Jillian E. Urban

Abnormal white matter integrity related to head impact exposure in a season of high school varsity football.

The aim of this study was to determine whether the cumulative effects of head impacts from a season of high school football produce magnetic resonance imaging (MRI) measureable changes in the brain in the absence of clinically diagnosed concussion. Players from a local high school football team were instrumented with the Head Impact Telemetry System (HITS™) during all practices and games. All players received pre- and postseason MRI, including diffusion tensor imaging (DTI). Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) was also conducted. Total impacts and risk-weighted cumulative exposure (RWE), including linear (RWELinear), rotational (RWERotational), and combined components (RWECP), were computed from the sensor data. Fractional, linear, planar, and spherical anisotropies (FA, CL, CP, and CS, respectively), as well as mean diffusivity (MD), were used to determine total number of abnormal white matter voxels defined as 2 standard deviations above or below the group mean. Delta (post-preseason) ImPACT scores for each individual were computed and compared to the DTI measures using Spearmans rank correlation coefficient. None of the players analyzed experienced clinical concussion (N=24). Regression analysis revealed a statistically significant linear relationship between RWECP and FA. Secondary analyses demonstrated additional statistically significant linear associations between RWE (RWECP and RWELinear) and all DTI measures. There was also a strong correlation between DTI measures and change in Verbal Memory subscore of the ImPACT. We demonstrate that a single season of football can produce brain MRI changes in the absence of clinical concussion. Similar brain MRI changes have been previously associated with mild traumatic brain injury.

Evaluation of Skull Cortical Thickness Changes With Age and Sex From Computed Tomography Scans

Head injuries resulting from motor vehicle crashes (MVC) are extremely common, yet the details of the mechanism of injury remain to be well characterized. Skull deformation is believed to be a contributing factor to some types of traumatic brain injury (TBI). Understanding biomechanical contributors to skull deformation would provide further insight into the mechanism of head injury resulting from blunt trauma. In particular, skull thickness is thought be a very important factor governing deformation of the skull and its propensity for fracture. Previously, age‐ and sex‐based skull cortical thickness changes were difficult to evaluate based on the need for cadaveric skulls. In this cross‐sectional study, skull thickness changes with age and sex have been evaluated at homologous locations using a validated cortical density‐based algorithm to accurately quantify cortical thickness from 123 high‐resolution clinical computed tomography (CT) scans. The flat bones of the skull have a sandwich structure; therefore, skull thickness was evaluated for the inner and outer tables as well the full thickness. General trends indicated an increase in the full skull thickness, mostly attributed to an increase in the thickness of the diploic layer; however, these trends were not found to be statistically significant. There was a significant relationship between cortical thinning and age for both tables of the frontal, occipital, and parietal bones ranging between a 36% and 60% decrease from ages 20 to 100 years in females, whereas males exhibited no significant changes. Understanding how cortical and full skull thickness changes with age from a wide range of subjects can have implications in improving the biofidelity of age‐ and sex‐specific finite element models and therefore aid in the prediction and understanding of TBI from impact and blast injuries.

Estimation of skull table thickness with clinical CT and validation with microCT

Brain injuries resulting from motor vehicle crashes (MVC) are extremely common yet the details of the mechanism of injury remain to be well characterized. Skull deformation is believed to be a contributing factor to some types of traumatic brain injury (TBI). Understanding biomechanical contributors to skull deformation would provide further insight into the mechanism of head injury resulting from blunt trauma. In particular, skull thickness is thought be a very important factor governing deformation of the skull and its propensity for fracture. Current computed tomography (CT) technology is limited in its ability to accurately measure cortical thickness using standard techniques. A method to evaluate cortical thickness using cortical density measured from CT data has been developed previously. This effort validates this technique for measurement of skull table thickness in clinical head CT scans using two postmortem human specimens. Bone samples were harvested from the skulls of two cadavers and scanned with microCT to evaluate the accuracy of the estimated cortical thickness measured from clinical CT. Clinical scans were collected at 0.488 and 0.625 mm in plane resolution with 0.625 mm thickness. The overall cortical thickness error was determined to be 0.078 ± 0.58 mm for cortical samples thinner than 4 mm. It was determined that 91.3% of these differences fell within the scanner resolution. Color maps of clinical CT thickness estimations are comparable to color maps of microCT thickness measurements, indicating good quantitative agreement. These data confirm that the cortical density algorithm successfully estimates skull table thickness from clinical CT scans. The application of this technique to clinical CT scans enables evaluation of cortical thickness in population‐based studies.

Motor vehicle crash-related subdural hematoma from real-world head impact data

Abstract Approximately 1,700,000 people sustain a traumatic brain injury (TBI) each year and motor vehicle crashes (MVCs) are a leading cause of hospitalization from TBI. Acute subdural hematoma (SDH) is a common intracranial injury that occurs in MVCs associated with high mortality and morbidity rates. In this study, SDH volume and midline shift have been analyzed in order to better understand occupant injury by correlating them to crash and occupant parameters. Fifty-seven head computed tomography (CT) scans were selected from the Crash Injury Research Engineering Network (CIREN) with Abbreviated Injury Scale (AIS) level 3+ SDH. Semi-automated methods were used to isolate the intracranial volume. SDH and additional occupant intracranial injuries were segmented across axial CT images, providing a total SDH injury volume. SDH volume was correlated to crash parameters and occupant characteristics. Results show a positive correlation between SDH volume and crash severity in near-side and frontal crashes. Additionally, the location of the resulting hemorrhage varied by crash type. Those with greater SDH volumes had significantly lower Glasgow Coma Scale (GCS) scores at the crash site in near-side crashes. Age and fracture type were found to be significant contributors to SDH volume. This study is a volumetric analysis of real world brain injuries and known MVC impacts. The results of this study demonstrate a relationship among SDH volume, crash mechanics, and occupant characteristics that provide a better understanding of the injury mechanisms of MVC-associated TBI.

Evaluation of morphological changes in the adult skull with age and sex.

The morphology of the brain and skull are important in the evaluation of the aging human; however, little is known about how the skull may change with age. The objective of this study was to evaluate the morphological changes of the adult skull using three‐dimensional geometric morphometric analysis of thousands of landmarks with the focus on anatomic regions that may be correlated with brain atrophy and head injury. Computed tomography data were collected between ages 20 and 100. Each scan was segmented using thresholding techniques. An atlas image of a 50th percentile skull was registered to each subject scan by computing a series of rigid, affine, and non‐linear transformations between atlas space and subject space. Landmarks on the atlas skull were transformed to each subject and partitioned into the inner and outer cranial vault and the cranial fossae. A generalized Procrustes analysis was completed for the landmark sets. The coordinate locations describing the shape of each region were regressed with age to generate a model predicting the landmark location with age. Permutation testing was performed to assess significant changes with age. For the males, all anatomic regions reveal significant changes in shape with age except for the posterior cranial fossa. For the females, only the middle cranial fossa and anterior cranial fossa were found to change significantly in shape. Results of this study are important for understanding the adult skull and how shape changes may pertain to brain atrophy, aging, and injury.

Development and validation of an atlas-based finite element brain model

Traumatic brain injury is a leading cause of disability and injury-related death. To enhance our ability to prevent such injuries, brain response can be studied using validated finite element (FE) models. In the current study, a high-resolution, anatomically accurate FE model was developed from the International Consortium for Brain Mapping brain atlas. Due to wide variation in published brain material parameters, optimal brain properties were identified using a technique called Latin hypercube sampling, which optimized material properties against three experimental cadaver tests to achieve ideal biomechanics. Additionally, falx pretension and thickness were varied in a lateral impact variation. The atlas-based brain model (ABM) was subjected to the boundary conditions from three high-rate experimental cadaver tests with different material parameter combinations. Local displacements, determined experimentally using neutral density targets, were compared to displacements predicted by the ABM at the same locations. Error between the observed and predicted displacements was quantified using CORrelation and Analysis (CORA), an objective signal rating method that evaluates the correlation of two curves. An average CORA score was computed for each variation and maximized to identify the optimal combination of parameters. The strongest relationships between CORA and material parameters were observed for the shear parameters. Using properties obtained through the described multiobjective optimization, the ABM was validated in three impact configurations and shows good agreement with experimental data. The final model developed in this study consists of optimized brain material properties and was validated in three cadaver impacts against local brain displacement data.

Subconcussive impacts and imaging findings over a season of contact sports

The effect of repeated subconcussive head impacts in youth and high school sports on the developing brain is poorly understood. Emerging neuroimaging data correlated with biomechanical exposure metrics are beginning to demonstrate relationships across a variety of modalities. The long-term consequences of these changes are unknown. A review of the currently available literature on the effect of subconcussive head impacts on youth and high school-age male football players provides compelling evidence for more focused studies of these effects in these vulnerable populations.

Validation performance comparison for finite element models of the human brain

Abstract The objective of this study was to compare the performance of six validated brain finite element (FE) models to localized brain motion validation data in five experimental configurations. Model performance was measured using the objective metric CORA (CORrelation and Analysis), where higher ratings represent better correlation. The KTH model achieved the highest average CORA rating, and the ABM received the highest average rating among models robustly validated against five cadaver impacts in three directions. This technique can be more frequently employed to build better models and, when associated limitations are well understood, to compare inter-model performance under similar conditions.

Detection of American Football Head Impacts Using Biomechanical Features and Support Vector Machine Classification

Accumulation of head impacts may contribute to acute and long-term brain trauma. Wearable sensors can measure impact exposure, yet current sensors do not have validated impact detection methods for accurate exposure monitoring. Here we demonstrate a head impact detection method that can be implemented on a wearable sensor for detecting field football head impacts. Our method incorporates a support vector machine classifier that uses biomechanical features from the time domain and frequency domain, as well as model predictions of head-neck motions. The classifier was trained and validated using instrumented mouthguard data from collegiate football games and practices, with ground truth data labels established from video review. We found that low frequency power spectral density and wavelet transform features (10~30 Hz) were the best performing features. From forward feature selection, fewer than ten features optimized classifier performance, achieving 87.2% sensitivity and 93.2% precision in cross-validation on the collegiate dataset (n = 387), and over 90% sensitivity and precision on an independent youth dataset (n = 32). Accurate head impact detection is essential for studying and monitoring head impact exposure on the field, and the approach in the current paper may help to improve impact detection performance on wearable sensors.

Head Impact Exposure in Youth Football: Comparing Age- and Weight-Based Levels of Play

Approximately 5,000,000 athletes play organized football in the United States, and youth athletes constitute the largest proportion with ∼3,500,000 participants. Investigations of head impact exposure (HIE) in youth football have been limited in size and duration. The objective of this study was to evaluate HIE of athletes participating in three age- and weight-based levels of play within a single youth football organization over four seasons. Head impact data were collected using the Head Impact Telemetry (HIT) System. Mixed effects linear models were fitted, and Wald tests were used to assess differences in head accelerations and number of impacts among levels and session type (competitions vs. practices). The three levels studied were levels A (n = 39, age = 10.8 ± 0.7 years, weight = 97.5 ± 11.8 lb), B (n = 48, age = 11.9 ± 0.5 years, weight = 106.1 ± 13.8 lb), and C (n = 32, age = 13.0 ± 0.5 years, weight = 126.5 ± 18.6 lb). A total of 40,538 head impacts were measured. The median/95th percentile linear head acceleration for levels A, B, and C was 19.8/49.4g, 20.6/51.0g, and 22.0/57.9g, respectively. Level C had significantly greater mean linear acceleration than both levels A (p = 0.005) and B (p = 0.02). There were a significantly greater number of impacts per player in a competition than in a practice session for all levels (A, p = 0.0005, B, p = 0.0019, and C, p < 0.0001). Athletes at lower levels experienced a greater percentage of their high magnitude impacts (≥ 80g) in practice, whereas those at the highest level experienced a greater percentage of their high magnitude impacts in competition. These data improve our understanding of HIE within youth football and are an important step in making evidence-based decisions to reduce HIE.