A Mechanical Brain Damage Framework Used to Model Abnormal Brain Tau Protein Accumulations of National Football League Players

Embedded finite element modeling of the mechanics of brain axonal fiber tracts under head impact conditions

Investigating and understanding the biomechanical kinematics and kinetics of human brain axonal fibers during head impact process is crucial to study the mechanisms of Traumatic Axonal Injury (TAI). Such a study may require the explicit incorporation of brain fiber tracts into the host brain in order to distinguish the mechanical states of axonal fibers and brain tissue. Herein we extend our previously developed human head model by using an embedded element method to include fiber tracts reconstructed from diffusion tensor images in a host brain with the purpose of numerically tracking the deformation state of axonal fiber tracts during a head impact simulation. The updated model is validated by comparing its prediction of intracranial pressures with experimental data, followed by a thorough study of the effects of element types used for fiber tracts and the stiffness ratios of fiber to host brain. The validated model is also used to predict and visualize the damaged region of fiber tracts during the head impact process based on different injury criteria. The model is promising in tracking the state of fiber tracts and can add more objective functions such as axonal fiber deformation if used in the future design optimization of head protective equipment such as a football helmet.

Impact of prior axonal injury on subsequent injury during brain tissue stretching - A mesoscale computational approach

Epidemiology studies of traumatic brain injury (TBI) show individuals with a prior history of TBI experience an increased risk of future TBI with a significantly more detrimental outcome. But the mechanisms through which prior head injuries may affect risks of injury during future head insults have not been identified. In this work, we show that prior brain tissue injury in the form of mechanically induced axonal injury and glial scar formation can facilitate future mechanically induced tissue injury. To achieve this, we use finite element computational models of brain tissue and a history-dependent pathophysiology-based mechanically-induced axonal injury threshold to determine the evolution of axonal injury and scar tissue formation and their effects on future brain tissue stretching. We find that due to the reduced stiffness of injured tissue and glial scars, the existence of prior injury can increase the risk of future injury in the vicinity of prior injury during future brain tissue stretching. The softer brain scar tissue is shown to increase the strain and strain rate in its vicinity by as much as 40% in its vicinity during dynamic stretching that reduces the global strain required to induce injury by 20% when deformed at 15 s-1 strain rate. The results of this work highlight the need to account for patient history when determining the risk of brain injury.

Mesoscale Simulation-based Parametric Study of Damage Potential in Brain Tissue Using Hyperelastic and Internal State Variable Models

Two-dimensional mesoscale finite element analysis (FEA) of a multi-layered brain tissue was performed to calculate the damage related average stress triaxiality and local maximum von Mises strain in the brain. The FEA was integrated with rate dependent hyperelastic and internal state variable (ISV) models respectively describing the behaviors of wet and dry brain tissues. Using the finite element results, a statistical method of design of experiments (DOE) was utilized to independently screen the relative influences of seven parameters related to brain morphology (sulcal width/depth, gray matter (GM) thickness, cerebrospinal fluid (CSF) thickness and brain lobe) and loading/environment conditions (strain rate and humidity) with respect to the potential damage growth/coalescence in the brain tissue. The results of the parametric study illustrated that the GM thickness and humidity were the two most crucial parameters affecting average stress triaxiality. For the local maximum von Mises strain at the depth of brain sulci, the brain lobe/region was the most influential factor. The conclusion of this investigation gives insight for the future development and refinement of a macroscale brain damage model incorporating information from lower length scale.

Impact-Induced Cortical Strain Concentrations at the Sulcal Base and Its Implications for Mild Traumatic Brain Injury

This study investigated impact-induced strain fields within brain tissue surrogates having different cortical gyrification. Two elastomeric surrogates, one representative of a lissencephalic brain and the other of a gyrencephalic brain, were drop impacted in unison at four different heights and in two different orientations. Each surrogate contained a radiopaque speckle pattern that was used to calculate strain fields. Two different approaches, digital image correlation (DIC) and a particle tracking method, enabled comparisons of full-field and localized strain responses. The DIC results demonstrated increased localized deviations from the mean strain field in the surrogate with a gyrified cortex. Particle tracking algorithms, defining four-node quadrilateral elements, were used to investigate the differences in the strain response of three regions: the base of a sulcus, the adjacent gyrus, and the internal capsule of the surrogates. The results demonstrated that the strains in the cortex were concentrated at the sulcal base. This mechanical mechanism of increased strain is consistent with neurodegenerative markers observed in postmortem analyses, suggesting a potential mechanism of local damage due to strain amplification at the sulcal bases in gyrencephalic brains. This strain amplification mechanism may be responsible for cumulative neurodegeneration from repeated subconcussive impacts. The observed results suggest that lissencephalic animal models, such as rodents, would not have the same modes of injury present in a gyrencephalic brain, such as that of a human. As such, a shift toward representative mild traumatic brain injury animal models having gyrencephalic cortical structures should be strongly considered.

Finite element analysis of a ram brain during impact under wet and dry horn conditions

In this study, ram impacts at 5.5 m/s are simulated through finite element analysis in order to study the mechanical response of the brain. A calibrated internal state variable inelastic constitutive model was implemented into the finite element code to capture the brain behavior. Also, constitutive models for the horns were calibrated to experimental data from dry and wet horn keratin at low and high strain rates. By investigating responses in the different keratin material states that occur in nature, the bounds of the ram brain response are quantified. An acceleration as high as 607 g's was observed, which is an order of magnitude higher than predicted brain injury threshold values. In the most extreme case, the maximum tensile pressure and maximum shear strains in the ram brain were 245 kPa and 0.28, respectively. Because the rams do not appear to sustain injury, these impacts could give insight to the threshold limits of mechanical loading that can be applied to the brain. Following this motivation, the brain injury metric values found in this research could serve as true injury metrics for human head impacts.

Craniocerebral Dynamic Response and Cumulative Effect of Damage Under Repetitive Blast

Soldiers suffer from multiple explosions in complex battlefield environment resulting in aggravated brain injuries. At present, researches mostly focus on the damage to human body caused by single explosion. In the repetitive impact study, small animals are mainly used for related experiments to study brain nerve damage. No in-depth research has been conducted on the dynamic response and damage of human brain under repetitive explosion shock waves. Therefore, this study use the Euler-Lagrange coupling method to construct an explosion shock wave-head fluid-structure coupling model, and numerically simulated the brain dynamic response subjected to single and repetitive blast waves, obtained flow field pressure, skull stress, skull displacement, intracranial pressure to analyze the brain damage. The simulation results of 100 g equivalent of TNT exploding at 1 m in front of the craniocerebral show that repetitive blast increase skull stress, intracranial pressure, skull displacement, and the damage of brain tissue changes from moderate to severe. Repetitive blasts show a certain cumulative damage effect, the severity of damage caused by double blast is 122.5% of single shock, and the severity of damage caused by triple blast is 105.9% of double blast and 131.5% of single blast. The data above shows that it is necessary to reduce soldiers' exposure from repetitive blast waves.

A mesoscale finite element modeling approach for understanding brain morphology and material heterogeneity effects in chronic traumatic encephalopathy

Chronic Traumatic Encephalopathy (CTE) affects a significant portion of athletes in contact sports but is difficult to quantify using clinical examinations and modeling approaches. We use an in silico approach to quantify CTE biomechanics using mesoscale Finite Element (FE) analysis that bridges with macroscale whole head FE analysis. The sulci geometry produces complex stress waves that interact with one another to create increased shear stresses at the sulci depth that are significantly larger than in analyses without sulci (from 0.5 to 18.0 kPa). Sulci peak stress concentration regions coincide with experimentally observed CTE sites documented in the literature. HighlightsSulci introduce stress localizations at their depth in the gray matterSulci stress fields interact to produce stress concentration sites in white matterDifferentiating brain tissue properties did not significantly affect peak stresses.

Player position in American football influences the magnitude of mechanical strains produced in the location of chronic traumatic encephalopathy pathology: A computational modelling study

American football players are frequently exposed to head impacts, which can cause concussions and may lead to neurodegenerative diseases such as chronic traumatic encephalopathy (CTE). Player position appears to influence the risk of concussion but there is limited work on its effect on the risk of CTE. Computational modelling has shown that large brain deformations during head impacts co-localise with CTE pathology in sulci. Here we test whether player position has an effect on brain deformation within the sulci, a possible biomechanical trigger for CTE. We physically reconstructed 148 head impact events from video footage of American Football games. Players were separated into 3 different position profiles based on the magnitude and frequency of impacts. A detailed finite element model of TBI was then used to predict Green-Lagrange strain and strain rate across the brain and in sulci. Using a one-way ANOVA, we found that in positions where players were exposed to large magnitude and low frequency impacts (e.g. defensive back and wide receiver), strain and strain rate across the brain and in sulci were highest. We also found that rotational head motion is a key determinant in producing large strains and strain rates in the sulci. Our results suggest that player position has a significant effect on impact kinematics, influencing the magnitude of deformations within sulci, which spatially corresponds to where CTE pathology is observed. This work can inform future studies investigating different player-position risks for concussion and CTE and guide design of prevention systems.

The Effectiveness of Regulations and Behavioral Interventions on Head Impacts and Concussions in Youth, High-School, and Collegiate Football: A Systematized Review

The purpose of this study was to assess the effectiveness of regulations and behavioral interventions on head impacts and concussions in youth, high-school, and collegiate football, using a systematic search strategy to identify relevant literature. Six databases were searched using key search terms related to three categories: football, head-injuries, and interventions. Studies that met inclusion criteria were included in the study and underwent data extraction. Twenty articles met inclusion criteria and were included in the final systematized review. Of the 20 included studies, 8 studies evaluated interventions in high-school football, 5 studies evaluated interventions in collegiate football, 6 studies evaluated interventions in youth football, and 1 study evaluated interventions in both, high-school and collegiate football. The four categories of interventions and regulations included rule changes, training, education/instruction/coaching tactical changes, and tackle football alternatives. Studies evaluating the effectiveness of interventions and regulations on reducing head impact exposures or head injuries have shown mixed results. Some regulations may be more effective than others, but methodological design and risk of bias pose limitations to generalize effects.

Validating Tackle Mechanics in American Football: Improving Safety and Performance

Research has helped to understand the risks of injuries of tackling in American football and rugby; however, approaches to teaching and analysis are not well-documented. Shoulder-led tackling has been proposed as a safer approach to tackling even though data on the effectiveness for safety and defensive performance is limited. Additionally, some have argued that safety and effectiveness are incompatible. The purpose of the study was to validate a specific sequence of tackling actions as a tool for teaching safer and more effective tackling skills. Results suggested tackle scores help predict presence of head contact, and that higher tackle scores were associated with reductions in Yards After Contact (YAC). Eight hundred and thirty-two (832) American high school football tackles were rated using a 12-element rating system. Estimated Structural Equation Modeling (ESEM) was employed to identify the factor structure of the elements with three factors identified: Track, Engage, and Finish. ANOVA, along with logistic and linear equation models were run to determine relationships between tackle scores and outcomes. Tackle scores predicted head-contact category (binary logistic regression accuracy = .76). Yards after contact (YAC) were significantly reduced [Finish factor: MANOVA F(3, 828) = 105.825, p < .001]. Construct and predictive validity were demonstrated and show that these tackle elements provide valid foci for teaching better tackling as well as analyzing both teaching effectiveness and performance.

The Shrinking Brain: Cerebral Atrophy Following Traumatic Brain Injury

Cerebral atrophy in response to traumatic brain injury is a well-documented phenomenon in both primary investigations and review articles. Recent atrophy studies focus on exploring the region-specific patterns of cerebral atrophy; yet, there is no study that analyzes and synthesizes the emerging atrophy patterns in a single comprehensive review. Here we attempt to fill this gap in our current knowledge by integrating the current literature into a cohesive theory of preferential brain tissue loss and by identifying common risk factors for accelerated atrophy progression. Our review reveals that observations for mild traumatic brain injury remain inconclusive, whereas observations for moderate-to-severe traumatic brain injury converge towards robust patterns: brain tissue loss is on the order of 5% per year, and occurs in the form of generalized atrophy, across the entire brain, or focal atrophy, in specific brain regions. The most common regions of focal atrophy are the thalamus, hippocampus, and cerebellum in gray matter and the corpus callosum, corona radiata, and brainstem in white matter. We illustrate the differences of generalized and focal gray and white matter atrophy on emerging deformation and stress profiles across the whole brain using computational simulation. The characteristic features of our atrophy simulations-a widening of the cortical sulci, a gradual enlargement of the ventricles, and a pronounced cortical thinning-agree well with clinical observations. Understanding region-specific atrophy patterns in response to traumatic brain injury has significant implications in modeling, simulating, and predicting injury outcomes. Computational modeling of brain atrophy could open new strategies for physicians to make informed decisions for whom, how, and when to administer pharmaceutical treatment to manage the chronic loss of brain structure and function.