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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).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).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.The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance.The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.

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.

Energy absorption is an important challenge shared by multiple different industries including manufacturing, transportation, and protective equipment. This paper presents a novel shock absorption system based on woven fabrics designed to fully collapse and absorb energy at a near-minimum force level. The proposed system exerts an approximately constant force across different impact velocities. This system utilizes a fixed-orifice hydraulic shock absorber with variable contact area over its displacement to provide a nearly-constant force which scales with impact energy. Using analytical fluid dynamics, the contact area needed to produce a constant minimum force is derived. A finite element model of this shock absorber is created to validate the concept. Different impact conditions are simulated. The results confirm that the proposed fabric shock absorber is capable of producing a nearly-constant force across different impact energies. In simulation, the fabric shock absorber follows the ideal constant force profile with an averaged efficiency of 77.8% ± 3.4% which is far above standard foams which have efficiency of only approximately 20%–40%. The proposed system is compared with a cylindrical damper to show performance improvements gained through variable area geometry. Potential applications of this technology include soft devices for space-constrained applications such as contact sport helmets.

Despite numerous research efforts, the precise mechanisms of concussion have yet to be fully uncovered. Clinical studies on high-risk populations, such as contact sports athletes, have become more common and give insight on the link between impact severity and brain injury risk through the use of wearable sensors and neurological testing. However, as the number of institutions operating these studies grows, there is a growing need for a platform to share these data to facilitate our understanding of concussion mechanisms and aid in the development of suitable diagnostic tools. To that end, this paper puts forth two contributions: 1) a centralized, open-source platform for storing and sharing head impact data, in collaboration with the Federal Interagency Traumatic Brain Injury Research informatics system (FITBIR), and 2) a deep learning impact detection algorithm (MiGNet) to differentiate between true head impacts and false positives for the previously biomechanically validated instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with FITBIR. We report 96% accuracy using MiGNet, based on a neural network model, improving on previous work based on Support Vector Machines achieving 91% accuracy, on an out of sample dataset of high school and collegiate football head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative research tool to be disseminated across multiple institutions towards creating a standardized dataset for furthering the knowledge of concussion biomechanics., 21 pages, 3 figures, 1 table

To analyze the mechanical properties in different regions of the brain in healthy adults in a wide age range: 26 to 76 years old. We used a multifrequency magnetic resonance elastography (MRE) protocol to analyze the effect of age on frequency-dependent (storage and loss moduli, G′ and G″, respectively) and frequency-independent parameters (μ1, μ2, and η, as determined by a standard linear solid model) of the cerebral parenchyma, cortical gray matter (GM), white matter (WM), and subcortical GM structures of 46 healthy male and female subjects. The multifrequency behavior of the brain and frequency-independent parameters were analyzed across different age groups. The annual change rate ranged from − 0.32 to − 0.36% for G′ and − 0.43 to − 0.55% for G″ for the cerebral parenchyma, cortical GM, and WM. For the subcortical GM, changes in G′ ranged from − 0.18 to − 0.23%, and G″ changed − 0.43%. Interestingly, males exhibited decreased elasticity, while females exhibited decreased viscosity with respect to age in some regions of subcortical GM. Significantly decreased values were also found in subjects over 60 years old. Values of G′ and G″ at 60 Hz and the frequency-independent μ2 of the caudate, putamen, and thalamus may serve as parameters that characterize the aging effect on the brain. The decrease in brain stiffness accelerates in elderly subjects. • We used a multifrequency MRE protocol to assess changes in the mechanical properties of the brain with age. • Frequency-dependent (storage moduli G′ and loss moduli G″) and frequency-independent (μ1, μ2, and η) parameters can bequantitatively measured by our protocol. • The decreased value of viscoelastic properties due to aging varies in different regions of subcortical GM in males and females, and the decrease in brain stiffness is accelerated in elderly subjects over 60 years old.

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 field. In the laboratory, we evaluated the efficacy of the padded helmet shell cover 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 - 39.6%), 18% at 5.5 m/s (range: -5.5 - 40.5%), and 10% at 7.4 m/s (range: -6.0 - 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., Comment: 49 references, 8 figures

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.

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 F

Respiratory failure complicates most critically ill patients with COVID-19 and is characterized by heterogeneous pulmonary parenchymal involvement, profound hypoxemia and pulmonary vascular injury. The high incidence of COVID-19 related respiratory failure has exposed critical shortages in the supply of mechanical ventilators, and providers with the necessary skills to treat. Traditional mass-produced ventilators rely on an internal compressor and mixer to moderate and control the gas mixture delivered to a patient. However, the current emergency has energized the pursuit of alternative designs, enabling greater flexibility in supply chain, manufacturing, storage, and maintenance considerations. To achieve this, we hypothesized that using the medical gasses and flow interruption strategy would allow for a high performance, low cost, functional ventilator. A low-cost ventilator designed and built-in accordance with the Emergency Use guidance from the US Food and Drug Administration (FDA) is presented wherein pressurized medical grade gases enter the ventilator and time limited flow interruption determines the ventilator rate and tidal volume. This simple strategy obviates the need for many components needed in traditional ventilators, thereby dramatically shortening the time from storage to clinical deployment, increasing reliability, while still providing life-saving ventilatory support. The overall design philosophy and its applicability in this new crisis is described, followed by both bench top and animal testing results used to confirm the precision, safety and reliability of this low cost and novel approach to mechanical ventilation. The ventilator meets and exceeds the critical requirements included in the FDA emergency use guidelines. The ventilator has received emergency use authorization from the FDA.

Collegiate football athletes are subject to repeated traumatic brain injuriesthat may cause brain injury. The hippocampus is composed of several distinct subfields with possible differential susceptibility to injury. The aim of this study is to determine whether there are longitudinal changes in hippocampal subfield volume in collegiate football. A prospective cohort study was conducted over a 5-year period tracking 63 football and 34 volleyball male collegiate athletes. Athletes underwent high-resolution structural magnetic resonance imaging, and automated segmentation provided hippocampal subfield volumes. At baseline, football (n = 59) athletes demonstrated a smaller subiculum volume than volleyball (n = 32) athletes (−67.77 mm(3); p = 0.012). A regression analysis performed within football athletes similarly demonstrated a smaller subiculum volume among those at increased concussion risk based on athlete position (p = 0.001). For the longitudinal analysis, a linear mixed-effects model assessed the interaction between sport and time, revealing a significant decrease in cornu ammonis area 1 (CA1) volume in football (n = 36) athletes without an in-study concussion compared to volleyball (n = 23) athletes (volume difference per year = −35.22 mm(3); p = 0.005). This decrease in CA1 volume over time was significant when football athletes were examined in isolation from volleyball athletes (p = 0.011). Thus, this prospective, longitudinal study showed a decrease in CA1 volume over time in football athletes, in addition to baseline differences that were identified in the downstream subiculum. Hippocampal changes may be important to study in high-contact sports.

Humans are susceptible to traumatic brain injuries from rapid head rotations that shear and stretch the brain tissue. Conversely, animals such as woodpeckers intentionally undergo repetitive head impacts without apparent injury. Here, we represent the head as the end effector of a rigid linkage cervical spine model to quantify how head angular accelerations are affected by the linkage positioning (head-neck configuration) and the soft tissue properties (muscles, ligaments, tendons).We developed a two-pivot manipulator model of the human cervical spine with passive torque elements to represent soft tissue forces. Passive torque parameters were fit against five human subjects undergoing mild laboratory head impacts with tensed and relaxed neck muscle activations. With this representation, we compared the effects of the linkage configuration dependent end-effector inertial properties and the soft tissue resistive forces on head impact rotation.Small changes in cervical spine positioning (5 degrees) can drastically affect the resulting rotational head accelerations (100%) following an impact by altering the effective end-effector inertia. Comparatively, adjusting the soft tissue torque elements from relaxed to tensed muscle activations had a smaller (30%) effect on maximum rotational head accelerations. Extending our analysis to a woodpecker rigid linkage model, we postulate that woodpeckers experience relatively minimal head impact rotation due to the configuration of their skeletal anatomy.Cervical spine positioning dictates the head angular acceleration following an impact, rather than the soft tissue torque elements.This analysis quantifies the importance of head positioning prior to impact, and may help us to explain why other species are naturally more resilient to head impacts than humans.

While many research efforts have focused on head impact exposure in professional soccer, there have been few studies characterizing exposure at the youth level. The aim of this study is to evaluate a new instrumentation approach and collect some of the first head impact exposure data for youth female soccer players. Athletes were instrumented with custom-fit mouthpieces that measure head impacts. Detailed video analysis was conducted to identify characteristics describing impact source (e.g., kick, header, throw). A total of 763 verified head impacts were collected over 23 practices and 8 games from 7 athletes. The median peak linear accelerations, rotational velocities, and rotational accelerations of all impacts were 9.4 g, 4.1 rad/s, and 689 rad/s2, respectively. Pairwise comparisons resulted in statistically significant differences in kinematics by impact source. Headers following a kicked ball had the highest accelerations and velocity when compared to headers from thrown or another header.

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.

Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in (1) the derivative order (angular velocity, angular acceleration, angular jerk), (2) the direction and (3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics about three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.

Sports concussion is a form of mild traumatic brain injury (mTBI) caused by an impulsive force transmitted to the head. While concussion is recognized as a complex pathophysiological process affecting the brain at multiple scales, the causal link between external load and cellular, molecular level damage in mTBI remains elusive. The present study proposes a multiscale framework to analyze concussion and demonstrates its applicability with a real-life concussion case. The multiscale analysis starts from inputting mouth guard-recorded head kinematic into a detailed finite element (FE) head model tailored to the subject's head and white matter (WM) tract morphology. The resulting WM tract-oriented strains are then extracted and input to histology-informed micromechanical models of corpus callosum subregions with axonal detail to obtain axolemma strains at a subcellular level. By comparing axolemma strains against mechanoporation thresholds obtained via molecular dynamics (MD) simulations, axonal damage is inferred corresponding to a likelihood of concussion, in line with clinical observation. This novel multiscale framework bridges the organ-to-molecule length scales and accounts both inter- and intra-subject regional variability, providing a new way of non-invasively predicting axonal damage and real-life concussion analysis. The framework may contribute to a better understanding of the mechanistic causes behind concussion. Statement of Significance This study reports a multiscale computational framework for concussion, for the first time revealing a picture of how a global impact to the head measured on the field transfers to the cellular level of axons and finally down to the molecular level. Demonstrated with a real-life concussion case using a detailed and subject-specific head model, the results show molecular level damage corresponds to a likelihood of concussion, in line with clinical observation. An insight into the multiscale mechanical consequences is critical for a better understanding of the complex pathophysiological process affecting the brain at impact, which today are still poorly understood. Analyzing the concussive injury mechanisms the whole way from brains to molecules may also have significant clinical relevance. We show that in a typical injury scenario, the axolemma sustains large enough strains to entail pore formation in the adjoining lipid bilayer. Proration is found to occur in bilayer regions lacking ganglioside lipids, which provides important implications for the treatment of brain injury and other related neurodegenerative diseases.

Objective: 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 was analyzed from 3,262 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 1,000 random partitions of training and test sets. The most important features in the classification included both low-frequency and high-frequency features, both linear acceleration features and angular velocity features. Different head impact types had different distributions of spectral densities in low-frequency and high-frequency ranges (e.g., the spectral densities of MMA impacts were higher in high-frequency range than in the low-frequency range). The type-specific regression showed a generally higher R^2-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., Comment: 33 pages, 5 figures

COVID-19 has become a significant burden on the healthcare systems in the United States and around the world, with many patients requiring invasive mechanical ventilation (IMV) to survive. Close monitoring of patients is critical, with total volume per breath (tidal volume) being one of the most important data points. However, ventilators are complex and expensive devices, typically in the range of tens of thousands of US dollars, and are challenging to manufacture, typically requiring months. Solutions which could augment the ventilator supply rapidly and at low cost in the United States and elsewhere would be valuable. In this paper, we present a standalone tidal volume measurement system consisting of a D-Lite spirometer, pressure sensor, microcontroller, and tubing with a cost of parts less than $50 USD. We also provide a model to predict the error in tidal volume measurements based on the pressure sensor used and the flow during ventilation. We validate this system and show that the tidal volume accuracy for flows above 10 L/min was within 10%. We envision this system being used to increase the ventilator supply in resource-constrained settings.

For the past 50 years, positive pressure ventilation has been a cornerstone of treatment for respiratory failure. Consensus surrounding the epidemiology of respiratory failure has permitted a relatively good fit between the supply of ventilators and the demand. However, the current COVID-19 pandemic has increased demand for mechanical ventilators well beyond supply. Respiratory failure complicates most critically ill patients with COVID-19 and is characterized by highly heterogeneous pulmonary parenchymal involvement, profound hypoxemia and pulmonary vascular injury. The profound increase in the incidence of respiratory failure has exposed critical shortages in the supply of mechanical ventilators, and those with the necessary skills to treat. While most traditional ventilators rely on an internal compressor and mixer to moderate and control the gas mixture delivered to a patient, the current emergency climate has catalyzed alternative designs that might enable greater flexibility in terms of supply chain, manufacturing, storage and maintenance. Design considerations of these “emergency response” ventilators have generally fallen into two categories: those that rely on mechanical compression of a known volume of gas and those powered by an internal compressor to deliver time cycled pressure- or volume-limited gas to the patient. The present work introduces a low-cost, ventilator designed and built in accordance with the Emergence Use guidance provided by the US Food and Drug Administration (FDA) wherein an external gas supply feeds into the ventilator and time limited flow interruption guarantees tidal volume. The goal of this device is to allow a patient to be treated by a single ventilator platform, capable of supporting the various treatment paradigms during a potential COVID-19 related hospitalization. This is a unique aspect of this design as it attempts to become a one-device-one-visit solution to the problem. The device is designed as a single use ventilator that is sufficiently robust to treat a patient being mechanically ventilated. The overall design philosophy and its applicability in this new crisis-laden world view is first described, followed by both bench top and animal testing results used to confirm the precision, capability, safety and reliability of this low cost and novel approach to mechanical ventilation during the COVID-19 pandemic. The ventilator is shown to perform in a range of critical requirements listed in the FDA emergency regulations and can safely and effectively ventilate a porcine subject. As of August 2020, only 13 emergency ventilators have been authorized by the FDA, and this work represents the first to publish animal data using the ventilator. This proof-of-concept provides support for this cost-effective, readily mass-produced ventilator that can be used to support patients when the demand for ventilators outstrips supply in hospital settings worldwide. More details for this project can be found at https://ventilator.stanford.edu/

The contact nature of American football has made head acceleration exposure a concern. We aimed to quantify the head kinematics associated with direct helmet contact and inertial head loading events in collegiate-level American football. A cohort of collegiate-level players were equipped with instrumented mouthguards synchronised with time-stamped multiple camera-view video footage of matches and practice. Video-verified contact events were identified as direct helmet contact or inertial head loading events and categorised as blocking, blocked, tackling, tackled or ground contact. Linear mixed-effects models were utilised to compare peak head kinematics between contact event categories. The timestamp-based cross-verification of the video analysis and instrumented mouthguard approach resulted in 200 and 328 direct helmet contact and inertial head loading cases, respectively. Median linear acceleration, angular acceleration and angular velocity for inertial head loading cases was greater than direct helmet contact events by 8% (p = 0.007), 55% (p < 0.001) and 4% (p = 0.007), respectively. Median head kinematics for all contact event categories appeared similar with no pairwise comparison resulting in statistical significance (p > 0.05). The study highlights the potential of combining qualitative video analysis with in-vivo head kinematics measurements. The findings suggest that a number of direct helmet contact events sustained in American football are of lower magnitude to what is sustained during regular play (i.e. from inertial head loading). Additionally, the findings illustrate the importance of including all contact events, including direct helmet contact and inertial head loading cases, when assessing head acceleration exposure and player load during a season of American football.

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. However, the evolution of fiber orientation in time during the impact and its potential influence on the tract-oriented strain remains unknown. 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.

Because of the relatively rigid coupling between the upper dentition and the skull, instrumented mouthguards have been shown to be a viable way of measuring head impact kinematics for assisting in understanding the underlying biomechanics of concussions. This has led various companies and institutions to further develop instrumented mouthguards. However, their use as a research tool for understanding concussive impacts makes quantification of their accuracy critical, especially given the conflicting results from various recent studies. Here we present a study that uses a pneumatic impactor to deliver impacts characteristic to football to a Hybrid III headform, in order to validate and compare five of the most commonly used instrumented mouthguards. We found that all tested mouthguards gave accurate measurements for the peak angular acceleration (mean relative error, MRE < 13%), the peak angular velocity (MRE < 8%), brain injury criteria values (MRE < 13%) and brain deformation (described as maximum principal strain and fiber strain, calculated by a convolutional neural network based brain model, MRE < 9%). Finally, we found that the accuracy of the measurement varies with the impact locations yet is not sensitive to the impact velocity for the most part., Annals of Biomedical Engineering: Concussion Biomechanics in Football

Mild traumatic brain injury (mTBI), more colloquially known as concussion, is common in contact sports such as American football, leading to increased scrutiny of head protective gear. Standardized laboratory impact testing, such as the yearly National Football League (NFL) helmet test, is used to rank the protective performance of football helmets, motivating new technologies to improve the safety of helmets relative to existing equipment. In this work, we hypothesized that a helmet which transmits a nearly constant minimum force will result in a reduced risk of mTBI. To evaluate the plausibility of this hypothesis, we first show that the optimal force transmitted to the head, in a reduced order model of the brain, is in fact a constant force profile. To simulate the effects of a constant force within a helmet, we conceptualize a fluid-based shock absorber system for use within a football helmet. We integrate this system within a computational helmet model and simulate its performance on the standard NFL helmet test impact conditions. The simulated helmet is compared with other helmet designs with different technologies. Computer simulations of head impacts with liquid shock absorption predict that, at the highest impact speed (9.3 m/s), the average brain tissue strain is reduced by 27.6% ± 9.3 compared to existing helmet padding when tested on the NFL helmet protocol. This simulation-based study puts forth a target benchmark for the future design of physical manifestations of this technology.

The head is kinematically constrained to the torso through the spine and thus, the spine dictates the amount of output head angular motion expected from an input impact. Here, we investigate the spinal kinematic constraint by analyzing the head instantaneous center of rotation (HICOR) with respect to the torso in head/neck sagittal extension and coronal lateral flexion during mild loads applied to 10 subjects. We found the mean HICOR location was near the C5-C6 intervertebral joint in sagittal extension, and T2-T3 intervertebral joint in coronal lateral flexion. Using the impulse-momentum relationship normalized by subject mass and neck length, we developed a non-dimensional analytical ratio between output angular velocity and input linear impulse as a function of HICOR location. The ratio was 0.65 and 0.50 in sagittal extension and coronal lateral flexion respectively, implying 30% greater angular velocities in sagittal extension given an equivalent impulse. Scaling to subject physiology also predicts larger required impulses given greater subject mass and neck length to achieve equivalent angular velocities, which was observed experimentally. Furthermore, the HICOR has greater motion in sagittal extension than coronal lateral flexion, suggesting the head and spine can be represented with a single inverted pendulum in coronal lateral flexion, but requires a more complex representation in sagittal extension. The upper cervical spine has substantial compliance in sagittal extension, and may be responsible for the complex motion and greater extension angular velocities. In analyzing the HICOR, we can gain intuition regarding the neck’s role in dictating head motion during external loading.

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

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different types of head impacts (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 different types of head impacts 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 each of 18 BIC respectively. The results show a significant difference in the relationship between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain in different head impact types. The accuracy of brain strain regression is generally decreasing if the BIC regression models are fit 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., Comment: 28 pages, 11 figures

A pre-computed brain response atlas (pcBRA) may have the potential to accelerate the investigation of the biomechanical mechanisms of traumatic brain injury on a large-scale. In this study, we further enhance the technique and evaluate its performance using six degree-of-freedom angular velocity profiles from dummy head impacts. Using the pcBRA to simplify profiles into acceleration-only shapes, sufficiently accurate strain estimates were obtained for impacts with a major dominating velocity peak. However, they were largely under-estimated when substantial deceleration occurred that reversed the direction of the angular velocity. For these impacts, estimation accuracy was substantially improved with a biphasic profile simplification (average correlation coefficient and linear regression slope of 0.92 ± 0.03 and 0.95 ± 0.07 for biphasic shapes, respectively, vs. 0.80 ± 0.06 and 0.80 ± 0.08 for acceleration-only shapes). Peak maximum principal strain (ɛ p) and cumulative strain damage measure (CSDM) from the estimated strains consistently correlated stronger than kinematic metrics with respect to the baseline ɛ p and CSDM from the directly simulated responses, regardless of the brain region, and by a large margin (e.g., correlation of 0.93 vs. 0.75 compared to Brain Injury Criterion (BrIC) for ɛ p in the whole-brain, and 0.91 vs. 0.47 compared to BrIC for CSDM in the corpus callosum). These findings further support the pre-computation technique for accurate, real-time strain estimation, which could be important to accelerate model-based brain injury studies in the future.

Whereas the diagnosis of moderate and severe traumatic brain injury (TBI) is readily visible on current medical imaging paradigms (magnetic resonance imaging [MRI] and computed tomography [CT] scanning), a far greater challenge is associated with the diagnosis and subsequent management of mild TBI (mTBI), especially concussion which, by definition, is characterized by a normal CT. To investigate whether the integrity of the blood-brain barrier (BBB) is altered in a high-risk population for concussions, we studied professional mixed martial arts (MMA) fighters and adolescent rugby players. Additionally, we performed the linear regression between the BBB disruption defined by increased gadolinium contrast extravasation on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on MRI and multiple biomechanical parameters indicating the severity of impacts recorded using instrumented mouthguards in professional MMA fighters. MMA fighters were examined pre-fight for a baseline and again within 120 h post-competitive fight, whereas rugby players were examined pre-season and again post-season or post-match in a subset of cases. DCE-MRI, serological analysis of BBB biomarkers, and an analysis of instrumented mouthguard data, was performed. Here, we provide pilot data that demonstrate disruption of the BBB in both professional MMA fighters and rugby players, dependent on the level of exposure. Our data suggest that biomechanical forces in professional MMA and adolescent rugby can lead to BBB disruption. These changes on imaging may serve as a biomarker of exposure of the brain to repetitive subconcussive forces and mTBI.

Background and purpose The brain's stiffness measurements from magnetic resonance elastography (MRE) strongly depend on actuation frequencies, which makes cross-study comparisons challenging. We performed a preliminary study to acquire optimal sets of actuation frequencies to accurately obtain rheological parameters for the whole brain (WB), white matter (WM), and gray matter (GM). Methods Six healthy volunteers aged between 26 and 72 years old went through MRE with a modified single-shot spin-echo echo planar imaging pulse sequence embedded with motion encoding gradients on a 3T scanner. Frequency-independent brain material properties and best-fit material model were determined from the frequency-dependent brain tissue response data (20 -80 Hz), by comparing four different linear viscoelastic material models (Maxwell, Kelvin-Voigt, Springpot, and Zener). During the material fitting, spatial averaging of complex shear moduli (G*) obtained under single actuation frequency was performed, and then rheological parameters were acquired. Since clinical scan time is limited, a combination of three actuation frequencies that would provide the most accurate approximation and lowest fitting error was determined for WB, WM, and GM by optimizing for the lowest Bayesian information criterion (BIC). Results BIC scores for the Zener and Springpot models showed these models approximate the multifrequency response of the tissue best. The best-fit frequency combinations for the reference Zener and Springpot models were identified to be 30-60-70 and 30-40-80 Hz, respectively, for the WB. Conclusions Optimal sets of actuation frequencies to accurately obtain rheological parameters for WB, WM, and GM were determined from shear moduli measurements obtained via 3-dimensional direct inversion. We believe that our study is a first-step in developing a region-specific multifrequency MRE protocol for the human brain.

In studying traumatic brain injury (TBI), it has been long hypothesized that the head is more vulnerable to injury from impacts in certain directions or locations, as the relationship between impact force and the resulting neurological outcome is complex and can vary significantly between individual cases. Many studies have identified head angular acceleration to be the putative cause of brain trauma, but it is not well understood how impact location can affect the resulting head kinematics and tissue strain. Here, we identify the susceptibility of the head to accelerations and brain strain from normal forces at contact points across the surface of the skull and jaw using a three-dimensional, 20-degree-of-freedom rigid-body head and cervical spine model. We find that head angular acceleration and brain tissue strain resulting from an input force can vary by orders of magnitude based on impact location on the skull, with the mandible as the most vulnerable region. Conversely, head linear acceleration is not sensitive to contact location. Using these analyses, we present an optimization scheme to distribute helmet padding thickness to minimize angular acceleration, resulting in a reduction of angular acceleration by an estimated 25% at the most vulnerable contact point compared to uniform thickness padding. This work gives intuition behind the relationship between input force and resulting brain injury risk, and presents a framework for developing and evaluating novel head protection gear.

Accurate positioning of cells is a fundamental task for many procedures in assisted reproductive technologies (e.g. intracytoplasmic sperm injection or preimplantation genetic diagnosis) to extract or insert materials into and from the cell without causing damage to it. The current method of manual manipulation is based on a trial and error procedure performed by skilled embryologists, where they use two different micropipettes to rotate the cell and immobilize it in the desired orientation. This procedure is time consuming, inconsistent and has a low efficiency. Attempts to automate the process presented in the literature have not yet been implemented in IVF clinics because their high degree of automation requires extensive changes to the current systems used in the clinics. We designed a system that can easily be integrated into standard equipment of IVF clinics and allows automated as well as manual manipulation of cells. The system uses vibrations induced by a surface transducer at the pipette holder to rotate the cell around the pipette tip axis, resulting in 2D motion. To detect if the polar body is in the desired position after a vibration burst, we developed a polar body detection algorithm. We performed simulations and experiments to confirm that vibrations at the natural frequencies of the system cause rotation around the pipette tip axis. Experimental results show that the system is capable of positioning the polar body in plane in less than 5.41 seconds.

Navigating surgical tools in the dynamic and tortuous anatomy of the lung's airways requires accurate, real-time localization of the tools with respect to the preoperative scan of the anatomy. Such localization can inform human operators or enable closed-loop control by autonomous agents, which would require accuracy not yet reported in the literature. In this paper, we introduce a deep learning architecture, called OffsetNet, to accurately localize a bronchoscope in the lung in real-time. After training on only 30 minutes of recorded camera images in conserved regions of a lung phantom, OffsetNet tracks the bronchoscope's motion on a held-out recording through these same regions at an update rate of 47 Hz and an average position error of 1.4 mm. Because this model performs poorly in less conserved regions, we augment the training dataset with simulated images from these regions. To bridge the gap between camera and simulated domains, we implement domain randomization and a generative adversarial network (GAN). After training on simulated images, OffsetNet tracks the bronchoscope's motion in less conserved regions at an average position error of 2.4 mm, which meets conservative thresholds required for successful tracking., 7 pages, 10 figures