Approximately 3.5 million youth athletes play tackle football in the United States alone.1 Football has gained attention for its relatively high rate of concussion injuries compared to other sports. Specifically, concussions account for 9.6% of all youth football injuries.2 Concussions are a serious injury, but the majority of head impacts experienced by football players are subconcussive. Although these impacts do not result in acute signs and symptoms of concussion, evidence suggests that accumulated repetitive subconcussive impacts may be associated with long-term neurological damage and can cause changes in the brain after a single season.3,4 As adolescence is a time of brain growth and development, understanding head impact exposure (HIE) is of utmost importance to mitigate adverse effects of head impacts in youth football. To understand subconcussive HIE in youth football, researchers have instrumented the helmets of athletes with head impact sensors. Recent efforts have combined on-field video with head impact sensor data to characterize HIE in practice drills5,6 and understand the effects of level of play,7 player performance metrics,8 and individual characteristics9-11 on peak kinematics. Additionally, other studies have considered the effect of how athletes engage in contact on peak kinematics, including closing distance (ie, the distance traveled prior to contact)7,12,13 and source of impact (ie, contact with a player or contact with a surface).11,12,14 Most studies to date are presented from one side of the ball since only one team during a game is instrumented.1,5,6,15 Since most concussions in football result from player-to-player contact, 87.8% according to Kerr et al,16 there is value in studying the head impact biomechanics of opposing athletes involved in the same contact scenario. Prior studies have improved our understanding of HIE in football and underscore the importance of contact characteristics on peak kinematics7,11,13; however, the dynamic response of the brain has not yet been widely considered when describing HIE. To better understand the effects of repetitive subconcussive head impacts, finite element (FE) models can be used to measure the biomechanical response of the brain to impacts such as tensile and compressive strain of the tissue.17 The biomechanical response of the brain is directionally dependent18 and, therefore, strain in the brain may be more indicative of the mechanistic effects of repetitive HIE and resulting damage to the brain compared to the more commonly used peak kinematic metrics. A variety of FE models have been used to evaluate brain strain and many have concluded that rotational kinematics are a strong predictor of strain magnitude.18-23 For example, Ji et al21 used the Dartmouth Head Injury Model and the Simulated Injury Monitor to simulate brain strain at the youth, high school, and collegiate levels. This study identified that rotational acceleration is an important predictor of brain strain, and that magnitude, and duration of rotational acceleration should be considered in kinematics-based injury metrics. In addition, Darling et al24 used a FE model of a football helmet and the human body to measure strain rates of frontal oblique and crown impact conditions seen during football impacts, and concluded that impacts have the potential to lead to axonal injuries and neuronal cell death. The atlas-based brain model (ABM), an anatomically accurate brain FE model, developed from the geometry of the International Consortium for Brain Mapping brain atlas, was used in this study to estimate the strain response of the brain during paired contact scenarios (ie, opposing athletes involved in the same contact).23 The International Consortium for Brain Mapping brain template represents a structural average of high-resolution scans of young adult brains.23 The resolution of the atlas allows for representation of detailed structures of the brain including the sulci and gyri that play a pivotal role in brain strain and stress predictions. Additionally, the ABM model contains a greater number of nodes and elements (approximately 2 million), compared to other FE models of the brain,21 allowing for greater detail in the strain prediction. The high anatomical specificity provided by the ABM allows for improvements in brain strain predictions compared to more simplified models.17,23 Recently, Miller et al17 used the ABM to study the brain response to head impacts in youth football players and identified the influence impact location has on magnitude of brain strain. The results from this study also establish the importance of considering the directional dependence and relative contribution of axes of rotation when evaluating HIE. In the current study, the ABM was used to calculate brain deformation metrics, including maximum principal tensile and compressive strain, for each paired contact scenario. The brain deformation metrics as well as peak kinematics were compared among different contact characteristics to identify scenarios where players may be more vulnerable to injury. The objective of this study is to evaluate and compare different HIE variables, including brain deformation metrics and peak kinematics, across contact characteristics in a unique data set, where opposing players were instrumented with head impact sensors. This study first considers contact characteristics across the entire data set of head impacts collected and the effect of contact characteristics on kinematic and strain-based metrics. In the second part of the study, pairwise differences in contact pairs are analyzed to relate on-field kinematic measurements to tissue-level strain-based metrics and understand the correlation between kinematic and strain-based metrics when evaluating HIE among players.
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