Control subjects were male and female athletes recruited from 3 local high schools before the start of their athletic season. Control subjects were removed from analysis when there were (1) a history of concussion; (2) indeterminate data regarding a possible previous concussion; and (3) poor data quality, missing data, or other technical problems with subject data or records.
One hundred seventy control subjects (range 11-18, mean age 15.5 years) were analyzed; note is made that there was only 1 subject aged 11 years; the next lowest age of a subject was 13 years. We had incomplete sex information for the control population; therefore, an exact distribution of this demographic detail could not be calculated. However, based on the number of available sex-identified control subjects, we estimated that the male-female ratio in the control group was roughly 2:1.
Assessments were performed on a combined eye-tracking, stimulation, and analysis system (I-Portal VNG). Video data from both eyes were recorded at 100 Hz and synchronized with stimulus presentation. Tests were conducted in dim light with the subject seated in front of a wide white reflective screen or other featureless surface. Full-field OKN stimuli were created by a rotating projector. Other visual stimuli were projected by a 650-nm laser onto the display/testing surface. Auditory stimuli were presented using a 5-V piezoelectric buzzer. All stimulus ranges given later had an estimated variance of 10% during this study.
Subjects performed 6 smooth pursuit (SP) tasks, 3 horizontal and 3 vertical. Subjects tracked a laser-projected target that travelled with a sinusoidal velocity profile across −10° to 10° of visual angles at 3 frequency profiles: 0.1, 0.75, and 1.25 Hz for horizontal, and 0.1, 0.5, and 0.75 Hz for vertical (see Table 2 for details). Assessed measures included position and velocity gain, velocity gain asymmetry, the presence of saccadic movement, and the latency to initiate SP tracking.
For saccade testing, targets were displayed consecutively at pseudorandom locations along horizontal or vertical axes. We evaluated latency to initiate a saccade, accuracy, and peak velocity relative to a normative threshold and as a function of saccade amplitude (saccadic “main sequence”).31 Subjects also performed predictive saccade tests (timing designed to evaluate prediction of target movement) and antisaccade tests (subjects instructed to consciously look in the opposite direction of the target).
Analysis of saccade results revealed that, for technical reasons, the individual stimulus presentation times were more variable with an estimated mean of 80-ms longer duration (per stimulus) for control subject test sessions than sessions for concussed subjects (for all saccade test types, including the saccades and reaction time test, below). This anomaly may have affected saccade final accuracy measurements (accuracy after all corrective saccades) and predictability of saccades in the predictive saccades test. Despite this technical discrepancy, we retained saccade metrics in our results since the only measures we report here as significant are low saccadic velocities on the saccades and reaction time tests, a measure not impacted by the discrepancy.
The OKN response was tested using a horizontally moving field of illuminated dots created by a rotating projector at an estimated 23.4° or 70.3°/s (separate tests) that subjects viewed passively. Stimuli moved in one direction for 10 seconds, paused briefly, and then reversed for 10 seconds (initial direction was not the same for all subjects). We measured velocity gain during the slow nystagmus phases (ie, the ratio of eye vs stimulus velocity), as the eyes tracked the stimulus. We computed asymmetry between the leftward and rightward directions as follows:
where Gaindir1 and Gaindir2 are the gain values computed separately for the 2 stimulus directions. We also assessed the OKN fast phases by constructing a main sequence plot (velocity vs amplitude) similar to that used for saccade analysis.31 Our metric of fast-phase performance is the integrated area under an exponential fit performed on the main sequence graph (maximum of positive vs negative areas).
We included 3 vestibular function tests. For subjective visual vertical and horizontal (SVV and SVH) tests, subjects used left and right buttons to rotate a laser-drawn line until it was either vertical or horizontal. For spontaneous nystagmus (SN), a fixation target was presented for 5.65 seconds, followed by a period of 10 seconds with no fixation target.
We assessed reaction time (RT) responses using button presses in response to visual and auditory stimuli (separate tests). To reduce the effects of erroneous button presses during the RT tests, the first valid response time and all values outside the range of 80 to 1500 ms were removed from the RTs. Then all outliers outside 3 standard deviations from the mean of the remaining points for each subject were removed. The retained points were used in the calculation of mean RT and its standard deviation.
In the saccade and RT test (SRT), we combined a standard prosaccade task with the visual RT test to assess oculomotor, cognitive, and manual interactions (see Table 2 for details). Subjects were instructed to look toward the lighted target when it moved, and press either the left or right button depending on whether the target moved to the left or right relative to its last position. The automatic removal of RT values performed for the other RT tests was not performed for the SRT test.
Performance measures were computed using the assessment system software (I-Portal and VEST, Neuro Kinetics Inc, Pittsburgh, Pennsylvania). Data were calibrated for position by the comparison of eye movement to fixation positions with known displacement, and refined as needed by position comparison in the slowest SP tests. Acquired data for the OVRT tests were then manually analyzed in the VEST software by a data analyst blinded to the concussion status of each subject (control vs concussion group). Data were filtered or partially removed on a test-by-test basis by manual adjustment of VEST software controls according to standard operating guidelines/procedures for the removal of artifacts (eg, blinks, recording noise, temporary failures of eye tracking, shifting of goggles, erroneous responses, or ones not related to the task) to separate eye movement signals from other recording noise, and to segregate saccadic activity from pursuit activity. Individual tests for some subjects were removed from analysis when the data quality was judged (not blinded to concussion diagnosis in all instances) to be inadequate for accurate measurement or produced analytic errors.
Subsequent population analyses were performed on the acquired measures using SPSS (IBM, Armonk, New York), Excel (Microsoft, Redmond, Washington), and custom software written in LabVIEW (National Instruments, Austin, Texas). All significance values reported for comparison of control versus concussed subjects were by the Mann-Whitney U test, although we report the distributions using customary parametric statistics (mean ± standard deviation), even though some of our variables did not adhere to a normal distribution. A subset of all measures produced by the software was assessed (to reduce the number of variables compared) resulting in 87 variables. To avoid type I statistical errors, we chose a highly conservative α value of .000575 (.05/87 variables) when determining comparison significance. To assess whether significantly different metrics were different at later time points following injury, we compared 16 variables (Mann-Whitney U) for the subset of concussed patients tested 22 or more days after injury (n = 10) versus the control group, using an α value of .00313 (.05/16 variables).
We constructed standard regression models using measures from the test batteries, with concussion/nonconcussion group membership as the dependent variable. The first regression model was a “Forward: conditional” logistic model (SPSS Statistics, version 23), used to identify a small viable subset of variables, which produced good classification results (entry and removal values for variable score statistics were 0.005 and 0.01, respectively). The procedure initially identified 5 variables; however, 2 of these (horizontal saccade final accuracy and antisaccades undershoot) were removed from consideration in model building because of the confound created by different saccade test stimulus durations (see the Saccades subsection, earlier). The variables identified by this method were then reassessed and subjected to cross-validation using custom-built software written in LabVIEW. This custom software constructed linear regression models from the identified variable set using the LabVIEW “Solve Linear Equations” function (which finds the solution matrix X for AX = Y matrix equations). Standard cross-validation was performed by leaving a random subset of cases out of model generation (5 from both the concussion and control groups), and computing the model classification success on the subset left out. This “leave-out” validation was repeated 500 times, and the mean and standard deviation of classification accuracies are reported. Results fields from individual tests were imputed (for regression, but not Mann-Whitney U comparison) if the test was missing from the data (eg, the test was not acquired), or if the fields were removed from analysis because the test could not be reliably analyzed. Imputation for each field was performed by standard “Hot Deck” imputation, meaning that replacement values were randomly drawn from the field's values among other subjects in the same group (control group or concussion group). Cutoff thresholds other than 0.5 (eg, Table 3) were chosen manually, but selected to reflect where models achieved a nearly even ratio between sensitivity and specificity.
We compared the results of 87 OVRT test metrics between 170 controls and 50 concussed subjects. Metrics that were significantly different are presented first. Following these results are the classification models (concussion vs control) developed using logistic and standard linear regression techniques. After identifying 16 significant concussion-related metrics, we also evaluated these metrics for the subset of 10 subjects evaluated 22 or more days after injury.
Smooth pursuit tracking
In our study population, initiation latency (see Figure 1) was significantly different for horizontal SP tests performed at 0.75 Hz (concussion 253 ± 106 ms; control 214 ± 59 ms; P < .000575) and at 1.25 Hz (concussion 243 ± 59 ms; control 225 ± 98 ms; P < .000575), and for vertical SP tests performed at 0.5 Hz (concussion 238 ± 49 ms; control 213 ± 65 ms; P < .000575) and 0.75 Hz (concussion 225 ± 58 ms; control 207 ± 75 ms; P < .000575). At 1.25 Hz (horizontal), concussion subjects also demonstrated a significantly reduced position gain (eye position/stimulus position) relative to control subjects (concussion 0.72 ± 0.18; control 0.82 ± 0.17; P < .000575), and reduced velocity gain (concussion 0.67 ± 0.18; control 0.77 ± 0.17; P < .000575).
Significant differences were not seen for saccades during the random horizontal, vertical, and antisaccades tests, or during the predictive saccade test. In addition, no significant difference was seen for prosaccade errors on the antisaccades test.
In the combined SRT test, in which subjects were required to respond with both saccadic movement and button presses, we found that the percentage of saccade velocities that fell below a normative velocity threshold was significantly higher for the concussion group than for controls (concussion 7.2% ± 11.5%; control 1.8% ± 4.7%; P < .000575; Figure 2).
Horizontal OKN response was tested at 2 velocities: 23.4° (low speed) and 70.3° (high speed; Figure 3A) of visual angle/second, and we measured the slow-phase nystagmus velocity gain. This gain measure was computed as a mean of the velocity of all nystagmus beats for a subject relative to stimulus velocity (see Figure 3B), and was significantly reduced for the concussion group during the low-speed OKN test (concussion 0.78 ± 0.17; control 0.95 ± 0.09; P < .000575), and the high-speed OKN test (concussion 0.35 ± 0.20; control 0.72 ± 0.15; P < .000575). Concussion subjects had significantly more gain asymmetry (unequal velocity gain in the leftward and rightward nystagmus directions) during the high-speed OKN test (concussion 21.2 ± 18.9; control 8.6 ± 9.8; P < .000575), and a higher variability in velocity gains as measured by the gain standard deviation across all nystagmus beats for each subject (low speed, concussion 3.9 ± 1.9; control 2.8 ± 1.6; P < .000575; high speed, concussion 13.6 ± 4.5; control 11.2 ± 4.1; P < .000575).
We also evaluated the fast phases of OKN nystagmus beats at the 2 speeds, and quantified fast-phase velocity performance as an area under a main sequence curve fit (see Methods; Figure 3C). This fast-phase area value was significantly reduced in concussion subjects for the high-speed OKN test (concussion 8868 ± 2541; control 10035 ± 1383; P < .000575), suggesting a reduction in the velocity/distance ratio of fast-phase nystagmus beats in patients with concussion.
In addition, we evaluated the 16 metrics that were significantly different between the concussed and control groups for the subgroup of 10 patients with concussion recorded at 22 days or more postinjury. OKN gain metrics were the only metrics significantly different in this postconcussive group, namely low speed gain (22 plus-day concussion gain 0.77 ± 0.16; control 0.95 ± 0.09; P < .00313), and high speed gain (22 plus-day concussion gain 0.23 ± 0.14; control 0.72 ± 0.15; P < .00313; Table 4 and Figure 3D), as well as high-speed gain standard deviation (22 plus-day concussion 14.7 ± 2.6, control 11.2 ± 4.1, P < .00313).
Manual RT was not significantly different for the standard visual RT or auditory RT tests. In the more complex SRT test, however, a significant increase in left, right, and mean button press latency was observed for the concussion group (mean latency, concussion 550 ± 167 ms; control 440 ± 122 ms; P < .000575).
No significant differences were seen for measures in the SVH, SVV, or SN tests.
Separation of groups
Because of the many significantly altered OVRT metrics between concussion and control groups, we hypothesized that a combination of multiple metrics could create a strong clinical indicator of concussion condition. To test this hypothesis, we constructed a logistic regression classification model using our measurements as inputs, and membership in the concussion group as outputs.
At a default cutoff threshold of 0.5, the model correctly classified 91.4% of the subjects in the model training set. The model had a specificity of 95.9% (163 out of 170 controls classified correctly) but a sensitivity of only 76.0% (38 out of 50 concussion subjects). At a more optimized threshold of 0.28, the model correctly classified 89.1% of subjects, with a specificity of 88.8% and a sensitivity of 90.0% (see Table 3). As a measure of classification accuracy across thresholds, we generated a standard receiver-operating characteristic curve (see Figure 4) and measured an area under the curve of 0.96.
Forward conditional regression adds variables in steps, and in our model the regression terminated after 3 steps, choosing 3 significant variables: high-speed OKN gain, SRT button latency, and 0.5-Hz vertical SP initiation latency.
To test for overfitting, we constructed (nonlogistic) linear regression cross-validation models using these 3 variables, computing model coefficients using one set of subjects and tested on another (see Methods for details). The mean success rates over all 500 cross-validation models (cutoff threshold of 0.365, see Table 3) remained robust (sensitivity 86.8% ± 15.1%; specificity 89.9% ± 13.4%; overall 88.3% ± 9.7%).
A growing number of studies have investigated the effects of head injuries and concussions on oculomotor,9–25,32–37 vestibular,10,11,18,19,26 and reaction time 27–30 (OVRT) performance. In this study we investigated whether combined OVRT measures can reliably distinguish concussed subjects from similarly aged controls in a population of high school students. The tests used in this study are fundamental assessments of vestibular and oculomotor function, many with a long history of use in the field of VNG.38,39 Also, the neural pathways controlling saccadic, SP, OKN, and other nystagmus movements have been extensively mapped and published, and, importantly, found to cover a broad range of neural territory,40–42 making them ideal for assessments of general brain injury and/or dysfunction. We evaluated measurements independently by comparison of values between the 2 groups, and then collectively by constructing regression models.
Both approaches revealed metrics strongly related to the presence of a concussion, including alterations in SP tracking, delays in SP initiation, delayed RT and lower velocity on a combined saccade and button press task (SRT), and dramatically impaired optokinetic response during OKN tests. Importantly, OKN gains and gain variability were the only significant metrics observed in the subjects tested more than 3 weeks after injury (n = 10). This finding suggests that concussions can induce oculomotor deficits that extend beyond the first few weeks of recovery. Interestingly, the literature connecting objective optokinetic test results to concussions appears nonexistent, whereas symptoms induced by OKN testing have been reported.43 It remains to be determined whether OKN can serve as a reliable indicator of long-term consequences of mTBI, and as a potential biomarker of protracted recovery.
Despite vestibular effects of concussion reported elsewhere,10,11,18,19,26 vestibular deficits were absent from our results. Importantly, the VNG device used in this study did not have the most robust tests of vestibular function, such as full body rotation tests. For the SVV, SVH, and SN tests, our lack of vestibular findings may also have resulted from performing tests in dim light (not complete darkness), meaning that orienting or stabilizing cues may have been visible in the periphery for some subjects.
With this study we constructed multiple permutations of a regression model that reliably classified subjects as either control or concussed. Three variables were identified by this modeling: optokinetic gain (OKN test), RT latency in the SRT test, and initiation latency during SP. Given that there were 16 variables identified as significantly different between the 2 populations, it is worth noting that other combinations of OVRT metrics might create models with similar classification accuracy.
There were several limitations of this study. First, our demographic data regarding the sex of controls were inadvertently incomplete preventing a straightforward number and proportion of males and females, although from all available records the proportion was approximately 2:1. Test data quality was poor for many controls due to suboptimal testing techniques resulting in the removal of the controls from the subject group; testing imprecision and inaccuracy were minimized in the concussed subject group. Also, previous concussion history for control subjects was determined by self-report, and confirmation of concussion history by other means (eg, medical records) was not performed. Concussed subjects were assessed at a wide range of time points following their injury (ie, from 1 to 328 days; mean 22.1, median 9); clearly a more restrictive time frame for evaluation would have enhanced the overall quality and interpretation of the test data, given that OVRT deficits appear to be variable, progressing and changing over time as underlying neurophysiological injury responses progress,19 resulting in a shifting (and not necessarily improving) profile of impairments. This possibility is supported by the observation that only 3 of the significant measures found for all subjects, OKN gain and gain variability metrics, were also significantly altered in the subgroup of concussed subjects who were recorded more than 3 weeks after injury, a finding that suggests the potential importance of OKN metrics in the longitudinal assessment of concussed patients during variable periods of recovery.
In summary, this study found that multiple OVRT metrics were strongly associated with the presence of a concussion both acute and chronic, suggesting their clinical utility in concussion assessment. These results indicate that concussions produce a broad range of motor and behavioral deficits that can be quantified objectively with high precision and accuracy using OVRT metrics. Given the feasibility of multivariate models as demonstrated in this study, combined with the potential for measurements to shift over the course of recovery, future studies employing a longitudinal series of assessments are warranted.
1. Laborey M, Masson F, Ribéreau-Gayon R, Zongo D, Salmi LR, Lagarde E. Specificity of postconcussion symptoms at 3 months after mild traumatic brain injury: results from a comparative cohort study. J Head Trauma Rehabil. 2014;29(1):E28–E36.
2. Covassin T, Elbin RJ III, Stiller-Ostrowski JL, Kontos AP. Immediate postconcussion assessment and cognitive testing (ImPACT) practices of sports medicine professionals. J Athl Train. 2009;44(6):639–644.
3. Coldren RL, Kelly MP, Parish RV, Dretsch M, Russell ML. Evaluation of the Military Acute Concussion
Evaluation for use in combat operations more than 12 hours after injury. Mil Med. 2010;175(7):477–481.
4. Resch J, Driscoll A, McCaffrey N, et al ImPact test-retest reliability: reliably unreliable? J Athl Train. 2013;48(3):506–511.
5. Jagoda AS, Cantrill SV, Wears RL, et al Clinical policy: neuroimaging and decision making in adult mild traumatic brain injury in the acute setting. Ann Emerg Med. 2002;40(2):231–249.
6. Ono K, Wada K, Takahara T, Shirotani T. Indications for computed tomography in patients with mild head injury. Neurol Med Chir (Tokyo). 2007;47(7):291–298.
7. Johnson B, Hallett M, Slobounov S. Follow-up evaluation of oculomotor
performance with fMRI in the subacute phase of concussion
. Neurology. 2015;85(13):1163–1166.
8. Johnson B, Zhang K, Hallett M, Slobounov S. Functional neuroimaging of acute oculomotor
deficits in concussed athletes. Brain Imag Behav. 2015;9:564–573.
9. Kontos AP, Sufrinko A, Elbin RJ, Puskar A, Collins MW. Reliability and associated risk factors for performance on the vestibular
/ocular motor screening (VOMS) tool in healthy collegiate athletes. Am J Sports Med. 2016;44(6):1400–1406.
10. Balaban C, Hoffer ME, Szczupak M, et al Oculomotor
, and reaction time
tests in mild traumatic brain injury. PLoS One. 2016;11(9):e0162168.
12. Cifu DX, Wares JR, Hoke KW, Wetzel PA, Gitchel G, Carne W. Differential eye movements in mild traumatic brain injury versus normal controls. J Head Trauma Rehabil. 2015;30(1):21–28.
13. Contreras R, Kolster R, Voss HU, Ghajar J, Suh M, Bahar S. Eye-target synchronization in mild traumatic brain-injured patients. J Biol Phys. 2008;34(3/4):381–392.
14. DiCesare CA, Kiefer AW, Nalepka P, Myer GD. Quantification and analysis of saccadic and smooth pursuit
eye movements and fixations to detect oculomotor
deficits. Behav Res Methods. 2017;49(1):258–266.
15. Glass I, Groswasser Z, Groswasser-Reider I. Impersistent execution of saccadic eye movements after traumatic brain injury. Brain Inj. 1995;9(8):769–775.
16. Heitger MH, Anderson TJ, Jones RD, et al Eye movement and visuomotor arm movement deficits following mild closed head injury. Brain. 2004;127(pt 3):575–590.
17. Heitger MH, Anderson TJ, Jones RD. Saccade sequences as markers for cerebral dysfunction following mild closed-head injury. Prog Brain Res. 2002;140:433–448.
18. Mucha A, Collins MW, Elbin RJ, et al A brief vestibular
/ocular motor screening (VOMS) assessment to evaluate concussions: preliminary findings. Am J Sports Med. 2014;42(10):2479–2486.
19. Hoffer ME, Balaban C, Szczupak M, et al The use of oculomotor
, and reaction time
tests to assess mild traumatic brain injury (mTBI) over time. Laryngoscope Investig Otolaryngol. 2017;2(4):157–165.
20. Maruta J, Lee SW, Jacobs EF, Ghajar J. A unified science of concussion
. Ann NY Acad Sci. 2010;1208:58–66.
21. Samadani U, Ritlop R, Reyes M, et al Eye tracking detects disconjugate eye movements associated with structural traumatic brain injury and concussion
. J Neurotrauma. 2015;32(8):548–556.
22. Suh M, Kolster R, Sarkar R, et al Deficits in predictive smooth pursuit
after mild traumatic brain injury. Neurosci Lett. 2006;401(1/2):108–113.
23. Suh M, Basum S, Kolster R, et al Increased oculomotor
deficits during target blanking as an indicator of mild traumatic brain injury. Neurosci Lett. 2006;410(3):203–207.
24. Szymanowicz D, Ciuffreda KJ, Thiagarajan P, Ludlam DP, Green W, Kapoor N. Vergence in mild traumatic brain injury: a pilot study. J Rehab Res Dev. 2012;49(7):1083–1100.
25. Tyler CW, Likova LT, Mineff KN, Elsaid AM, Nicholas SC. Consequences of traumatic brain injury for human vergence dynamics. Front Neurol. 2015;5:282.
26. Hoffer ME, Balaban C, Gottshall K, Balough BJ, Maddox MR, Penta JR. Blast exposure: vestibular
consequences and associated characteristics. Otol Neurotol. 2010;31(2):232–236.
27. Halterman CI, Langan J, Drew A, et al Tracking the recovery of visuospatial attention deficits in mild traumatic brain injury. Brain. 2006;129(pt 3):747–753.
28. Hetherington CR, Stuss DT, Finlayson MA. Reaction time
and variability 5 and 10 years after traumatic brain injury. Brain Inj. 1996;10:473–486.
29. Segalowitz SJ, Dywan J, Unsal A. Attentional factors in response time variability after traumatic brain injury: an ERP study. J Int Neuropsychol Soc. 1997;3:95–107.
30. Stuss DT, Pogue J, Buckle L, Bondar J. Characterization of stability of performance in patients with traumatic brain injury: variability and consistency on reaction time
test. Neuropsychology. 1994;8:316–324.
31. Bahill AT, Clark MR, Stark L. The main sequence, a tool for studying human eye movements. Math Biosc. 1975;24:191–204.
32. Crevits L, Hanse MC, Tummers P, Van Maele G. Antisaccades and remembered saccades in mild traumatic brain injury. J Neurol. 2000;247:179–182.
33. Drew AS, Langan J, Halterman C, Osternig LR, Chou LS, van Donkelaar P. Attentional disengagement dysfunction following mTBI assessed with the gap saccade task. Neurosci Lett. 2007;417(1):61–65.
34. Heitger MH, Macaskill MR, Jones RD, Anderson TJ. The impact of mild closed-head injury on involuntary saccadic adaptation: evidence for the preservation of implicit motor learning. Brain Inj. 2005;19(2):109–117.
35. Heitger MH, Jones RD, Dalrymple-Alford JC, Frampton CM, Ardagh MW, Anderson TJ. Motor deficits and recovery during the first year following mild closed-head injury. Brain Inj. 2006;20(8):807–824.
36. Heitger MH, Jones RD, Dalrymple-Alford JC, Frampton CM, Ardagh MW, Anderson TJ. Mild head injury—a close relationship between motor function at 1 week postinjury and overall recovery at 3 and 6 months. J Neurol Sci. 2007;253(1/2):34–47.
37. Heitger MH, Jones RD, Macleod AD, Snell DL, Frampton CM, Anderson TJ. Impaired eye movements in postconcussion syndrome indicate suboptimal brain function beyond the influence of depression, malingering or intellectual ability. Brain. 2009;132(pt 10):2850–2870.
38. Shepard NT, Telian SA. Practical Management of the Balance Disorder Patient. San Diego, CA: Singular Publishing Group; 1996.
39. Jacobson GP, Shepard NT. Balance Function Assessment and Management. San Diego, CA: Plural Publishing, Inc; 2008.
40. Krauzlis RJ. Recasting the smooth pursuit
eye movement system. J Neurophysiol. 2004;91(2):591–603.
41. Munoz DP, Everling S. Look away: the anti-saccade task and the voluntary control of eye movement. Nat Rev Neurosci. 2004;5(3):218–228.
42. Lynch JC, Hoover JE, Strick PL. Input to the primate frontal eye field from the substantia nigra, superior colliculus, and dentate nucleus demonstrated by transneuronal transport. Exp Brain Res. 1994;100(1):181–186.
43. Wright WG, Tierney RT, McDevitt J. Visual-vestibular
processing deficits in mild traumatic brain injury. J Vestib Res. 2017;27(1):27–37.
Keywords:Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.
concussion; oculomotor; optokinetic; OVRT; reaction time; smooth pursuit; vestibular; video-oculography