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Resting state electroencephalography (rsEEG) is widely used to investigate intrinsic brain activity, with the potential for detecting neurophysiological abnormalities in clinical conditions from neurodegenerative disease to developmental disorders. When interpreting quantitative rsEEG changes, a key question is: how much deviation from a healthy normal brain state indicates a clinically significant change? Here, we build on the existing rsEEG variability literature by quantifying how this baseline rsEEG range can be attributed to common but underinvestigated sources of variability: experiment day, time of day, and pre-recording exercise level. We found that even within individuals, frequency band powers and entropy measures can vary by 7% (sample entropy and relative alpha power) to 28% (absolute delta power). Absolute and relative delta power increased significantly after running, while relative theta power decreased significantly. Relative beta and gamma power were significantly higher in the afternoon compared to morning trials. Sample entropy and alpha power were relatively consistent. The coefficients of variability we found are similar to some clinical rsEEG effect sizes identified in prior literature, bringing into question the clinical significance of these effect sizes. Furthermore, time of day and activity level accounted for more rsEEG variability than experiment day, indicating the potential to reduce variability by controlling for these factors in repeated-measures studies.

Abstract There is growing concern that repetitive soccer headers may have negative long-term consequences on brain health. However, inconsistent and low-quality heading exposure measurements limit past investigations of this effect. Here we conducted a comprehensive heading exposure analysis across all players on a university women’s soccer team for over two calendar years (36 unique athletes), quantifying both game and practice exposure during all in-season and off-season periods, with over ten thousand video-confirmed headers. Despite an average of approximately 2 headers per day, players’ daily exposures ranged from 0 to 45 headers, accumulating to highly variable total exposure of 2–223 headers over each in-season/off-season period. Overall, practices and off-season periods accounted for 70% and 45% of headers, respectively. Impact sensor data showed that heading kinematics fell within a tight distribution, but sensors could not capture full heading exposure due to factors such as compliance. With first-of-its-kind complete heading exposure data, we recommend exposure assessments be made on an individual level and include practice/off-season collection in addition to games and competitive seasons. Commonly used group statistics do not capture highly variable exposures, and individualized complete heading exposure tracking needs to be incorporated in future study designs for confirming the potential brain injury risk associated with soccer heading.

Abstract Head impacts are highly prevalent in sports and there is a pressing need to investigate the potential link between head impact exposure and brain injury risk. Wearable impact sensors and manual video analysis have been utilized to collect impact exposure data. However, wearable sensors suffer from high deployment cost and limited accuracy, while manual video analysis is a long and resource-intensive task. Here we develop and apply DeepImpact, a computer vision algorithm to automatically detect soccer headers using soccer game videos. Our data-driven pipeline uses two deep learning networks including an object detection algorithm and temporal shift module to extract visual and temporal features of video segments and classify the segments as header or nonheader events. The networks were trained and validated using a large-scale professional-level soccer video dataset, with labeled ground truth header events. The algorithm achieved 95.3% sensitivity and 96.0% precision in cross-validation, and 92.9% sensitivity and 21.1% precision in an independent test that included videos of five professional soccer games. Video segments identified as headers in the test data set correspond to 3.5 min of total film time, which can be reviewed through additional manual video verification to eliminate false positives. DeepImpact streamlines the process of manual video analysis and can help to collect large-scale soccer head impact exposure datasets for brain injury research. The fully video-based solution is a low-cost alternative for head impact exposure monitoring and may also be expanded to other sports in future work.