Comparison of head impact measurements via an instrumented mouthguard and an anthropometric testing device

Active elite rugby participation is associated with altered precentral cortical thickness

There is growing concern that elite rugby participation may negatively influence brain health, but the underlying mechanisms are unclear. Cortical thickness is a widely applied biomarker of grey matter structure, but there is limited research into how it may be altered in active professional rugby players. Cross-sectional MRI data from 44 active elite rugby players, including 21 assessed within 1 week of head injury, and 47 healthy controls were analysed. We investigated how active elite rugby participation with and without sub-acute traumatic brain injury influenced grey matter structure using whole cortex and region of interest cortical thickness analyses. Relationships between cortical thickness and biomarkers of traumatic brain injury, including fractional anisotropy, plasma neurofilament light and glial fibrillary acidic protein, were also examined. In whole-cortex analyses, precentral cortical thickness in the right hemisphere was lower in rugby players compared with controls, which was due to reductions in non-injured players. Post hoc region of interest analyses showed non-injured rugby players had reduced cortical thickness in the inferior precentral sulcal thickness bilaterally (P = 0.005) and the left central sulcus (P = 0.037) relative to controls. In contrast, players in the sub-acute phase of mild traumatic brain injury had higher inferior precentral sulcal cortical thickness in the right hemisphere (P = 0.015). Plasma glial fibrillary acidic protein, a marker of astrocyte activation, was positively associated with right inferior precentral sulcal cortical thickness in injured rugby players (P = 0.0012). Elite rugby participation is associated with localized alterations in cortical thickness, specifically in sulcal motor regions. Sub-acute changes after mild traumatic brain injury are associated with evidence of astrocytic activation. The combination of cortical thickness and glial fibrillary acidic protein may be useful in understanding the pathophysiological relationship between sporting head injury and brain health.

An Instrumented Mouthguard for Real-Time Measurement of Head Kinematics under a Large Range of Sport Specific Accelerations

BACKGROUND

Head impacts in sports can produce brain injuries. The accurate quantification of head kinematics through instrumented mouthguards (iMG) can help identify underlying brain motion during injurious impacts. The aim of the current study is to assess the validity of an iMG across a large range of linear and rotational accelerations to allow for on-field head impact monitoring.

METHODS

Drop tests of an instrumented helmeted anthropometric testing device (ATD) were performed across a range of impact magnitudes and locations, with iMG measures collected concurrently. ATD and iMG kinematics were also fed forward to high-fidelity brain models to predict maximal principal strain.

RESULTS

The impacts produced a wide range of head kinematics (16-171 g, 1330-10,164 rad/s2 and 11.3-41.5 rad/s) and durations (6-18 ms), representing impacts in rugby and boxing. Comparison of the peak values across ATD and iMG indicated high levels of agreement, with a total concordance correlation coefficient of 0.97 for peak impact kinematics and 0.97 for predicted brain strain. We also found good agreement between iMG and ATD measured time-series kinematic data, with the highest normalized root mean squared error for rotational velocity (5.47 ± 2.61%) and the lowest for rotational acceleration (1.24 ± 0.86%). Our results confirm that the iMG can reliably measure laboratory-based head kinematics under a large range of accelerations and is suitable for future on-field validity assessments.

A sensor-enabled cloud-based computing platform for computational brain biomechanics

BACKGROUND AND OBJECTIVES

Driven by the risk of repetitive head trauma, sensors have been integrated into mouthguards to measure head impacts in contact sports and military activities. These wearable devices, referred to as "instrumented" or "smart" mouthguards are being actively developed by various research groups and organizations. These instrumented mouthguards provide an opportunity to further study and understand the brain biomechanics due to impact. In this study, we present a brain modeling service that can use information from these sensors to predict brain injury metrics in an automated fashion.

METHODS

We have built a brain modeling platform using several of Amazon's Web Services (AWS) to enable cloud computing and scalability. We use a custom-built cloud-based finite element modeling code to compute the physics-based nonlinear response of the intracranial brain tissue and provide a frontend web application and an application programming interface for groups working on head impact sensor technology to include simulated injury predictions into their research pipeline.

RESULTS

The platform results have been validated against experimental data available in literature for brain-skull relative displacements, brain strains and intracranial pressure. The parallel processing capability of the platform has also been tested and verified. We also studied the accuracy of the custom head surfaces generated by Avatar 3D.

CONCLUSION

We present a validated cloud-based computational brain modeling platform that uses sensor data as input for numerical brain models and outputs a quantitative description of brain tissue strains and injury metrics. The platform is expected to generate transparent, reproducible, and traceable brain computing results.

Sex differences in neck strength and head impact kinematics in university rugby union players

Globally, over three million women participate in rugby union, yet injury prevention and training strategies are predominantly based on androcentric data. These strategies may have limited generalisability to females, given the cervical spine is more susceptible to whiplash and less adept at resisting inertial loading. A total of 53 university rugby union players (25 female, 28 male, 20.7 ± 1.8 years) had their isometric neck strength measured. Bespoke instrumented mouthguards were used to record the magnitude of head impact events in six female and seven male competitive matches. Mean female maximal isometric neck strength was 47% lower than male. Independent samples Mann-Whitney U tests showed no significant differences for peak linear head acceleration (female: median 11.7 g, IQR 7.9 g; male: median 12.5 g, IQR 7.0 g p=.23) or peak rotational head acceleration (female: median 800.2 rad·s^-2, IQR 677.7 rad·s^-2; male: median 849.4 rad·s^-2, IQR 479.8 rad·s^-2; p=.76), despite the mean male body mass being 24% greater than female. Coded video analysis revealed substantial differences in head-impact mechanisms; uncontrolled whiplash dominated >50% of all recorded female impact events and <0.5% in males. Direct head-to-ground impacts comprised 26.1% of female and 9.7% of male impacts, with whiplash occurring in 78.0% and 0.5%, respectively. Overall, the data provided in this study do not support the generalisation of male-derived training and injury-prevention data to female rugby athletes. These results suggest a considerable research effort is required to identify specific weakness of female rugby players and derive appropriate training, injury prevention and return to play protocols.

Laboratory Validation of Instrumented Mouthguard for Use in Sport

Concussion is an inherent risk of participating in contact, combat, or collision sports, within which head impacts are numerous. Kinematic parameters such as peak linear and rotational acceleration represent primary measures of concussive head impacts. The ability to accurately measure and categorise such impact parameters in real time is important in health and sports performance contexts. The purpose of this study was to assess the accuracy of the latest HitIQ Nexus A9 instrumented mouthguard (HitIQ Pty. Ltd. Melbourne Australia) against reference sensors in an aluminium headform. The headform underwent drop testing at various impact intensities across the NOCSAE-defined impact locations, comparing the peak linear and rotational acceleration (PLA and PRA) as well as the shapes of the acceleration time-series traces for each impact. Mouthguard PLA and PRA measurements strongly correlated with (R² = 0.996 and 0.994 respectively), and strongly agreed with (LCCC = 0.997) the reference sensors. The root mean square error between the measurement devices was 1 ± 0.6g for linear acceleration and 47.4 ± 35 rad/s² for rotational acceleration. A Bland-Altman analysis found a systematic bias of 1% for PRA, with no significant bias for PLA. The instrumented mouthguard displayed high accuracy when measuring head impact kinematics in a laboratory setting.

Mouthguards in dentistry: Current recommendations for dentists

Properly fitted mouthguards reduce the risk and severity of orofacial injury, to both hard and soft tissues, preventing thousands of dollars of trauma management. In this review, findings from recent research will be evaluated to discuss the strengths and limitations of the different types of mouthguards, including their indications by sport. Design, ideal dimensions, and other characteristics will also be explored. Additionally, patient education and motivation will be examined, with a focus on the dentist's role in this regard. Finally, in addition to proper oral hygiene, the importance of proper mouthguard maintenance and evaluation will be discussed. This review will therefore be able to act as a guide for dentists looking to provide patients of all ages with personal protective equipment and stay up-to-date on recent developments in this branch of the sports dentistry field.

Using Wearable Sensors and a Convolutional Neural Network for Catch Detection in American Football

Highly efficient training is a must in professional sports. Presently, this means doing exercises in high number and quality with some sort of data logging. In American football many things are logged, but there is no wearable sensor that logs a catch or a drop. Therefore, the goal of this paper was to develop and verify a sensor that is able to do exactly that. In a first step a sensor platform was used to gather nine degrees of freedom motion and audio data of both hands in 759 attempts to catch a pass. After preprocessing, the gathered data was used to train a neural network to classify all attempts, resulting in a classification accuracy of 93%. Additionally, the significance of each sensor signal was analysed. It turned out that the network relies most on acceleration and magnetometer data, neglecting most of the audio and gyroscope data. Besides the results, the paper introduces a new type of dataset and the possibility of autonomous training in American football to the research community.