Six Degree-of-Freedom Measurements of Human Mild Traumatic Brain Injury

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.

Influence of centroid acceleration acquisition and filtering class on head injury criterion evaluation

BACKGROUND

Although the Head Injury Criteria (HIC) has been widely applied to assess head impact injuries, it faces two outstanding problems: 1) HIC is affected strongly by the cut-off frequency when processing acceleration signals. And these cut-off frequencies are experiential and lack unified guidelines; 2) If the head was impacted on a different part, should the corresponding HIC threshold be the same? If these problems are not resolved, it could potentially lead to a critical misinterpretation of the safety assessment.

METHODS

Finite element method was used to reconstruct head impacts. The head model includes tissues like skull, brainstem, cerebrospinal fluid, etc. The head model was impacted in the frontal, occipital, parietal or lateral direction with different impact velocities. Acceleration signals of the head model were extracted directly from the skull and the head centroid node. To obtain a robust HIC, the filtering class of acceleration signals were analyzed carefully. Then, the relation between rigid body HIC and the centroid node HIC were studied systematically.

RESULTS

When the filtering class of rigid body acceleration and centroid node acceleration reached the cut-off frequency, the corresponding derivative of HIC tended to change smoothly. Using these cut-off frequencies, robust HICs were obtained. The rigid body HIC far exceeded that of centroid node HIC, such as 8, 9, 14 and 31 times exceeded in the frontal, occipital, parietal and lateral impact conditions, respectively. Moreover, approximate linear relations were found between the rigid body HIC and the centroid node HIC in different impact directions, respectively. From these relations, the injury thresholds of rigid body HIC of various directions were given quantitatively.

CONCLUSIONS

The rational filtering class like CFC 800 and CFC 700 were given for rigid body HIC and centroid node HIC, respectively. The rigid body HIC had a significant discrepancy from the centroid node HIC. Linear relations between the rigid body HIC and centroid node HIC were found, and their slopes changed with impact directions. From these relations, we can adjust the injury thresholds reasonably if the head receives different impacts. These findings can effectively enhance the applicability of HIC.

American Football On-Field Head Impact Kinematics: Influence of Acceleration Signal Characteristics on Peak Maximal Principal Strain

Recorded head kinematics from head-impact measurement devices (HIMd) are pivotal for evaluating brain stress and strain through head finite element models (hFEM). The variability in kinematic recording windows across HIMd presents challenges as they yield inconsistent hFEM responses. Despite establishing an ideal recording window for maximum principal strain (MPS) in brain tissue, uncertainties persist about the impact characteristics influencing vulnerability when this window is shortened. This study aimed to scrutinize factors within impact kinematics affecting the reliability of different recording windows on whole-brain peak MPS using a validated hFEM. Utilizing 53 on-field head impacts recorded via an instrumented mouthguard during a Canadian varsity football game, 10 recording windows were investigated with varying pre- and post-impact-trigger durations. Tukey pair-wise comparisons revealed no statistically significant differences in MPS responses for the different recording windows. However, specific impacts showed marked variability up to 40%. It was found, through correlation analyses, that impacts with lower peak linear acceleration exhibited greater response variability across different pre-trigger durations. Signal shape, analyzed through spectral analysis, influenced the time required for MPS development, resulting in specific impacts requiring a prolonged post-trigger duration. This study adds to the existing consensus on standardizing HIMd acquisition time windows and sheds light on impact characteristics leading to peak MPS variation across different head impact kinematic recording windows. Considering impact characteristics in research assessments is crucial, as certain impacts, affected by recording duration, may lead to significant errors in peak MPS responses during cumulative longitudinal exposure assessments.

The Impact of Drop Test Conditions on Brain Strain Location and Severity: A Novel Approach Using a Deep Learning Model

In contact sports such as rugby, players are at risk of sustaining traumatic brain injuries (TBI) due to high-intensity head impacts that generate high linear and rotational accelerations of the head. Previous studies have established a clear link between high-intensity head impacts and brain strains that result in concussions. This study presents a novel approach to investigating the effect of a range of laboratory controlled drop test parameters on regional peak and mean maximum principal strain (MPS) predictions within the brain using a trained convolutional neural network (CNN). The CNN is publicly available at https://github.com/Jilab-biomechanics/CNN-brain-strains . The results of this study corroborate previous findings that impacts to the side of the head result in significantly higher regional MPS than forehead impacts. Forehead impacts tend to result in the lowest region-averaged MPS values for impacts where the surface angle was at 0° and 45°, while side impacts tend to result in higher regional peak and mean MPS. The absence of a neck in drop tests resulted in lower regional peak and mean MPS values. The results indicated that the relationship between drop test parameters and resulting regional peak and mean MPS predictions is complex. The study's findings offer valuable insights into how deep learning models can be used to provide more detailed insights into how drop test conditions impact regional MPS. The novel approach used in this paper to predict brain strains can be applied in the development of better methods to reduce the brain strain resulting from head accelerations such as protective sports headgear.

Development of a head-weighted injury criterion for evaluation of multiple types of AIS 4+ injuries for vulnerable road users

Vulnerable Road users (VRUs) often suffer multiple fatal head injury types simultaneously in road accidents. In this study, a head-weighted injury criterion (HWIC4) was proposed for assessing the risk of head AIS 4+ injuries considering multiple injury types. Firstly, the kinematic characteristics of VRUs in the 50 in-depth accidents were reconstructed by using multi-body system models, and head injuries were reconstructed using eight head kinematic-based injury criteria and eight brain tissue injury criteria via the THUMS (Ver. 4.0.2) head finite element model. The predictive capability of each injury criterion to predict head AIS 4+ injuries was assessed and four better predictors (HIC15, angular acceleration, coup pressure, and maximum principal strain) were selected. The different head injury types and the weighting parameters for each injury type were taken into account in the development of HWIC4. Finally, the effectiveness and evaluation of HWIC4 for head AIS 4+ injury was validated based on the area under of receiver operating characteristic (AUROC) curve and reconstruction results from 10 additional selected accident cases. The results showed that HWIC4 has a good predictive capability for head AIS 4+ injuries with an AUROC of 0.983, which means that HWIC4 is superior and more reliable than a single head injury criterion. This knowledge further improves the capability of head injury criteria to predict head AIS 4+ injuries.

Shear shock wave injury in vivo: High frame-rate ultrasound observation and histological assessment

Using high frame-rate ultrasound and ¡1μm sensitive motion tracking we previously showed that shear waves at the surface of ex vivo and in situ brains develop into shear shock waves deep inside the brain, with destructive local accelerations. However post-mortem tissue cannot develop injuries and has different viscoelastodynamic behavior from in vivo tissue. Here we present the ultrasonic measurement of the high-rate shear shock biomechanics in the in vivo porcine brain, and histological assessment of the resulting axonal pathology. A new biomechanical model of brain injury was developed consisting of a perforated mylar surface attached to the brain and vibrated using an electromechanical shaker. Using a custom sequence with 8 interleaved wide beam emissions, brain imaging and motion tracking were performed at 2900 images/s. Shear shock waves were observed for the first time in vivo wherein the shock acceleration was measured to be 2.6 times larger than the surface acceleration ( 95g vs. 36g). Histopathology showed axonal damage in the impacted side of the brain from the brain surface, accompanied by a local shock-front acceleration of >70g. This shows that axonal injury occurs deep in the brain even though the shear excitation was at the brain surface, and the acceleration measurements support the hypothesis that shear shock waves are responsible for deep traumatic brain injuries.

Assessment of brain response in operators subject to recoil force from firing long-range rifles

Mild traumatic brain injury (mTBI) may be caused by occupational hazards military personnel encounter, such as falls, shocks, exposure to blast overpressure events, and recoil from weapon firing. While it is important to protect against injurious head impacts, the repeated exposure of Canadian Armed Forces (CAF) service members to sub-concussive events during the course of their service may lead to a significant reduction in quality of life. Symptoms may include headaches, difficulty concentrating, and noise sensitivity, impacting how personnel complete their duties and causing chronic health issues. This study investigates how the exposure to the recoil force of long-range rifles results in head motion and brain deformation. Direct measurements of head kinematics of a controlled population of military personnel during firing events were obtained using instrumented mouthguards. The experimentally measured head kinematics were then used as inputs to a finite element (FE) head model to quantify the brain strains observed during each firing event. The efficacy of a concept recoil mitigation system (RMS), designed to mitigate loads applied to the operators was quantified, and the RMS resulted in lower loading to the operators. The outcomes of this study provide valuable insights into the magnitudes of head kinematics observed when firing long-range rifles, and a methodology to quantify effects, which in turn will help craft exposure guidelines, guide training to mitigate the risk of injury, and improve the quality of lives of current and future CAF service members and veterans.

Capturing Head Impacts in Boxing: A Video-Based Comparison of Three Wearable Sensors

Wearable sensors are used to quantify head impacts in athletes, but recent work has shown that the number of events recorded may not be accurate. This study aimed to compare the number of head acceleration events recorded by three wearable sensors during boxing and assess how impact type and location affect the triggering of acceleration events. Seven boxers were equipped with an instrumented mouthguard, a skin patch, and a headgear patch. Contacts to participants' heads were identified via three video cameras over 115 sparring rounds. The resulting 5168 video-identified events were used as reference to quantify the sensitivity, specificity, and positive predictive value (PPV) of the sensors. The mouthguard, skin patch, and headgear patch recorded 695, 1579, and 1690 events, respectively, yielding sensitivities of 35%, 86%, and 78%, respectively, and specificities of 90%, 76%, and 75%, respectively. The mouthguard, skin patch, and headgear patch yielded 693, 1571, and 1681 true-positive events, respectively, leading to PPVs for head impacts over 96%. All three sensors were more likely to be triggered by punches landing near the sensor and cleanly on the head, although the mouthguard's sensitivity to impact location varied less than the patches. While the use of head impact sensors for assessing injury risks remains uncertain, this study provides valuable insights into the capabilities and limitations of these sensors in capturing video-verified head impact events.

Neuroimaging, wearable sensors, and blood-based biomarkers reveal hyperacute changes in the brain after sub-concussive impacts

Impacts in mixed martial arts (MMA) have been studied mainly in regard to the long-term effects of concussions. However, repetitive sub-concussive head impacts at the hyperacute phase (minutes after impact), are not understood. The head experiences rapid acceleration similar to a concussion, but without clinical symptoms. We utilize portable neuroimaging technology - transcranial Doppler (TCD) ultrasound and functional near infrared spectroscopy (fNIRS) - to estimate the extent of pre- and post-differences following contact and non-contact sparring sessions in nine MMA athletes. In addition, the extent of changes in neurofilament light (NfL) protein biomarker concentrations, and neurocognitive/balance parameters were determined following impacts. Athletes were instrumented with sensor-based mouth guards to record head kinematics. TCD and fNIRS results demonstrated significantly increased blood flow velocity (p = 0.01) as well as prefrontal (p = 0.01) and motor cortex (p = 0.04) oxygenation, only following the contact sparring sessions. This increase after contact was correlated with the cumulative angular acceleration experienced during impacts (p = 0.01). In addition, the NfL biomarker demonstrated positive correlations with angular acceleration (p = 0.03), and maximum principal and fiber strain (p = 0.01). On average athletes experienced 23.9 ± 2.9 g peak linear acceleration, 10.29 ± 1.1 rad/s peak angular velocity, and 1,502.3 ± 532.3 rad/s2 angular acceleration. Balance parameters were significantly increased following contact sparring for medial-lateral (ML) center of mass (COM) sway, and ML ankle angle (p = 0.01), illustrating worsened balance. These combined results reveal significant changes in brain hemodynamics and neurophysiological parameters that occur immediately after sub-concussive impacts and suggest that the physical impact to the head plays an important role in these changes.

A Morphologically Individualized Deep Learning Brain Injury Model

The brain injury modeling community has recommended improving model subject specificity and simulation efficiency. Here, we extend an instantaneous (< 1 sec) convolutional neural network (CNN) brain model based on the anisotropic Worcester Head Injury Model (WHIM) V1.0 to account for strain differences due to individual morphological variations. Linear scaling factors relative to the generic WHIM along the three anatomical axes are used as additional CNN inputs. To generate training samples, the WHIM is randomly scaled to pair with augmented head impacts randomly generated from real-world data for simulation. An estimation of voxelized peak maximum principal strain of the whole-brain is said to be successful when the linear regression slope and Pearson's correlation coefficient relative to directly simulated do not deviate from 1.0 (when identical) by more than 0.1. Despite a modest training dataset (N = 1363 vs. ∼5.7 k previously), the individualized CNN achieves a success rate of 86.2% in cross-validation for scaled model responses, and 92.1% for independent generic model testing for impacts considered as complete capture of kinematic events. Using 11 scaled subject-specific models (with scaling factors determined from pre-established regression models based on head dimensions and sex and age information, and notably, without neuroimages), the morphologically individualized CNN remains accurate for impacts that also yield successful estimations for the generic WHIM. The individualized CNN instantly estimates subject-specific and spatially detailed peak strains of the entire brain and thus, supersedes others that report a scalar peak strain value incapable of informing the location of occurrence. This tool could be especially useful for youths and females due to their anticipated greater morphological differences relative to the generic model, even without the need for individual neuroimages. It has potential for a wide range of applications for injury mitigation purposes and the design of head protective gears. The voxelized strains also allow for convenient data sharing and promote collaboration among research groups.

Hyper-acute effects of sub-concussive soccer headers on brain function and hemodynamics

Introduction

Sub-concussive head impacts in soccer are drawing increasing research attention regarding their acute and long-term effects as players may experience thousands of headers in a single season. During these impacts, the head experiences rapid acceleration similar to what occurs during a concussion, but without the clinical implications. The physical mechanism and response to repetitive impacts are not completely understood. The objective of this work was to examine the immediate functional outcomes of sub-concussive level impacts from soccer heading in a natural, non-laboratory environment.

Methods

Twenty university level soccer athletes were instrumented with sensor-mounted bite bars to record impacts from 10 consecutive soccer headers. Pre- and post-header measurements were collected to determine hyper-acute changes, i.e., within minutes after exposure. This included measuring blood flow velocity using transcranial Doppler (TCD) ultrasound, oxyhemoglobin concentration using functional near infrared spectroscopy imaging (fNIRS), and upper extremity dual-task (UEF) neurocognitive testing.

Results

On average, the athletes experienced 30.7 ± 8.9 g peak linear acceleration and 7.2 ± 3.1 rad/s peak angular velocity, respectively. Results from fNIRS measurements showed an increase in the brain oxygenation for the left prefrontal cortex (PC) (p = 0.002), and the left motor cortex (MC) (p = 0.007) following the soccer headers. Additional analysis of the fNIRS time series demonstrates increased sample entropy of the signal after the headers in the right PC (p = 0.02), right MC (p = 0.004), and left MC (p = 0.04).

Discussion

These combined results reveal some variations in brain oxygenation immediately detected after repetitive headers. Significant changes in balance and neurocognitive function were not observed in this study, indicating a mild level of head impacts. This is the first study to observe hemodynamic changes immediately after sub-concussive impacts using non-invasive portable imaging technology. In combination with head kinematic measurements, this information can give new insights and a framework for immediate monitoring of sub-concussive impacts on the head.

Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics

BACKGROUND

Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo, and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification.

METHODS

Data were analyzed from 3262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, mixed martial arts). To test the classifier robustness, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards. Finally, with the classifier, type-specific, nearest-neighbor regression models were built for brain strain.

RESULTS

The classifier reached a median accuracy of 96% over 1000 random partitions of training and test sets. The most important features in the classification included both low- and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low- and high-frequency ranges (e.g., the spectral densities of mixed martial arts impacts were higher in the high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R2 value than baseline models without classification.

CONCLUSION

The machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.

Efficient Generation of Pretraining Samples for Developing a Deep Learning Brain Injury Model via Transfer Learning

The large amount of training samples required to develop a deep learning brain injury model demands enormous computational resources. Here, we study how a transformer neural network (TNN) of high accuracy can be used to efficiently generate pretraining samples for a convolutional neural network (CNN) brain injury model to reduce computational cost. The samples use synthetic impacts emulating real-world events or augmented impacts generated from limited measured impacts. First, we verify that the TNN remains highly accurate for the two impact types (N = 100 each; [Formula: see text] of 0.948-0.967 with root mean squared error, RMSE, ~ 0.01, for voxelized peak strains). The TNN-estimated samples (1000-5000 for each data type) are then used to pretrain a CNN, which is further finetuned using directly simulated training samples (250-5000). An independent measured impact dataset considered of complete capture of impact event is used to assess estimation accuracy (N = 191). We find that pretraining can significantly improve CNN accuracy via transfer learning compared to a baseline CNN without pretraining. It is most effective when the finetuning dataset is relatively small (e.g., 2000-4000 pretraining synthetic or augmented samples improves success rate from 0.72 to 0.81 with 500 finetuning samples). When finetuning samples reach 3000 or more, no obvious improvement occurs from pretraining. These results support using the TNN to rapidly generate pretraining samples to facilitate a more efficient training strategy for future deep learning brain models, by limiting the number of costly direct simulations from an alternative baseline model. This study could contribute to a wider adoption of deep learning brain injury models for large-scale predictive modeling and ultimately, enhancing safety protocols and protective equipment.

Development, validation and a case study: The female finite element head model (FeFEHM)

BACKGROUND AND OBJECTIVE

Traumatic brain injuries are one of the leading causes of death and disability in the world. To better understand the interactions and forces applied in different constituents of the human head, several finite element head models have been developed throughout the years, for offering a good cost-effective and ethical approach compared to experimental tests. Once validated, the female finite element head model (FeFEHM) will allow a better understanding of injury mechanisms resulting in neuronal damage, which can later evolve into neurodegenerative diseases.

METHODS

This work encompasses the approached methodology starting from medical images and finite element modelling until the validation process using novel experimental data of brain displacements conducted on human cadavers. The material modelling of the brain is performed using an age-specific characterization of the brain using microindentation at dynamic rates and under large deformation, with a similar age to the patient used to model the FeFEHM.

RESULTS

The numerical displacement curves are in good accordance with the experimental data, displaying similar peak times and values, in all three anatomical planes. The case study result shows a similarity between the pressure fields of the FeFEHM compared to another model, highlighting the future potential of the model.

CONCLUSIONS

The initial objective was met, and a new female finite element head model has been developed with biofidelic brain motion. This model will be used for the assessment of repetitive impact scenarios and its repercussions on the female brain.

Padded Helmet Shell Covers in American Football: A Comprehensive Laboratory Evaluation with Preliminary On-Field Findings

Protective headgear effects measured in the laboratory may not always translate to the field. In this study, we evaluated the impact attenuation capabilities of a commercially available padded helmet shell cover in the laboratory and on the field. In the laboratory, we evaluated the padded helmet shell cover's efficacy in attenuating impact magnitude across six impact locations and three impact velocities when equipped to three different helmet models. In a preliminary on-field investigation, we used instrumented mouthguards to monitor head impact magnitude in collegiate linebackers during practice sessions while not wearing the padded helmet shell covers (i.e., bare helmets) for one season and whilst wearing the padded helmet shell covers for another season. The addition of the padded helmet shell cover was effective in attenuating the magnitude of angular head accelerations and two brain injury risk metrics (DAMAGE, HARM) across most laboratory impact conditions, but did not significantly attenuate linear head accelerations for all helmets. Overall, HARM values were reduced in laboratory impact tests by an average of 25% at 3.5 m/s (range: 9.7 to 39.6%), 18% at 5.5 m/s (range: - 5.5 to 40.5%), and 10% at 7.4 m/s (range: - 6.0 to 31.0%). However, on the field, no significant differences in any measure of head impact magnitude were observed between the bare helmet impacts and padded helmet impacts. Further laboratory tests were conducted to evaluate the ability of the padded helmet shell cover to maintain its performance after exposure to repeated, successive impacts and across a range of temperatures. This research provides a detailed assessment of padded helmet shell covers and supports the continuation of in vivo helmet research to validate laboratory testing results.

Diffusion Tensor Imaging Reveals Elevated Diffusivity of White Matter Microstructure that Is Independently Associated with Long-Term Outcome after Mild Traumatic Brain Injury: A TRACK-TBI Study

Diffusion tensor imaging (DTI) literature on single-center studies contains conflicting results regarding acute effects of mild traumatic brain injury (mTBI) on white matter (WM) microstructure and the prognostic significance. This larger-scale multi-center DTI study aimed to determine how acute mTBI affects WM microstructure over time and how early WM changes affect long-term outcome. From Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI), a cohort study at 11 United States level 1 trauma centers, a total of 391 patients with acute mTBI ages 17 to 60 years were included and studied at two weeks and six months post-injury. Demographically matched friends or family of the participants were the control group (n = 148). Axial diffusivity (AD), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were the measures of WM microstructure. The primary outcome was the Glasgow Outcome Scale Extended (GOSE) score of injury-related functional limitations across broad life domains at six months post-injury. The AD, MD, and RD were higher and FA was lower in mTBI versus friend control (FC) at both two weeks and six months post-injury throughout most major WM tracts of the cerebral hemispheres. In the mTBI group, AD and, to a lesser extent, MD decreased in WM from two weeks to six months post-injury. At two weeks post-injury, global WM AD and MD were both independently associated with six-month incomplete recovery (GOSE <8 vs = 8) even after accounting for demographic, clinical, and other imaging factors. DTI provides reliable imaging biomarkers of dynamic WM microstructural changes after mTBI that have utility for patient selection and treatment response in clinical trials. Continued technological advances in the sensitivity, specificity, and precision of diffusion magnetic resonance imaging hold promise for routine clinical application in mTBI.

Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis

OBJECTIVE

Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM).

METHODS

We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021).

RESULTS

PC1 explained variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy.

CONCLUSION

The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance.

SIGNIFICANCE

The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.

Consensus Head Acceleration Measurement Practices (CHAMP): Laboratory Validation of Wearable Head Kinematic Devices

Wearable devices are increasingly used to measure real-world head impacts and study brain injury mechanisms. These devices must undergo validation testing to ensure they provide reliable and accurate information for head impact sensing, and controlled laboratory testing should be the first step of validation. Past validation studies have applied varying methodologies, and some devices have been deployed for on-field use without validation. This paper presents best practices recommendations for validating wearable head kinematic devices in the laboratory, with the goal of standardizing validation test methods and data reporting. Key considerations, recommended approaches, and specific considerations were developed for four main aspects of laboratory validation, including surrogate selection, test conditions, data collection, and data analysis. Recommendations were generated by a group with expertise in head kinematic sensing and laboratory validation methods and reviewed by a larger group to achieve consensus on best practices. We recommend that these best practices are followed by manufacturers, users, and reviewers to conduct and/or review laboratory validation of wearable devices, which is a minimum initial step prior to on-field validation and deployment. We anticipate that the best practices recommendations will lead to more rigorous validation of wearable head kinematic devices and higher accuracy in head impact data, which can subsequently advance brain injury research and management.

Drop Test Kinematics Using Varied Impact Surfaces and Head/Neck Configurations for Rugby Headgear Testing

World Rugby employs a specific drop test method to evaluate headgear performance, but almost all researchers use a different variation of this method. The aim of this study was, therefore, to quantify the differences between variations of the drop testing method using a Hybrid III headform and neck in the following impact setups: (1) headform only, with a flat steel impact surface, approximating the World Rugby method, (2 and 3) headform with and without a neck, respectively, onto a flat MEP pad impact surface, and (4) headform and neck, dropped onto an angled MEP pad impact surface. Each variation was subject to drop heights of 75-600 mm across three orientations (forehead, side, and rear boss). Comparisons were limited to the linear and rotational acceleration and rotational velocity for simplicity. Substantial differences in kinematic profile shape manifested between all drop test variations. Peak accelerations varied highly between variations, but the peak rotational velocities did not. Drop test variation also significantly changed the ratios of the peak kinematics to each other. This information can be compared to kinematic data from field head impacts and could inform more realistic impact testing methods for assessing headgear.

Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts

In a previous study, we found that the relationship between brain strain and kinematic features cannot be described by a generalized linear model across different types of head impacts. In this study, we investigate if such a linear relationship exists when partitioning head impacts using a data-driven approach. We applied the K-means clustering method to partition 3161 impacts from various sources including simulation, college football, mixed martial arts, and car crashes. We found piecewise multivariate linearity between the cumulative strain damage (CSDM; assessed at the threshold of 0.15) and head kinematic features. Compared with the linear regression models without partition and the partition according to the types of head impacts, K-means-based data-driven partition showed significantly higher CSDM regression accuracy, which suggested the presence of piecewise multivariate linearity across types of head impacts. Additionally, we compared the piecewise linearity with the partitions based on individual features used in clustering. We found that the partition with maximum angular acceleration magnitude at 4706 rad/s² led to the highest piecewise linearity. This study may contribute to an improved method for the rapid prediction of CSDM in the future.

Should anthropometric differences between the commonly used pedestrian computational biomechanics models and Chinese population be taken into account when predicting pedestrian head kinematics and injury in vehicle collisions in China?

Computational biomechanics models play a key role in predicting/evaluating pedestrian head kinematics and injury risk in car-to-pedestrian collisions. The human multibody models most commonly used in car-to-pedestrian collision reconstruction, such as pedestrian model by The Netherlands Organisation for Applied Scientific Research TNO, are built using the anthropometry of Western European population as defined in TNO (2013) human multibody model database. In this study, we investigate the effects of the anthropometric differences between the Western European and Chinese populations on the pedestrian head kinematics and injury responses predicted using multibody models. The comparison was conducted through car-to-pedestrian collision simulations using pedestrian multibody models representing anthropometric characteristics of Western European and Chinese populations, three typical vehicle shapes (sedan, SUV and minivan), five initial vehicle impact speeds (30, 35, 40, 45, 50 km/h), and six pedestrian walking postures. The results indicate that the change of pedestrian model anthropometry (from Western European to Chinese) exerts appreciable effects on both the predicted initial boundary conditions of the head-to-windscreen impact (in particular the head-to-windscreen impact angle) and the head injury indices in the impact with the road surface (secondary impact).

American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism

Brain strain is increasingly being used in helmet design and safety performance evaluation as it is generally considered as the primary mechanism of concussion. In this study, we investigate whether different helmet designs can meaningfully alter brain strains using two commonly used metrics, peak maximum principal strain (MPS) of the whole brain and cumulative strain damage measure (CSDM). A convolutional neural network (CNN) that instantly produces detailed brain strains is first tested for accuracy for helmeted head impacts. Based on N = 144 impacts in 12 impact conditions from three random and representative helmet models, we conclude that the CNN is sufficiently accurate for helmet testing applications, for elementwise MPS (success rate of 98.6%), whole-brain peak MPS and CSDM (coefficient of determination of 0.977 and 0.980, with root mean squared error of 0.015 and 0.029, respectively). We then apply the technique to 23 football helmet models (N = 1104 impacts) to reproduce elementwise MPS. Assuming a concussion would occur when peak MPS or CSDM exceeds a threshold, we sweep their thresholds across the value ranges to evaluate the number of predicted hypothetical concussions that different helmets sustain across the impact conditions. Relative to the 12 impact conditions tested, we find that the "best" and "worst" helmets differ by an average of 22.5% in terms of predicted concussions, ranging from 0 to 42% (the latter achieved at the threshold value of 0.28 for peak MPS and 0.4 for CSDM, respectively). Such a large variation among helmets in strain-based concussion predictions demonstrate that helmet designs can still be optimized in a clinically meaningful way. The robustness and accuracy of the CNN tool also suggest its potential for routine use for helmet design and safety performance evaluation in the future. The CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains .

Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact

Real-time dynamic simulation remains a significant challenge for spatiotemporal data of high dimension and resolution. In this study, we establish a transformer neural network (TNN) originally developed for natural language processing and a separate convolutional neural network (CNN) to estimate five-dimensional (5D) spatiotemporal brain-skull relative displacement resulting from impact (isotropic spatial resolution of 4 mm with temporal resolution of 1 ms). Sequential training is applied to train (N = 5184 samples) the two neural networks for estimating the complete 5D displacement across a temporal duration of 60 ms. We find that TNN slightly but consistently outperforms CNN in accuracy for both displacement and the resulting voxel-wise four-dimensional (4D) maximum principal strain (e.g., root mean squared error (RMSE) of ~1.0% vs. ~1.6%, with coefficient of determination, R ² >0.99 vs. >0.98, respectively, and normalized RMSE (NRMSE) at peak displacement of 2%-3%, based on an independent testing dataset; N = 314). Their accuracies are similar for a range of real-world impacts drawn from various published sources (dummy, helmet, football, soccer, and car crash; average RMSE/NRMSE of ~0.3 mm/~4%-5% and average R ² of ~0.98 at peak displacement). Sequential training is effective for allowing instantaneous estimation of 5D displacement with high accuracy, although TNN poses a heavier computational burden in training. This work enables efficient characterization of the intrinsically dynamic brain strain in impact critical for downstream multiscale axonal injury model simulation. This is also the first application of TNN in biomechanics, which offers important insight into how real-time dynamic simulations can be achieved across diverse engineering fields.

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.

Physics-Informed Machine Learning Improves Detection of Head Impacts

In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Monitoring player impacts is vitally important to understanding and protecting from injuries like concussion. Typically, to analyze this data, a combination of video analysis and sensor data is used to ascertain the recorded events are true impacts and not false positives. In fact, due to the nature of using wearable devices in sports, false positives vastly outnumber the true positives. Yet, manual video analysis is time-consuming. This imbalance leads traditional machine learning approaches to exhibit poor performance in both detecting true positives and preventing false negatives. Here, we show that by simulating head impacts numerically using a standard Finite Element head-neck model, a large dataset of synthetic impacts can be created to augment the gathered, verified, impact data from mouthguards. This combined physics-informed machine learning impact detector reported improved performance on test datasets compared to traditional impact detectors with negative predictive value and positive predictive values of 88 and 87% respectively. Consequently, this model reported the best results to date for an impact detection algorithm for American football, achieving an F₁ score of 0.95. In addition, this physics-informed machine learning impact detector was able to accurately detect true and false impacts from a test dataset at a rate of 90% and 100% relative to a purely manual video analysis workflow. Saving over 12 h of manual video analysis for a modest dataset, at an overall accuracy of 92%, these results indicate that this model could be used in place of, or alongside, traditional video analysis to allow for larger scale and more efficient impact detection in sports such as American Football.

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.

Evaluation of the head protection effectiveness of cyclist helmets using full-scale computational biomechanics modelling of cycling accidents

INTRODUCTION

Cycling is a popular choice for urban transportation. Helmets are important and the most popular means of head protection for cyclists. However, a debate about the effectiveness of helmets in protecting a cyclist's head from injury continues.

METHOD

We employed computational biomechanics methods to analyze the head protection effectiveness of nine off-the-shelf-helmets for two typical impact scenarios that occur in cycling accidents: cyclist's head impacting a kerb (kerb-impact) and cyclist skidding (skidding impact) on the road surface. We conducted drop tests for all nine analyzed helmets, and used the test data for validation of the corresponding helmet finite element (FE) models created in this study. The validated helmet models were then used in the full-scale computer simulations (FE analysis for the skull, brain and helmet, and multibody dynamics for the remaining segments of the cyclist's body) of the cycling accidents for cyclists wearing a helmet and without a helmet.

RESULTS

The results indicate that helmets can reduce both the peak linear acceleration of the cyclist head center of gravity (COG) and the risk of cyclist skull fracture. However, higher rotational acceleration of the head COG was predicted for cyclists wearing helmets. The results obtained using the injury criteria that rely on the brain deformations (maximum shear strain MPS and cumulative strain damage measure CSDM) suggest that helmets may offer protection in all the analyzed cyclist impact scenarios. However, the predicted level of protection varies for different helmets and impact scenarios with appreciable variations in the predictions obtained using different injury criteria. Reduction in the maximum principal strain (MPS_0.98) for helmeted cyclists was predicted for both impact scenarios. In contrast, wearing the helmet reduced the CSDM only for the skidding impact scenario. For the kerb-impact scenario, no clear influence of the helmet on the predicted CSDM was observed.

Head Impact Exposure and Biomechanics in University Varsity Women's Soccer

Soccer is a unique sport where players purposefully and voluntarily use their unprotected heads to manipulate the direction of the ball. There are limited soccer head impact exposure data to further study brain injury risks. The objective of the current study was to combine validated mouthpiece sensors with comprehensive video analysis methods to characterize head impact exposure and biomechanics in university varsity women’s soccer. Thirteen female soccer athletes were instrumented with mouthpiece sensors to record on-field head impacts during practices, scrimmages, and games. Multi-angle video was obtained and reviewed for all on-field activity to verify mouthpiece impacts and identify contact scenarios. We recorded 1307 video-identified intentional heading impacts and 1011 video-verified sensor impacts. On average, athletes experienced 1.83 impacts per athlete-exposure, with higher exposure in practices than games/scrimmages. Median and 95th percentile peak linear and peak angular accelerations were 10.0, 22.2 g, and 765, 2296 rad/s², respectively. Long kicks, top of the head impacts and jumping headers resulted in the highest impact kinematics. Our results demonstrate the importance of investigating and monitoring head impact exposure during soccer practices, as well as the opportunity to limit high-kinematics impact exposure through heading technique training and reducing certain contact scenarios.

Quantifying head impacts and neurocognitive performance in collegiate boxers

Head impacts and neurocognition were quantified in 27 intercollegiate male boxers engaged in two, two-minute sparring rounds. Head impacts were measured using Instrumented Boxing Headgear (IBH). Pre and post-sparring neurocognitive performance was compared using two computerized neuropsychological test batteries (CNTs): Immediate Post-concussion Assessment and Cognitive Testing (ImPACT™) and Automated Neuropsychological Assessment Metrics - Military Battery (ANAM4® MIL). An average of 27.63 ± 17.87 impacts above the 9.6 g IBH threshold were recorded per boxer, with average peak linear acceleration of 23.48 ± 15.20 g and average peak rotational acceleration of 1761.40 ± 1064.34 rad/s². Small, but measurable declines in delayed memory and improvement in response time from pre- to post-bout were noted. Number of impacts and concussion history predicted degraded memory performance. This is a runique quantificationof head impacts in collegiate boxing, which were similar in frequency and location, but lower in magnitude as compared to amateur boxing. Improved understanding of impact kinematics may enhance safety in boxing and other contact sports. Subtle post-bout decrements in delayed memory performance and mild improvement in response time reinforce prior research and provide evidence of congruence in our two CNT assessments, which may facilitate comparisons of outcomes across settings utilizing these tests.

Characterizing Head Impact Exposure in Men and Women During Boxing and Mixed Martial Arts

Background:

The accumulation of subconcussive impacts has been implicated in permanent neurological impairment. A gap in understanding the relationship between head impacts and neurological function is the lack of precise characterization and quantification of forces that individuals experience during sports training and competition.

Purpose:

To characterize impact exposure during training and competition among male and female athletes participating in boxing and mixed martial arts (MMA) via an instrumented custom-fit Impact Monitoring Mouthguard (IMM).

Study Design:

Cross-sectional study; Level of evidence, 3.

Methods:

Twenty-three athletes (n = 4 women) were provided a custom-fit IMM. The IMM monitored impacts during sparring and competition. All training and competition sessions were videotaped. Video and IMM data were synchronized for post hoc data verification of true positives and substantiation of impact location. IMM data were collected from boxing and MMA athletes at a collaborating site. For each true-positive impact, peak linear acceleration and peak angular acceleration were calculated. Wilcoxon rank sum tests were used to evaluate potential differences in sport, activity type, and sex with respect to each outcome. Differences in impact location were assessed via Kruskal-Wallis tests.

Results:

IMM data were collected from 53 amateur training sessions and 6 competitions (session range, 5-20 minutes). A total of 896 head impacts (men, n = 786; women, n = 110) were identified using IMM data and video verification: 827 in practice and 69 during competition. MMA and boxers experienced a comparable number of impacts per practice session or competition. In general, MMA impacts produced significantly higher peak angular acceleration than did boxing impacts (P < .001) and were more varied in impact location on the head during competitions. In terms of sex, men experienced a greater number of impacts than women per practice session. However, there was no significant difference between men and women in terms of impact magnitude.

Conclusion:

Characteristic profiles of head impact exposure differed between boxing and MMA athletes; however, the impact magnitudes were not significantly different for male and female athletes.

Head Impact Sensor Triggering Bias Introduced by Linear Acceleration Thresholding

Contact sports players frequently sustain head impacts, most of which are mild impacts exhibiting 10-30 g peak head center-of-gravity (CG) linear acceleration. Wearable head impact sensors are commonly used to measure exposure and typically detect impacts using a linear acceleration threshold. However, linear acceleration across the head can substantially vary during 6-degree-of-freedom motion, leading to triggering biases that depend on sensor location and impact condition. We conducted an analytical investigation with impact characteristics extracted from on-field American football and soccer data. We assumed typical mouthguard sensor locations and evaluated whether simulated multi-directional impacts would trigger recording based on per-axis or resultant acceleration thresholding. Across 1387 impact directions, a 10g peak CG linear acceleration impact would trigger at only 24.7% and 31.8% of directions based on a 10 g per-axis and resultant acceleration threshold, respectively. Anterior impact locations had lower trigger rates and even a 30 g impact would not trigger recording in some directions. Such triggering biases also varied by sensor location and linear-rotational head kinematics coupling. Our results show that linear acceleration-based impact triggering could lead to considerable bias in head impact exposure measurements. We propose a set of recommendations to consider for sensor manufacturers and researchers to mitigate this potential exposure measurement bias.

Instantaneous Brain Strain Estimation for Automotive Head Impacts via Deep Learning

Efficient brain strain estimation is critical for routine application of a head injury model. Lately, a convolutional neural network (CNN) has been successfully developed to estimate spatially detailed brain strains instantly and accurately in contact sports. Here, we extend its application to automotive head impacts, where impact profiles are typically more complex with longer durations. Head impact kinematics (N=458) from two public databases were used to generate augmented impacts (N=2694). They were simulated using the anisotropic Worcester Head Injury Model (WHIM) V1.0, which provided baseline elementwise peak maximum principal strain (MPS). For each augmented impact, rotational velocity (v_rot) and the corresponding rotational acceleration (a_rot) profiles were concatenated as static images to serve as CNN input. Three training strategies were evaluated: 1) "baseline", using random initial weights; 2) "transfer learning", using weight transfer from a previous CNN model trained on head impacts drawn from contact sports; and 3) "combined training", combining previous training data from contact sports (N=5661) for training. The combined training achieved the best performances. For peak MPS, the CNN achieved a coefficient of determination (R²) of 0.932 and root mean squared error (RMSE) of 0.031 for the real-world testing dataset. It also achieved a success rate of 60.5% and 94.8% for elementwise MPS, where the linear regression slope, k, and correlation coefficient, r, between estimated and simulated MPS did not deviate from 1.0 (when identical) by more than 0.1 and 0.2, respectively. Cumulative strain damage measure (CSDM) from the CNN estimation was also highly accurate compared to those from direct simulation across a range of thresholds (R² of 0.899-0.943 with RMSE of 0.054-0.069). Finally, the CNN achieved an average k and r of 0.98±0.12 and 0.90±0.07, respectively, for six reconstructed car crash impacts drawn from two other sources independent of the training dataset. Importantly, the CNN is able to efficiently estimate elementwise MPS with sufficient accuracy while conventional kinematic injury metrics cannot. Therefore, the CNN has the potential to supersede current kinematic injury metrics that can only approximate a global peak MPS or CSDM. The CNN technique developed here may offer enhanced utility in the design and development of head protective countermeasures, including in the automotive industry. This is the first study aimed at instantly estimating spatially detailed brain strains for automotive head impacts, which employs >8.8 thousand impact simulations generated from ~1.5 years of nonstop computations on a high-performance computing platform.

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.

Head Impact Research Using Inertial Sensors in Sport: A Systematic Review of Methods, Demographics, and Factors Contributing to Exposure

BACKGROUND

The number and magnitude of head impacts have been assessed in-vivo using inertial sensors to characterise the exposure in various sports and to help understand their potential relationship to concussion.

OBJECTIVES

We aimed to provide a comprehensive review of the field of in-vivo sensor acceleration event research in sports via the summary of data collection and processing methods, population demographics and factors contributing to an athlete's exposure to sensor acceleration events.

METHODS

The systematic search resulted in 185 cohort or cross-sectional studies that recorded sensor acceleration events in-vivo during sport participation.

RESULTS

Approximately 5800 participants were studied in 20 sports using 18 devices that included instrumented helmets, headbands, skin patches, mouthguards and earplugs. Female and youth participants were under-represented and ambiguous results were reported for these populations. The number and magnitude of sensor acceleration events were affected by a variety of contributing factors, suggesting sport-specific analyses are needed. For collision sports, being male, being older, and playing in a game (as opposed to a practice), all contributed to being exposed to more sensor acceleration events.

DISCUSSION

Several issues were identified across the various sensor technologies, and efforts should focus on harmonising research methods and improving the accuracy of kinematic measurements and impact classification. While the research is more mature for high-school and collegiate male American football players, it is still in its early stages in many other sports and for female and youth populations. The information reported in the summarised work has improved our understanding of the exposure to sport-related head impacts and has enabled the development of prevention strategies, such as rule changes.

CONCLUSIONS

Head impact research can help improve our understanding of the acute and chronic effects of head impacts on neurological impairments and brain injury. The field is still growing in many sports, but technological improvements and standardisation of processes are needed.

An Overview of the Effectiveness of Bicycle Helmet Designs in Impact Testing

Cycling accidents are the leading cause of sports-related head injuries in the US. Conventional bicycle helmets typically consist of polycarbonate shell over Expanded Polystyrene (EPS) foam and are tested with drop tests to evaluate a helmet’s ability to reduce head kinematics. Within the last decade, novel helmet technologies have been proposed to mitigate brain injuries during bicycle accidents, which necessitates the evaluation of their effectiveness in impact testing as compared to conventional helmets. In this paper, we reviewed the literature to collect and analyze the kinematic data of drop test experiments carried out on helmets with different technologies. In order to provide a fair comparison across different types of tests, we clustered the datasets with respect to their normal impact velocities, impact angular momentum, and the type of neck apparatus. When we analyzed the data based on impact velocity and angular momentum clusters, we found that the bicycle helmets that used rotation damping based technology, namely MIPS, had significantly lower peak rotational acceleration (PRA) and Generalized Acceleration Model for Brain Injury Threshold (GAMBIT) as compared to the conventional EPS liner helmets (p < 0.01). SPIN helmets had a superior performance in PRA compared to conventional helmets (p < 0.05) in the impact angular momentum clustered group, but not in the impact-velocity clustered comparisons. We also analyzed other recently developed helmets that primarily use collapsible structures in their liners, such as WaveCel and Koroyd. In both of the impact velocity and angular momentum groups, helmets based on the WaveCel technology had significantly lower peak linear acceleration (PLA), PRA, and GAMBIT at low impact velocities as compared to the conventional helmets, respectively (p < 0.05). The protective gear with the airbag technology, namely Hövding, also performed significantly better compared to the conventional helmets in the analyzed kinematic-based injury metrics (p < 0.001), possibly due to its advantage in helmet size and stiffness. We also observed that the differences in the kinematic datasets strongly depend on the type of neck apparatus. Our findings highlight the importance and benefits of developing new technologies and impact testing standards for bicycle helmet designs for better prevention of traumatic brain injury (TBI).

Toward a Comprehensive Delineation of White Matter Tract-Related Deformation

Finite element (FE) models of the human head are valuable instruments to explore the mechanobiological pathway from external loading, localized brain response, and resultant injury risks. The injury predictability of these models depends on the use of effective criteria as injury predictors. The FE-derived normal deformation along white matter (WM) fiber tracts (i.e., tract-oriented strain) recently has been suggested as an appropriate predictor for axonal injury. However, the tract-oriented strain only represents a partial depiction of the WM fiber tract deformation. A comprehensive delineation of tract-related deformation may improve the injury predictability of the FE head model by delivering new tract-related criteria as injury predictors. Thus, the present study performed a theoretical strain analysis to comprehensively characterize the WM fiber tract deformation by relating the strain tensor of the WM element to its embedded fiber tract. Three new tract-related strains with exact analytical solutions were proposed, measuring the normal deformation perpendicular to the fiber tracts (i.e., tract-perpendicular strain), and shear deformation along and perpendicular to the fiber tracts (i.e., axial-shear strain and lateral-shear strain, respectively). The injury predictability of these three newly proposed strain peaks along with the previously used tract-oriented strain peak and maximum principal strain (MPS) were evaluated by simulating 151 impacts with known outcome (concussion or non-concussion). The results preliminarily showed that four tract-related strain peaks exhibited superior performance than MPS in discriminating concussion and non-concussion cases. This study presents a comprehensive quantification of WM tract-related deformation and advocates the use of orientation-dependent strains as criteria for injury prediction, which may ultimately contribute to an advanced mechanobiological understanding and enhanced computational predictability of brain injury.

Identifying Factors Associated with Head Impact Kinematics and Brain Strain in High School American Football via Instrumented Mouthguards

Repeated head impact exposure and concussions are common in American football. Identifying the factors associated with high magnitude impacts aids in informing sport policy changes, improvements to protective equipment, and better understanding of the brain's response to mechanical loading. Recently, the Stanford Instrumented Mouthguard (MiG2.0) has seen several improvements in its accuracy in measuring head kinematics and its ability to correctly differentiate between true head impact events and false positives. Using this device, the present study sought to identify factors (e.g., player position, helmet model, direction of head acceleration, etc.) that are associated with head impact kinematics and brain strain in high school American football athletes. 116 athletes were monitored over a total of 888 athlete exposures. 602 total impacts were captured and verified by the MiG2.0's validated impact detection algorithm. Peak values of linear acceleration, angular velocity, and angular acceleration were obtained from the mouthguard kinematics. The kinematics were also entered into a previously developed finite element model of the human brain to compute the 95th percentile maximum principal strain. Overall, impacts were (mean ± SD) 34.0 ± 24.3 g for peak linear acceleration, 22.2 ± 15.4 rad/s for peak angular velocity, 2979.4 ± 3030.4 rad/s² for peak angular acceleration, and 0.262 ± 0.241 for 95th percentile maximum principal strain. Statistical analyses revealed that impacts resulting in Forward head accelerations had higher magnitudes of peak kinematics and brain strain than Lateral or Rearward impacts and that athletes in skill positions sustained impacts of greater magnitude than athletes in line positions. 95th percentile maximum principal strain was significantly lower in the observed cohort of high school football athletes than previous reports of collegiate football athletes. No differences in impact magnitude were observed in athletes with or without previous concussion history, in athletes wearing different helmet models, or in junior varsity or varsity athletes. This study presents novel information on head acceleration events and their resulting brain strain in high school American football from our advanced, validated method of measuring head kinematics via instrumented mouthguard technology.

Time Window of Head Impact Kinematics Measurement for Calculation of Brain Strain and Strain Rate in American Football

Wearable devices have been shown to effectively measure the head's movement during impacts in sports like American football. When a head impact occurs, the device is triggered to collect and save the kinematic measurements during a predefined time window. Then, based on the collected kinematics, finite element (FE) head models can calculate brain strain and strain rate, which are used to evaluate the risk of mild traumatic brain injury. To find a time window that can provide a sufficient duration of kinematics for FE analysis, we investigated 118 on-field video-confirmed football head impacts collected by the Stanford Instrumented Mouthguard. The simulation results based on the kinematics truncated to a shorter time window were compared with the original to determine the minimum time window needed for football. Because the individual differences in brain geometry influence these calculations, we included six representative brain geometries and found that larger brains need a longer time window of kinematics for accurate calculation. Among the different sizes of brains, a pre-trigger time of 40 ms and a post-trigger time of 70 ms were found to yield calculations of brain strain and strain rate that were not significantly different from calculations using the original 200 ms time window recorded by the mouthguard. Therefore, approximately 110 ms is recommended for complete modeling of impacts for football.

Potential Mechanisms of Acute Standing Balance Deficits After Concussions and Subconcussive Head Impacts: A Review

Standing balance deficits are prevalent after concussions and have also been reported after subconcussive head impacts. However, the mechanisms underlying such deficits are not fully understood. The objective of this review is to consolidate evidence linking head impact biomechanics to standing balance deficits. Mechanical energy transferred to the head during impacts may deform neural and sensory components involved in the control of standing balance. From our review of acute balance-related changes, concussions frequently resulted in increased magnitude but reduced complexity of postural sway, while subconcussive studies showed inconsistent outcomes. Although vestibular and visual symptoms are common, potential injury to these sensors and their neural pathways are often neglected in biomechanics analyses. While current evidence implies a link between tissue deformations in deep brain regions including the brainstem and common post-concussion balance-related deficits, this link has not been adequately investigated. Key limitations in current studies include inadequate balance sampling duration, varying test time points, and lack of head impact biomechanics measurements. Future investigations should also employ targeted quantitative methods to probe the sensorimotor and neural components underlying balance control. A deeper understanding of the specific injury mechanisms will inform diagnosis and management of balance deficits after concussions and subconcussive head impact exposure.

Effective Head Impact Kinematics to Preserve Brain Strain

Conventional kinematics-based brain injury metrics often approximate peak maximum principal strain (MPS) of the whole brain but ignore the anatomical location of occurrence. In this study, we develop effective impact kinematics consisting of peak rotational velocity and the associated rotational axis to preserve not only peak MPS but also spatially detailed MPS. A pre-computed brain response atlas (pcBRA) serves as a common reference. A training dataset (N = 3069) is used to develop a convolutional neural network (CNN) to automate impact simplification. When preserving peak MPS alone, the CNN-estimated effective peak rotational velocity achieves a coefficient of determination ([Formula: see text]) of ~ 0.96 relative to the directly identified counterpart, far outperforming nominal peak velocity from the resultant profiles ([Formula: see text] of ~ 0.34). Impacts from a subset of data (N = 1900) are also successfully matched with pcBRA idealized impacts based on elementwise MPS, where their regression slope and Pearson correlation coefficient do not deviate from 1.0 (when identical) by more than 0.1. The CNN-estimated effective peak rotation velocity and rotational axis are sufficiently accurate for ~ 73.5% of the impacts. This is not possible for the nominal peak velocity or any other conventional injury metric. The performance may be further improved by expanding the pcBRA to include deceleration and focusing on region-wise strains. This study establishes a new avenue to reduce an arbitrary head impact into an idealized but actual "impact mode" characterized by triplets of basic kinematic variables. They retain specific physical interpretations of head impact and may be an advancement over state-of-the-art kinematics-based scalar metrics for more effective impact comparison in the future.

Evaluation of Head Injury Criteria for Injury Prediction Effectiveness: Computational Reconstruction of Real-World Vulnerable Road User Impact Accidents

This study evaluates the effectiveness of various widely used head injury criteria (HICs) in predicting vulnerable road user (VRU) head injuries due to road traffic accidents. Thirty-one real-world car-to-VRU impact accident cases with detailed head injury records were collected and replicated through the computational biomechanics method; head injuries observed in the analyzed accidents were reconstructed by using a finite element (FE)-multibody (MB) coupled pedestrian model [including the Total Human Model for Safety (THUMS) head-neck FE model and the remaining body segments of TNO MB pedestrian model], which was developed and validated in our previous study. Various typical HICs were used to predict head injuries in all accident cases. Pearson's correlation coefficient analysis method was adopted to investigate the correlation between head kinematics-based injury criteria and the actual head injury of VRU; the effectiveness of brain deformation-based injury criteria in predicting typical brain injuries [such as diffuse axonal injury diffuse axonal injury (DAI) and contusion] was assessed by using head injury risk curves reported in the literature. Results showed that for head kinematics-based injury criteria, the most widely used HICs and head impact power (HIP) can accurately and effectively predict head injury, whereas for brain deformation-based injury criteria, the maximum principal strain (MPS) behaves better than cumulative strain damage measure (CSDM0.15 and CSDM0.25) in predicting the possibility of DAI. In comparison with the dilatation damage measure (DDM), MPS seems to better predict the risk of brain contusion.

The relationship between brain injury criteria and brain strain across different types of head impacts can be different

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different head impact types (e.g. sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across head impact types has not been evaluated. Physiologically, brain strain is often considered the key parameter of brain injury. To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact types, linear regression was used to model 95% maximum principal strain, 95% maximum principal strain at the corpus callosum and cumulative strain damage (15%) on 18 BIC. The results show significantly different relationships between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain across head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fitted on a dataset with a different type of head impact rather than on the dataset with the same type. Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.

Instantaneous Whole-Brain Strain Estimation in Dynamic Head Impact

Head injury models are notoriously time consuming and resource demanding in simulations, which prevents routine application. Here, we extend a convolutional neural network (CNN) to instantly estimate element-wise distribution of peak maximum principal strain (MPS) of the entire brain (>36 k speedup accomplished on a low-end computing platform). To achieve this, head impact rotational velocity and acceleration temporal profiles are combined into two-dimensional images to serve as CNN input for training and prediction of MPS. Compared with the directly simulated counterparts, the CNN-estimated responses (magnitude and distribution) are sufficiently accurate for 92.1% of the cases via 10-fold cross-validation using impacts drawn from the real world (n = 5661; range of peak rotational velocity in augmented data extended to 2-40 rad/sec). The success rate further improves to 97.1% for "in-range" impacts (n = 4298). When using the same CNN architecture to train (n = 3064) and test on an independent, reconstructed National Football League (NFL) impact dataset (n = 53; 20 concussions and 33 non-injuries), 51 out of 53, or 96.2% of the cases, are sufficiently accurate. The estimated responses also achieve virtually identical concussion prediction performances relative to the directly simulated counterparts, and they often outperform peak MPS of the whole brain (e.g., accuracy of 0.83 vs. 0.77 via leave-one-out cross-validation). These findings support the use of CNN for accurate and efficient estimation of spatially detailed brain strains across the vast majority of head impacts in contact sports. Our technique may hold the potential to transform traumatic brain injury (TBI) research and the design and testing standards of head protective gears by facilitating the transition from acceleration-based approximation to strain-based design and analysis. This would have broad implications in the TBI biomechanics field to accelerate new scientific discoveries. The pre-trained CNN is freely available online at https://github.com/Jilab-biomechanics/CNN-brain-strains.

Rapid Estimation of Entire Brain Strain Using Deep Learning Models

OBJECTIVE

Many recent studies have suggested that brain deformation resulting from a head impact is linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even though several finite element (FE) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these FE head models is limited due to the time-consuming nature of FE simulations. This work aims to accelerate the process of brain deformation calculation and thus improve the potential for clinical applications.

METHODS

We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 2511 total head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts.

RESULTS

The proposed deep learning head model can calculate the maximum principal strain (Green Lagrange) for every element in the entire brain in less than 0.001s with an average root mean squared error of 0.022, and with a standard deviation of 0.001 over twenty repeats with random data partition and model initialization.

CONCLUSION

Trained and tested using the dataset of 2511 head impacts, this model can be applied to various sports in the calculation of brain strain with accuracy, and its applicability can even further be extended by incorporating data from other types of head impacts.

SIGNIFICANCE

In addition to the potential clinical application in real-time brain deformation monitoring, this model will help researchers estimate the brain strain from a large number of head impacts more efficiently than using FE models.

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.

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.