mild traumatic brain injury (mTBI) or sport-related concussion (SRC) has generated widespread interest over the past decade. Recent research efforts have started to define the epidemiology and etiology of these injuries in American football players ; however, the clinical diagnosis of SRC is significantly limited by athletes' willingness to report symptoms and clinicians' personal practices. 1,2 There is evidence specifically related to the underreporting of 3,4 concussion in youth sports. Given this underreporting, objective neuroimaging measures are needed. Recent advances in neuroimaging techniques, specifically magnetic resonance diffusion tensor imaging (DTI), have allowed for the evaluation of underlying structural alterations to the brain after 5 head impacts. Diffusion tensor imaging has shown efficacy in the evaluation of athletes who are exposed to repetitive subconcussive head impacts (SCI), even in the absence of a clinically diagnosed concussion. Diffusion tensor imaging–derived diffusivity measures allow for objective evaluation of microscale 6–8 white matter (WM) alterations that may occur with head impacts, regardless of visible symptoms in the athlete. The 4 measureable metrics used for DTI are fractional anisotropy (FA), radial diffusivity (RD), axial diffusivity (AD), and mean diffusivity (MD), all derived from the diffusion tensor reconstructed from the DTI data. Fractional anisotropy is the normalized variance of the 3 eigenvalues, which is an index for the degree of diffusion asymmetry. Mean diffusivity is the average of the 3 eigenvalues. The AD is the eigenvalue along the principle eigenvector, whereas the RD is the average of the eigenvalues along the 2 other eigenvectors. Of these measures, RD, AD, and MD have consistently shown sensitivity in detecting WM alterations in athletes in contact sports. 6–11 For instance, DTI alterations have been associated with various pathologies because reductions in AD and RD indicate degraded axonal integrity and inflammation. 6–8 Specifically, alterations in AD reflect degraded axonal membrane integrity, 12,13 and alterations in RD are linked to extracellular space changes. 14,15 Although the anatomical effects of 16 head impacts in high school (HS), collegiate, and professional levels of football competition have been more extensively studied, there has been much less investigation of the largest participation population, youth below HS age. 17
football participation has decreased in recent years, due, in part, to highly publicized concerns regarding SRC. This trend is concerning when considering the benefits experienced by athletes who participate in 18,19 football. Youth sports participation is associated with social, psychological, and physical benefits that carry over into adulthood and establish a foundation for lifelong health. With physical inactivity being a top cause of death worldwide, 20–24 it is imperative that children be encouraged to form healthy habits for physical activity early in life. 25 In addition, the nature of 26 football as a cooperative and interdependent team sport encourages athletes of diverse backgrounds to come together while working toward a common goal. 27
There has been recent speculation whether children's brains are relatively more susceptible to the potentially deleterious effects of
head impacts when compared with adults, potentially related to the timing of microstructural maturation of brain matter during childhood. Between the ages of 10 to 12 years, young boys undergo a critical period 28,29 in which myelination, 30–33 cerebral blood flow, 34,35 and brain regions such as the amygdala and hippocampus 31 rapidly mature. Likewise, topological improvements in WM brain networks 36–39 and enhanced information processing through synaptic pruning 40 are observed during, or shortly after, this period. Although initial research theorized that brain plasticity may augment youth brain development more than what is seen in adults, 41 some recent studies suggest that young adults are more susceptible to prolonged recovery and poor outcomes after concussions. 42 The potential consequences of head injury on cerebral development, in contrast to the documented psychological and physical benefits of regular exercise and sports participation, make it imperative to characterize sporting head impact–related changes in brain health at the youth level. The purpose of this pilot cohort study was to compare the WM integrity and total 43–47 head impacts of a cohort of youth football (YFB) players relative to varsity HS football players over the course of one competitive season. Congruent with previous work in HS, we hypothesized that YFB would exhibit significant reductions in WM integrity over the course of one competitive season. Furthermore, we hypothesized the magnitude of these changes would be greater for YFB during this developmental stage than for HS 8,48 football players. METHODS
The Children's Hospital Medical Center Institutional Review Board (IRB) approved this study. Twenty-one male varsity HS
football players and 12 YFB players from a single area in the Midwestern United States were included in the analysis. The DTI data for the 21 HS football players have been reported in a previous study. In this study, these HS athletes (age: 17.33 ± 0.73 years) were compared with a new younger cohort (age: 13.08 ± 0.64 years) who participated in a full season of 7 football. Subject assent and parental consent were obtained before study participation. Instrumentation and Procedures
Prospective longitudinal magnetic resonance imaging (MRI) data were acquired at preseason and postseason for all participants. Preseason testing took place before the start of the first practice. The postseason testing took place after the last competitive event, with the interval between the last competitive event and postseason imaging between 1 and 20 days (median = 7.0 days) for the HS group and 1 to 59 days [median = 4.5 days for the YFB group (
P = 0.75, the Mann–Whitney U test)]. Of note, the larger range for YFB relative to HS was due to 2 YFB subjects who had conflicts attending the postseason MRI testing session. Our nonsignificant Mann–Whitney U test indicates that YFB attended the postseason MRI testing session within a similar time interval as HS. During the study period, 50 practices and 10 regular season games were completed for the HS group, whereas 28 practices and 9 games were completed for the YFB group. Athlete attendance was tracked by an athletic trainer for every practice and game session to calculate athlete exposures (AEs). The athletic trainer was also responsible for the assessment of any suspected SRC. Magnetic Resonance Imaging Data Acquisition
Magnetic resonance imaging data were all acquired on a 3 Tesla Phillips Achieva MRI scanner (Philips Medical Systems, Best, the Netherlands) equipped with a SENSE 32 channel head coil. The DTI data were acquired with a single-shot spin-echo echo-planar imaging sequence with the following specifications: repetition time = 9000 msec; echo time = 83 msec; field of view = 256 × 256 mm; acquisition matrix = 128 × 128; in-plane resolution = 2 × 2 mm; slice thickness = 2 mm; and number of slices = 72 slices. Diffusion weighed images were acquired along 61 noncollinear directions with 7 nondiffusion weighted images (b0 = 1000 s/mm
2). A high-resolution 3D T1-weighted anatomical data set (1 × 1 × 1 mm) and a susceptibility weighted imaging (SWI) were also acquired. One board-certified neuroradiologist (JLL) evaluated all anatomical images (3D T1-weighted and SWI images) for potential traumatic abnormalities and/or incidental findings and was blinded to the purpose of the study. After reviewing all the data sets (T1-weighted and SWI), no traumatic abnormalities or signs of preseason to postseason changes in anatomical images were identified in any of the participants. The MRI protocol for data acquisition was kept unchanged for all participants at both preseason and postseason time points in this study.
The Functional MRI of the Brain (FMRIB) Software Library (FSL) software package (
, version 5.0.9) was used in the DTI data processing and analysis with standard procedures including skull stripping (bet function), eddy current and head motion artifact correction (eddy_correct), and the calculation of DTI measures (dtifit) as previously published and implemented in sports-related head impact studies from our group. www.fmrib.ox.ac.uk/fsl The following 4 commonly used DTI measures were calculated using standard methods: FA, MD, AD, and RD. 6–8 The tract-based spatial statistics (TBSS) approach was used in the image analysis. 49 Standard TBSS analysis procedures were followed with a skeleton threshold of 0.2. 50 51–53 Head Impact Surveillance
Head impacts were recorded using the GForceTracker (GFT; GForceTracker, Markham, Ontario) accelerometer device, as previously described. The device was affixed to the inside of each 7,8 football helmet in the same position and orientation. Accelerometer data were collected for all practices and games. The accelerometers recorded peak linear acceleration and rotational velocity of the head (ie, 6 degrees of freedom) by directly measuring 3 axes of linear acceleration and 3 axes of angular acceleration. Before the initial exposure (ie, first practice), each accelerometer was calibrated according to device specifications and relative to the placement of the sensor in each helmet. Previous evaluation of GFT accelerometers indicate that they provide a suitable impact-monitoring device across multiple helmet styles with coefficients of determination reported at r 2 = 0.82 for peak linear acceleration. The accelerometers were programmed to record data above 10 g. Acceleration data were collected at 3000 Hz. 54 Statistics
Preseason to postseason DTI measures, including FA, MD, AD, and RD, were tested in both HS and YFB group. The TBSS approach was used to test the within-group longitudinal change between the 2 time points at each voxel along the WM skeleton as generated in the TBSS analysis. For each participant, a difference map between the 2 time points was calculated for each DTI measure and used in a one-sample
t test to assess the longitudinal change for each group. Next, independent t tests were used to determine whether there was a significant group difference in the preseason to postseason DTI alteration. The randomize function from FSL with a permutation of 5000 times was used to generate t test statistical results with correction for multiple comparison through the threshold-free cluster enhancement method. All the 55 t tests were performed 2-sided at significance level of P < 0.05. In the voxelwise TBSS analysis of the group difference of preseason to postseason DTI change, the difference was adjusted for the number of head impacts (a priori threshold set at 10 g) because of the significant group differences in season-long head impact exposure. The time interval between the last game/practice and the postseason imaging was also included as a covariate in the analysis because of its large variability, although there was no statistically significant group difference in this parameter. We also repeated the above analyses with the athletes who had concussion during the season (2 from the HS group and 1 from the YFB group) excluded. In addition, we extracted the median value of the DTI metrics from the significant regions as determined in the within-group testing of preseason to postseason change and tested group differences. A 2-sample t test was used in these latter region of interest–based comparisons. As a secondary analysis, we also tested the preseason to postseason DTI change within each group and the group difference of the preseason to postseason DTI change with the athletes who experienced concussion during the season (2 from the HS group and 1 from the YFB group) excluded. Since the results were similar, this latter part of the data was included in the Supplemental Material (see Figures S1-S3 and Table S1, Supplemental Digital Content 1, ). https://links.lww.com/JSM/A210
In both study groups, exploratory correlation analyses were performed using the Pearson correlation between head impact exposure (number of hits, cumulative g-force, and average g-force/hit) at difference g-force threshold levels (all, > 20, >50, > 100 g) and the change in DTI values within the corresponding WM regions that showed the significant preseason to postseason change. In addition, we also tested the correlation in each of the subregions in the areas with significant preseason to postseason reduction in MD, AD, or RD (as listed in
Table 1). Our hypothesis was that the level of head impact exposure will be positively correlated with the change in DTI, ie, the greater head impact exposure (eg, greater number of hits, higher average g-force/hit, or higher cumulative g-force) is positively correlated with the magnitude of the change in DTI values. TABLE 1.:
Number of Voxels in Different WM Regions With Significant Within-Group Pre- to Post-Season DTI Change and/or Significant Between-Group Difference
YFB participants amassed a total of 438 AEs (105 game and 333 practice exposures) versus HS 1425 AEs (272 game and 1153 practice exposures). The YFB group had one reported SRC (incidence rate 2.28 per 1000 AEs). There were 3 SRC among HS (incidence rate 2.02 per 1000 AEs). Athletes who sustained concussions were all cleared to return to participation by their medical physician and completed a return-to-play protocol before the postseason scan. Independent-samples
t tests revealed that during practice, HS had more total impacts per player compared with YFB ( P = 0.04). For games, HS had more total impacts and impacts over 100 g compared with YFB ( P = 0.007, P = 0.04, respectively) ( Figure 1). Two of the athletes who sustained concussions from the HS group and the one from the YFB group had complete imaging data and were included in the primary imaging data analyses. One concussed athlete from the HS group did not complete MRI and was excluded from the final imaging analysis. Figure 1.:
Represents the average total number of impacts per player greater than 25 g and average number of impacts per player greater than 100 g for both practice and game situations.
As reported in previous work,
with HS 7,8,43–48 football athletes, significant preseason to postseason reductions in MD, AD, and RD ( P < 0.05, corrected) were found in extensive WM areas ( Figure 2 and Table 1). Significant preseason to postseason AD reduction was also found in the YFB group ( P < 0.05, corrected) but was more limited in extent ( Figure 3 and Table 1). Based on the voxelwise TBSS analysis, some WM areas, including the body and splenium of the corpus callosum, right superior corona radiata, and right superior longitudinal fasciculus ( Figure 4 and Table 1) presented significantly less preseason to postseason AD reduction in the YFB group when compared with the HS group (corrected P < 0.05, adjusted for head impact (>10 g) and time interval between last/game/practice and postseason imaging). No significant finding was observed in MD or RD in the YFB group. Figure 2.: White matter regions with significantly preseason to postseason reduction in (A) MD; (B) AD; and (C) RD reduction in the HS group (n = 21, all P < 0.05, corrected). Figure 3.: White matter areas with significantly preseason to postseason AD reduction in the YFB group (n = 12, P < 0.05, corrected). Figure 4.: White matter areas with significantly lower preseason to postseason AD reduction in YFB group when compared with the HS group (corrected P < 0.05, adjusted for head impact exposure during the season and time interval between the last game/practice and postseason imaging).
Furthermore, within the WM areas of the HS group that yielded significant preseason to postseason AD reduction, the percentage of AD reduction in the HS group (2.47 ± 0.96%) was significantly greater than the YFB group (0.38 ± 1.22%) when controlling for total impacts (thresholded at 10 g) and time interval between last/game/practice and postseason imaging,
F(1.00, 29.00) = 28.77, P < 0.001, partial n 2 = 0.50. Within the limited WM areas of the YFB group that yielded significant preseason to postseason AD reduction, the percentage of AD reduction in the YFB group (3.16 ± 1.88%) was significantly greater than the HS group (0.53 ± 1.78%) when controlling for total impacts (thresholded at 10 g) and time interval between last/game/practice and postseason imaging, F(1.00, 29.00) = 14.00, P < 0.001, partial n 2 = 0.33.
In the exploratory correlation analysis, no statistically significant correlation was found between any of the head exposure indices and the MD, AD, or RD reduction in the corresponding group and WM regions that presented significant preseason to postseason DTI change. In addition, we also tested the correlation in each of the subregions in the WM areas with significant preseason to postseason reduction in MD, AD, or RD (as listed in
Table 1). In the HS group, only one subregion of the entire WM areas that showed significant preseason to postseason AD reduction, the right superior longitudinal fasciculus, had a significant correlation with AD reduction in this region and the cumulative g-force thresholded at greater than 100 g (r = 0.442, n = 21, P = 0.045). In the YFB group, no significant correlation was found between head exposure measures and the AD reduction in either the entire region with significant AD reduction or any of its subregions ( Table 1).
When the 3 athletes with
concussion experienced during the season were excluded from the analysis, the within-group preseason to postseason comparison of DTI measures and the between-group comparison of the preseason to postseason DTI change demonstrated results similar to the findings as described above (see Figures S1-S3 and Table S1, Supplemental Digital Content 1, ). One difference is that the YFB group (n = 11) presented a significant increase in FA in some WM regions (see https://links.lww.com/JSM/A210 Figure S2.B and Table S1, Supplemental Digital Content 1, ), which was absent when the concussed athlete was included in the analysis ( https://links.lww.com/JSM/A210 Figure 3 and Table 1). In addition, the group difference of the preseason to postseason AD reduction was not statistically significant at level of P < 0.05 (corrected). However, there was a trend-level group difference ( P < 0.1, corrected) of AD reduction, with the location of these areas (see Figure S3, Supplemental Digital Content 1, ) similar to the areas where the significant group differences ( https://links.lww.com/JSM/A210 P < 0.05, corrected) were found in the analysis when all participants included ( Figure 4). DISCUSSION
The purpose of the current study was to test objectively whether YFB athletes demonstrate more significant WM alterations than HS athletes after experiencing repetitive
head impacts across a single season of tackle football. This was accomplished by tracking head impact exposures throughout a competitive season in YFB and HS and comparing preseason and postseason changes in WM microstructure. Both age groups demonstrated reductions in AD from preseason to postseason. These reductions were region-specific in each group. Contrary to our hypothesis, the HS group demonstrated greater WM microstructure changes than the YFB group as evidenced by more widespread AD reduction. Areas with significantly reduced AD in the HS group included primarily the corpus callosum, anterior and posterior limbs of the internal capsule, corona radiata, posterior thalamic radiation, external capsule, cingulum, and superior longitudinal fasciculus ( Figure 2B). The YFB group showed significant AD reduction in more limited regions relative to HS athletes, with significant changes for YFB only occurring within the anterior corona radiata, superior corona radiata, and external capsule ( Figure 3 and Table 1). In our initial correlation analysis, significant association was found in the HS athletes between the AD reduction in the right superior longitudinal fasciculus and higher cumulative g-force (thresholded at 100 g) experienced during the season. However, no significant correlation was found in the YFB group. Because of the small sample size and the moderate strength in the correlation, we are not able to correct for multiple comparison error and establish a direct association between head impact exposure and the AD reduction in this study, a goal that needs to be achieved in a future prospective study with large sample size.
With current speculation raising concerns regarding the safety of youth participation in contact sports in general and in
football in particular, the findings of the current study add a unique perspective into these concerns by providing longitudinal DTI data for a small cohort in this age group. Stamm et al examined National 28 Football League retirees who had an age of first exposure (AFE) to football before 12 (AFE < 12) years of age and after 12 years of age (AFE ≥ 12) and concluded that athletes in the AFE <12 cohort experience a higher incidence of cognitive, behavioral, and emotional impairments later in life. The authors suggest that these changes might be due to damage to the corpus callosum during a vital stage of development. Although some evidence supports this notion, these investigations are typically retrospective and are confounded by comorbidities, accumulating head exposure throughout the lifetime, as well as other biopsychosocial factors. Researchers have attempted to overcome methodological limitations of previous research of retired NFL players by using stricter exclusionary criteria, 29 including history of brain surgery and tumors, HIV or AIDS, significant head injuries from nonathletic trauma, mTBI after NFL career that resulted in minutes of lost consciousness, open heart surgery, organ transplant surgery, carotid artery surgery, chemotherapy or radiology treatment for brain or spinal cord cancers, renal failure requiring dialysis, and significant drug or alcohol abuse both during and after playing career. Solomon et al 56 attempted to cross-validate the results of Stamm et al 57 but were unable to replicate the previous findings while controlling for age, body mass index, 28 concussion history, professional football experience, and learning disability. A significant correlation between premorbid learning disabilities (ie, attention deficit hyperactivity disorder) and 2 ImPACT composite scores was reported, indicating that learning disorders in the AFE <12 cohort may have contributed to the findings of AFE <12 years being related to later life neurocognitive functioning. Although these investigations furthered our cumulative understanding of behavioral functioning and head injury, retrospective analyses are still limited by the failure to assess brain changes on completion of sports participation at a young age.
In our group of YFB players (average age of 13.08 years), DTI alterations in the corpus callosum were not identified after a season of competitive
football. The size of WM regions in the corpus callosum with significant DTI alterations between preseason and postseason for YFB was negligible (only one voxel, Table 1). Specifically, our data indicated that the AD reduction in the YFB group was significantly lower in the corpus callosum ( Table 1) when compared with the change in the HS group after adjusting for head impact exposure. This regional difference in susceptibility may be related to development differences in some way specifically due in part to vasculature-mediated changes after head exposure, or relative differences in brain motion with impacts in this age group. This is an important area for future research. In addition, the difference in corpus callosum and the more general observation that the regions in the brain showing significant AD reduction is more limited in the YFB group relative to the HS group could also be confounded by the significant group difference of the head impact level. Although the head impact level was included in the statistical analysis, we cannot rule out this potential influence, especially when statistics were tested with a relatively moderate sample size. 58
Most studies that have used diffusion tensor imaging to prospectively investigate repetitive SCI have focused on HS and collegiate athletes.
Thus far, there has been little consensus among the results of these studies because some have found both increases and decreases in measures of FA over the preseason to postseason time interval. 7,10,59–61 These FA measures are dependent on magnitudes of change of both AD and RD and are insensitive to structural alterations if AD and RD change in the same direction. Because of the possible inaccuracy of measures, the DTI metrics of RD, AD, and, by association, MD may provide the best sensitivity and specificity to quantify brain injury from repetitive 59–61 head impacts. Davenport et al 62 used regression analysis to reveal a significant relationship between exposure to linear and rotational impacts and changes in all of the DTI metrics measured (MD and fractional, spherical, linear, and planar anisotropy) over the course of a season. Bazarian et al 10 found that both decreases and increases in MD were present after the season concluded and again at a 6-month follow-up. Finally, Myer et al 59 found decreases in MD, AD, and RD over the course of a HS 7 football season.
An additional important finding from this investigation is that the HS group sustained significantly more
head impacts compared with the YFB group ( P < 0.001). This is consistent with existing evidence that the cumulative number of head impacts during a competitive season increases with the level of play. Furthermore, significant linear correlations have been described between head impact exposures during the course of a season and alterations in DTI and diffusion kurtosis imaging measures from preseason to postseason. 63,64 Increasing numbers of SCI have also been associated with neurological impairments, specifically in memory, new learning, and ocular function. 9–11,65,66 Given these described trends, it is sensible to limit head impact exposure during practices and games. Previous studies have illustrated the effectiveness of practice modifications in decreasing cumulative 67–70 head impacts, including decreasing the number of practices, limiting contact drills, and altering the equipment worn during practice. The adoption of these modifications is easiest at the youth level, where the focus of participation is less heavily weighted toward winning games, but rather, learning the fundamental skills, strategy, and teamwork associated with success. 63,71
A significant strength of this study is its prospective nature. However, as with any study, our results should be interpreted cautiously and study limitations noted. Two such limitations are a comparatively small sample size for YFB participants, and an increased number of days between last head impact exposure and postseason scan for the YFB group compared with the HS group. And, although there were no significant differences between groups, future studies should consider controlling for time from last competition to follow-up. Moreover, some of the observable differences between YFB and HS groups may be due to the differential, age-expected developmental changes seen in the 2 groups, although this phenomenon is likely mitigated given the study was conducted over a single season.
Significant preseason to postseason AD reduction was found in both YFB and HS study groups after one season of
football. After adjusting for head impact exposure, the extent of brain involvement was greater in the HS group than the YFB group. Although regional differences in involvement may relate to developmental differences between our 2 studied groups, our results did not confirm recent speculation that younger children are more susceptible to the deleterious effects of repetitive head impacts compared with their older counterparts. Future studies with larger sample sizes and longitudinal methods consisting of multiple seasons of competitive play are needed to further investigate the age-dependent and region-specific vulnerability of WM to head impact exposure in contact sports. References
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