Context: Measuring head impact exposure is a critical step toward understanding the mechanism and prevention of sportrelated mild traumatic brain (concussion) injury, as well as the possible effects of repeated subconcussive impacts.Objective: To quantify the frequency and location of head impacts that individual players received in 1 season among 3 collegiate teams, between practice and game sessions, and among player positions.Design: Cohort study. Setting: Collegiate football field. Patients or Other Participants: One hundred eighty-eight players from 3 National Collegiate Athletic Association football teams.Intervention(s): Participants wore football helmets instrumented with an accelerometer-based system during the 2007 fall season.Main Outcome Measure(s): The number of head impacts greater than 10g and location of the impacts on the player's helmet were recorded and analyzed for trends and interactions among teams (A, B, or C), session types, and player positions using Kaplan-Meier survival curves.Results: The total number of impacts players received was nonnormally distributed and varied by team, session type, and player position. The maximum number of head impacts for a single player on each team was 1022 (team A), 1412 (team B), and 1444 (team C). The median number of head impacts on each team was 4.8 (team A), 7.5 (team B), and 6.6 (team C) impacts per practice and 12.1 (team A), 14.6 (team B), and 16.3 (team C) impacts per game. Linemen and linebackers had the largest number of impacts per practice and per game. Offensive linemen had a higher percentage of impacts to the front than to the back of the helmet, whereas quarterbacks had a higher percentage to the back than to the front of the helmet.Conclusions: The frequency of head impacts and the location on the helmet where the impacts occur are functions of player position and session type. These data provide a basis for quantifying specific head impact exposure for studies related to understanding the biomechanics and clinical aspects of concussion injury, as well as the possible effects of repeated subconcussive impacts in football.

Recent research has suggested a possible link between sports-related concussions and neurodegenerative processes, highlighting the importance of developing methods to accurately quantify head impact tolerance. The use of kinematic parameters of the head to predict brain injury has been suggested because they are indicative of the inertial response of the brain. The objective of this study isto characterize the rotational kinematics of the head associated with concussive impacts using a large head acceleration dataset collected from human subjects. The helmets of 335 football players were instrumented with accelerometer arrays that measured head acceleration following head impacts sustained during play, resulting in data for 300,977 subconcussive and 57 concussive head impacts. The average subconcussive impact had a rotational acceleration of 1230 rad/s 2 and a rotational velocity of 5.5 rad/s, while the average concussive impact had a rotational acceleration of 5022 rad/s 2 and a rotational velocity of 22.3 rad/s. An injury risk curve was developed and a nominal injury value of 6383 rad/s 2 associated with 28.3 rad/s represents 50% risk of concussion. These data provide an increased understanding of the biomechanics associated with concussion, and they provide critical insight into injury mechanisms, human tolerance to mechanical stimuli, and injury prevention techniques.

In American football, impacts to the helmet and the resulting head accelerations are the primary cause of concussion injury and potentially chronic brain injury. The purpose of this study was to quantify exposures to impacts to the head (frequency, location and magnitude) for individual collegiate football players and to investigate differences in head impact exposure by player position. A total of 314 players were enrolled at three institutions and 286,636 head impacts were recorded over three seasons. The 95th percentile peak linear and rotational acceleration and HITsp (a composite severity measure) were 62.7g, 4378 rad/s2, and 32.6, respectively. These exposure measures as well as the frequency of impacts varied significantly by player position and by helmet impact location. Running backs (RB) and quarter backs (QB) received the greatest magnitude head impacts, while defensive line (DL), offensive line (OL) and line backers (LB) received the most frequent head impacts (more than twice as many than any other position). Impacts to the top of the helmet had the lowest peak rotational acceleration (2387 rad/s2), but the greatest peak linear acceleration (72.4 g), and were the least frequent of all locations (13.7%) among all positions. OL and QB had the highest (49.2%) and the lowest (23%.7%) frequency, respectively, of front impacts. QB received the greatest magnitude (70.8g and 5428 rad/s2) and the most frequent (44% and 38.9%) impacts to the back of the helmet. This study quantified head impact exposure in collegiate football, providing data that is critical to advancing the understanding of the biomechanics of concussive injuries and sub-concussive head impacts.

Sports-related concussion is a major public health problem in the United States and yet its biomechanical mechanisms remain unclear. In vitro studies demonstrate axonal elongation as a potential injury mechanism; however, current response-based injury predictors (e.g., maximum principal strain, ε(ep)) typically do not incorporate axonal orientations. We investigated the significance of white matter (WM) fiber orientation in strain estimation and compared fiber strain (ε(n)) with ε(ep) for 11 athletes with a clinical diagnosis of concussion. Geometrically accurate subject-specific head models with high mesh quality were created based on the Dartmouth Head Injury Model (DHIM), which was successfully validated (performance categorized as "good" to "excellent"). For WM regions estimated to be exposed to high strains using a range of injury thresholds (0.09-0.28), substantial differences existed between ε(n) and ε(ep) in both distribution (Dice coefficient of 0.13-0.33) and extent (∼ 5-10-fold differences), especially at higher threshold levels and higher rotational acceleration magnitudes. For example, an average of 3.2% vs. 29.8% of WM was predicted above an optimal threshold of 0.18 established from an in vivo animal study using ε(n) and ε(ep), respectively, with an average Dice coefficient of 0.14. The distribution of WM regions with high ε(n) was consistent with typical heterogeneous patterns of WM disruptions in diffuse axonal injury, and the group-wise extent at the optimal threshold matched well with the percentage of WM voxels experiencing significant longitudinal changes of fractional anisotropy and mean diffusivity (3.2% and 3.44%, respectively) found from a separate independent study. These results suggest the significance of incorporating WM microstructural anisotropy in future brain injury studies.

This study suggests a relationship between head impact exposure, white matter diffusion measures, and cognition over the course of a single season, even in the absence of diagnosed concussion, in a cohort of college athletes. Further work is needed to assess whether such effects are short term or persistent.