Acute Changes in Plasma Total Tau Levels Are ...

JOURNAL OF NEUROTRAUMA 35:260–266 (January 15, 2018) ª Mary Ann Liebert, Inc. DOI: 10.1089/neu.2017.5376

Acute Changes in Plasma Total Tau Levels Are Independent of Subconcussive Head Impacts in College Football Players

Downloaded by Johns Hopkins Univ e-journal package from www.liebertpub.com at 03/09/18. For personal use only.

Keisuke Kawata,1 Leah H. Rubin,2 Leroy Wesley,3 Jong Hyun Lee,3 Thomas Sim,4 Masahiro Takahagi,5 Al Bellamy,5 Ryan Tierney,3 and Dianne Langford3

Abstract

Athletes in contact sports sustain repetitive subconcussive head impacts in a brief window, yet neurophysiological sequelae from repetitive subconcussion remain unclear. This prospective longitudinal study examined a relationship between changes in plasma Tau protein levels and subconcussive impact kinematic data in 23 Division I collegiate football players during a series of pre-season practices. Plasma measures for Tau and S100b proteins, symptom scores, and near point of convergence were obtained at pre-season baseline and pre-/post-practices. During each practice, impact frequency and linear and rotational head accelerations were recorded via an accelerometer-embedded mouth guard. There were significant elevations in plasma Tau levels at all post-practice time-points, compared with those of pre-practice and baseline levels. However, the highest degree of elevation in plasma Tau was observed after the first practice, for which players sustained the lowest number of hits and magnitudes for these hits. Subconcussive impact exposure during practice (e.g., head impact frequency and magnitude) did not predict increased plasma Tau levels. Concussion history and years of football experience also were unrelated to changes in plasma Tau levels. Increases in plasma Tau levels were associated with increases in S100b levels only after the first practice. There were no significant associations between changes in Tau levels, symptom scores, or near point of convergence. These data suggest that the changes in levels of circulating Tau protein were independent of subconcussive head impact exposure, pointing to the possibility that other factors may have played roles in changes in plasma Tau levels. Keywords: biomarker; head impact kinematics; subconcussion; Tau; traumatic brain injury

Introduction

H

ead impacts or rapid acceleration-deceleration of the body or torso that cause the brain to ‘‘slosh’’ within the cranium commonly occur in collision or contact sports without signs and symptoms associated with clinical diagnosis of concussion.1 These milder forms of head injury are called subconcussion and accumulating evidence suggests that exposure to repeated subconcussive head impacts may cause long-term damage.1–3 Because of the asymptomatic nature of subconcussion, athletes frequently sustain repetitive subconcussive head impacts in a short interval, with the average football athlete at college and high school levels receiving hundreds to close to 1000 subconcussive head impacts in a single season.2,4–9 To date, numerous studies have attempted to assess effects of repeated subconcussion; however, none have yielded reliable biomarkers for detecting brain injury. Tau is of particular interest given that neuronal axons are particularly vulnerable to shear-strain forces of the head. For example,

linear and rotational head acceleration and deceleration can cause micro-scale displacements of the brain, where axons are twisted, stretched, and shortened.10 When the accelerations are abrupt and of high magnitudes, Tau protein, which functions as a structural element stabilizing microtubules in the axonal cytoskeleton, can be disrupted and disarticulate the microtubule network. On the other hand, at lower magnitude accelerations, the Tau proteins can elongate to enable microtubules to slide relative to one another without damaging axonal structure.11 Yet, if the force is applied repetitively, the compensatory mechanisms may be compromised, triggering a disruption of neuronal connectivity12,13 and perturbation of sensory systems.14–16 Several lines of evidence suggest that Tau maybe a useful marker of repetitive exposure to mild traumatic brain injury in athletes. First, plasma protein Tau levels were increased in ice hockey players with sports-related concussion, compared with preseason baseline levels.17 Moreover, in these studies, the highest plasma Tau levels were observed immediately post-concussion and

1

Department of Kinesiology, School of Public Health, Indiana University, Bloomington, Indiana. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland. 3 Department of Neuroscience, Lewis Katz School of Medicine, 4Department of Kinesiology, College of Public Health, 5Department of Athletics, Temple University, Philadelphia, Pennsylvania. 2

260


PLASMA TAU IN REPETITIVE SUBCONCUSSION

261

levels declined during rehabilitation. Military service members and civilians also showed higher plasma Tau levels post-concussion, compared with healthy controls.18,19 Second, repeated exposure to subconcussive head impacts increased plasma Tau levels in professional boxers after a round of boxing, compared with controls.20 Moreover, the plasma Tau levels in boxers decreased back to the levels observed in controls after 2 weeks of having no blows to the head. Similarly to boxers, greater self-reported exposure to repetitive subconcussive head impacts were associated with higher plasma Tau levels among former National Football League players.21 Third, in some but not all studies,21 higher levels of plasma Tau were associated with clinical outcomes including return to play.17,22 The contributions of subconcussive impact kinematics, such as frequency and magnitude of impacts, on acute and longer-term changes in plasma Tau levels have yet to be examined. In this prospective longitudinal investigation of Division I collegiate football players, we examined acute changes in plasma total Tau levels. We subsequently analyzed the potential factors, such as impact frequency and magnitude, concussion history, and years of football experience, that may drive the changes in plasma Tau. We hypothesized that acute increases in plasma Tau levels would be detected from pre- to post-practices, with the increases being driven by frequency and magnitude of hits sustained. Our secondary analysis included testing to see if changes in plasma Tau were correlated to the pattern observed in a glial marker (S100b) and clinical measurements (near point of convergence and symptom scores) that were reported previously in this participant population.15,23 This study is the first of its kind to investigate subconcussive impact–dependent changes in plasma Tau levels, filling a critical gap regarding the effects of repetitive head impacts on neuronal integrity as measured by total Tau.

Table 1. Demographics, Position, and Head Impact Kinematics Participants (n = 23)

Variables Demographics Age, M (SD) Body mass index, M (SD) Years football experience, M (SD) Number of previous concussions, n (%) None 1 3 Position, n (%) Linemen (OL, DL) Linebacker, tight end Skill players (WR, DB, RB, QB) Special team Impact kinematics, median (IQR){ Number of hits PLA (g) PRA (rad/sec2)

20.52 (1.34) 30.21 (4.58) 9.10 (4.76) 12 (52) 9 (39) 2 (9) 7 5 9 2

(30) (22) (39) (9)

26 (38) 817.83 (856.61) 44,612.57 (46,177.05)

{ Based on the sum from four practice impact kinematic data collections (see Methods section). M, mean; SD, standard deviation; OL, offensive lineman; DL, defensive lineman; WR, wide receiver; DB, defensive back; RB, running back; QB, quarterback; IQR, interquartile range; PLA, peak linear acceleration; PRA, peak rotational acceleration.

University Institutional Review Board approved the study and participants gave written informed consent. Study procedures

Methods Participants Twenty-three Division I collegiate football players volunteered for study participation. The study was conducted at pre-season physical examinations during a series of no contact and full-contact training-camp practices. None of the 23 players sustained concussion during the study period. Inclusion criteria included being a currently active collegiate football team member. Exclusion criteria were any history of head, neck, or face injury 1 year prior to study entry and/or any neurological disorders. Participants refrained from substances influencing the central nervous system (e.g., stimulants), and alcohol use was prohibited. The Temple

During pre-season physical examination, participants were fitted with the Vector mouth guard (i1 Biometrics, Inc., Kirkland, WA) to measure the frequency (number of hits) and magnitude of head accelerations (peak linear and rotational accelerations). After a brief incubation in boiling water, the mouth guard was fitted to each participant’s bite for a secure custom fit. Demographic information (e.g., age, body mass index) and blood samples were collected (Table 1). Previous history of concussion and years of football experience were self-reported. During training camp practices, head impact data were collected from four practices with intervals of 3–4 days between measures, starting from the first helmet-only non-contact (Pads-OFF), first full-gear full-contact practice (Pads-ON1), and two other fullgear practices (Pads-ON2 and Pads-ON3; Table 2).

Table 2. Impact Kinematics from Each Practice Practice day st

st

1 OFF

1 ON

2nd ON

3rd ON

# of Hits

Avg. (SD) Sum

1.67 (2.4) 40

11.35 (10.2) 261

5.09 (3.8) 117

8.39 (8.5) 193

PLA (g)

Avg. (SD) Sum

53.41 (81.2) 1281.78

337.85 (321.6) 7770.47

142.65 (106.1) 3280.99

253.13 (270.6) 5821.88

PRA (rad/s2)

Avg. (SD) Sum

3765.79 (5725.0) 90,378.91

18,723.35 (17337.9) 430,637.01

8706.63 (6671.3) 200,252.60

16,563.33 (19,587.5) 380,956.52

OFF, helmet-only non-contact practice; ON, full-gear full-contact practice; Avg., impact per player; SD, standard deviation; Sum, total number of impacts as a group; PLA, peak linear acceleration; PRA, peak rotational acceleration.

262

KAWATA ET AL.


anticoagulant (BD Bioscience). Plasma was separated by centrifugation (1500 · g, 15 min) and stored at -80C until analyses. Tau concentrations in plasma samples were measured by a digital array technology (Simoa; Quanterix Corporation), which uses a single molecule enzyme-linked immunoarray method previously described.25 The Simoa Human Total Tau assay uses a combination of a Tau 5 monoclonal capture antibodies (Covance) that reacts with a linear epitope in the mid-region of all Tau isoforms, and HT7 and BT2 monoclonal detection antibodies (Thermo Fisher Scientific) that react with a linear epitope in the N-terminus of total Tau. The researchers who performed the analyses were blinded to the time-points, demographic information, and head impact kinematic data. All assays were run in duplicate. The limit of detection for the assay is 0.012 pg/mL, which is more than 1000-fold more sensitive than conventional immunoassays. The intra-assay coefficient of variation was below 10%.

FIG. 1. Tau concentrations (pg/mL). Baseline (black circles) and before (gray circles) and after (white circles) each practice in the overall sample. Box plots indicate data distribution at each timepoint with red lines showing mean and black lines showing median. Head impact measurement This study used an instrumented Vector mouth guard for measuring linear and rotational head kinematics during impact as previously described.16,23 The mouth guard employs a triaxial accelerometer (ADXL377; Analog Devices, Norwood, MA) with 200 g maximum per axis to sense linear acceleration. For rotational kinematics, a triaxial rotational rate gyroscope (L3GD20H; ST Microelectrics, Geneva, Switzerland) was employed.16,24 Accelerometer and gyroscope data were low-pass filtered at 180 and 40 Hz cutoff, respectively. When a preset threshold for a peak linear acceleration (PLA) magnitude exceeded 10.0 g, 16 pre-trigger and 80 post-trigger samples with a standard hit duration of 93.75 msec of all impact data were transmitted wirelessly through the antenna transmitter to the sideline antenna and computer, then stored on a secure internet database. From raw impact data extracted from the server, the number of hits, PLA, and peak rotational acceleration (PRA) were used for further analyses. Blood collection and Tau measurements Venous blood samples were collected at each time-point into vacutainer sterile tubes with the ethylenediaminetetraacetic acid

S100b measurements S100b measurements were performed using sandwich-based enzyme-linked immunosorbent assay kits (EMD Millipore, Billerica, MA), as shown in our recent publication.23 The lowest detection limit of the assay is 0.0028 ng/mL and the intra-assay coefficient of variation is below 2.9%. Fluorescent signals measured by a microplate reader (BioTek EL800; Winooski, VT) were converted into ng/mL as per standard curve concentrations. The researcher performing the assay was blinded from time-points and participant information. Near point of convergence The near point of convergence measures the closest point to which one can maintain convergence while focusing on an object before diplopia occurs.26 Please refer to our recent study for details.16 Briefly, participants were seated with their head in a neutral anatomical position. The accommodative ruler (Bernell Inc.) rested on the participant’s philtrum, and an accommodative target (reduced-size Snellen chart) was adjusted horizontally to the participant’s eye level. The target was moved down the length of the ruler toward the eyes at a rate of approximately 1 to 2 cm/ sec. Near point of convergence was recorded when the tester observed eye misalignment or when participants verbally signaled experiencing diplopia. On verbal signal, the tester stopped moving the target and recorded the distance between the participant and object. The assessment was repeated twice, and mean near point of convergence was used for analysis.

FIG. 2. Tau levels across study duration in the overall sample. Data points represent the estimated mean and standard error at each time-point. ***p < 0.001 and **p < 0.01, compared with respective pre-practice value. ###p < 0.001, ##p < 0.001, and #p < 0.05, compared with baseline.

PLASMA TAU IN REPETITIVE SUBCONCUSSION Table 3. Results from the Mixed Effects Regression Analyses Showing the Magnitude of Change in Tau Levels from Baseline to Post-Practices and Pre- to Post-Practices within the Overall Sample

PADS


OFF ON 1 ON 2 ON 3

Baseline – pre-practice change B (SE) -0.55 -1.30 -2.19 -2.52

(0.44) (0.45)** (0.39)*** (0.40)***

Baseline – post-practice change B (SE) 2.90 0.18 -0.84 -0.62

(0.44)*** (0.45) (0.39)* (0.40)

Pre-/ post-practice change B (SE) 3.44 1.49 1.34 1.90

(0.47)*** (0.49)** (0.40)** (0.41)***

***p < 0.001; **p < 0.01; *p < 0.05. B, unstandardized beta weight; SE, standard error; OFF, helmet-only non-contact practice; ON, full-gear full-contact practice.

Symptom checklist Participants were instructed to rate the presence of any symptom at each time-point using the symptom checklist, a subset of the Sports Concussion Assessment Tool 3.27 Statistical analyses Mixed-effects regression models (MRM) with random intercept were used to examine changes and factors that influence changes in total Tau levels over time. The first model examined changes in Tau

263 levels across the study duration (12 time-points; time treated categorically). Of interest were the comparison of Tau levels from baseline to each time-point and comparisons between each pre- to post-practice assessment. Based on findings from the initial model, all subsequent models focused on the change in Tau levels (postminus pre-) across study duration (four time-points). These models examined the influence of kinematics (e.g., hits, PLA, PRA; timevarying factors), concussion history (yes vs. no), and years of football experience (< 10 years vs. >10 years) on changes in Tau levels. Predictor variables included time, kinematics/group variable, and the time · kinematics/group variable interaction. All MRM models were analyzed with SAS (v.9.4 for Windows) and significance was set at p < 0.05.

Results Demographic and kinematic data for the 23 participants for which Tau levels were measured are summarized in Table 1. Impact kinematics from each practice are shown in Table 2. Comparison of head impact kinematics between Pads-OFF and Pads-ON shows that during the Pads-OFF practice, the group sustained 40 impacts as a group (1.67 hits/participant) with an average PLA of 53.41 g and a PRA of 3765.79 rad/sec2 (Table 2), whereas during Pads-ON practice days, the group sustained between 117-261 hits (5.09-11.35 hits/participant) with average PLA ranging from 142.65-337.85 g and PRA ranging from 8706.63 – 18,723.35 rad/sec2 (Table 2). Figure 1 shows the individual data points for each player across the study duration. Figure 2 shows the changes in Tau

FIG. 3. Association between kinematic variables and fold changes in Tau levels. Associations are depicted between fold change in Tau to total number of hits (A), sum of PLA (B), and sum of PRA (C), average PLA. PLA, peak linear acceleration; PRA, peak rotational acceleration.


264 levels across the study duration. The test of the overall effect of time was significant (F [8, 132] = 23.58; p < 0.001). Tau levels were similar at baseline and Pads-OFF1-pre ( p = 0.21; Table 1). Relative to baseline, Tau levels were significantly lower at all Pads-ON-pre time-points ( p < 0.01) and higher at most Pads-ONpost time-points ( p < 0.05) (Table 3). Tau levels increased from Pads-OFF-pre to Pads-OFF-post ( p < 0.001) and from all PadsON-pre to Pads-ON-post time-points ( p < 0.01). Moreover, the magnitude of the Tau level change from pre- to post- time-points differed across the study duration after controlling for baseline levels (F [3, 44] = 6.73; p < 0.0001). Tau significantly increased from pre- to post-practice on Pads-OFF ( p < 0.0001) but did not increase significantly on any of the Pads-ON practice days ( p > 0.13). Unexpectedly, none of the kinematics measures (hits, PRA, PLA) or group level variables (concussion history, years of football experience) were associated with pre- to post-practice changes in Tau levels ( p > 0.26; Table 3; Fig. 3). To gain a better understanding of pre-/post-practice changes in Tau levels across the study duration, we examined changes in S100b expression, near point convergence, and self-reported clinical symptoms in relation to changes in Tau levels. The association between S100b and Tau levels changed over time (S100b · time interaction; p = 0.03; Fig. 4). Specifically, S100b expression changes were initially associated with changes in Tau levels

KAWATA ET AL. during Pads-OFF (B [unstandardized beta weight] = 34.76; standard error [SE] = 9.66; p = 0.001). However, this association became attenuated across Pads-ON practices (Day 1: B = 16.56, SE = 9.50, p = 0.09; Day 2: B = 7.49, SE = 5.58, p = 0.18; Day 3: B = 0.66, SE = 5.44, p = 0.90). There were no significant associations between changes in near point convergence or symptoms in relation to change in Tau levels ( p > 0.54; data not shown).

Discussion This study examined the potential contributions of subconcussive head impact kinematics to acute changes in plasma total Tau protein levels in Division-I college football players. The results were unexpected, as changes in blood levels of Tau were independent of the frequency and magnitude of subconcussive head impacts. Instead, Tau levels in blood increased most significantly after the non-contact practice (first practice during the training camp) where players sustained 40 hits as a group (1.67 hits/participant), compared with between 117-261 hits as a group (5.09-11.35 hits/participant) over three subsequent full-contact practices. The pre-practice levels and the magnitudes of the acute changes in plasma total Tau steadily declined in the Pads-ON practices over time, although players were exposed to greater numbers and higher magnitudes of repetitive

FIG. 4. Raw association between pre-/post-practices changes in S100b expression and pre-/post-practice changes in Tau levels as a function of time-point: first helmet-only non-contact (Pads-OFF), first full-gear full-contact practice (Pads-ON1), and two other full-gear practices (Pads-ON 2 and Pads-ON 3).


PLASMA TAU IN REPETITIVE SUBCONCUSSION subconcussive head impacts than in the non-contact practice. Reasons for these results are unknown. Brain-derived proteins can travel into the blood stream in the context of increased permeability of the blood–brain barrier28,29 or via the glymphatic system.30–32 While participants did sustain some head impacts during the non-contact practice, the Pads-OFF practice consisted mainly of various running drills and skill-related training. The increase in plasma Tau after the Pads-OFF practice may be attributed to increased glymphatic clearance or increased blood–brain barrier permeability as the vigorous exercise during the Pads-OFF practice (which was the first practice of the training camp) could have triggered the acute clearance of pre-existing Tau as speculated by at least one other study.22 While this trend was not observed in S100b level changes in this cohort,16 exercise effects for changes in S100b have been reported by some other studies, although findings remain controversial. Conversely, sequential reports using mouse traumatic brain injury models of moderate closed-head injury demonstrated that after head trauma, glymphatic clearance can be reduced by 60% and lead to accumulation of Tau in the brain parenchyma.23,30,31 While it remains impossible to translate the degree of glymphatic suppression in the mouse traumatic brain injury model to subconcussive head impacts in football players, in the present study there was a trend for reduction in plasma Tau levels as players continued to participate in full-contact practices with increasing numbers and magnitudes of subconcussive blows. It is unknown if glymphatic clearance was affected in the current study. Our results could also suggest that after repeated subconcussive blows, Tau may be accumulating in the brain since a number of neuroimaging studies confirm that both acute and chronic exposure to subconcussive head impacts disrupt brain connectivity and increase axonal diffusion.12,13,33 Changes in plasma S100b in these same participants, compared with Tau levels, are divergent in that unlike Tau, changes in plasma S100b levels were associated with the frequency and magnitude of impacts received.23 A limitation of the study is a lack of data on exercise parameters. It is challenging to monitor heat rate, running distance, and body acceleration and deceleration in contact-intensive sports like football. However vest-based activity sensors that detect these parameters without being compromised by body contacts have been utilized.34–36 Like head impact and exercise measurements, it also is important to account for sleep components in future studies, as the glymphatic system is reported to efflux waste products during sleep.37 A second limitation of the study is that Tau was measured immediately before practice and within 1 hour post-practice. Longitudinal measures of Tau over hours or days post-practice would give a broader picture of Tau patterns, as reported by previous studies.22 In summary, while data reporting changes in plasma Tau are available in post-concussion studies, evidence for changes in Tau after repetitive subconcussive head impacts is lacking. The present study suggests that increases in plasma Tau protein levels are independent of subconcussive head impact frequencies and magnitudes. Future longitudinal studies with unified neural and cardiovascular assessments are warranted to delineate complex relationships among repetitive subconcussive head impacts, axonal integrity, exercise, and circulating Tau levels. Moreover, the current study serves as an excellent bridge for follow-up studies in conjunction with longitudinal measures of Tau and neuroimaging. These data likely would not be available in the short-term, given that establishing a diagnostic criteria for chronic traumatic encephalopathy where accumulated Tau in the brain is a pathological hallmark requires almost life-long tracking of subjects.38,39 Yet, the present study provides information to our

265 current knowledge regarding circulating Tau after repetitive subconcussive head impacts.

Acknowledgments This work was supported by the Pennsylvania Athletic Trainers’ Society research grant (to K. Kawata), generous support from Athole G. Jacobi, MD, and the Marianne Garman Burton Foundation for Caregivers (to D. Langford), and a seed grant from Temple University Office of the Vice Provost for Research (to D. Langford). Sponsors had no role in the design or execution of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. Author Disclosure Statement No competing financial interests exist. References 1. Bailes, J.E., Petraglia, A.L., Omalu, B.I., Nauman, E., and Talavage, T. (2013). Role of subconcussion in repetitive mild traumatic brain injury. J. Neurosurgery 119, 1235–1245. 2. Broglio, S.P., Eckner, J.T., Martini, D., Sosnoff, J.J., Kutcher, J.S., and Randolph, C. (2011). Cumulative head impact burden in high school football. J. Neurotrauma 28, 2069–2078. 3. McKee, A.C., Alosco, M.L., and Huber, B.R. (2016). Repetitive head impacts and chronic traumatic encephalopathy. Neurosurg. Clin. North Am. 27, 529–535. 4. Duma, S.M., Manoogian, S.J., Bussone, W.R., Brolinson, P.G., Goforth, M.W., Donnenwerth, J.J., Greenwald, R.M., Chu, J.J. and Crisco, J.J. (2005). Analysis of real-time head accelerations in collegiate football players. Clin. J. Sport Med. 15, 3–8. 5. Beckwith, J.G., Greenwald, R.M., Chu, J.J., Crisco, J.J., Rowson, S., Duma, S.M., Broglio, S.P., McAllister, T.W., Guskiewicz, K.M., Mihalik, J.P., Anderson, S., Schnebel, B., Brolinson, P.G., and Collins, M.W. (2013). Timing of concussion diagnosis is related to head impact exposure prior to injury. Med. Sci. Sports Exerc. 45, 747–754. 6. Talavage, T.M., Nauman, E.A., Breedlove, E.L., Yoruk, U., Dye, A.E., Morigaki, K.E., Feuer, H., and Leverenz, L.J. (2014). Functionallydetected cognitive impairment in high school football players without clinically-diagnosed concussion. J. Neurotrauma 31, 327–338. 7. Guskiewicz, K.M., Mihalik, J.P., Shankar, V., Marshall, S.W., Crowell, D.H., Oliaro, S.M., Ciocca, M.F., and Hooker, D.N. (2007). Measurement of head impacts in collegiate football players: relationship between head impact biomechanics and acute clinical outcome after concussion. Neurosurgery 61, 1244–1252 8. Guskiewicz, K.M. and Mihalik, J.P. (2011). Biomechanics of sport concussion: quest for the elusive injury threshold. Exerc. Sport Sci. Rev. 39, 4–11. 9. Schnebel, B., Gwin, J.T., Anderson, S., and Gatlin, R. (2007). In vivo study of head impacts in football: a comparison of National Collegiate Athletic Association Division I versus high school impacts. Neurosurgery 60, 490–495. 10. Meythaler, J.M., Peduzzi, J.D., Eleftheriou, E., and Novack, T.A. (2001). Current concepts: diffuse axonal injury-associated traumatic brain injury. Arch. Phys. Med. Rehabil. 82, 1461–1471. 11. Ahmadzadeh, H., Smith, D.H., and Shenoy, V.B. (2014). Viscoelasticity of tau proteins leads to strain rate-dependent breaking of microtubules during axonal stretch injury: predictions from a mathematical model. Biophys. J. 106, 1123–1133. 12. Bahrami, N., Sharma, D., Rosenthal, S., Davenport, E.M., Urban, J.E., Wagner, B., Jung, Y., Vaughan, C.G., Gioia, G.A., Stitzel, J.D., Whitlow, C.T., and Maldjian, J.A. (2016). Subconcussive head impact exposure and white matter tract changes over a single season of youth football. Radiology 281, 919–926. 13. Slobounov, S.M., Walter, A., Breiter, H.C., Zhu, D.C., Bai, X., Bream, T., Seidenberg, P., Mao, X., Johnson, B., and Talavage, T.M. (2017). The effect of repetitive subconcussive collisions on brain integrity in

266

14. 15. 16.

17.


18.

19.

20.

21.

22. 23.

24.

25.

26. 27.

collegiate football players over a single football season: a multi-modal neuroimaging study. NeuroImage Clin. 14, 708–718. Hwang, S., Ma, L., Kawata, K., Tierney, R., and Jeka, J.J. (2017). Vestibular dysfunction after subconcussive head impact. J. Neurotrauma 34, 8–15. Kawata, K., Tierney, Ryan, Phillips, Jackie, and John Jeka (2016). Effect of repetitive sub-concussive head impacts on ocular near point of convergence. Int. J. Sports Med. 37, 405–410. Kawata, K., Rubin, L.H., Lee, J.H., Sim, T., Takahagi, M., Szwanki, V., Bellamy, A., Darvish, K., Assari, S., Henderer, J.D., Tierney, R., and Langford, D. (2016). Association of football subconcussive head impacts with ocular near point of convergence. JAMA Ophthalmol. 134, 763–769. Shahim, P., Tegner, Y., Wilson, D.H., Randall, J., Skillback, T., Pazooki, D., Kallberg, B., Blennow, K., and Zetterberg, H. (2014). Blood biomarkers for brain injury in concussed professional ice hockey players. JAMA Neurol. 71, 684–692. Olivera, A., Lejbman, N., Jeromin, A., French, L.M., Kim, H.S., Cashion, A., Mysliwiec, V., Diaz-Arrastia, R., and Gill, J. (2015). Peripheral total tau in military personnel who sustain traumatic brain injuries during deployment. JAMA Neurol. 72, 1109–1116. Bulut, M., Koksal, O., Dogan, S., Bolca, N., Ozguc, H., Korfali, E., Ilcol, Y.O., and Parklak, M. (2006). Tau protein as a serum marker of brain damage in mild traumatic brain injury: preliminary results. Adv. Ther. 23, 12–22. Neselius, S., Zetterberg, H., Blennow, K., Randall, J., Wilson, D., Marcusson, J., and Brisby, H. (2013). Olympic boxing is associated with elevated levels of the neuronal protein tau in plasma. Brain Inj. 27, 425–433. Alosco, M.L., Tripodis, Y., Jarnagin, J., Baugh, C.M., Martin, B., Chaisson, C.E., Estochen, N., Song, L., Cantu, R.C., Jeromin, A., and Stern, R.A. (2017). Repetitive head impact exposure and later-life plasma total tau in former National Football League players. Alzheimers Dement. (Amst.). 7, 33–40. Gill, J., Merchant-Borna, K., Jeromin, A., Livingston, W., and Bazarian, J. (2017). Acute plasma tau relates to prolonged return to play after concussion. Neurology 88, 595–602. Kawata, K., Rubin, L.H., Takahagi, M., Lee, J., Sim, T., Szwanki, V., Bellamy, A., Tierney, R., and Langford, D. (2017). Subconcussive impact-dependent increase in plasma S100beta levels in collegiate football players. J. Neurotrauma 34, 2254–2260. Camarillo, D.B., Shull, P.B., Mattson, J., Shultz, R., and Garza, D. (2013). An instrumented mouthguard for measuring linear and angular head impact kinematics in American football. Ann. Biomed. Eng. 41, 1939–1949. Rissin, D.M., Fournier, D.R., Piech, T., Kan, C.W., Campbell, T.G., Song, L., Chang, L., Rivnak, A.J., Patel, P.P., Provuncher, G.K., Ferrell, E.P., Howes, S.C., Pink, B.A., Minnehan, K.A., Wilson, D.H., and Duffy, D.C. (2011). Simultaneous detection of single molecules and singulated ensembles of molecules enables immunoassays with broad dynamic range. Anal. Chem. 83, 2279–2285. Hung, G.K., Ciuffreda, K.J., and Semmlow, J.L. (1986). Static vergence and accommodation: population norms and orthoptics effects. Documenta ophthalmologica. Adv. Ophthalmol. 62, 165–179. McCrory, P., Meeuwisse, W.H., Aubry, M., Cantu, B., Dvorak, J., Echemendia, R.J., Engebretsen, L., Johnston, K., Kutcher, J.S., Raftery, M., Sills, A., Benson, B.W., Davis, G.A., Ellenbogen, R.G., Guskiewicz, K., Herring, S.A., Iverson, G.L., Jordan, B.D., Kissick, J., McCrea, M., McIntosh, A.S., Maddocks, D., Makdissi, M., Purcell, L., Putukian, M., Schneider, K., Tator, C.H., and Turner, M. (2013). Consensus statement on concussion in sport: the 4th International Conference on Concussion in Sport held in Zurich, November 2012. Br. J. Sports Med. 47, 250–258.

KAWATA ET AL. 28. Chodobski, A., Zink, B.J., and Szmydynger-Chodobska, J. (2011). Blood-brain barrier pathophysiology in traumatic brain injury. Transl. Stroke Res. 2, 492–516. 29. Alves, J.L. (2014). Blood-brain barrier and traumatic brain injury. J. Neurosci. Res. 92, 141–147. 30. Iliff, J.J., Chen, M.J., Plog, B.A., Zeppenfeld, D.M., Soltero, M., Yang, L., Singh, I., Deane, R., and Nedergaard, M. (2014). Impairment of glymphatic pathway function promotes tau pathology after traumatic brain injury. J. Neurosci. 34, 16180–16193. 31. Iliff, J.J., Wang, M., Liao, Y., Plogg, B.A., Peng, W., Gundersen, G.A., Benveniste, H., Vates, G.E., Deane, R., Goldman, S.A., Nagelhus, E.A., and Nedergaard, M. (2012). A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid beta. Sci. Transl. Med. 4, 147ra111. 32. Plog, B.A., Dashnaw, M.L., Hitomi, E., Peng, W., Liao, Y., Lou, N., Deane, R., and Nedergaard, M. (2015). Biomarkers of traumatic injury are transported from brain to blood via the glymphatic system. J. Neurosci. 35, 518–526. 33. Johnson, B., Neuberger, T., Gay, M., Hallett, M., and Slobounov, S. (2014). Effects of subconcussive head trauma on the default mode network of the brain. J. Neurotrauma 31, 1907–1913. 34. Macutkiewicz, D. and Sunderland, C. (2011). The use of GPS to evaluate activity profiles of elite women hockey players during matchplay. J. Sports Sci. 29, 967–973. 35. Waldron, M., Worsfold, P., Twist, C., and Lamb, K. (2011). Concurrent validity and test-retest reliability of a global positioning system (GPS) and timing gates to assess sprint performance variables. J. Sports Sci. 29, 1613–1619. 36. Wisbey, B., Montgomery, P.G., Pyne, D.B., and Rattray, B. (2010). Quantifying movement demands of AFL football using GPS tracking. J. Sci. Med. Sport 13, 531–536. 37. Xie, L., Kang, H., Xu, Q., Chen, M.J., Liao, Y., Thiyagarajan, M., O’Donnell, J., Christensen, D.J., Nicholson, C., Iliff, J.J., Takano, T., Deane, R., and Nedergaard, M. (2013). Sleep drives metabolite clearance from the adult brain. Science 342, 373–377. 38. McKee, A.C., Stern, R.A., Nowinski, C.J., Stein, T.D., Alvarez, V.E., Daneshvar, D.H., Lee, H.S., Wojtowicz, S.M., Hall, G., Baugh, C.M., Riley, D.O., Kubilus, C.A., Cormier, K.A., Jacobs, M.A., Martin, B.R., Abraham, C.R., Ikezu, T., Reichard, R.R., Wolozin, B.L., Budson, A.E., Goldstein, L.E., Kowall, N.W., and Cantu, R.C. (2013). The spectrum of disease in chronic traumatic encephalopathy. Brain 136, 43–64. 39. Goldstein, L.E., Fisher, A.M., Tagge, C.A., Zhang, X.L., Velisek, L., Sullivan, J.A., Upreti, C., Kracht, J.M., Ericsson, M., Wojnarowicz, M.W., Goletiani, C.J., Maglakelidze, G.M., Casey, N., Moncaster, J.A., Minaeva, O., Moir, R.D., Nowinski, C.J., Stern, R.A., Cantu, R.C., Geiling, J., Blusztajn, J.K., Wolozin, B.L., Ikezu, T., Stein, T.D., Budson, A.E., Kowall, N.W., Chargin, D., Sharon, A., Saman, S., Hall, G.F., Moss, W.C., Cleveland, R.O., Tanzi, R.E., Stanton, P.K., and McKee, A.C. (2012). Chronic traumatic encephalopathy in blastexposed military veterans and a blast neurotrauma mouse model. Sci. Transl. Med. 4, 134ra160.

Address correspondence to: Dianne Langford, PhD Lewis Katz School of Medicine at Temple University MERB 750, Department of Neuroscience 3500 North Broad Street Philadelphia, PA 19140 E-mail: [email protected]