Longitudinal Alterations of Cerebral Blood Flow in High-Contact Sports.

OBJECTIVE: Repetitive head trauma is common in high-contact sports. Cerebral blood flow (CBF) can measure changes in brain perfusion that could indicate injury. Longitudinal studies with a control group are necessary to account for interindividual and developmental effects. We investigated whether exposure to head impacts causes longitudinal CBF changes. METHODS: We prospectively studied 63 American football (high-contact cohort) and 34 volleyball (low-contact controls) male collegiate athletes, tracking CBF using 3D pseudocontinuous arterial spin labeling magnetic resonance imaging for up to 4 years. Regional relative CBF (rCBF, normalized to cerebellar CBF) was computed after co-registering to T1-weighted images. A linear mixed effects model assessed the relationship of rCBF to sport, time, and their interaction. Within football players, we modeled rCBF against position-based head impact risk and baseline Standardized Concussion Assessment Tool score. Additionally, we evaluated early (1-5 days) and delayed (3-6 months) post-concussion rCBF changes (in-study concussion). RESULTS: Supratentorial gray matter rCBF declined in football compared with volleyball (sport-time interaction p = 0.012), with a strong effect in the parietal lobe (p = 0.002). Football players with higher position-based impact-risk had lower occipital rCBF over time (interaction p = 0.005), whereas players with lower baseline Standardized Concussion Assessment Tool score (worse performance) had relatively decreased rCBF in the cingulate-insula over time (interaction effect p = 0.007). Both cohorts showed a left-right rCBF asymmetry that decreased over time. Football players with an in-study concussion showed an early increase in occipital lobe rCBF (p = 0.0166). INTERPRETATION: These results suggest head impacts may result in an early increase in rCBF, but cumulatively a long-term decrease in rCBF. ANN NEUROL 2023;94:457-469.

MR elastography frequency-dependent and independent parameters demonstrate accelerated decrease of brain stiffness in elder subjects.

OBJECTIVES: To analyze the mechanical properties in different regions of the brain in healthy adults in a wide age range: 26 to 76 years old. METHODS: 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. RESULTS: 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. CONCLUSION: 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. KEY POINTS: • 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.

Characterizing head impact exposure in youth female soccer with a custom-instrumented mouthpiece.

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.

Vulnerable Locations on the Head to Brain Injury and Implications for Helmet Design.

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.

Head Impact Kinematics Estimation With Network of Inertial Measurement Units.

Wearable sensors embedded with inertial measurement units have become commonplace for the measurement of head impact biomechanics, but individual systems often suffer from a lack of measurement fidelity. While some researchers have focused on developing highly accurate, single sensor systems, we have taken a parallel approach in investigating optimal estimation techniques with multiple noisy sensors. In this work, we present a sensor network methodology that utilizes multiple skin patch sensors arranged on the head and combines their data to obtain a more accurate estimate than any individual sensor in the network. Our methodology visually localizes subject-specific sensor transformations, and based on rigid body assumptions, applies estimation algorithms to obtain a minimum mean squared error estimate. During mild soccer headers, individual skin patch sensors had over 100% error in peak angular velocity magnitude, angular acceleration magnitude, and linear acceleration magnitude. However, when properly networked using our visual localization and estimation methodology, we obtained kinematic estimates with median errors below 20%. While we demonstrate this methodology with skin patch sensors in mild soccer head impacts, the formulation can be generally applied to any dynamic scenario, such as measurement of cadaver head impact dynamics using arbitrarily placed sensors.

Validation of a Custom Instrumented Retainer Form Factor for Measuring Linear and Angular Head Impact Kinematics.

Head impact exposure in popular contact sports is not well understood, especially in the youth population, despite recent advances in impact-sensing technology which has allowed widespread collection of real-time head impact data. Previous studies indicate that a custom-instrumented mouthpiece is a superior method for collecting accurate head acceleration data. The objective of this study was to evaluate the efficacy of mounting a sensor device inside an acrylic retainer form factor to measure six-degrees-of-freedom (6DOF) head kinematic response. This study compares 6DOF mouthpiece kinematics at the head center of gravity (CG) to kinematics measured by an anthropomorphic test device (ATD). This study found that when instrumentation is mounted in the rigid retainer form factor, there is good coupling with the upper dentition and highly accurate kinematic results compared to the ATD. Peak head kinematics were correlated with r2 > 0.98 for both rotational velocity and linear acceleration and r2 = 0.93 for rotational acceleration. These results indicate that a rigid retainer-based form factor is an accurate and promising method of collecting head impact data. This device can be used to study head impacts in helmeted contact sports such as football, hockey, and lacrosse as well as nonhelmeted sports such as soccer and basketball. Understanding the magnitude and frequency of impacts sustained in various sports using an accurate head impact sensor, such as the one presented in this study, will improve our understanding of head impact exposure and sports-related concussion.

Microfluidic analysis of oocyte and embryo biomechanical properties to improve outcomes in assisted reproductive technologies.

Measurement of oocyte and embryo biomechanical properties has recently emerged as an exciting new approach to obtain a quantitative, objective estimate of developmental potential. However, many traditional methods for probing cell mechanical properties are time consuming, labor intensive and require expensive equipment. Microfluidic technology is currently making its way into many aspects of assisted reproductive technologies (ART), and is particularly well suited to measure embryo biomechanics due to the potential for robust, automated single-cell analysis at a low cost. This review will highlight microfluidic approaches to measure oocyte and embryo mechanics along with their ability to predict developmental potential and find practical application in the clinic. Although these new devices must be extensively validated before they can be integrated into the existing clinical workflow, they could eventually be used to constantly monitor oocyte and embryo developmental progress and enable more optimal decision making in ART.

AI-based denoising of head impact kinematics measurements with convolutional neural network for traumatic brain injury prediction.

OBJECTIVE: Wearable devices are developed to measure head impact kinematics but are intrinsically noisy because of the imperfect interface with human bodies. This study aimed to improve the head impact kinematics measurements obtained from instrumented mouthguards using deep learning to enhance traumatic brain injury (TBI) risk monitoring. METHODS: We developed one-dimensional convolutional neural network (1D-CNN) models to denoise mouthguard kinematics measurements for tri-axial linear acceleration and tri-axial angular velocity from 163 laboratory dummy head impacts. The performance of the denoising models was evaluated on three levels: kinematics, brain injury criteria, and tissue-level strain and strain rate. Additionally, we performed a blind test on an on-field dataset of 118 college football impacts and a test on 413 post-mortem human subject (PMHS) impacts. RESULTS: On the dummy head impacts, the denoised kinematics showed better correlation with reference kinematics, with relative reductions of 36% for pointwise root mean squared error and 56% for peak absolute error. Absolute errors in six brain injury criteria were reduced by a mean of 82%. For maximum principal strain and maximum principal strain rate, the mean error reduction was 35% and 69%, respectively. On the PMHS impacts, similar denoising effects were observed and the peak kinematics after denoising were more accurate (relative error reduction for 10% noisiest impacts was 75.6%). CONCLUSION: The 1D-CNN denoising models effectively reduced errors in mouthguard-derived kinematics measurements on dummy and PMHS impacts. SIGNIFICANCE: This study provides a novel approach for denoising head kinematics measurements in dummy and PMHS impacts, which can be further validated on more real-human kinematics data before real-world applications.

Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types.

OBJECTIVE: The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM. METHODS: To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). RESULTS: The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than [Formula: see text] in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models. CONCLUSION: The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate. SIGNIFICANCE: This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.

Finite element evaluation of an American football helmet featuring liquid shock absorbers for protecting against concussive and subconcussive head impacts.

Introduction: Concern has grown over the potential long-term effects of repeated head impacts and concussions in American football. Recent advances in impact engineering have yielded the development of soft, collapsible, liquid shock absorbers, which have demonstrated the ability to dramatically attenuate impact forces relative to existing helmet shock absorbers. Methods: To further explore how liquid shock absorbers can improve the efficacy of an American football helmet, we developed and optimized a finite element (FE) helmet model including 21 liquid shock absorbers spread out throughout the helmet. Using FE models of an anthropomorphic test headform and linear impactor, a previously published impact test protocol representative of concussive National Football League impacts (six impact locations, three velocities) was performed on the liquid FE helmet model and four existing FE helmet models. We also evaluated the helmets at three lower impact velocities representative of subconcussive football impacts. Head kinematics were recorded for each impact and used to compute the Head Acceleration Response Metric (HARM), a metric factoring in both linear and angular head kinematics and used to evaluate helmet performance. The head kinematics were also input to a FE model of the head and brain to calculate the resulting brain strain from each impact. Results: The liquid helmet model yielded the lowest value of HARM at 33 of the 36 impact conditions, offering an average 33.0% (range: -37.5% to 56.0%) and 32.0% (range: -2.2% to 50.5%) reduction over the existing helmet models at each impact condition in the subconcussive and concussive tests, respectively. The liquid helmet had a Helmet Performance Score (calculated using a summation of HARM values weighted based on injury incidence data) of 0.71, compared to scores ranging from 1.07 - 1.21 from the other four FE helmet models. Resulting brain strains were also lower in the liquid helmet. Discussion: The results of this study demonstrate the promising ability of liquid shock absorbers to improve helmet safety performance and encourage the development of physical prototypes of helmets featuring this technology. The implications of the observed reductions on brain injury risk are discussed.

Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation.

Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI). However, the overfitting to simulated impacts and the lack of generalizability caused by distributional shift of different head impact datasets hinders the broad clinical applications of current MLHMs. We propose brain deformation estimators that integrates unsupervised domain adaptation with a deep neural network to predict whole-brain maximum principal strain (MPS) and MPS rate (MPSR). With 12,780 simulated head impacts, we performed unsupervised domain adaptation on on-field head impacts from 302 college football (CF) impacts and 457 mixed martial arts (MMA) impacts using domain regularized component analysis (DRCA) and cycle-GAN-based methods. The new model improved the MPS/MPSR estimation accuracy, with the DRCA method significantly outperforming other domain adaptation methods in prediction accuracy (p<0.001): MPS RMSE: 0.027 (CF) and 0.037 (MMA); MPSR RMSE: 7.159 (CF) and 13.022 (MMA). On another two hold-out testsets with 195 college football impacts and 260 boxing impacts, the DRCA model significantly outperformed the baseline model without domain adaptation in MPS and MPSR estimation accuracy (p<0.001). The DRCA domain adaptation reduces the MPS/MPSR estimation error to be well below TBI thresholds, enabling accurate brain deformation estimation to detect TBI in future clinical applications.

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.

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.

A low-cost, highly functional, emergency use ventilator for the COVID-19 crisis.

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.

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.

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.

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.

Viscoelasticity of children and adolescent brains through MR elastography.

Magnetic Resonance Elastography (MRE) is an elasticity imaging technique that allows a safe, fast, and non-invasive evaluation of the mechanical properties of biological tissues in vivo. Since mechanical properties reflect a tissue's composition and arrangement, MRE is a powerful tool for the investigation of the microstructural changes that take place in the brain during childhood and adolescence. The goal of this study was to evaluate the viscoelastic properties of the brain in a population of healthy children and adolescents in order to identify potential age and sex dependencies. We hypothesize that because of myelination, age dependent changes in the mechanical properties of the brain will occur during childhood and adolescence. Our sample consisted of 26 healthy individuals (13 M, 13 F) with age that ranged from 7-17 years (mean: 11.9 years). We performed multifrequency MRE at 40, 60, and 80 Hz actuation frequencies to acquire the complex-valued shear modulus G = G' + iG″ with the fundamental MRE parameters being the storage modulus (G'), the loss modulus (G″), and the magnitude of complex-valued shear modulus (|G|). We fitted a springpot model to these frequency-dependent MRE parameters in order to obtain the parameter α, which is related to tissue's microstructure, and the elasticity parameter k, which was converted to a shear modulus parameter (µ) through viscosity (η). We observed no statistically significant variation in the parameter µ, but a significant increase of the microstructural parameter α of the white matter with increasing age (p < 0.05). Therefore, our MRE results suggest that subtle microstructural changes such as neural tissue's enhanced alignment and geometrical reorganization during childhood and adolescence could result in significant biomechanical changes. In line with previously reported MRE data for adults, we also report significantly higher shear modulus (µ) for female brains when compared to males (p < 0.05). The data presented here can serve as a clinical baseline in the analysis of the pediatric and adolescent brain's viscoelasticity over this age span, as well as extending our understanding of the biomechanics of brain development.

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.

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.