Multi-directional dynamic model for traumatic brain injury detection

Traumatic brain injury (TBI) is a complex injury that is hard to predict and diagnose, with many studies focused on associating head kinematics to brain injury risk. Recently, there has been a push towards using computationally expensive finite element (FE) models of the brain to create tissue deformation metrics of brain injury. Here, we developed a 3 degree-of-freedom lumped-parameter brain model, built based on the measured natural frequencies of a FE brain model simulated with live human impact data, to be used to rapidly estimate peak brain strains experienced during head rotational accelerations. On our dataset, the simplified model correlates with peak principal FE strain by an R2 of 0.80. Further, coronal and axial model displacement correlated with fiber-oriented peak strain in the corpus callosum with an R2 of 0.77. Using the maximum displacement predicted by our brain model, we propose an injury criteria and compare it against a number of existing rotational and translational kinematic injury metrics on a dataset of head kinematics from 27 clinically diagnosed injuries and 887 non-injuries. We found that our proposed metric performed comparably to peak angular acceleration, linear acceleration, and angular velocity in classifying injury and non-injury events. Metrics which separated time traces into their directional components had improved deviance to those which combined components into a single time trace magnitude. Our brain model can be used in future work as a computationally efficient alternative to FE models for classifying injuries over a wide range of loading conditions.Comment: 10 figures, 3 table

Resting State EEG Variability and Implications for Interpreting Clinical Effect Sizes

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

Comparison of video-based and sensor-based head impact exposure

<div><p>Previous research has sought to quantify head impact exposure using wearable kinematic sensors. However, many sensors suffer from poor accuracy in estimating impact kinematics and count, motivating the need for additional independent impact exposure quantification for comparison. Here, we equipped seven collegiate American football players with instrumented mouthguards, and video recorded practices and games to compare video-based and sensor-based exposure rates and impact location distributions. Over 50 player-hours, we identified 271 helmet contact periods in video, while the instrumented mouthguard sensor recorded 2,032 discrete head impacts. Matching video and mouthguard real-time stamps yielded 193 video-identified helmet contact periods and 217 sensor-recorded impacts. To compare impact locations, we binned matched impacts into frontal, rear, side, oblique, and top locations based on video observations and sensor kinematics. While both video-based and sensor-based methods found similar location distributions, our best method utilizing integrated linear and angular position only correctly predicted 81 of 217 impacts. Finally, based on the activity timeline from video assessment, we also developed a new exposure metric unique to American football quantifying number of cross-verified sensor impacts per player-play. We found significantly higher exposure during games (0.35, 95% CI: 0.29–0.42) than practices (0.20, 95% CI: 0.17–0.23) (p<0.05). In the traditional impacts per player-hour metric, we observed higher exposure during practices (4.7) than games (3.7) due to increased player activity in practices. Thus, our exposure metric accounts for variability in on-field participation. While both video-based and sensor-based exposure datasets have limitations, they can complement one another to provide more confidence in exposure statistics.</p></div

Activity classifications for tracking player activity and identifying helmet contact activities with high sensitivity.

<p>Tracked player marked with a red arrow. (A) Raters identified Helmet Contact activities whenever the tracked player’s head overlapped with an opposing player. (B) Body Contact activities when there was contact not involving the head. (C) No Contact activities when player was in play, but not actively in contact. (D) Obstructed View activities when there was no clear view of the player’s head. (E) Idle activities when players were observed on the sideline, or otherwise not in play. Finally, (F) Not in Video activities when tracked player was not in the video.</p

Overview of tiered video assessment for collecting head impact exposure dataset.

<p>(A) Multiple video angles were collected for each practice and game, with at least one camera capturing an end-zone view and one camera capturing a sideline view. (B) Video was trimmed by technicians to only include play footage. (C) Trained raters performed a first round of video assessment, tracking specific players and labeling their activity. (D) A second round of video assessment performed by one of the authors confirmed Helmet Contact activities.</p

Impact location vectors and mouthguard kinematics processing.

<p>(A) Locations are binned into front, front oblique, side, rear oblique, rear, and top impacts. (B) Video-based helmet contact periods were qualitatively binned into impact locations during second round video assessment by the rating author. For sensor-based head impacts, kinematics were processed by first integrating or differentiating sensor linear acceleration and angular velocity signals to obtain linear velocity, linear position, angular acceleration, and angular position (represented with XYZ Euler angles). Peak motion (angular or linear acceleration, velocity, or position) vectors were found by identifying the peak magnitude and determining the 3 degree-of-freedom components. Peak linear acceleration, velocity, and position vectors were binned directly. We also incorporated peak angular motion vectors to correct respective peak linear motion vectors.</p

Second round video assessment for specific helmet contact activity identification.

<p>Multiple videos were used to confirm Helmet Contact activities. Red arrows mark the tracked player, with blue arrows marking other players. End-zone videos show helmet overlap, but sideline video showed (A) definitive head contact and (B) no helmet contact.</p