• Kian Merchant-Borna, Patrick Asselin, Darren Narayan, Beau Abar, Courtney M C Jones, and Jeffrey J Bazarian.
• Department of Emergency Medicine, University of Rochester School of Medicine and Dentistry, 265 Crittenden Blvd, Box 655C, Rochester, NY, 14642, USA. kian.merchant-borna@rochester.edu.
• Ann Biomed Eng. 2016 Dec 1; 44 (12): 3679-3692.

AbstractOne football season of sub-concussive head blows has been shown to be associated with subclinical white matter (WM) changes on diffusion tensor imaging (DTI). Prior research analyses of helmet-based impact metrics using mean and peak linear and rotational acceleration showed relatively weak correlations to these WM changes; however, these analyses failed to account for the emerging concept that neuronal vulnerability to successive hits is inversely related to the time between hits (TBH). To develop a novel method for quantifying the cumulative effects of sub-concussive head blows during a single season of collegiate football by weighting helmet-based impact measures for time between helmet impacts. We further aim to compare correlations to changes in DTI after one season of collegiate football using weighted cumulative helmet-based impact measures to correlations using non-weighted cumulative helmet-based impact measures and non-cumulative measures. We performed a secondary analysis of DTI and helmet impact data collected on ten Division III collegiate football players during the 2011 season. All subjects underwent diffusion MR imaging before the start of the football season and within 1 week of the end of the football season. Helmet impacts were recorded at each practice and game using helmet-mounted accelerometers, which computed five helmet-based impact measures for each hit: linear acceleration (LA), rotational acceleration (RA), Gadd Severity Index (GSI), Head Injury Criterion (HIC15), and Head Impact Technology severity profile (HITsp). All helmet-based impact measures were analyzed using five methods of summary: peak and mean (non-cumulative measures), season sum-totals (cumulative unweighted measures), and season sum-totals weighted for time between hits (TBH), the interval of time from hit to post-season DTI assessment (TUA), and both TBH and TUA combined. Summarized helmet-based impact measures were correlated to statistically significant changes in fractional anisotropy (FA) using bivariate and multivariable correlation analyses. The resulting R 2 values were averaged in each of the five summary method groups and compared using one-way ANOVA followed by Tukey post hoc tests for multiple comparisons. Total head hits for the season ranged from 431 to 1850. None of the athletes suffered a clinically evident concussion during the study period. The mean R 2 value for the correlations using cumulative helmet-based impact measures weighted for both TUA and TBH combined (0.51 ± 0.03) was significantly greater than the mean R 2 value for correlations using non-cumulative HIMs (vs. 0.19 ± 0.04, p < 0.0001), unweighted cumulative helmet-based impact measures (vs. 0.27 + 0.03, p < 0.0001), and cumulative helmet-based impact measures weighted for TBH alone (vs. 0.34 ± 0.02, p < 0.001). R 2 values for weighted cumulative helmet-based impact measures ranged from 0.32 to 0.77, with 60% of correlations being statistically significant. Cumulative GSI weighted for TBH and TUA explained 77% of the variance in the percent of white matter voxels with statistically significant (PWMVSS) increase in FA from pre-season to post-season, while both cumulative GSI and cumulative HIC15 weighted for TUA accounted for 75% of the variance in PWMVSS decrease in FA. A novel method for weighting cumulative helmet-based impact measures summed over the course of a football season resulted in a marked improvement in the correlation to brain WM changes observed after a single football season of sub-concussive head blows. Our results lend support to the emerging concept that sub-concussive head blows can result in sub-clinical brain injury, and this may be influenced by the time between hits. If confirmed in an independent data set, our novel method for quantifying the cumulative effects of sub-concussive head blows could be used to develop threshold-based countermeasures to prevent the accumulation of WM changes with multiple seasons of play.

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