either defined as cavitation if the bubble growth is elastic and reversible or as fracture if the cavity growth is by material failure and irreversible. In any way, bubble growth can cause damage to soft materials (e.g., tissue) by inducing high strain and strain-rate deformation. Additionally, a high-strength pressure wave is generated upon the collapse of the bubble. Therefore, it is crucial to identify the critical condition of spontaneous bubble growth in soft materials. Experimental and theoretical observations have agreed that the onset of bubble growth in soft materials requires higher tensile pressure than pure water. The extra tensile pressure is required since the cavitating bubble needs to overcome the elastic and surface energy in soft materials. In this manuscript, we developed two models to study and quantify the extra tensile pressure for different gelatin concentrations. Both the models are then compared with the existing cavitation onset criteria of rubber-like materials. Validation is done with the experimental results of threshold tensile pressure for different gelatin concentrations. Both models can moderately predict the extra tensile pressure within the intermediate range of gelatin concentrations (3-7% [w/v]). For low concentration (∼1%), the network's non-affinity plays a significant role and must be incorporated. On the other hand, for higher concentrations (∼10%), the entropic deformation dominates, and the strain energy formulation is not adequate.

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.

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.

Biomechanical Analysis of Head Subjected to Blast Waves and the Role of Combat Protective Headgear Under Blast Loading: A Review

Blast-induced traumatic brain injury (bTBI) is a rising health concern of soldiers deployed in modern-day military conflicts. For bTBI, blast wave loading is a cause, and damage incurred to brain tissue is the effect. There are several proposed mechanisms for the bTBI, such as direct cranial entry, skull flexure, thoracic compression, blast-induced acceleration, and cavitation that are not mutually exclusive. So the cause-effect relationship is not straightforward. The efficiency of protective headgears against blast waves is relatively unknown as compared with other threats. Proper knowledge about standard problem space, underlying mechanisms, blast reconstruction techniques, and biomechanical models are essential for protective headgear design and evaluation. Various researchers from cross disciplines analyze bTBI from different perspectives. From the biomedical perspective, the physiological response, neuropathology, injury scales, and even the molecular level and cellular level changes incurred during injury are essential. From a combat protective gear designer perspective, the spatial and temporal variation of mechanical correlates of brain injury such as surface overpressure, acceleration, tissue-level stresses, and strains are essential. This paper outlines the key inferences from bTBI studies that are essential in the protective headgear design context.

Nonlinear plane waves in saturated porous media with incompressible constituents

We consider the propagation of nonlinear plane waves in porous media within the framework of the Biot–Coussy biphasic mixture theory. The tortuosity effect is included in the model, and both constituents are assumed incompressible (Yeoh-type elastic skeleton, and saturating fluid). In this case, the linear dispersive waves governed by Biot’s theory are either of compression or shear-wave type, and nonlinear waves can be classified in a similar way. In the special case of a neo-Hookean skeleton, we derive the explicit expressions for the characteristic wave speeds, leading to the hyperbolicity condition. The sound speeds for a Yeoh skeleton are estimated using a perturbation approach. Then we arrive at the evolution equation for the amplitude of acceleration waves. In general, it is governed by a Bernoulli equation. With the present constitutive assumptions, we find that longitudinal jump amplitudes follow a nonlinear evolution, while transverse jump amplitudes evolve in an almost linearly degenerate fashion.

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.

Localizing Clinical Patterns of Blast Traumatic Brain Injury Through Computational Modeling and Simulation

Blast traumatic brain injury is ubiquitous in modern military conflict with significant morbidity and mortality. Yet the mechanism by which blast overpressure waves cause specific intracranial injury in humans remains unclear. Reviewing of both the clinical experience of neurointensivists and neurosurgeons who treated service members exposed to blast have revealed a pattern of injury to cerebral blood vessels, manifested as subarachnoid hemorrhage, pseudoaneurysm, and early diffuse cerebral edema. Additionally, a seminal neuropathologic case series of victims of blast traumatic brain injury (TBI) showed unique astroglial scarring patterns at the following tissue interfaces: subpial glial plate, perivascular, periventricular, and cerebral gray-white interface. The uniting feature of both the clinical and neuropathologic findings in blast TBI is the co-location of injury to material interfaces, be it solid-fluid or solid-solid interface. This motivates the hypothesis that blast TBI is an injury at the intracranial mechanical interfaces. In order to investigate the intracranial interface dynamics, we performed a novel set of computational simulations using a model human head simplified but containing models of gyri, sulci, cerebrospinal fluid (CSF), ventricles, and vasculature with high spatial resolution of the mechanical interfaces. Simulations were performed within a hybrid Eulerian—Lagrangian simulation suite (CTH coupled via Zapotec to Sierra Mechanics). Because of the large computational meshes, simulations required high performance computing resources. Twenty simulations were performed across multiple exposure scenarios—overpressures of 150, 250, and 500 kPa with 1 ms overpressure durations—for multiple blast exposures (front blast, side blast, and wall blast) across large variations in material model parameters (brain shear properties, skull elastic moduli). All simulations predict fluid cavitation within CSF (where intracerebral vasculature reside) with cavitation occurring deep and diffusely into cerebral sulci. These cavitation events are adjacent to high interface strain rates at the subpial glial plate. Larger overpressure simulations (250 and 500kPa) demonstrated intraventricular cavitation—also associated with adjacent high periventricular strain rates. Additionally, models of embedded intraparenchymal vascular structures—with diameters as small as 0.6 mm—predicted intravascular cavitation with adjacent high perivascular strain rates. The co-location of local maxima of strain rates near several of the regions that appear to be preferentially damaged in blast TBI (vascular structures, subpial glial plate, perivascular regions, and periventricular regions) suggest that intracranial interface dynamics may be important in understanding how blast overpressures leads to intracranial injury.

Testing the blast response of foam inserts for helmets

Modern era combat helmets have different iterations and configurations to offer greater protection from blunt impact or ballistic penetration to suit the theatre of operation, although there are currently no standards for blast protection. Moreover, incorporation of blast protection into the same constrained mass-volume envelope is extremely challenging as there is very little space for a material to absorb or dissipate the shockwave. Foam padding is fitted in contemporary combat helmet designs for comfort and standoff purposes. Examples were subjected to blastwaves generated from an air-driven shocktube, along with open cell polyurethane foam specimens of varying pores per inch and thicknesses to. Whilst the range of samples tested did not afford any superior blast mitigation behaviour over the foam already present in helmets, they exhibited comparable performance with a lower mass. There also appears to be positive correlation between increased mass and increased impulse transmitted through the foam. The literature suggests that multiple mechanisms of damage for blast induced mild Traumatic Brain Injury (bTBI) can be caused by the helmet itself, therefore additional protection from a blunt or ballistic impact may increase the risk of damage from a blast insult.

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.

Smoothed particle hydrodynamic modelling of the cerebrospinal fluid for brain biomechanics: Accuracy and stability

The Cerebrospinal Fluid (CSF) can undergo shear deformations under head motions. Finite Element (FE) models, which are commonly used to simulate biomechanics of the brain, including traumatic brain injury, employ solid elements to represent the CSF. However, the limited number of elements paired with shear deformations in CSF can decrease the accuracy of their predictions. Large deformation problems can be accurately modelled using the mesh-free Smoothed Particle Hydrodynamics (SPH) method, but there is limited previous work on using this method for modelling the CSF. Here we explored the stability and accuracy of key modelling parameters of an SPH model of the CSF when predicting relative brain/skull displacements in a simulation of an in vivo mild head impact in human. The Moving Least Squares (MLS) SPH formulation and Ogden rubber material model were found to be the most accurate and stable. The strain and strain rate in the brain differed across the SPH and FE models of CSF. The FE mesh anchored the gyri, preventing them from experiencing the level of strains seen in the in vivo brain experiments and predicted by the SPH model. Additionally, SPH showed higher levels of strains in the sulci compared to the FE model. However, tensile instability was found to be a key challenge of the SPH method, which needs to be addressed in future. Our study provides a detailed investigation of the use of SPH and shows its potential for improving the accuracy of computational models of brain biomechanics.

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.

Cerebral perfusion is associated with blast exposure in military personnel without moderate or severe TBI

Due to the use of improvised explosive devices, blast exposure and mild traumatic brain injury (mTBI) have become hallmark injuries of the Iraq and Afghanistan wars. Although the mechanisms of the effects of blast on human neurobiology remain active areas of investigation, research suggests that the cerebrovasculature may be particularly vulnerable to blast via molecular processes that impact cerebral blood flow. Given that recent work suggests that blast exposure, even without a subsequent TBI, may have negative consequences on brain structure and function, the current study sought to further understand the effects of blast exposure on perfusion. One hundred and eighty military personnel underwent pseudo-continuous arterial spin labeling (pCASL) imaging and completed diagnostic and clinical interviews. Whole-brain analyses revealed that with an increasing number of total blast exposures, there was significantly increased perfusion in the right middle/superior frontal gyri, supramarginal gyrus, lateral occipital cortex, and posterior cingulate cortex as well as bilateral anterior cingulate cortex, insulae, middle/superior temporal gyri and occipital poles. Examination of other neurotrauma and clinical variables such as close-range blast exposures, mTBI, and PTSD yielded no significant effects. These results raise the possibility that perfusion may be an important neural marker of brain health in blast exposure.

Cerebrospinal Fluid Cavitation as a Mechanism of Blast-Induced Traumatic Brain Injury: A Review of Current Debates, Methods, and Findings

Cavitation has gained popularity in recent years as a potential mechanism of blast-induced traumatic brain injury (bTBI). This review presents the most prominent debates on cavitation; how bubbles can form or exist within the cerebrospinal fluid (CSF) and brain vasculature, potential mechanisms of cellular, and tissue level damage following the collapse of bubbles in response to local pressure fluctuations, and a survey of experimental and computational models used to address cavitation research questions. Due to the broad and varied nature of cavitation research, this review attempts to provide a necessary synthesis of cavitation findings relevant to bTBI, and identifies key areas where additional work is required. Fundamental questions about the viability and likelihood of CSF cavitation during blast remain, despite a variety of research regarding potential injury pathways. Much of the existing literature on bTBI evaluates cavitation based off its prima facie plausibility, while more rigorous evaluation of its likelihood becomes increasingly necessary. This review assesses the validity of some of the common assumptions in cavitation research, as well as highlighting outstanding questions that are essential in future work.

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.

Brain response of a computational head model for prescribed skull kinematics and simulated football helmet impact boundary conditions

Computational human body models (HBM) present a novel approach to predict brain response in football impact scenarios, with prescribed kinematic boundary conditions for the HBM skull typically used at present. However, computational optimization of helmets requires simulation of the coupled helmet and HBM model; which is much more complex and has not been assessed in the context of brain deformation and existing simplified approaches. In the current study, two boundary conditions and the resulting brain deformations were compared using a HBM head model: (1) a prescribed skull kinematics (PK) boundary condition using measured head kinematics from experimental impacts; and (2) a novel detailed simulation of a HBM head and neck, helmet and linear impactor (HBM-S). While lateral and rear impacts exhibited similar levels of maximum principal strain (MPS) in the brain tissue using both boundary conditions, differences were noted in the frontal orientation (at 9.3 m/s, MPS was 0.39 for PK, 0.54 for HBM-S). Importantly, both PK and HBM-S boundary conditions produced a similar distribution of MPS throughout the brain for each impact orientation considered. Within the corpus callosum and thalamus, high MPS was associated with lateral impacts and lower values with frontal and rear impacts. The good correspondence of both boundary conditions is encouraging for future optimization of helmet designs. A limitation of the PK approach is the need for experimental head kinematics data, while the HBM-S can predict brain response for varying impact conditions and helmet configurations, with potential as a tool to improve helmet protection performance.

Periventricular White Matter Alterations From Explosive Blast in a Large Animal Model: Mild Traumatic Brain Injury or "Subconcussive" Injury?

The neuropathology of mild traumatic brain injury in humans resulting from exposure to explosive blast is poorly understood as this condition is rarely fatal. A large animal model may better reflect the injury patterns in humans. We investigated the effect of explosive blasts on the constrained head minimizing the effects of whole head motion. Anesthetized Yucatan minipigs, with body and head restrained, were placed in a 3-walled test structure and exposed to 1, 2, or 3 explosive blast shock waves of the same intensity. Axonal injury was studied 3 weeks to 8 months postblast using β-amyloid precursor protein immunohistochemistry. Injury was confined to the periventricular white matter as early as 3-5 weeks after exposure to a single blast. The pattern was also present at 8 months postblast. Animals exposed to 2 and 3 blasts had more axonal injury than those exposed to a single blast. Although such increases in axonal injury may relate to the longer postblast survival time, it may also be due to the increased number of blast exposures. It is possible that the injury observed is due to a condition akin to mild traumatic brain injury or subconcussive injury in humans, and that periventricular injury may have neuropsychiatric implications.

Correlating Tissue Mechanics and Spinal Cord Injury: Patient-Specific Finite Element Models of Unilateral Cervical Contusion Spinal Cord Injury in Non-Human Primates

Non-human primate (NHP) models are the closest approximation of human spinal cord injury (SCI) available for pre-clinical trials. The NHP models, however, include broader morphological variability that can confound experimental outcomes. We developed subject-specific finite element (FE) models to quantify the relationship between impact mechanics and SCI, including the correlations between FE outcomes and tissue damage. Subject-specific models of cervical unilateral contusion SCI were generated from pre-injury MRIs of six NHPs. Stress and strain outcomes were compared with lesion histology using logit analysis. A parallel generic model was constructed to compare the outcomes of subject-specific and generic models. The FE outcomes were correlated more strongly with gray matter damage (0.29 < R² < 0.76) than white matter (0.18 < R² < 0.58). Maximum/minimum principal strain, Von-Mises and Tresca stresses showed the strongest correlations (0.31 < R² < 0.76) with tissue damage in the gray matter while minimum principal strain, Von-Mises stress, and Tresca stress best predicted white matter damage (0.23 < R² < 0.58). Tissue damage thresholds varied for each subject. The generic FE model captured the impact biomechanics in two of the four models; however, the correlations between FE outcomes and tissue damage were weaker than the subject-specific models (gray matter [0.25 < R² < 0.69] and white matter [R² < 0.06] except for one subject [0.26 < R² < 0.48]). The FE mechanical outputs correlated with tissue damage in spinal cord white and gray matters, and the subject-specific models accurately mimicked the biomechanics of NHP cervical contusion impacts.

Materials Characterization of Cranial Simulants for Blast-Induced Traumatic Brain Injury

INTRODUCTION

The mechanical response of brain tissue to high-speed forces in the blast and blunt traumatic brain injury is poorly understood. Object-to-object variation and interspecies differences are current limitations in animal and cadaver studies conducted to study damage mechanisms. Biofidelic and transparent tissue simulants allow the use of high-speed optical diagnostics during a blast event, making it possible to observe deformations and damage patterns for comparison to observed injuries seen post-mortem in traumatic brain injury victims.

METHODS

Material properties of several tissue simulants were quantified using standard mechanical characterization techniques, that is, shear rheometric, tensile, and compressive testing.

RESULTS

Polyacrylamide simulants exhibited the best optical and mechanical property matching with the fewest trade-offs in the design of a cranial test object. Polyacrylamide gels yielded densities of ~1.04 g/cc and shear moduli ranging 1.3-14.55 kPa, allowing gray and white matter simulant tuning to a 30-35% difference in shear for biofidelity.

CONCLUSIONS

These materials are intended for use as layered cranial phantoms in a shock tube and open field blasts, with focus on observing phenomena occurring at the interfaces of adjacent tissue simulant types or material-fluid boundaries. Mechanistic findings from these studies may be used to inform the design of protective gear to mitigate blast injuries.

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.

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

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

Variations in Constitutive Properties of the Fluid Elicit Divergent Vibrational and Pressure Response Under Shock Wave Loading

We performed a characterization of the shock wave loading on the response of the specimen representing a simplified head model. A polycarbonate cylinder (2-in. outer diameter, wall thickness: 0.06 or 0.12 in.) was filled with two fluids: pure de-ionized water and 40% glycerol in water, which differ only slightly in their constitutive material properties. These two fluids were selected to represent the cerebrospinal fluid and cerebral blood, using their high strain rate viscosity as a primary selection criterion. The model specimen was exposed to a single shock wave with two nominal intensities: 70 and 130 kPa overpressure. The response of the model was measured using three strain gauges and three pressure sensors, one mounted on the front face of the cylinder and two embedded in the cylinder to measure the pressure inside of the fluid. We noted several discriminant characteristics in the collected data, which indicate that the type of fluid is strongly influencing the response. The vibrations of the cylinder walls are strongly correlated with the fluid kind. The similarity analysis via the Pearson coefficient indicated that the pressure waveforms in the fluid are only moderately correlated, and these results were further corroborated by Euclidean distance analysis. Continuous wavelet transform of pressure waveforms revealed that the frequency response is strongly correlated with the properties of the fluid. The observed differences in strain and pressure modalities stem from relatively small differences in the properties of the fluids used in this study.

Simulation of the Strain Amplification in Sulci Due to Blunt Impact to the Head

Traumatic brain injury (TBI) has become a concern in sports, automobile accidents and combat operations. A better understanding of the mechanics leading to a TBI is required to cope with both the short-term life-threatening effects and long-term effects of TBIs, such as the development chronic traumatic encephalopathy (CTE). Kornguth et al. (1) proposed that an inflammatory and autoimmune process initiated by a water hammer effect at the bases of the sulci of the brain is a mechanism of TBI leading to CTE. A major objective of this study is to investigate whether the water hammer effect is present due to blunt impacts through the use of computational models. Frontal blunt impacts were simulated with 2D finite element models developed to capture the biofidelic geometry of a human head. The models utilized the Arbitrary Lagrangian Eulerian (ALE) method to model a layer of cerebrospinal fluid (CSF) as a deforming fluid allowing for CSF to move in and out of sulci. During the simulated impacts, CSF was not observed to be driven into the sulci during the transient response. However, elevated shear strain levels near the base of the sulci were exhibited. Further, increased shear strain was present when differentiation between white and gray matter was taken into account. Both of the results support clinical observations of (1).