The Effect of Muscle Activation on Head Kinematics During Non-injurious Head Impacts in Human Subjects

Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports

Head acceleration measurement sensors are now widely deployed in the field to monitor head kinematic exposure in contact sports. The wealth of impact kinematics data provides valuable, yet challenging, opportunities to study the biomechanical basis of mild traumatic brain injury (mTBI) and subconcussive kinematic exposure. Head impact kinematics are translated into brain mechanical responses through physics-based computational simulations using validated brain models to study the mechanisms of injury. First, this article reviews representative legacy and contemporary brain biomechanical models primarily used for blunt impact simulation. Then, it summarizes perspectives regarding the development and validation of these models, and discusses how simulation results can be interpreted to facilitate injury risk assessment and head acceleration exposure monitoring in the context of contact sports. Recommendations and consensus statements are presented on the use of validated brain models in conjunction with kinematic sensor data to understand the biomechanics of mTBI and subconcussion. Mainly, there is general consensus that validated brain models have strong potential to improve injury prediction and interpretation of subconcussive kinematic exposure over global head kinematics alone. Nevertheless, a major roadblock to this capability is the lack of sufficient data encompassing different sports, sex, age and other factors. The authors recommend further integration of sensor data and simulations with modern data science techniques to generate large datasets of exposures and predicted brain responses along with associated clinical findings. These efforts are anticipated to help better understand the biomechanical basis of mTBI and improve the effectiveness in monitoring kinematic exposure in contact sports for risk and injury mitigation purposes.

Quantifying the Effect of Sex and Neuroanatomical Biomechanical Features on Brain Deformation Response in Finite Element Brain Models

Recent automotive epidemiology studies have concluded that females have significantly higher odds of sustaining a moderate brain injury or concussion than males in a frontal crash after controlling for multiple crash and occupant variables. Differences in neuroanatomical features, such as intracranial volume (ICV), have been shown between male and female subjects, but how these sex-specific neuroanatomical differences affect brain deformation is unknown. This study used subject-specific finite element brain models, generated via registration-based morphing using both male and female magnetic resonance imaging scans, to investigate sex differences of a variety of neuroanatomical features and their effect on brain deformation; additionally, this study aimed to determine the relative importance of these neuroanatomical features and sex on brain deformation metrics for a single automotive loading environment. Based on the Bayesian linear mixed models, sex had a significant effect on ICV, white matter volume and gray matter volume, as well as a section of cortical gray matter regions' thicknesses and volumes; however, after these neuroanatomical features were accounted for in the statistical model, sex was not a significant factor in predicting brain deformation. ICV had the highest relative effect on the brain deformation metrics assessed. Therefore, ICV should be considered when investigating both brain injury biomechanics and injury risk.

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.

‘Falling heads’: investigating reflexive responses to head–neck perturbations

Background

Reflexive responses to head–neck perturbations affect the injury risk in many different situations ranging from sports-related impact to car accident scenarios. Although several experiments have been conducted to investigate these head–neck responses to various perturbations, it is still unclear why and how individuals react differently and what the implications of these different responses across subjects on the potential injuries might be. Therefore, we see a need for both experimental data and biophysically valid computational Human Body Models with bio-inspired muscle control strategies to understand individual reflex responses better.

Methods

To address this issue, we conducted perturbation experiments of the head–neck complex and used this data to examine control strategies in a simulation model. In the experiments, which we call ’falling heads’ experiments, volunteers were placed in a supine and a prone position on a table with an additional trapdoor supporting the head. This trapdoor was suddenly released, leading to a free-fall movement of the head until reflexive responses of muscles stopped the downwards movement.

Results

We analysed the kinematic, neuronal and dynamic responses for all individuals and show their differences for separate age and sex groups. We show that these results can be used to validate two simple reflex controllers which are able to predict human biophysical movement and modulate the response necessary to represent a large variability of participants.

Conclusions

We present characteristic parameters such as joint stiffness, peak accelerations and latency times. Based on this data, we show that there is a large difference in the individual reflexive responses between participants. Furthermore, we show that the perturbation direction (supine vs. prone) significantly influences the measured kinematic quantities. Finally, ’falling heads’ experiments data are provided open-source to be used as a benchmark test to compare different muscle control strategies and to validate existing active Human Body Models directly.

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.

Sex Differences in Neck Strength Force and Activation Patterns in Collegiate Contact Sport

The purpose of this study was to assess changes in cervical musculature throughout contact-heavy collegiate ice hockey practices during a regular season of NCAA Division III ice hockey teams. In this cross-sectional study, 36 (male n = 13; female n = 23) ice hockey players participated. Data were collected over 3 testing sessions (baseline; pre-practice; post-practice). Neck circumference, neck length, head-neck segment length, isometric strength and electromyography (EMG) activity for flexion and extension were assessed. Assessments were completed approximately 1h before a contact-heavy practice and 15 min after practice. For sternocleidomastoid (SCM) muscles, males had significantly greater peak force and greater time to peak force versus females. For both left and right SCMs, both sexes had significantly greater peak EMG activity pre-practice versus baseline, and right (dominant side) SCM time to peak EMG activity was decreased post-practice compared to pre-practice. There were no significant differences for EMG activity of the upper trapezius musculature, over time or between sexes. Sex differences observed in SCM force and activation patterns of the dominant side SCM may contribute to head stabilization during head impacts. Our study is the first investigation to report changes in cervical muscle strength in men's and women's ice hockey players in the practical setting.

Cervical Muscle Activation Characteristics and Head Kinematics in Males and Females Following Acoustic Warnings and Impulsive Head Forces

Sex, head and neck posture, and cervical muscle preparation are contributing factors in the severity of head and neck injuries. However, it is unknown how these factors modulate the head kinematics. In this study, twenty-four (16 male and 8 female) participants experienced 50 impulsive forces to their heads with and without an acoustic warning. Female participants demonstrated a 71 ms faster (p = 0.002) muscle activation onset compared to males after warning. The magnitude of muscle activation was not significant between sexes. Females exhibited 21% (p < 0.008) greater peak angular velocity in all force directions and 18% (p < 0.04) greater peak angular acceleration in sagittal plane compared to males. Females exhibited 15% (p = 0.03) greater peak linear acceleration compared to males only in sagittal flexion. Preparation attenuated head kinematics significantly (p < 0.03) in 11 out of 18 investigated head kinematics for both sexes. A warning eliciting a startle response 420 ms prior to the impact resulted in significant attenuation of all measured head kinematics in sagittal extension (p < 0.037). In conclusion, both sex and warning type were significant factors in head kinematics. These data provide insight into the complex relationship of muscle activation and sex, and may help identify innovative strategies to reduce head and neck injury risk in sports.

The role of neck muscle co-contraction and postural changes in head kinematics after safe head impacts: Investigation of head/neck injury reduction

Concerns surrounding concussions from impacts to the head necessitate research to generate new knowledge about ways to prevent them and reduce risk. In this paper, we report the relative temporal characteristics of the head resulting from neck muscle co-contraction and postural changes following a sudden force applied to the head in four different directions. In the two "prepared" conditions (i.e., co-contraction and postural), participants experienced impulsive forces to the head after hearing a warning. The warning given for the postural condition informed both the direction and timing of the impulsive force. Participants responded to the postural warning by altering their head posture, whereas in the co-contraction warning, the force direction was unknown to them, and they were asked to isometrically co-contract their neck muscles after the warning. Peak angular velocity reduced by 29% in sagittal extension, 18% in sagittal flexion, and 23% in coronal lateral flexion in prepared vs. unwarned conditions. Peak linear acceleration was attenuated by 15% in sagittal extension, 8% in sagittal flexion, and 18% in coronal lateral flexion in prepared vs. unwarned conditions. Changes in peak angular acceleration were not uniform. We also measured a significant delay in the peak angular velocity (22 vs. 44.8 ms) and peak angular acceleration (7 vs. 20 ms) after peak linear acceleration in prepared compared to unwarned conditions. An increase in muscle activation significantly reduced the peak angular velocity and linear acceleration. Gross head movement was significantly decreased with preparation. These findings suggest that a warning prior to impact can reduce head kinematics associated with injury.

Toward subject-specific evaluation: methods of evaluating finite element brain models using experimental high-rate rotational brain motion

Computational models of the brain have become the gold standard in biomechanics to understand, predict, and mitigate traumatic brain injuries. Many models have been created and evaluated with limited experimental data and without accounting for subject-specific morphometry of the specimens in the dataset. Recent advancements in the measurement of brain motion using sonomicrometry allow for a comprehensive evaluation of brain model biofidelity using a high-rate, rotational brain motion dataset. In this study, four methods were used to determine the best technique to compare nodal displacement to experimental brain motion, including a new morphing method to match subject-specific inner skull geometry. Three finite element brain models were evaluated in this study: the isotropic GHBMC and SIMon models, as well as an anisotropic model with explicitly embedded axons (UVA-EAM). Using a weighted cross-correlation score (between 0 and 1), the anisotropic model yielded the highest average scores across specimens and loading conditions ranging from 0.53 to 0.63, followed by the isotropic GHBMC with average scores ranging from 0.46 to 0.58, and then the SIMon model with average scores ranging from 0.36 to 0.51. The choice of comparison method did not significantly affect the cross-correlation score, and differences of global strain up to 0.1 were found for the morphed geometry relative to baseline models. The morphed or scaled geometry is recommended when evaluating computational brain models to capture the subject-specific skull geometry of the experimental specimens.

Repeatability and Biofidelity of a Physical Surrogate Neck Model Fit to a Hybrid III Head

In helmet impact testing, parameters including acceleration and velocity are measured using instrumented head-neck models that are meant to be mechanically realistic (i.e. biofidelic) stand-ins, or surrogates, for humans. Currently available models of the human neck are designed primarily for application in automotive crash testing, and their applicability in assessment of helmets is often questioned. The object of the present work is to document the mechanical design, repeatability, and biofidelity in low speed impact of a new neck model that we apply with a Hybrid III head. Focusing on Hybrid III head kinematics measured during impacts at 2 to 6 m/s, the co-efficient of variance of repeated measures of kinematics was generally less than 10%. Differences in kinematics between identical copies of the neck was less than 20% when tested with helmets, and less than 7% when the head was not helmeted. In parallel testing using a Hybrid III head-neck, the co-efficient of variance in repeated measures was less than 4% and the kinematics significantly differed from those measured using the new neck. CORAplus scores for the new neck were approximately 0.70 when compared against data for human subjects with passive neck muscles experiencing impact at 2 m/s.

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach

Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20–40 rad/s), durations (30–60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

Cervical Muscle Activation Due to an Applied Force in Response to Different Types of Acoustic Warnings

Mild traumatic brain injury (mTBI) and whiplash-associated disorder are the most common head and neck injuries and result from a sudden head or body acceleration. The head and neck injury potential is correlated with the awareness, level of muscle activation, and posture changes at the time of the perturbation. Environmental acoustic stimuli or a warning system can influence muscle activation and posture during a head perturbation. In this study, different acoustic stimuli, including Non-Directional, Directional, and Startle, were provided 1000 ms before a head impact, and the amplitude and timing of cervical muscle electromyographic (EMG) data were characterized based on the type of warning. The startle warning resulted in 49% faster and 80% greater EMG amplitude compared to the Directional and Non-Directional warnings after warning and before the impact. The post-impact peak EMG amplitudes in Unwarned trials were lower by 18 and 21% in the retraction and rebound muscle groups, respectively, compared to any of the warned conditions. When there was no warning before the impact, the retraction and rebound muscle groups also reached their maximum activation 38 and 54 ms sooner, respectively, compared to the warned trials. Based on these results, the intensity and complexity of information that a warning sound carries change the muscle response before and after a head impact and has implications for injury potential.

Integrating material properties from magnetic resonance elastography into subject-specific computational models for the human brain

Advances in brain imaging and computational methods have facilitated the creation of subject-specific computational brain models that aid researchers in investigating brain trauma using simulated impacts. The emergence of magnetic resonance elastography (MRE) as a non-invasive mechanical neuroimaging tool has enabled in vivo estimation of material properties at low-strain, harmonic loading. An open question in the field has been how this data can be integrated into computational models. The goals of this study were to use a novel MRI dataset acquired in human volunteers to generate models with subject-specific anatomy and material properties, and then to compare simulated brain deformations to subject-specific brain deformation data under non-injurious loading. Models of five subjects were simulated with linear viscoelastic (LVE) material properties estimated directly from MRE data. Model predictions were compared to experimental brain deformation acquired in the same subjects using tagged MRI. Outcomes from the models matched the spatial distribution and magnitude of the measured peak strain components as well as the 95^th percentile in-plane peak strains within 0.005 mm/mm and maximum principal strain within 0.012 mm/mm. Sensitivity to material heterogeneity was also investigated. Simulated brain deformations from a model with homogenous brain properties and a model with brain properties discretized with up to ten regions were very similar (a mean absolute difference less than 0.0015 mm/mm in peak strains). Incorporating material properties directly from MRE into a biofidelic subject-specific model is an important step toward future investigations of higher-order model features and simulations under more severe loading conditions.

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models

Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subject-specific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subject-specific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinary research between the biomechanics and neuroimaging fields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis.