Detecting Neurocognitive and Neurophysiological Changes as a Result of Subconcussive Blows Among High School Football Athletes
2014; Volume: 6; Issue: 3 Linguagem: Inglês
10.3928/19425864-20140507-02
ISSN1942-5872
AutoresKatherine M. Breedlove, Evan L. Breedlove, Meghan E. Robinson, Victoria N. Poole, Jeffrey R. King, Paul Rosenberger, M Rasmussen, Thomas M. Talavage, Larry J. Leverenz, Eric A. Nauman,
Tópico(s)Sports injuries and prevention
ResumoOriginal Research freeDetecting Neurocognitive and Neurophysiological Changes as a Result of Subconcussive Blows Among High School Football Athletes Katherine Morigaki Breedlove, MS, ATC, , , MS, ATC Evan L. Breedlove, MS, , , MS Meghan Robinson, PhD, , , PhD Victoria N. Poole, BS, , , BS Jeffrey R. King III, BS, , , BS Paul Rosenberger, BS, , , BS Matthew Rasmussen, BS, , , BS Thomas M. Talavage, PhD, , , PhD Larry J. Leverenz, PhD, ATC, , and , PhD, ATC Eric A. Nauman, PhD, , PhD Katherine Morigaki Breedlove, MS, ATC , Evan L. Breedlove, MS , Meghan Robinson, PhD , Victoria N. Poole, BS , Jeffrey R. King III, BS , Paul Rosenberger, BS , Matthew Rasmussen, BS , Thomas M. Talavage, PhD , Larry J. Leverenz, PhD, ATC , and Eric A. Nauman, PhD Published Online:May 07, 2014https://doi.org/10.3928/19425864-20140507-02Cited by:1PDFAbstract ToolsAdd to favoritesDownload CitationsTrack CitationsCopy LTI LinkHTMLAbstractPDF ShareShare onFacebookTwitterLinkedInRedditEmail SectionsMoreAbstractRecent work suggests that subconcussive head impacts may contribute to long-term neurodegeneration; however, the risk thresholds are unknown. It was hypothesized that the number of head impacts could quantify the risk of developing abnormal neurophysiology. Twenty-one high school boys (ages 14 to 18) were evaluated over the course of 1 football season. A combination of the ImPACT, functional magnetic resonance imaging (fMRI), and helmet telemetry was used. The number of head impacts throughout the football season was subsequently compared with the fraction of players flagged by either the ImPACT or fMRI. A minimum of 1 ImPACT score was flagged for 54.5% of asymptomatic participants, and 72.7% were flagged by fMRI. Seven assessments were flagged by both. Larger numbers of hits corresponded with a larger fraction of players being flagged. A substantial number of asymptomatic athletes exhibit neurophysiological changes in-season. The number of head impacts was a risk factor for the development of neurophysiological changes. [Athletic Training & Sports Health Care. 2014;6(3):119–127.]IntroductionThe exact cause of long-term neurocognitive complications following participation in contact athletics is unclear, and it has been suggested that neurological deterioration is the result of a combination of concussive and multiple subconcussive impacts.1 Although the current diagnosis criteria for concussion relies heavily on the presence of symptoms such as headache, loss of consciousness, or slowed reaction time,2 subconcussive blows do not result in acute clinical symptoms. As a result, subconcussive blows are difficult to study, and research regarding their effects has only recently appeared in the literature.In an initial study, Talavage et al3 demonstrated that asymptomatic, nonconcussed high school football players could exhibit measurable changes in neurocognitive performance and neurophysiology, as measured by the Immediate Post-Concussion Assessment and Cognitive Test (ImPACT; ImPACT Applications, Inc; Pittsburgh, Pennsylvania) and functional magnetic resonance imaging (fMRI). Breedlove et al4 subsequently noted correlations between these measured changes and the number of subconcussive blows sustained throughout the season. Furthermore, these correlations were also found to exist for symptomatic athletes, suggesting that neurophysiological changes are linked to cumulative, subconcussive impacts.Given the possibility that repetitive, low-magnitude head impacts are linked to altered cognitive function, the purpose of this article is to use existing data from an ongoing research study to evaluate the possibility of quantifying the risk of developing abnormal neurophysiology due to repetitive subconcussive impacts to the head. We hypothesized that the number of head impacts sustained during the season would be predictive of neurological impairment. These data would be valuable to clinicians in making informed decisions about the risk of repetitive head impacts.MethodParticipantsAll research protocols were approved by the Purdue University Institutional Review Board prior to initiation of the research project, and participants or their guardians (if under age 18) provided informed consent. Participants were recruited from a single high school's football team during the 2010 football season. No participants reported any history of learning disabilities or attention deficit disorders.During the 2010 football season, 21 participants (age range = 14 to 18 years) consented to be part of the study and their helmets were equipped with the Head Impact Telemetry (HIT) System (Simbex, LLC, Lebanon, New Hampshire). Nine participants were included in the study during the 2009 football season, and 12 were new to the study in 2010. Because the focus of this study was on the effect of subconcussive blows, athletes who experienced a concussion diagnosed by the team physician or team athletic trainer were excluded. Four players were diagnosed with a concussion and were thereby eliminated from this data set. An additional inclusion criterion was being able to complete a baseline examination prior to the beginning of contact drills and completing an in-season examination. Four players did not complete an in-season examination and were excluded.The remaining 13 participants all completed at least 1 in-season examination within 48 hours of their most recent competition. Five completed a second in-season examination and 2 completed 3 in-season examinations. The order in which they returned for testing was based on their number of head impacts. Each week, a player was chosen from 3 different groups comprising the upper 50% of recorded hits, the lower 50% of recorded hits, and the most head impacts to the top front of the helmet. Participants were invited back until everyone had completed an examination. Student schedule conflicts prevented us from having all participants complete the same number of in-season examinations. The high school season was 13 weeks long, and the team competed in 11 games and 1 scripted scrimmage.Helmet Telemetry and Head Impact MonitoringThe HIT System was installed in the participants' helmets prior to the beginning of practices and was continuously present for the duration of the football season. The system, which is designed to not interfere with the helmet's function, consists of 6 uniaxial accelerometers placed around existing helmet padding.5,6 The location of blows and their corresponding linear acceleration is estimated by the system software and transmitted to an external computer system and antenna array.7 The HIT System has been validated for counting hits, determining approximate contact location, and estimating the peak translational acceleration magnitude.5,6 However, the system incorrectly assumes zero rotational acceleration about the superior–inferior axis, resulting in inaccurate rotational acceleration data4; therefore, rotational acceleration data were not included in this study. For each player, the average of number of hits per week during football activity (preseason and regular season), the cumulative number of hits, and the cumulative magnitude of the hits (accelerations measured in multiples of the gravitational constant [Gs]) were recorded. Cumulative magnitude was selected because of its traditional role as a predictor of neurotrauma,8 and number of head impacts was selected because of its apparent role in subconcussive impairment.3,4Neurocognitive TestingAll participants underwent neurocognitive examinations using the ImPACT, a Windows- or Web-based computerized neurocognitive testing program. It is one of the most commonly used computerized neurocognitive assessment programs in numerous sports at all levels internationally.9,10 The online version of the ImPACT was used for this study ( http://www.impacttest.com). ImPACT was administered by a certified athletic trainer experienced in administering the test. Participants were tested individually using the same computer, and they were tested in the same room for all examinations. Participants were also given the same instructions prior to each test and were tested at approximately the same time of day (Saturday and Sunday afternoons).The ImPACT measures 5 aspects of cognitive functioning: verbal recognition memory, spatial recognition memory, visual working memory and cognitive speed, memory and visual–motor speed, and verbal working memory and cognitive speed.11 It consists of 8 tasks that result in 5 subscores: visual memory, verbal memory, reaction time, visual motor speed, and impulse control.12 Completion requires approximately 20 to 30 minutes.12 The elements of the test battery can be varied over a wide range to minimize artificial increases in score due to repetition.13 Also included in the test is a postconcussion symptom scale that includes 22 self-reported symptoms commonly associated with concussion, such as photosensitivity, irritability, dizziness, and insomnia. ImPACT best practices recommends baseline tests for all participants.14,15 Substantial deviations from baseline, as measured by the reliable change index,16 are highlighted by the program. These composite scores are flagged on the clinical report for further attention by a clinician.For the purposes of this study, the verbal memory composite score, visual memory composite score, visual motor speed composite score, reaction time composite score, and total symptom composite score were considered. The impulse control composite score was not included in our analysis. That score is used primarily to identify submaximal effort or a misunderstanding of test instructions.13Note that none of the tests (baseline or in-season) were identified as invalid. The ImPACT provides a validity index to determine whether a test is valid. The computer will indicate that the test is invalid if a participant's test results in more than 30 incorrect on the Xs and Os task, an impulse control greater than 30, a score less than 69% correct for word memory, a score less than 50% for design memory, and fewer than 8 letters correctly identified on the 3-letters task.17Functional Magnetic Resonance ImagingAll participants underwent fMRI testing conducted by experienced MRI operators using a 3T General Electric (Waukesha, Wisconsin) Signa HDx machine as part of participant baseline and follow-up examinations. This imaging technique reveals task-dependent changes in neurometabolism18 that occur due to neural damage or concussion-induced decoupling from neurometabolism.Detailed descriptions of our fMRI methods have been presented elsewhere.3,4 Each participant completed 3 n-back working memory tasks: the 0-back, 1-back, and 2-back tasks.19 The n-back task has been shown to be sensitive to concussion-induced changes in working memory, particularly in the dorsal and ventral prefrontal cortex.20 The fMRI n-back task does not yield significant changes between repeated tests in healthy controls,21 and results are generally insensitive to the effects of alcohol and drugs.22,23For the purpose of intersubject comparisons, participants' brains were mapped to the Talairach–Tournoux atlas brain24 and segmented into 116 anatomical regions of interest (ROIs)25,26 using the MarsBaR toolbox.27 Functional MRI postprocessing was performed to obtain a t statistic for the contrast between the 2-back and 1-back tasks at each voxel in the brain image. ROI averages were constructed from these voxel-based t statistics. Consistent with our previous work, ROI averages were recentered by subtraction of the global contrast average4 to partially control for random session-specific factors28 including, but not limited to, caffeine,29 recent food intake,30 level of participant rest,31 and participant motion during the task.Determination of Flagged Regions of InterestFor each season, a pooled preseason mean and standard deviation was calculated from the fMRI data for each ROI. In-season scans were compared with the preseason scans on an ROI basis by constructing a 95% confidence interval (CI) about the preseason average. An ROI from an in-season scan was flagged as deviant if it lay outside this 95% CI. A similar CI-based approach has been used in other fMRI work.32–34 In addition, a CI is readily related to a z score, which is a typical approach used for identifying outlier scans in clinical applications of diffusion tensor imaging.35As the focus of the current analysis is higher-level cognition (ie, whole-brain function and not individual ROIs), the total number of deviant ROIs was computed for each in-season scan. A number of random deviant ROIs is expected. However, if the observation of deviant ROIs is thought of as a Bernoulli trial with a 5% chance of false–positive rate, then no more than 10 deviant ROIs are expected at random (95% CI).4 Therefore, any in-season session in which 11 or more deviant ROIs were detected was considered abnormal and was deemed to have been flagged by fMRI.ResultsOn the basis of the data obtained from the HIT System, the players experienced a range of 17.7 to 153.5 average hits per week, with a mean and standard deviation of 71.7 ± 39.3 hits per week. The corresponding cumulative number of hits for the season ranged from 86 to 1996, with a mean and standard deviation of 582.8 ± 444.3 hits.Examinations of the 13 nonconcussed participants who participated in the study resulted in a total of 22 in-season ImPACTs. Twelve of these 22 (54.5%) in-season tests of asymptomatic participants had a minimum of 1 composite score flagged (Table 1). The number of flagged composite scores ranged from 1 to 4 of the 5 included scores, and all flagged changes resulted from a decrease in performance. For the fMRI analysis, the 22 in-season assessments demonstrated a range of 3 to 43 deviant ROIs (average = 15.1 ± 8.5 ROIs), with 16 sessions (72.7%) considered as flagged (Table 2). Seven assessments were flagged by both the ImPACT and fMRI. The 10 most frequently deviant ROIs among those flagged by fMRI (Table 3) and those not flagged by fMRI (Table 4) were also tabulated, and the regions affected are largely distinct.Table 1 Summary of ImPACT ScoresIN-SEASON ASSESSMENTNO. (%) FLAGGEDTOTAL TESTS ADMINISTEREDFirst8 (53.3)15Second3 (60)5Third1 (50)2Total12 (54.5)22Table 2 Summary of Abnormal Regions of InterestIN-SEASON ASSESSMENTNO. (%) FLAGGEDTOTAL TESTS ADMINISTEREDFirst11 (73)15Second3 (60)5Third1 (50)2Total16 (72.7)22Table 3 Ten Most Frequently Deviant Regions of Interest Among Participants Flagged by Functional Magnetic Resonance ImagingREGION OF INTERESTFREQUENCY DEVIANT (%)Uvula, right50Orbitofrontal gyrus, middle left44Supramarginal gyrus, left39Postcentral gyrus, left39Frontal operculum, inferior left33Middle temporal gyrus, right33Thalamus, left33Declive, left33Cingulate gyrus, anterior left28Cuneus, right28Table 4 Ten Most Frequently Deviant Regions of Interest Among Participants Not Flagged by Functional Magnetic Resonance ImagingREGION OF INTERESTFREQUENCY DEVIANT (%)Supramarginal gyrus, left56Supramarginal gyrus, right33Middle temporal gyrus, right33Precentral gyrus, right22Caudate nucleus, right22Angular gyrus, left22Paracentral lobule, right22Orbitofrontal gyrus, medial right22Cingulate gyrus, middle right11Hippocampus, right11Deviant neurological assessments (ie, flagged ImPACT composite scores and flagged deviant fMRI ROI counts) were plotted against both the cumulative magnitude (g) of head impacts and the cumulative number of head impacts sustained at the time of the scan (Figure 1). The data were divided into 2 groups based on number of head impacts, using 500 cumulative hits at the time of the test as a convenient approximation of the median. This yielded 12 tests with fewer than 500 cumulative hits and 10 tests with more than 500 cumulative hits. As shown in Table 5, 11 of 12 examinations below 500 cumulative hits were flagged (ImPACT only: 3; fMRI only: 4; ImPACT and fMRI: 4) and all 10 examinations above 500 cumulative hits were flagged (ImPACT only: 2; fMRI only: 5; ImPACT and fMRI: 3). No pattern relative to the cumulative magnitude was observed. Given that only 1 participant was not flagged by fMRI or ImPACT in-season, it was not possible to compute the relative odds of being flagged above and below the threshold. However, the odds of being flagged by fMRI above the threshold were greater than the odds of being flagged by ImPACT above the threshold (odds ratio = 1.4 ± 0.3).Figure 1. Deviant neurological assessments (ie, flagged ImPACT composite scores or flagged deviant functional magnetic resonance imaging regions of interest counts) were plotted against both the cumulative magnitude (g) of head impacts and the cumulative number of head impacts sustained at the time of the assessment. Although there was no discernible relationship between deviant assessments and the cumulative magnitude of head impacts, they were dependent on the cumulative number of head impacts. Data were divided into 2 groups based on the number of head impacts, resulting in 11 of 12 tests with fewer than 500 cumulative hits being flagged, as were all 10 tests associated with more than 500 cumulative hits. (Abbreviations: CI, confidence interval; ImPACT, Immediate Post-Concussion Assessment and Cognitive Test.)Table 5 Cumulative Hits Versus Cumulative Magnitude Plot Results 500 CUMULATIVE HITS PER SEASONNormal evaluations10ImPACT flagged evaluations32fMRI flagged evaluations45Flagged by both ImPACT and fMRI43Total1210Abbreviations: fMRI, functional magnetic resonance imaging; ImPACT, Immediate Post-Concussion Assessment and Cognitive Test.When deviant assessments were plotted against both the cumulative magnitude of head impacts and the average number of hits per week, 13 examinations fell below an average of 65 hits per week and 9 were above 65 hits per week (Table 6; Figure 2). Twelve of 13 tests below 65 hits per week were flagged (ImPACT only: 3; fMRI only: 5; ImPACT and fMRI: 4) and all examinations greater than 65 hits per week were flagged (ImPACT only: 2; fMRI only: 4; ImPACT and fMRI: 3). Again, no pattern relative to the cumulative magnitude was observed. No association was noted between the threshold and the relative odds of being flagged by fMRI or ImPACT (odds ratio = 1.08 ± 0.2).Table 6 Average Hits per Week Versus Cumulative Magnitude Plot Results 65 AVERAGE HITS/WEEKNormal evaluations10ImPACT flagged evaluations32fMRI flagged evaluations54Flagged by both ImPACT and fMRI43Total139Abbreviations: fMRI, functional magnetic resonance imaging.; ImPACT, Immediate Post-Concussion Assessment and Cognitive TestFigure 2. Deviant assessments plotted against both the cumulative magnitude (g) of head impacts and the average number of hits per week. Although there was no discernible relationship between cumulative magnitude and the likelihood of being flagged by the ImPACT or functional magnetic resonance imaging, a total of 12 of the 13 examinations below an average of 65 hits per week were flagged, as were all 9 above 65 hits per week. (Abbreviations: CI, confidence interval; ImPACT, Immediate Post-Concussion Assessment and Cognitive Test.)DiscussionPrevious studies have indicated that asymptomatic athletes can exhibit substantive changes in their neurophysiology during the season.3,4 The goal of the current study was to quantify the likelihood of impairment in asymptomatic high school athletes playing football as measured by ImPACT and fMRI analysis. Both the ImPACT and fMRI are valuable yet distinctly different tools for quantifying neurological deficits. A combination of ImPACT and fMRI analysis flagged 95.5% of in-season evaluations. The ImPACT flagged 54.5% of evaluations, fMRI flagged 72.7% of evaluations, and 31.8% were flagged by both fMRI and the ImPACT (Table 7). It should be noted that although the participants exhibited measurable changes in brain physiology or neurocognitive function, none presented symptoms that would have prompted a health care professional to remove them from participation.Table 7 Total Evaluation BreakdownNO. (%) OF EVALUATIONSTOTAL EVALUATIONSNormal evaluations1 (4.6)22ImPACT flagged evaluations12 (54.5)22fMRI flagged evaluations16 (72.7)22Flagged by both ImPACT and fMRI7 (31.8)22Abbreviations: fMRI, functional magnetic resonance imaging; ImPACT, Immediate Post-Concussion Assessment and Cognitive Test.No obvious clinical implications were observed in the pattern of deviant ROIs among either participants who were flagged by fMRI or those who were not. Tables 3–4 indicate that the regions most frequently deviant among the 2 groups were largely distinct, which may suggest that changes in the fMRI flagged population were structurally different from those who were not flagged. Note that the majority of participants not flagged by fMRI were flagged by the ImPACT because only 1 participant was flagged by neither. It is notable that the ROIs most frequently deviant among participants not flagged by fMRI were the left and right supramarginal gyrus (Table 4). The supramarginal gyrus has been identified as a key region in verbal working memory36; therefore, these changes may correspond with flagged performance deficits on the ImPACT.To examine the role of subconcussive hits, our population focused on players with large or small numbers of hits. We expected that players with high numbers of head impacts would exhibit deficiencies at a higher rate than players with low numbers of hits, but that was not observed. Only 1 low hit evaluation was not considered abnormal either by the ImPACT or our fMRI analysis. Although it was true that high numbers of hits resulted in either abnormal ImPACT or fMRI scores, low hit totals generally did also.The observation that a large percentage of participants are flagged after sustaining large numbers of hits is consistent with the idea of cumulative subconcussive damage. However, it is not immediately clear why so many participants were also flagged without sustaining large numbers of hits. In the case of players flagged by the ImPACT, it is not definitive whether the lower scores represent real neurocognitive deficits. Resch et al12 noted that a group of nonathlete controls with no daily head trauma were flagged by the ImPACT at 22.2% (45 days after baseline) and 28.9% (50 days after baseline), which provides a proxy false–positive rate. Also of note, Broglio et al37 found that 38% of concussed players whose symptoms had resolved were flagged by the ImPACT on at least 1 composite score. This supports the idea that neurocognitive deficits may exist without symptoms.37 These previously reported data, combined with the 54.5% flagging rate observed in the current study, substantially exceed the false–positive rate reported by Resch et al12 and suggest that at least some portion of the flagged players experienced real neurocognitive deficits. The fMRI assessment used here was mainly concerned with identifying substantial and widespread changes in neurophysiology. The 95% CIs, based on precontact averages, represent a conservative approach to identifying changes, given that baseline activation patterns may vary substantially among participants. In addition, our approach focused on quantifying broad neurophysiological changes, using the number of deviant ROIs as a metric of neurophysiological change, rather than an analysis of each individual ROI. Although the widespread changes detected by our fMRI analysis did not always correspond with neurocognitive deficits detected by the ImPACT, the changes are of a sufficient effect size to warrant further investigation, given that significant changes were not observed in a control population.21Because the ROIs are anatomically defined and deviant regions are not necessarily members of any single cognitive circuit, this approach is likely not sensitive to the same neurological changes as the neurocognitive tests incorporated in the ImPACT. It is possible that an alternative fMRI analysis would yield results that more strongly correlate with the ImPACT; however, such an analytical approach is not present in the literature. Although widespread changes in neurophysiology may not result in cognitive impairment, these changes merit further investigation.Several limitations of this study exist. First, there was a small participant pool due to only 1 year of observation. This limitation restricts the ability to generalize the results of this study, but the high percentage of athletes flagged by the ImPACT, fMRI, or the combination of both during the season strongly suggests that long-term follow-up studies should be performed to determine whether physiological damage is accruing from season to season. An additional potential limitation of this study is a result of the sampling method used. To ensure that we obtained the widest possible range of hit numbers, athletes were chosen based on whether they exhibited especially high or low numbers of head impacts, as well as those with the most blows to the top front of the head. In particular, it should be noted that the latter group was considered because previous work suggested there may be profound damage to the dorsolateral prefrontal cortex.38,39 Ultimately, this sampling method may have excluded athletes with hit numbers near the median across the population. However, it should be noted that athletes were flagged at a high rate whether they took a large number of hits or a small number of hits, so we can expect that the data are representative of the players on this particular team. Future work is required to determine the nature of the effect in the broader population.ConclusionAsymptomatic players were flagged by the ImPACT, fMRI, or the combination thereof at a considerably higher rate than expected. In particular, the ImPACT flagged players at more than twice the previously reported rate of false–positives. These data suggest that subconcussive hits are important, but considerably more work is required to determine whether these results generalize to more teams and age groups.Implications for Clinical PracticeAlthough it is clear that more research must be conducted to come to any decisive conclusions leading to clinical intervention, some preliminary actions could be taken to protect student–athletes and decrease their risk of long-term damage.There is some discussion as to whether hit count may be used as a way of preventing damage or impairment due to blows to the head. The current study showed that even a low number of head impacts could cause changes in some players, so hit counts in the pure sense may not be the answer. However, it is clear that the athletic trainer and team physician should pay close attention to players who receive a large number of blows to the head and check those players for any signs or symptoms that might indicate changes in brain activity or impairment.Although some controversy exists regarding the false–positive rates of computerized neurocognitive tests, it might be wise to test the student–athlete annually, rather than biannually as recommended, and compare that score with the student–athlete's original baseline. Finally, if the accumulation of subconcussive blows is important to brain health, then we must promote the techniques and development of skills to reduce the incidence of those blows. The medical staff should lead the way in this effort to make the sport safer.1.McKee AC, Cantu RC, Nowinski CJ, et al.Chronic traumatic encephalopathy in athletes: progressive tauopathy after repetitive head injury. 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Tamer, Jr, for his assistance with data collection.PDF downloadAddress correspondence to Eric A. Nauman, PhD, 585 Purdue Mall, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47906; e-mail: [email protected]edu.Ms Breedlove and Dr Leverenz are from the Department of Health and Kinesiology, Mr Breedlove and Dr Nauman are from the School of Mechanical Engineering, Dr Robinson, Ms Poole, Mr Rasmussen, and Dr Talavage are from the Weldon School of Biomedical Engineering, and Mr King and Mr Rosenberger are from the School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana. Ms Poole is also from the Department of Basic Medical Sciences, and Dr Nauman is also from the Weldon School of Biomedical Engineering and the Department of Medical Sciences, Purdue University.This work was supported by grants from the Indiana State Department of Health Spinal Cord and Brain Injury Research Fund, General Electric Healthcare, and through the National Science Foundation and National Defense Science and Engineering Graduate Fellowships.The authors have disclosed no potential conflicts of interest, financial or otherwise. Received4/16/13Accepted1/22/14
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