Andrew Post

Finite element analysis of the effect of loading curve shape on brain injury predictors.

Prediction of traumatic and mild traumatic brain injury is an important factor in managing their prevention. Currently, the prediction of these injuries is limited to peak linear and angular acceleration loading curves derived from laboratory reconstructions. However it remains unclear as to what aspect of these loading curves contributes to brain tissue damage. This research uses the University College Dublin Brain Trauma Model (UCDBTM) to analyse three distinct loading curve shapes meant to represent different helmet loading scenarios. The loading curves were applied independently in each axis of linear and angular acceleration and their effect on currently used predictors of TBI and mTBI was examined. Loading curve shape A had a late time to peak, B an early time to peak and C had a consistent plateau. The areas under the curve for all three loading curve shapes were identical. The results indicate that loading curve A produced consistently higher maximum principal strains and Von Mises stress than the other two loading curve types. Loading curve C consistently produced the lowest values of maximum principal strain and Von Mises stress, with loading curve B being lowest in only 2 cases. The areas of peak Von Mises stress and Principal strain also varied depending on loading curve shape and acceleration input.

A Multiscale Computational Approach to Estimating Axonal Damage under Inertial Loading of the Head

A computational modeling framework is developed to estimate the location and degree of diffuse axonal injury (DAI) under inertial loading of the head. DAI is one of the most common pathological features of traumatic brain injury and is characterized by damage to the neural axons in the white matter regions of the brain. We incorporate the microstructure of the white matter (i.e., the fiber orientations and fiber dispersion) through the use of diffusion tensor imaging (DTI), and model the white matter with an anisotropic, hyper-viscoelastic constitutive model. The extent of DAI is estimated using an axonal strain injury criterion. A novel injury analysis method is developed to quantify the degree of axonal damage in the fiber tracts of the brain and identify the tracts that are at the greatest risk for functional failure. Our modeling framework is applied to analyze DAI in a real-life ice hockey incident that resulted in concussive injury. To simulate the impact, two-dimensional finite element (FE) models of the head were constructed from detailed MRI and DTI data and validated using available human head experimental data. Acceleration loading curves from accident reconstruction data were then applied to the FE models. The rotational (rather than translational) accelerations were shown to dominate the injury response, which is consistent with previous studies. Through this accident reconstruction, we demonstrate a conceptual framework to estimate the degree of axonal injury in the fiber tracts of the human brain, enabling the future development of relationships between computational simulation and neurocognitive impairment.

Examination of the relationship between peak linear and angular accelerations to brain deformation metrics in hockey helmet impacts

Ice hockey is a contact sport which has a high incidence of brain injury. The current methods of evaluating protective devices use peak resultant linear acceleration as their pass/fail criteria, which are not fully representative of brain injuries as a whole. The purpose of this study was to examine how the linear and angular acceleration loading curves from a helmeted impact influence currently used brain deformation injury metrics. A helmeted Hybrid III headform was impacted in five centric and non-centric impact sites to elicit linear and angular acceleration responses. These responses were examined through the use of a brain model. The results indicated that when the helmet is examined using peak resultant linear acceleration alone, they are similar and protective, but when a 3D brain deformation response is used to examine the helmets, there are risks of brain injury with lower linear accelerations which would pass standard certifications for safety.

Mechanisms of brain impact injuries and their prediction: A review

Brain injuries are a significant contributor to morbidity and mortality. Currently a great deal of controversy exists concerning the mechanism of these injuries and concussion in particular. The following review discusses the anatomical mechanisms which are known to cause brain injuries and the variables currently used to predict their incidence. This review examines how current engineering techniques and measurements are being used to research brain injury mechanisms. The text examines the past and current measurement techniques and their benefits and drawbacks when it comes to predicting brain injuries and understanding brain injury research. Finally, future methods of quantifying brain injury are discussed with concluding remarks concerning the efficacy of current measurement techniques to predict brain injuries.

Rotational Acceleration, Brain Tissue Strain, and the Relationship to Concussion

The mechanisms of concussion have been investigated by many researchers using a variety of methods. However, there remains much debate over the relationships between head kinematics from an impact and concussion. This review presents the links between research conducted in different disciplines to better understand the relationship between linear and rotational acceleration and brain strains that have been postulated as the root cause of concussion. These concepts are important when assigning performance variables for helmet development, car design, and protective innovation research.

Current and future concepts in helmet and sports injury prevention

Since the introduction of head protection, a decrease in sports-related traumatic brain injuries has been reported. The incidence of concussive injury, however, has remained the same or on the rise. These trends suggest that current helmets and helmet standards are not effective in protecting against concussive injuries. This article presents a literature review that describes the discrepancy between how helmets are designed and tested and how concussions occur. Most helmet standards typically use a linear drop system and measure criterion such as head Injury criteria, Gadd Severity Index, and peak linear acceleration based on research involving severe traumatic brain injuries. Concussions in sports occur in a number of different ways that can be categorized into collision, falls, punches, and projectiles. Concussive injuries are linked to strains induced by rotational acceleration. Because helmet standards use a linear drop system simulating fall-type injury events, the majority of injury mechanisms are neglected. In response to the need for protection against concussion, helmet manufacturers have begun to innovate and design helmets using other injury criteria such as rotational acceleration and brain tissue distortion measures via finite-element analysis. In addition to these initiatives, research has been conducted to develop impact protocols that more closely reflect how concussions occur in sports. Future research involves a better understanding of how sports-related concussions occur and identifying variables that best describe them. These variables can be used to guide helmet innovation and helmet standards to improve the quality of helmet protection for concussive injury.Since the introduction of head protection, a decrease in sports-related traumatic brain injuries has been reported. The incidence of concussive injury, however, has remained the same or on the rise. These trends suggest that current helmets and helmet standards are not effective in protecting against concussive injuries. This article presents a literature review that describes the discrepancy between how helmets are designed and tested and how concussions occur. Most helmet standards typically use a linear drop system and measure criterion such as head Injury criteria, Gadd Severity Index, and peak linear acceleration based on research involving severe traumatic brain injuries. Concussions in sports occur in a number of different ways that can be categorized into collision, falls, punches, and projectiles. Concussive injuries are linked to strains induced by rotational acceleration. Because helmet standards use a linear drop system simulating fall-type injury events, the majority of injury mechanisms are neglected. In response to the need for protection against concussion, helmet manufacturers have begun to innovate and design helmets using other injury criteria such as rotational acceleration and brain tissue distortion measures via finite-element analysis. In addition to these initiatives, research has been conducted to develop impact protocols that more closely reflect how concussions occur in sports. Future research involves a better understanding of how sports-related concussions occur and identifying variables that best describe them. These variables can be used to guide helmet innovation and helmet standards to improve the quality of helmet protection for concussive injury.

Traumatic brain injuries: the influence of the direction of impact.

BACKGROUND Head impact direction has been identified as an influential risk factor in the risk of traumatic brain injury (TBI) from animal and anatomic research; however, to date, there has been little investigation into this relationship in human subjects. If a susceptibility to certain types of TBI based on impact direction was found to exist in humans, it would aid in clinical diagnoses as well as prevention methods for these types of injuries. OBJECTIVE To examine the influence of impact direction on the presence of TBI lesions, specifically, subdural hematomas, subarachnoid hemorrhage, and parenchymal contusions. METHODS Twenty reconstructions of falls that resulted in a TBI were conducted in a laboratory based on eyewitness, interview, and medical reports. The reconstructions involved impacts to a Hybrid III anthropometric dummy and finite element modeling of the human head to evaluate the brain stresses and strains for each TBI event. RESULTS The results showed that it is likely that increased risk of incurring a subdural hematoma exists from impacts to the frontal or occipital regions, and parenchymal contusions from impacts to the side of the head. There was no definitive link between impact direction and subarachnoid hemorrhage. In addition, the results indicate that there is a continuum of stresses and strain magnitudes between lesion types when impact location is isolated, with subdural hematoma occurring at lower magnitudes for frontal and occipital region impacts, and contusions lower for impacts to the side. CONCLUSION This hospital data set suggests that there is an effect that impact direction has on TBI depending on the anatomy involved for each particular lesion.

The influence of dynamic response and brain deformation metrics on the occurrence of subdural hematoma in different regions of the brain

OBJECT The purpose of this study was to examine how the dynamic response and brain deformation of the head and brain-representing a series of injury reconstructions of which subdural hematoma (SDH) was the outcome-influence the location of the lesion in the lobes of the brain. METHODS Sixteen cases of falls in which SDH was the outcome were reconstructed using a monorail drop rig and Hybrid III headform. The location of the SDH in 1 of the 4 lobes of the brain (frontal, parietal, temporal, and occipital) was confirmed by CT/MR scan examined by a neurosurgeon. RESULTS The results indicated that there were minimal differences between locations of the SDH for linear acceleration. The peak resultant rotational acceleration and x-axis component were larger for the parietal lobe than for other lobes. There were also some differences between the parietal lobe and the other lobes in the z-axis component. Maximum principal strain, von Mises stress, shear strain, and product of strain and strain rate all had differences in magnitude depending on the lobe in which SDH was present. The parietal lobe consistently had the largest-magnitude response, followed by the frontal lobe and the occipital lobe. CONCLUSIONS The results indicated that there are differences in magnitude for rotational acceleration and brain deformation metrics that may identify the location of SDH in the brain.

Characterization of persistent concussive syndrome using injury reconstruction and finite element modelling.

Concussions occur 1.7 million times a year in North America, and account for approximately 75% of all traumatic brain injuries (TBI). Concussions usually cause transient symptoms but 10 to 20% of patients can have symptoms that persist longer than a month. The purpose of this research was to use reconstructions and finite element modeling to determine the brain tissue stresses and strains that occur in impacts that led to persistent post concussive symptoms (PCS) in hospitalized patients. A total of 21 PCS patients had their head impacts reconstructed using computational, physical and finite element methods. The dependent variables measured were maximum principal strain, von Mises stress (VMS), strain rate, and product of strain and strain rate. For maximum principal strain alone there were large regions of brain tissue incurring 30 to 40% strain. This large field of strain was also evident when using strain rate, product of strain and strain rate. In addition, VMS also showed large magnitudes of stress throughout the cerebrum tissues. The distribution of strains throughout the brain tissues indicated peak responses were always present in the grey matter (0.481), with the white matter showing significantly lower strains (0.380) (p<0.05). The impact conditions of the PCS cases were severe in nature, with impacts against non-compliant surfaces (concrete, steel, ice) resulting in higher brain deformation. PCS biomechanical parameters were shown to fit between those that have been shown to cause transient post concussive symptoms and those that lead to actual pathologic damage like contusion, however, values of all metrics were characterized by large variance and high average responses. This data supports the theory that there exists a progressive continuum of impacts that lead to a progressive continuum of related severity of injury from transient symptoms to pathological damage.

A centric/non-centric impact protocol and finite element model methodology for the evaluation of American football helmets to evaluate risk of concussion

American football reports high incidences of head injuries, in particular, concussion. Research has described concussion as primarily a rotation dominant injury affecting the diffuse areas of brain tissue. Current standards do not measure how helmets manage rotational acceleration or how acceleration loading curves influence brain deformation from an impact and thus are missing important information in terms of how concussions occur. The purpose of this study was to investigate a proposed three-dimensional impact protocol for use in evaluating football helmets. The dynamic responses resulting from centric and non-centric impact conditions were examined to ascertain the influence they have on brain deformations in different functional regions of the brain that are linked to concussive symptoms. A centric and non-centric protocol was used to impact an American football helmet; the resulting dynamic response data was used in conjunction with a three-dimensional finite element analysis of the human brain to calculate brain tissue deformation. The direction of impact created unique loading conditions, resulting in peaks in different regions of the brain associated with concussive symptoms. The linear and rotational accelerations were not predictive of the brain deformation metrics used in this study. In conclusion, the test protocol used in this study revealed that impact conditions influences the region of loading in functional regions of brain tissue that are associated with the symptoms of concussion. The protocol also demonstrated that using brain deformation metrics may be more appropriate when evaluating risk of concussion than using dynamic response data alone.