A Dose Relationship Between Brain Functional Connectivity and Cumulative Head Impact Exposure in Collegiate Water Polo Players.

A growing body of evidence suggests that chronic, sport-related head impact exposure can impair brain functional integration and brain structure and function. Evidence of a robust inverse relationship between the frequency and magnitude of repeated head impacts and disturbed brain network function is needed to strengthen an argument for causality. In pursuing such a relationship, we used cap-worn inertial sensors to measure the frequency and magnitude of head impacts sustained by eighteen intercollegiate water polo athletes monitored over a single season of play. Participants were evaluated before and after the season using computerized cognitive tests of inhibitory control and resting electroencephalography. Greater head impact exposure was associated with increased phase synchrony [r (16) > 0.626, p < 0.03 corrected], global efficiency [r (16) > 0.601, p < 0.04 corrected], and mean clustering coefficient [r (16) > 0.625, p < 0.03 corrected] in the functional networks formed by slow-wave (delta, theta) oscillations. Head impact exposure was not associated with changes in performance on the inhibitory control tasks. However, those with the greatest impact exposure showed an association between changes in resting-state connectivity and a dissociation between performance on the tasks after the season [r (16) = 0.481, p = 0.043] that could also be attributed to increased slow-wave synchrony [F (4, 135) = 113.546, p < 0.001]. Collectively, our results suggest that athletes sustaining the greatest head impact exposure exhibited changes in whole-brain functional connectivity that were associated with altered information processing and inhibitory control

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 test sets 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 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 figure

Classification of head impacts based on the spectral density of measurable kinematics

Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are less accurate across the variety of impacts that patients may undergo. We investigated the spectral characteristics of different head impact types with kinematics classification. 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), reaching a median accuracy of 96% over 1,000 random partitions of training and test sets. To test the classifier on data from different measurement devices, another 271 lab-reconstructed impacts were obtained from 5 other instrumented mouthguards with the classifier reaching over 96% accuracy. 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). Finally, with the classifier, type-specific, nearest-neighbor regression models were built for 95th percentile maximum principal strain, 95th percentile maximum principal strain in corpus callosum, and cumulative strain damage (15th percentile). This showed a generally higher R2-value than baseline models. The classifier enables a better understanding of the impact kinematics in different sports, and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation. Key words: traumatic brain injury, head impacts, classification, impact kinematicsComment: 16 pages, 5 figure

Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation

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 in 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

Linking capacity development to GOOS monitoring networks to achieve sustained ocean observation

Developing enduring capacity to monitor ocean life requires investing in people and their institutions to build infrastructure, ownership, and long-term support networks. International initiatives can enhance access to scientific data, tools and methodologies, and develop local expertise to use them, but without ongoing engagement may fail to have lasting benefit. Linking capacity development and technology transfer to sustained ocean monitoring is a win-win proposition. Trained local experts will benefit from joining global communities of experts who are building the comprehensive Global Ocean Observing System (GOOS). This two-way exchange will benefit scientists and policy makers in developing and developed countries. The first step toward the GOOS is complete: identification of an initial set of biological Essential Ocean Variables (EOVs) that incorporate the Group on Earth Observations (GEO) Essential Biological Variables (EBVs), and link to the physical and biogeochemical EOVs. EOVs provide a globally consistent approach to monitoring where the costs of monitoring oceans can be shared and where capacity and expertise can be transferred globally. Integrating monitoring with existing international reporting and policy development connects ocean observations with agreements underlying many countries' commitments and obligations, including under SDG 14, thus catalyzing progress toward sustained use of the ocean. Combining scientific expertise with international capacity development initiatives can help meet the need of developing countries to engage in the agreed United Nations (UN) initiatives including new negotiations for the conservation and sustainable use of marine biological diversity of areas beyond national jurisdiction, and the needs of the global community to understand how the ocean is changing

Estimation of Light-use Efficiency of Terrestrial Ecosystem from Space: A Status Report

A critical variable in the estimation of gross primary production of terrestrial ecosystems is light-use efficiency (LUE), a value that represents the actual efficiency of a plant's use of absorbed radiation energy to produce biomass. Light-use efficiency is driven by the most limiting of a number of environmental stress factors that reduce plants' photosynthetic capacity; these include short-term stressors, such as photoinhibition, as well as longer-term stressors, such as soil water and temperature. Modeling LUE from remote sensing is governed largely by the biochemical composition of plant foliage, with the past decade seeing important theoretical and modeling advances for understanding the role of these stresses on LUE. In this article we provide a summary of the tower-, aircraft-, and satellite-based research undertaken to date, and discuss the broader scalability of these methods, concluding with recommendations for ongoing research possibilities

Adherence to self-managed exercises for patients with persistent subacromial pain: the Ad-Shoulder feasibility study

© 2021, The Author(s). Background: Exercise is recommended for patients with subacromial pain. It has been suggested that good exercise adherence improves clinical outcomes. Despite this, little attention has been paid to the need for behavioural frameworks to enhance adherence to home exercise programmes for patients with subacromial pain. Methods: A feasibility study with pre-post design was used. Participants aged > 18 years, with subacromial pain, who had received conservative treatment during the past 6 months, were recruited. The Ad-Shoulder intervention consisted of 1–5 individual sessions provided over 3 months and was based on 5 self-management skills, which aimed to enhance the patients’ self-efficacy and adherence to self-managed exercises. The primary objectives were assessed according to predefined progression criteria: (1) the recruitment rate (10 patients enrolled within 12 weeks), (2) follow-up rate (≥ 80% on all self-reported measures), (3) objective physical activity measures (≥ 80% of participants would contribute valid data at each time point), (4) adherence with the self-managed exercises (≥ 80% of the participants would adhere to ≥ 80% of the assigned home exercise programme), (5) fidelity of the delivery of the intervention (the therapists delivered the intervention according to the protocol) and (6) adverse events (< 30% would report adverse events (including mild)). The results were reported using descriptive statistics. Results: Eleven patients were recruited during 16 weeks. Ten patients completed the self-reported measures at baseline and week 12. Objective physical activity measures were successfully obtained for 100% (11/11) at baseline, 64% (7/11) at week six and 82% at week 12. Fifty-five percent (6/11) of the participants satisfactorily completed at least 80% of their home exercise programme. All sessions were delivered according to the protocol. None of the patients reported any adverse events. Conclusions: Objective physical activity data measures at baseline and week 12, follow-up, the physiotherapists’ fidelity to the intervention and adverse events met our pre-specified progression criteria. Recruitment and adherence to the self-managed exercise programme were both below the anticipated level. Further intervention development is necessary to understand whether adherence to the self-managed exercises could be enhanced and additional methods of recruitment would need to be considered, including additional recruitment sites, in any planning for a future main trial. Trial registration: ClinicalTrials.gov, NCT04190836, Registered December 9, 2019—retrospectively registered