IntroductionRepetitive head impacts (RHI) sustained in contact sports are thought to be necessary for the long-term development of chronic traumatic encephalopathy (CTE). Our objectives were to: 1) characterize the magnitude and persistence of RHI-induced white matter (WM) changes; 2) determine their relationship to kinematic measures of RHI; and 3) explore their clinical relevance.MethodsProspective, observational study of 10 Division III college football players and 5 non-athlete controls during the 2011-12 season. All subjects underwent diffusion tensor imaging (DTI), physiologic, cognitive, and balance testing at pre-season (Time 1), post-season (Time 2), and after 6-months of no-contact rest (Time 3). Head impact measures were recorded using helmet-mounted accelerometers. The percentage of whole-brain WM voxels with significant changes in fractional anisotropy (FA) and mean diffusivity (MD) from Time 1 to 2, and Time 1 to 3 was determined for each subject and correlated to head impacts and clinical measures.ResultsTotal head impacts for the season ranged from 431–1,850. No athlete suffered a clinically evident concussion. Compared to controls, athletes experienced greater changes in FA and MD from Time 1 to 2 as well as Time 1 to 3; most differences at Time 2 persisted to Time 3. Among athletes, the percentage of voxels with decreased FA from Time 1 to 2 was positively correlated with several helmet impact measures. The persistence of WM changes from Time 1 to 3 was also associated with changes in serum ApoA1 and S100B autoantibodies. WM changes were not consistently associated with cognition or balance.ConclusionsA single football season of RHIs without clinically-evident concussion resulted in WM changes that correlated with multiple helmet impact measures and persisted following 6 months of no-contact rest. This lack of WM recovery could potentially contribute to cumulative WM changes with subsequent RHI exposures.

Objective:To determine whether tau changes after sport-related concussion (SRC) relate to return to play (RTP).Methods:Collegiate athletes underwent preseason plasma sampling and cognitive testing and were followed. After a SRC (n = 46), athletes and controls (n = 37) had sampling at 6 hours, and at 24 hours, 72 hours, and 7 days after SRC. A sample of 21 nonathlete controls were compared at baseline. SRC athletes were grouped by long (>10 days, n = 23) and short (≤10 days, n = 18) RTP. Total tau was measured using an ultrasensitive immunoassay.Results:Both SRC and athlete controls had significantly higher mean tau at baseline compared to nonathlete healthy controls (F101,3 = 19.644, p < 0.01). Compared to SRC athletes with short RTP, those with long RTP had higher tau concentrations overall, after controlling for sex (F39,1 = 3.59, p = 0.022), compared to long RTP athletes, at 6 (p < 0.01), 24 (p < 0.01), and 72 hours (p = 0.02). Receiver operator characteristic analyses showed that higher plasma tau 6 hours post-SRC was a significant predictor of RTP >10 days (area under the curve 0.81; 95% confidence interval 0.62–0.97, p = 0.01).Conclusions:Elevated plasma tau concentration within 6 hours following a SRC was related to having a prolonged RTP, suggesting that tau levels may help inform RTP.

The midbrain is biomechanically susceptible to force loading from repetitive subconcussive head impacts (RSHI), is a site of tauopathy in chronic traumatic encephalopathy (CTE), and regulates functions (e.g., eye movements) often disrupted in concussion. In a prospective longitudinal design, we demonstrate there are reductions in midbrain white matter integrity due to a single season of collegiate football, and that the amount of reduction in midbrain white matter integrity is related to the amount of rotational acceleration to which players’ brains are exposed. We then replicate the observation of reduced midbrain white matter integrity in a retrospective cohort of individuals with frank concussion, and further show that variance in white matter integrity is correlated with levels of serum-based tau, a marker of blood-brain barrier disruption. These findings mean that noninvasive structural MRI of the midbrain is a succinct index of both clinically silent white matter injury as well as frank concussion.

The impact of sub-concussive head hits (sub-CHIs) has been recently investigated in American football players, a population at risk for varying degrees of post-traumatic sequelae. Results show how sub-CHIs in athletes translate in serum as the appearance of reporters of blood-brain barrier disruption (BBBD), how the number and severity of sub-CHIs correlate with elevations of putative markers of brain injury is unknown. Serum brain injury markers such as UCH-L1 depend on BBBD. We investigated the effects of sub-CHIs in collegiate football players on markers of BBBD, markers of cerebrospinal fluid leakage (serum beta 2-transferrin) and markers of brain damage. Emergency room patients admitted for a clinically-diagnosed mild traumatic brain injury (mTBI) were used as positive controls. Healthy volunteers were used as negative controls. Specifically this study was designed to determine the use of UCH-L1 as an aid in the diagnosis of sub-concussive head injury in athletes. The extent and intensity of head impacts and serum values of S100B, UCH-L1, and beta-2 transferrin were measured pre- and post-game from 15 college football players who did not experience a concussion after a game. S100B was elevated in players experiencing the most sub-CHIs; UCH-L1 levels were also elevated but did not correlate with S100B or sub-CHIs. Beta-2 transferrin levels remained unchanged. No correlation between UCH-L1 levels and mTBI were measured in patients. Low levels of S100B were able to rule out mTBI and high S100B levels correlated with TBI severity. UCH-L1 did not display any interpretable change in football players or in individuals with mild TBI. The significance of UCH-L1 changes in sub-concussions or mTBI needs to be further elucidated.

We conducted a prospective study to identify genome-wide changes in peripheral gene expression before and after sports-related concussion (SRC). A total of 253 collegiate contact athletes underwent collection of peripheral blood mononuclear cells (PBMCs) before the sport season (baseline). Sixteen athletes who subsequently developed an SRC, along with 16 non-concussed teammate controls, underwent repeat collection of PBMCs within 6 h of injury (acutely). Concussed athletes underwent additional sample collection at 7 days post-injury (sub-acutely). Messenger RNA (mRNA) expression at baseline was compared with mRNA expression acutely and sub-acutely post-SRC. To estimate the contribution of physical exertion to gene changes, baseline samples from athletes who subsequently developed an SRC were compared with samples from uninjured teammate controls collected at the acute time-point. Clinical outcome was determined by changes in post-concussive symptoms, postural stability, and cognition from baseline to the sub-acute time-point. SRC athletes had significant changes in mRNA expression at both the acute and sub-acute time-points. There were no significant expression changes among controls. Acute transcriptional changes centered on interleukins 6 and 12, toll-like receptor 4, and NF-κB. Sub-acute gene expression changes centered on NF-κB, follicle stimulating hormone, chorionic gonadotropin, and protein kinase catalytic subunit. All SRC athletes were clinically back to baseline by Day 7. In conclusion, acute post-SRC transcriptional changes reflect regulation of the innate immune response and the transition to adaptive immunity. By 7 days, transcriptional activity is centered on regulating the hypothalamic-pituitary-adrenal axis. Future efforts to compare expressional changes in fully recovered athletes with those who do not recover from SRC could suggest putative targets for therapeutic intervention.

BackgroundThe on-field diagnosis of sports-related concussion (SRC) is complicated by the lack of an accurate and objective marker of brain injury.PurposeTo compare subject-specific changes in the astroglial protein, S100B, before and after SRC among collegiate and semi-professional contact sport athletes, and compare these changes to differences in S100B before and after non-contact exertion.Study DesignLongitudinal cohort study.MethodsFrom 2009–2011, we performed a prospective study of athletes from Munich, Germany, and Rochester, New York, USA. Serum S100B was measured in all SRC athletes at pre-season baseline, within 3 hours of injury, and at days 2, 3 and 7 post-SRC. Among a subset of athletes, S100B was measured after non-contact exertion but before injury. All samples were collected identically and analyzed using an automated electrochemiluminescent assay to quantify serum S100B levels.ResultsForty-six athletes (30 Munich, 16 Rochester) underwent baseline testing. Thirty underwent additional post-exertion S100B testing. Twenty-two athletes (16 Rochester, 6 Munich) sustained a SRC, and 17 had S100B testing within 3 hours post-injury. The mean 3-hour post-SRC S100B was significantly higher than pre-season baseline (0.099±0.008 µg/L vs. 0.058±0.006 µg/L, p = 0.0002). Mean post-exertion S100B was not significantly different than the preseason baseline. S100B levels at post-injury days 2, 3 and 7 were significantly lower than the 3-hour level, and not different than baseline. Both the absolute change and proportional increase in S100B 3-hour post-injury were accurate discriminators of SRC from non-contact exertion without SRC (AUC 0.772 and 0.904, respectively). A 3-hour post-concussion S100B >0.122 µg/L and a proportional S100B increase of >45.9% over baseline were both 96.7% specific for SRC.ConclusionsRelative and absolute increases in serum S100B can accurately distinguish SRC from sports-related exertion, and may be a useful adjunct to the diagnosis of SRC.

The aim of this study was to evaluate longitudinal changes in the diffusion characteristics of brain white matter (WM) in collegiate athletes at three time points: prior to the start of the football season (T1), after one season of football (T2), followed by six months of no-contact rest (T3). Fifteen male collegiate football players and 5 male non-athlete student controls underwent diffusion MR imaging and computerized cognitive testing at all three timepoints. Whole-brain tract-based spatial statistics (TBSS) were used to compare fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), and trace between all timepoints. Average diffusion values were obtained from statistically significant clusters for each individual. No athlete suffered a concussion during the study period. After one season of play (T1 to T2), we observed a significant increase in trace in a cluster located in the brainstem and left temporal lobe, and a significant increase in FA in the left parietal lobe. After six months of no-contact rest (T2 to T3), there was a significant decrease in trace and FA in clusters that were partially overlapping or in close proximity with the initial clusters (T1 to T2), with no significant changes from T1 to T3. Repetitive head impacts (RHI) sustained during a single football season may result in alterations of the brain's WM in collegiate football players. These changes appear to return to baseline after 6 months of no-contact rest, suggesting remission of WM alterations. Our preliminary results suggest that collegiate football players might benefit from periods without exposure to RHI.

One 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 (HIC), 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 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 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 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 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 ma...