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State-of-the-Art Review

Diagnostic Tests for Concussion

Is Vision Part of the Puzzle?

Ventura, Rachel E. MD, PhD; Jancuska, Jeffrey M. BA; Balcer, Laura J. MD, MSCE; Galetta, Steven L. MD

Editor(s): Biousse, Valérie MD; Galetta, Steven MD

Author Information
Journal of Neuro-Ophthalmology: March 2015 - Volume 35 - Issue 1 - p 73-81
doi: 10.1097/WNO.0000000000000223


Traumatic brain injury (TBI) is a major cause of morbidity and mortality worldwide. The Centers for Disease Control and Prevention have estimated that there are between 1.4 and 3.8 million sports-related mild traumatic brain injuries (mTBI) annually in the United States alone, although this approximation is based on emergency room rather than outpatient visits (1–3). Concussion, a mild form of mTBI (4) (Table 1), remains underreported based on anonymous surveys of collegiate athletes (6), in part reflecting a misconception that concussion is a benign brain injury.


Concussion is defined as a direct or indirect impulse to the head or body with accompanying neurological symptoms (7, 8). Visual complaints are especially common in concussion (see Supplemental Digital Content, Table E1, Sustaining one concussion increases the risk of the second concussion in the same season by threefold (9) and concussed athletes who return to play before complete recovery are vulnerable to the rare, but potentially catastrophic second impact syndrome (9–11). Concussion has also been associated with long-term sequelae including neurodegenerative disorders (12–14). Given that concussions may have devastating short- and long-term effects, tools that improve our assessment and management are critical.

We will first explore deficits in visual function after concussion, which can help in both screening and monitoring the recovery of TBI symptoms. Then, we will review the most widely used concussion tests, some of which are now vision-based.


The cognitive control of eye movements requires pathways involving fronto–parietal circuits and subcortical nuclei (15), many of which are particularly vulnerable to concussion. Common neuro-ophthalmic findings in concussion include abnormalities in saccades/antisaccades, smooth pursuit, vergence, accommodation, the vestibular–ocular reflex, and photosensitivity (Table 2). In the first 10 days after mTBI, patients have been found to have impaired antisaccades, prolonged saccadic latencies, higher directional errors, poorer spatial accuracy, and impaired memory-guided saccades (16,17). Patients with postconcussive syndrome 3–5 months after their injury perform worse on antisaccade testing, memory-guided saccades, and a self-paced saccade test in which subjects look back and forth between two points as rapidly as possible, when compared with patients who have had mTBI and recovered well (18). In addition, the gap saccade test has been studied in which subjects fixate on a central target and then, after varying amounts of time, fixate on a peripheral target. Patients with acute mTBI had longer saccadic reaction times when there is a short temporal gap between the central and peripheral targets, but not when the temporal gap is longer, suggesting difficulties in disengaging attention (19). These studies demonstrate abnormalities in saccadic function with concussion that, in part, relate to impairments in executive function (as probed with antisaccade tests), attention (as with the gap saccade test), and memory (as with memory-guided saccade tests) (Table 2) (16–20). Symptom resolution often corresponds with return of normal saccadic function (18). These saccadic tests have been studied in the research setting and require the use of a computer and video-oculography and thus have limited clinical application at this time. The absence of baseline testing, calibration issues, poor attention, and medication effects could be other issues that complicate the routine testing of eye movements. Saccadic dysfunction as assessed clinically may be present in approximately 30% of patients with mTBI (21).

Tests of saccades and pursuit can be used to assess higher cognitive functions using research paradigms.

Smooth pursuit requires attention, anticipation, and working memory as well as smooth and at times, saccadic eye movements to fixate on a moving target (22). Suh and colleagues (23,24) correlated mTBI with decreased target prediction, increased eye position error, and variability of eye position using a circular tracking test. Temporarily extinguishing the target, which necessitates more predictive tracking, resulted in more abnormal findings. When tested clinically, it was found that 60% of mTBI patients had abnormalities in pursuit eye movements (21).

Convergence abnormalities have been reported in 47%–64% of patients with concussion (21,25). Additionally, 65% of concussed patients vs. only 15% of controls have abnormalities in accommodative amplitude (21). Patients with sports-related concussions often have symptomatic complaints attributable to either convergence or accommodative insufficiency, such as headaches, “sore eyes,” words coming in and out of focus, and losing one's place while reading (21). Peripheral and central mechanisms of vertigo also commonly occur with concussions. In addition, patients often complain of increased light sensitivity, which is possibly due to meningeal irritation, migraine, or driven through central pathways such as the thalamus (26). Ocular motor palsies and other cranial nerve abnormalities are unlikely to occur in concussion as opposed to more severe forms of TBI, and their presence with concussion should alert the examiner to look for a preexisting structural abnormality such as an arteriovenous malformation, aneurysm, or tumor (27).

Testing individual eye movements is important, but requires clinical expertise which may limit widespread use on the sideline, particularly at the youth level where sports parents will be in charge of concussion assessment. Cost, standardization, accessibility, and reliability will be other considerations when developing sideline tools for concussion evaluation.


Concussion tests can be divided into those used for diagnosis and initial assessment, including symptom checklists and sideline evaluations, and those used for management, including computerized neurocognitive testing, neuroimaging, biomarkers, and electrophysiology.

Symptom Checklists

Symptom checklists rely on self-reporting among a list of symptoms (see Supplemental Digital Content, Table E2, and as such are susceptible to underreporting (28). In an online survey of 262 University of Pennsylvania athletes, 43% of participants with a history of concussion have hidden symptoms to stay in a game (6). Apart from suffering from potentially inaccurate reporting, symptom checklists may have a limited role in return-to-play guidelines because cognitive impairment may persist beyond symptom resolution (29), although they are helpful in monitoring recovery. Mucha and colleagues (30) have recently described a screening test, called the Vestibular/Ocular Motor Screening Assessment, in which symptoms are reported while subjects undergo a battery of vestibular and ocular provocations. They found that subjects who reported symptoms with the provocations of undergoing vestibular–ocular reflex testing or testing visual motion sensitivity were highly likely to be in the concussed group with an odds ratio of 3.89 and 3.37, respectively. Thus provoked symptoms may be a more accurate way to assess patients with possible concussions, but this methodology still suffers from biases inherent in patient self-reporting. Combining provoked symptoms with any objective findings on the vestibular and ocular examination may increase the likelihood of predicting subjects with concussion and also provide clues regarding inaccurate reporting.

Sideline Tests

King–Devick (K–D) Test

Since approximately 50% of the brain's circuits are related to vision (31), and many of these pathways are susceptible to injury in concussion, performance measures involving visual function are promising in sideline assessment. To perform the K–D test, the subject rapidly reads numbers on three test cards, with the score being the total time required in seconds (see Supplemental Digital Content, Figure E1, This typically takes 1–2 minutes. Rapid number naming requires a distributed network of saccade areas including those in the dorsolateral prefrontal cortex (DLPFC) that are responsible for anticipatory saccades (15,32). The K–D test also requires attention and language, as well as other areas involved in reading; K–D thereby tests functioning of the brainstem, cerebellum, and cerebral cortex (33,34). Eye movement abnormalities commonly occur with concussion and the K–D test allows them to be assessed without the clinical expertise that would otherwise be required. Like any functional test, K–D baseline scores can be potentially limited by those athletes who willfully attempt to sandbag the test. Understanding the typical range of scores and encouraging the athlete to read as fast as they can limit such activity. Factors that may limit sandbagging of functional tests is an important area for future investigation.

Studies of mixed martial arts fighters, boxers, collegiate athletes, professional hockey players, and rugby players consistently reveal on average a 5- to 7-second increase (worse) score immediately after concussion compared with baseline (33–36). Any worsening of the K–D score from baseline suggests the presence of a concussion (33–36). Scores are not worsened by routine exercise, and there is a learning effect with repeated testing (33). Furthermore, a control cohort studied at the same time as the concussed athlete group showed an improvement of K–D times. Given that other sideline tests such as the Standardized Assessment of Concussion (SAC) and Balance Error Scoring System (BESS; see below) do not assess eye movements, we studied whether the K–D would provide additional information in a cohort of University of Florida athletes (5). We found that the SAC and BESS even when used together as sideline tools failed to show abnormalities in 10% of the 20 concussions under study. With the addition of the K–D, all of the concussions could be identified. Thus, adding the K–D increased our ability to detect concussed athletes and complements the SAC and BESS as a performance measure.

Sport Concussion Assessment Tool, Third Edition (SCAT3)

The SCAT3 is a commonly used sideline tool that consists of a 22-item symptom checklist, cognitive and physical examination, the Glasgow Coma Scale, Maddocks questions (set of 5 questions that assess game-specific orientation and recent memory) (37, 38), the modified BESS, and the SAC. The SCAT3 takes 15–20 minutes to complete and was compiled by a consensus committee based on best available measures (39). A composite score reflects the quantity of questions in each section rather than the importance and may not be as helpful as using each component alone (39). Baseline testing is required due to individual variability (40–42). The SCAT3 does not test all areas, such as vision, limiting its use as a sole indicator for concussion diagnosis or for return-to-play, although it can provide supportive information (43). The lack of a vision test in the SCAT3 is a current gap despite its widespread use a sideline concussion tool.

Standardized Assessment of Concussion

The SAC is a component of SCAT3 that measures cognitive areas including orientation, immediate memory, concentration, and delayed recall (Fig. 1) (44–48). In one study, only 50% of 28 concussed collegiate athletes had abnormal SAC testing. The test was able to capture some concussed athletes with normal BESS and K–D scores and seems to have value as a complementary test (5). One should note that the scores may be artificially inflated since athletes can memorize sections of the test (49).

FIG. 1:
Standardized assessment of concussion test examines orientation, memory, and concentration function (42). Total possible of 30 points.

Balance Error Scoring System and Other Tests of Balance

The BESS, another component of the SCAT3, tests balance (see Supplemental Digital Content, Figure E2, Likely due to its subjective nature, there is a great variability in scoring for the BESS. The interrater reliability intraclass correlation (ICC) has been reported as 0.57, with intrarater reliability ICC's of 0.74 (50). Given this variability, particularly between raters, it is prudent to have the same individual measure baseline and postconcussive BESS scores when possible. Performance on the BESS also can vary over the course of the season, as it is affected by the sport played, history of ankle injury, and fatigue (51–55).

Portable inertia sensors may help to obtain more objective and sensitive measures of balance. One study found that although the BESS alone could not distinguish controls from those with a recent history of concussion and balance complaints, the addition of a portable inertia sensor enabled detection of significant differences (56). Additionally, Wii Balance Boards provide another objective balance test and have been shown to have improved validity (0.99) and test–retest reliability (0.88) over the BESS (57).

Head Impact Telemetry System

Head impact telemetry system is an investigational tool in which a series of accelerometers incorporated into the padding of a football helmet provide data on the magnitude and location of impact (58). Factors such as a rotational acceleration greater than 5500 radians per second, linear acceleration greater than approximately 96 g, and location of impact can be predictive for concussion (58). By analyzing these measurements on the field, athletes at risk for concussive injury could potentially be identified, many of whom may show evidence of structural compromise using diffusion tensor imaging (DTI) even without a clinical presentation (59). Individuals with impacts below the predicted concussion threshold can still have a concussion, making complementary assessments necessary (49).

Eye Movement Tracking Devices

Use of a portable head-mounted video-based eye tracker to detect abnormalities in eye movements has been studied in the research setting on a limited basis. Cifu and colleagues (60) recently reported use of an eye tracking device on 60 military subjects with persistent concussive symptoms vs. 26 controls and found that those with the concussive symptoms had significantly larger saccadic position errors, smaller saccadic amplitudes, smaller predicted peak velocities, smaller peak accelerations, and also abnormalities in pursuit velocities. Further work is required to determine how acutely these eye movement changes occur and the exact time course for recovery.

Immediate Postconcussion Assessment Cognitive Test and Other Computerized Neurocognitive Tests

Immediate postconcussion assessment cognitive test (ImPACT) is a computerized neuropsychological test battery that takes 20 minutes to complete. Deficits detected by ImPACT correlate with traditional neuropsychological testing (61) and with functional magnetic resonance imaging (fMRI) findings such as altered activation of the DLPFC (62). Visual subscores on ImPACT correlate with the K–D test (5). The software incorporates statistical techniques to account for normal test score variability over time and can usually detect intentional “sandbagging” by flagging athletes with scores on certain subscales that are below predefined values (63,64). Meaningful postinjury ImPACT results require baseline testing (65,66), since factors such as attention-deficit hyperactivity disorder and learning disabilities affect baseline scores (67). Eighty-three percent of athletes with concussion who completed the ImPACT battery did show cognitive impairment, although it is notable that 17% of those with concussion did not show any abnormality on the cognitive measures (68) and that up to 20%–40% of nonconcussed athletes demonstrated cognitive impairment (61,69,70). One should be cautious in the use of ImPACT in return-to-play decisions, given insufficient validity and test–retest reliability, and also because subjects may have ongoing metabolic abnormalities in spite of return of ImPACT scores to baseline (71). Other computerized programs have emerged, and further study of their ability to detect and guide return to play need to be performed. Computerized testing has its limitations and should be used by those skilled in the interpretation of such testing.


Conventional Magnetic Resonance Imaging and Computed Tomography

A commonly accepted definition of mTBI/concussion requires normal computed tomography imaging. Routine magnetic resonance imaging (MRI) is often normal as well (72,73), although susceptibility-weighted imaging sequences can detect microhemorrhages associated with subconcussive or concussive injury (74). One prospective study found that 4 of 19 patients with mTBI had brain atrophy as measured by MRI volumetry 3–7 months postinjury. However, this was a small study, and it did not include patients with sports-related concussions (75). Another study found that collegiate football players had decreased hippocampal volumes vs. controls, with an inverse relationship between left hippocampal volume and years of football played (76). Among the football players, those with a history of concussion had the smallest hippocampi (76).

Diffusion tensor imaging

DTI maps the diffusion of molecules, mainly water (77). Fractional anisotropy is a measurement of the fraction of diffusion magnitude (78) and has been shown to be a reliable marker of white-matter integrity (72,79). Significant DTI findings in players with a history of concussion compared with nonconcussed controls include widespread increase in fractional anisotropy and decreased trace and radial diffusivity in the right corona radiata, right posterior limb of the internal capsule, right superior temporal white matter (80), and optic radiations (81).

Maruta and colleagues (73) used video-oculography to record visual tracking of a moving target in a circular trajectory combined with DTI analysis of their concussed subjects. They found that large gaze error variability was associated with low fractional anisotropy values in areas known to be frequently compromised in concussion, such as the right anterior corona radiata, the left superior cerebellar peduncle, the genu of the corpus callosum, and a number of other brain regions.

DTI also has been studied in subjects with subconcussive impacts. In one study, nonconcussed athletes in contact sports were found to have significant changes in mean diffusivity in the corpus callosum and fractional anisotropy in the amygdala when compared with athletes in noncontact sports over the course of one season. Measurements of head impact detected with a helmet sensor correlated with changes in white-matter diffusivity in several brain regions, in spite of not having a clinical concussion (59). These studies highlight a role for DTI in further delineating the pathology associated with concussive or subconcussive brain injuries.

Functional Magnetic Resonance Imaging

Blood-oxygen-level-dependent (BOLD) fMRI detects changes in the oxygenation state of hemoglobin, thereby capturing oxygen consumption associated with neuronal activation (79). With concussion, changes in brain activation using fMRI are observed acutely and several months after injury even without clinical changes (82–87). Patients with severe postconcussion symptoms have increased activity in the normal working memory network that correlates with symptom severity (88,89). Conversely, patients with concussion have reduced activation in the DLPFC, insular cortex, anterior cingulate cortex, striatum, and medial frontal and temporal regions (90). Functional MRI studies on mTBI patients with concussion who were performing a visual working memory task showed decreased BOLD signal intensities in the right mid-DLPFC, which corresponded to severity of their postconcussive symptoms (91). However, one caveat is that it can be challenging to interpret fMRI changes given the complexity of neuronal circuitry (92).

Magnetic Resonance Spectroscopy

Magnetic resonance spectroscopy (MRS) measures the concentrations of molecules associated with brain metabolism (72,79). Concussion significantly lowers levels of gray matter glutamine and N-acetylaspartate (NAA) and increases levels of white matter creatine (Cr) and choline (Cho) (72,93,94). MRS studies on one cohort of patients with concussion who had symptom resolution in about 3 days found that it took 30 days for the NAA level and NAA/Cr ratio to return to baseline (94). Another study of patients with concussion who required about 15 days for symptomatic clinical recovery found that it took 45 days for the NAA/Cho ratio to return to baseline (95). MRS holds promise clinically in determining metabolic recovery from concussion and aiding in return-to-play decisions (94).

Positron Emission Tomography

Positron emission tomography (PET) scanning uses radiolabeled metabolic analogs to measure the rate of brain glucose metabolism (72,79). Mild TBI decreases glucose metabolism in the cerebellar vermis, pons, and medial temporal cortex (96). Decreases in glucose metabolism in concussed patients were found to correlate with cognitive disturbances (97). Further research is necessary to validate these studies and determine the potential clinical utility of PET scanning, with the ultimate goal of identifying individuals at risk for worse outcomes or neurodegeneration (98).


Concussion leads to abnormal electrical activity, such as smaller amplitudes of frontal N350 and parietal P300 evoked responses (99,100). Patients with multiple concussions have been found to have abnormal electrophysiological results even 2–3 years after their last injury, well after symptoms have resolved. This has been shown when patients perform a visual spatial attention and short-term memory task (101). Electrophysiological techniques, particularly when paired with visual tasks, provide insight into subclinical postconcussive abnormalities and may help to predict those vulnerable to long-term sequelae.


Concussion and mTBI have a multitude of effects on the visual system, necessitating a careful neuro-ophthalmic examination. Clinically, tests of saccades, pursuit, convergence, accommodation, vestibular–ocular reflex, and ocular misalignment are frequently abnormal. Sideline tests of visual performance, such as the K–D test, may be a sensitive means of screening for sports-related concussion. As a visual performance measure with an objective end point, this tool can be administered at the sideline by a nonprofessional.

The armamentarium for assessing concussion is largely under development. Tests under exploration for assessing long-term sequelae of mTBI include ocular coherence tomography, as it has been found that mice with blast injury have a decrease in the retinal nerve fiber layer 3 months postinjury (102). Other devices measuring afferent and efferent visual dysfunction are currently being developed, but their cost, efficiency, and need for expert interpretation may limit their widespread use.

Vision has also garnered attention for potential use in predicting subjects at risk of more severe head impacts. Harpham and colleagues (103) found that those athletes with low visual and sensory performance, including on such tasks as depth perception and visual reaction time, had a higher number of more severe impacts measured using head impact telemetry. This raises the question as to whether visual training can decrease the likelihood of severe head impacts, and further study in this area is ongoing.

We have highlighted the merits of many of the currently used tests, but, at present, there is no single test that alone can reliably diagnose concussion or determine when recovery has occurred. Clinical decision making based on examination and assessment of a wide variety of tools is still necessary. Given the potentially devastating long-term effects of repeated head trauma, it is important to be able to accurately assess even subclinical brain injury. A combination of visual processing tasks, neuroimaging, serum biomarkers, and electrophysiological recordings may allow further insights into subtle damage that has occurred from concussion and future clinical implications.


Category 1: a. Conception and design: Steven L. Galetta. b. Acquisition of data Rachel E. Ventura, Jeffrey Jancuska. c. Analysis and interpretation of data: Rachel E. Ventura, Steven L. Galetta. Category 2: a. Drafting the manuscript: Rachel E. Ventura, Jeffrey Jancuska. b. Revising it for intellectual content: Rachel E. Ventura, Steven L. Galetta, Laura J. Balcer. Category 3: a. Final approval of the completed manuscript: Rachel E. Ventura, Steven L. Galetta, Laura J. Balcer, Jeffrey Jancuska.


1. Meehan WP III, Micheli LJ. Concussion results in deficits in neurocognitive functioning. Preface Clin Sports Med. 2011;30:17–18.
2. Langlois JA, Rutland-Brown W, Wald MM. The epidemiology and impact of traumatic brain injury: a brief overview. J Head Trauma Rehabil. 2006;21:375–378.
3. Langlois JAR-BW, Thomas KE. Traumatic Brain Injury in the United States: Emergency Department visits, Hospitalizations, and Deaths. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 2004.
4. Menon DK, Schwab K, Wright DW, Maas AI; Demographics and Clinical Assessment Working Group of the International and Interagency Initiative toward Common Data Elements for Research on Traumatic Brain Injury and Psychological Health. Position statement: definition of traumatic brain injury. Arch Phys Med Rehabil. 2010;91:1637–1640.
5. Marinides Z, Galetta KM, Andrews CN, Wilson JA, Herman DC, Robinson CD, Smith MS, Bentley BC, Galetta SL, Balcer LJ, Clugston JR. Vision testing is additive to the sideline assessment of sports-related concussion. Neurol Clin Pract. 2014. Epub July 2014.
6. Torres DM, Galetta KM, Phillips HW, Dziemianowicz EM, Wilson JA, Dorman ES, Laudano E, Galetta SL, Balcer LJ. Sports-related concussion: anonymous survey of a collegiate cohort. Neurol Clin Pract. 2013;3:279–287.
7. Concussion (mild traumatic brain injury) and the team physician: a consensus statement. Med Sci Sports Exerc. 2006;38:395–399.
8. McCrory P, Johnston K, Meeuwisse W, Aubry M, Cantu R, Dvorak J, Graf-Baumann T, Kelly J, Lovell M, Schamasch P. International Symposium on Concussion in S. Summary and agreement statement of the 2nd International Conference on Concussion in Sport, Prague 2004. Clin J Sport Med. 2005;15:48–55.
9. Guskiewicz KM, Weaver NL, Padua DA, Garrett WE Jr. Epidemiology of concussion in collegiate and high school football players. Am J Sports Med. 2000;28:643–650.
10. Meaney DF, Smith DH. Biomechanics of concussion. Clin Sports Med. 2011;30:19–31, vii.
11. Boden BP, Tacchetti RL, Cantu RC, Knowles SB, Mueller FO. Catastrophic head injuries in high school and college football players. Am J Sports Med. 2007;35:1075–1081.
12. Halstead ME, Walter KD; Council on Sports Medicine and Fitness. American Academy of Pediatrics. Clinical report–sport-related concussion in children and adolescents. Pediatrics. 2010;126:597–615.
13. Sim A, Terryberry-Spohr L, Wilson KR. Prolonged recovery of memory functioning after mild traumatic brain injury in adolescent athletes. J Neurosurg. 2008;108:511–516.
14. Gavett BE, Stern RA, McKee AC. Chronic traumatic encephalopathy: a potential late effect of sport-related concussive and subconcussive head trauma. Clin Sports Med. 2011;30:179–188, xi.
15. White OB, Fielding J. Cognition and eye movements: assessment of cerebral dysfunction. J Neuroophthalmol. 2012;32:266–273.
16. Heitger MH, Anderson TJ, Jones RD, Dalrymple-Alford JC, Frampton CM, Ardagh MW. Eye movement and visuomotor arm movement deficits following mild closed head injury. Brain. 2004;127:575–590.
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. Heitger MH, Jones RD, Macleod AD, Snell DL, Frampton CM, Anderson TJ. Impaired eye movements in post-concussion syndrome indicate suboptimal brain function beyond the influence of depression, malingering or intellectual ability. Brain. 2009;132:2850–2870.
19. 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;417:61–65.
20. Kraus MF, Little DM, Donnell AJ, Reilly JL, Simonian N, Sweeney JA. Oculomotor function in chronic traumatic brain injury. Cogn Behav Neurol. 2007;20:170–178.
21. Capo-Aponte JE, Urosevich TG, Temme LA, Tarbett AK, Sanghera NK. Visual dysfunctions and symptoms during the subacute stage of blast-induced mild traumatic brain injury. Mil Med. 2012;177:804–813.
22. Barnes GR. Cognitive processes involved in smooth pursuit eye movements. Brain Cogn. 2008;68:309–326.
23. Suh M, Kolster R, Sarkar R, McCandliss B, Ghajar J; Cognitive and Neurobiological Research Consortium. Deficits in predictive smooth pursuit after mild traumatic brain injury. Neurosci Lett. 2006;401:108–113.
24. Suh M, Basu S, Kolster R, Sarkar R, McCandliss B, Ghajar J; Cognitive and Neurobiological Research Consortium. Increased oculomotor deficits during target blanking as an indicator of mild traumatic brain injury. Neurosci Lett. 2006;410:203–207.
25. Brahm KD, Wilgenburg HM, Kirby J, Ingalla S, Chang CY, Goodrich GL. Visual impairment and dysfunction in combat-injured servicemembers with traumatic brain injury. Optom Vis Sci. 2009;86:817–825.
26. Singman E. Automating the assessment of visual dysfunction after traumatic brain injury. Med Instrum. 2013;1:3. doi: 10.7243/2052-6962-1-3.
27. Dhaliwal A, West AL, Trobe JD, Musch DC. Third, fourth, and sixth cranial nerve palsies following closed head injury. J Neuroophthalmol. 2006;26:4–10.
28. Guskiewicz KM, McCrea M, Marshall SW, Cantu RC, Randolph C, Barr W, Onate JA, Kelly JP. Cumulative effects associated with recurrent concussion in collegiate football players: the NCAA Concussion Study. JAMA. 2003;290:2549–2555.
29. McCrea M, Barr WB, Guskiewicz K, Randolph C, Marshall SW, Cantu R, Onate JA, Kelly JP. Standard regression-based methods for measuring recovery after sport-related concussion. J Int Neuropsychol Soc. 2005;11:58–69.
30. Mucha A, Collins MW, Elbin RJ, Furman JM, Troutman-Enseki C, DeWolf RM, Marchetti G, Kontos AP. A brief vestibular/ocular motor screening (VOMS) assessment to evaluate concussions: preliminary findings. Am J Sports Med. 2014;42:2479–2486.
31. Felleman DJ, Van Essen DC. Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex. 1991;1:1–47.
32. Pierrot-Deseilligny C, Muri RM, Ploner CJ, Gaymard B, Demeret S, Rivaud-Pechoux S. Decisional role of the dorsolateral prefrontal cortex in ocular motor behaviour. Brain. 2003;126:1460–1473.
33. Galetta KM, Brandes LE, Maki K, Dziemianowicz MS, Laudano E, Allen M, Lawler K, Sennett B, Wiebe D, Devick S, Messner LV, Galetta SL, Balcer LJ. The King-Devick test and sports-related concussion: study of a rapid visual screening tool in a collegiate cohort. J Neurol Sci. 2011;309:34–39.
34. Galetta KM, Barrett J, Allen M, Madda F, Delicata D, Tennant AT, Branas CC, Maguire MG, Messner LV, Devick S, Galetta SL, Balcer LJ. The King-Devick test as a determinant of head trauma and concussion in boxers and MMA fighters. Neurology. 2011;76:1456–1462.
35. Galetta MS, Galetta KM, McCrossin J, Wilson JA, Moster S, Galetta SL, Balcer LJ, Dorshimer GW, Master CL. Saccades and memory: baseline associations of the King-Devick and SCAT2 SAC tests in professional ice hockey players. J Neurol Sci. 2013;328:28–31.
36. King D, Clark T, Gissane C. Use of a rapid visual screening tool for the assessment of concussion in amateur rugby league: a pilot study. J Neurol Sci. 2012;320:16–21.
37. Maddocks DL, Dicker GD, Saling MM. The assessment of orientation following concussion in athletes. Clin J Sport Med. 1995;5:32–35.
38. McCrory P, Meeuwisse W, Aubry M, Cantu B, Dvorak J, Echemendia R, Engebretsen L, Johnston K, Kutcher J, Raftery M, Sills A, Benson B, Davis G, Ellenbogen R, Guskiewicz K, Herring SA, Iverson G, Jordan B, Kissick J, McCrea M, McIntosh A, Maddocks D, Makdissi M, Purcell L, Putukian M, Schneider K, Tator C, Turner M. Consensus statement on concussion in sport–the 4th International Conference on concussion in sport held in Zurich, November 2012. Phys Ther Sport. 2013;14:e1–e13.
39. Guskiewicz KM, Register-Mihalik J, McCrory P, McCrea M, Johnston K, Makdissi M, Dvorak J, Davis G, Meeuwisse W. Evidence-based approach to revising the SCAT2: introducing the SCAT3. Br J Sports Med. 2013;47:289–293.
40. Jones NS, Walter KD, Caplinger R, Wright D, Raasch WG, Young C. Effects of education and language on baseline concussion screening tests in professional baseball players. Clin J Sport Med. 2014;24:284–288.
41. Valovich McLeod TC, Bay RC, Lam KC, Chhabra A. Representative baseline values on the sport concussion assessment tool 2 (SCAT2) in adolescent athletes vary by gender, grade, and concussion history. Am J Sports Med. 2012;40:927–933.
42. Jinguji TM, Bompadre V, Harmon KG, Satchell EK, Gilbert K, Wild J, Eary JF. Sport concussion assessment tool-2: baseline values for high school athletes. Br J Sports Med. 2012;46:365–370.
43. Luoto TM, Silverberg ND, Kataja A, Brander A, Tenovuo O, Ohman J, Iverson GL. Sport concussion assessment tool 2 in a civilian trauma sample with mild traumatic brain injury. J Neurotrauma. 2014;31:728–738.
44. McCrea M, Kelly JP, Randolph C, Kluge J, Bartolic E, Finn G, Baxter B. Standardized assessment of concussion (SAC): on-site mental status evaluation of the athlete. J Head Trauma Rehabil. 1998;13:27–35.
45. McCrea M, Kelly JP, Kluge J, Ackley B, Randolph C. Standardized assessment of concussion in football players. Neurology. 1997;48:586–588.
46. McCrea M. Standardized mental status testing on the sideline after sport-related concussion. J Athl Train. 2001;36:274–279.
47. Valovich McLeod TC, Barr WB, McCrea M, Guskiewicz KM. Psychometric and measurement properties of concussion assessment tools in youth sports. J Athl Train. 2006;41:399–408.
48. Valovich TC, Perrin DH, Gansneder BM. Repeat administration elicits a practice effect with the balance error scoring system but not with the standardized assessment of concussion in high school athletes. J Athl Train. 2003;38:51–56.
49. Dziemianowicz MS, Kirschen MP, Pukenas BA, Laudano E, Balcer LJ, Galetta SL. Sports-related concussion testing. Curr Neurol Neurosci Rep. 2012;12:547–559.
50. Finnoff JT, Peterson VJ, Hollman JH, Smith J. Intrarater and interrater reliability of the balance error scoring system (BESS). PM R. 2009;1:50–54.
51. Susco TM, Valovich McLeod TC, Gansneder BM, Shultz SJ. Balance recovers within 20 minutes after exertion as measured by the balance error scoring system. J Athl Train. 2004;39:241–246.
52. Wilkins JC, Valovich McLeod TC, Perrin DH, Gansneder BM. Performance on the balance error scoring system decreases after fatigue. J Athl Train. 2004;39:156–161.
53. Bressel E, Yonker JC, Kras J, Heath EM. Comparison of static and dynamic balance in female collegiate soccer, basketball, and gymnastics athletes. J Athl Train. 2007;42:42–46.
54. Docherty CL, Valovich McLeod TC, Shultz SJ. Postural control deficits in participants with functional ankle instability as measured by the balance error scoring system. Clin J Sport Med. 2006;16:203–208.
55. Burk JM, Munkasy BA, Joyner AB, Buckley TA. Balance error scoring system performance changes after a competitive athletic season. Clin J Sport Med. 2013;23:312–317.
56. King LA, Horak FB, Mancini M, Pierce D, Priest KC, Chesnutt J, Sullivan P, Chapman JC. Instrumenting the balance error scoring system for use with patients reporting persistent balance problems after mild traumatic brain injury. Arch Phys Med Rehabil. 2014;95:353–359.
57. Chang JO, Levy SS, Seay SW, Goble DJ. An alternative to the balance error scoring system: using a low-cost balance board to improve the validity/reliability of sports-related concussion balance testing. Clin J Sport Med. 2014;24:256–262.
58. Broglio SP, Schnebel B, Sosnoff JJ, Shin S, Fend X, He X, Zimmerman J. Biomechanical properties of concussions in high school football. Med Sci Sports Exerc. 2010;42:2064–2071.
59. McAllister TW, Ford JC, Flashman LA, Maerlender A, Greenwald RM, Beckwith JG, Bolander RP, Tosteson TD, Turco JH, Raman R, Jain S. Effect of head impacts on diffusivity measures in a cohort of collegiate contact sport athletes. Neurology. 2014;82:63–69.
60. 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. 2014;30:21–28.
61. Maerlender A, Flashman L, Kessler A, Kumbhani S, Greenwald R, Tosteson T, McAllister T. Examination of the construct validity of ImPACT computerized test, traditional, and experimental neuropsychological measures. Clin Neuropsychol. 2010;24:1309–1325.
62. Talavage TM, Nauman EA, Breedlove EL, Yoruk U, Dye AE, Morigaki KE, Feuer H, Leverenz LJ. Functionally-detected cognitive impairment in high school football players without clinically-diagnosed concussion. J Neurotrauma. 2014;31:327–338.
63. Schatz P, Glatts C. “Sandbagging” baseline test performance on ImPACT, without detection, is more difficult than it appears. Arch Clin Neuropsychol. 2013;28:236–244.
64. Erdal K. Neuropsychological testing for sports-related concussion: how athletes can sandbag their baseline testing without detection. Arch Clin Neuropsychol. 2012;27:473–479.
65. Gardner A, Shores EA, Batchelor J, Honan CA. Diagnostic efficiency of ImPACT and CogSport in concussed rugby union players who have not undergone baseline neurocognitive testing. Appl Neuropsychol Adult. 2012;19:90–97.
66. Echemendia RJ, Bruce JM, Bailey CM, Sanders JF, Arnett P, Vargas G. The utility of post-concussion neuropsychological data in identifying cognitive change following sports-related MTBI in the absence of baseline data. Clin Neuropsychol. 2012;26:1077–1091.
67. Zuckerman SL, Lee YM, Odom MJ, Solomon GS, Sills AK. Baseline neurocognitive scores in athletes with attention deficit-spectrum disorders and/or learning disability. J Neurosurg Pediatr. 2013;12:103–109.
68. Van Kampen DA, Lovell MR, Pardini JE, Collins MW, Fu FH. The “value added” of neurocognitive testing after sports-related concussion. Am J Sports Med. 2006;34:1630–1635.
69. Randolph C. Baseline neuropsychological testing in managing sport-related concussion: does it modify risk? Curr Sports Med Rep. 2011;10:21–26.
70. Broglio SP, Ferrara MS, Macciocchi SN, Baumgartner TA, Elliott R. Test-retest reliability of computerized concussion assessment programs. J Athl Train. 2007;42:509–514.
71. Mayers LB, Redick TS. Clinical utility of ImPACT assessment for postconcussion return-to-play counseling: psychometric issues. J Clin Exp Neuropsychol. 2012;34:235–242.
72. Pulsipher DT, Campbell RA, Thoma R, King JH. A critical review of neuroimaging applications in sports concussion. Curr Sports Med Rep. 2011;10:14–20.
73. Maruta J, Lee SW, Jacobs EF, Ghajar J. A unified science of concussion. Ann N Y Acad Sci. 2010;1208:58–66.
74. Helmer KG, Pasternak O, Fredman E, Preciado RI, Koerte IK, Sasaki T, Mayinger M, Johnson AM, Holmes JD, Forwell LA, Skopelja EN, Shenton ME, Echlin PS. Hockey concussion education project, part 1. Susceptibility-weighted imaging study in male and female ice hockey players over a single season. J Neurosurg. 2014;120:864–872.
75. Lannsjo M, Raininko R, Bustamante M, von Seth C, Borg J. Brain pathology after mild traumatic brain injury: an exploratory study by repeated magnetic resonance examination. J Rehabil Med. 2013;45:721–728.
76. Singh R, Meier TB, Kuplicki R, Savitz J, Mukai I, Cavanagh L, Allen T, Teague TK, Nerio C, Polanski D, Bellgowan PS. Relationship of collegiate football experience and concussion with hippocampal volume and cognitive outcomes. JAMA. 2014;311:1883–1888.
77. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996;111:209–219.
78. FitzGerald DB, Crosson BA. Diffusion weighted imaging and neuropsychological correlates in adults with mild traumatic brain injury. Int J Psychophysiol. 2011;82:79–85.
79. Prabhu SP. The role of neuroimaging in sport-related concussion. Clin Sports Med. 2011;30:103–114, ix.
80. Sasaki T, Pasternak O, Mayinger M, Muehlmann M, Savadjiev P, Bouix S, Kubicki M, Fredman E, Dahlben B, Helmer KG, Johnson AM, Holmes JD, Forwell LA, Skopelja EN, Shenton ME, Echlin PS, Koerte IK. Hockey concussion education project, part 3. White matter microstructure in ice hockey players with a history of concussion: a diffusion tensor imaging study. J Neurosurg. 2014;120:882–890.
81. Grossman EJ, Jensen JH, Babb JS, Chen Q, Tabesh A, Fieremans E, Xia D, Inglese M, Grossman RI. Cognitive impairment in mild traumatic brain injury: a longitudinal diffusional kurtosis and perfusion imaging study. AJNR Am J Neuroradiol. 2013;34:951–957.
82. McAllister TW, Saykin AJ, Flashman LA, Sparling MB, Johnson SC, Guerin SJ, Mamourian AC, Weaver JB, Yanofsky N. Brain activation during working memory 1 month after mild traumatic brain injury: a functional MRI study. Neurology. 1999;53:1300–1308.
83. McAllister TW, Sparling MB, Flashman LA, Guerin SJ, Mamourian AC, Saykin AJ. Differential working memory load effects after mild traumatic brain injury. Neuroimage. 2001;14:1004–1012.
84. Chen JK, Johnston KM, Petrides M, Ptito A. Recovery from mild head injury in sports: evidence from serial functional magnetic resonance imaging studies in male athletes. Clin J Sport Med. 2008;18:241–247.
85. Jantzen KJ, Anderson B, Steinberg FL, Kelso JA. A prospective functional MR imaging study of mild traumatic brain injury in college football players. AJNR Am J Neuroradiol. 2004;25:738–745.
86. Slobounov SM, Zhang K, Pennell D, Ray W, Johnson B, Sebastianelli W. Functional abnormalities in normally appearing athletes following mild traumatic brain injury: a functional MRI study. Exp Brain Res. 2010;202:341–354.
87. Chen JK, Johnston KM, Frey S, Petrides M, Worsley K, Ptito A. Functional abnormalities in symptomatic concussed athletes: an fMRI study. Neuroimage. 2004;22:68–82.
88. Smits M, Houston GC, Dippel DW, Wielopolski PA, Vernooij MW, Koudstaal PJ, Hunink MG, van der Lugt A. Microstructural brain injury in post-concussion syndrome after minor head injury. Neuroradiology. 2011;53:553–563.
89. Pardini JE, Pardini DA, Becker JT, Dunfee KL, Eddy WF, Lovell MR, Welling JS. Postconcussive symptoms are associated with compensatory cortical recruitment during a working memory task. Neurosurgery. 2010;67:1020–1027; discussion 7–8.
90. Chen JK, Johnston KM, Petrides M, Ptito A. Neural substrates of symptoms of depression following concussion in male athletes with persisting postconcussion symptoms. Arch Gen Psychiatry. 2008;65:81–89.
91. Gosselin N, Bottari C, Chen JK, Petrides M, Tinawi S, de Guise E, Ptito A. Electrophysiology and functional MRI in post-acute mild traumatic brain injury. J Neurotrauma. 2011;28:329–341.
92. Logothetis NK. What we can do and what we cannot do with fMRI. Nature. 2008;453:869–878.
93. Gasparovic C, Yeo R, Mannell M, Ling J, Elgie R, Phillips J, Doezema D, Mayer AR. Neurometabolite concentrations in gray and white matter in mild traumatic brain injury: an 1H-magnetic resonance spectroscopy study. J Neurotrauma. 2009;26:1635–1643.
94. Vagnozzi R, Signoretti S, Cristofori L, Alessandrini F, Floris R, Isgro E, Ria A, Marziali S, Zoccatelli G, Tavazzi B, Del Bolgia F, Sorge R, Broglio SP, McIntosh TK, Lazzarino G. Assessment of metabolic brain damage and recovery following mild traumatic brain injury: a multicentre, proton magnetic resonance spectroscopic study in concussed patients. Brain. 2010;133:3232–3242.
95. Vagnozzi R, Signoretti S, Floris R, Marziali S, Manara M, Amorini AM, Belli A, Di Pietro V, D'Urso S, Pastore FS, Lazzarino G, Tavazzi B. Decrease in N-acetylaspartate following concussion may be coupled to decrease in creatine. J Head Trauma Rehabil. 2013;28:284–292.
96. Peskind ER, Petrie EC, Cross DJ, Pagulayan K, McCraw K, Hoff D, Hart K, Yu CE, Raskind MA, Cook DG, Minoshima S. Cerebrocerebellar hypometabolism associated with repetitive blast exposure mild traumatic brain injury in 12 Iraq war veterans with persistent post-concussive symptoms. Neuroimage. 2011;54(suppl 1):S76–S82.
97. Nariai T, Inaji M, Tanaka Y, Hiura M, Hosoda C, Ishii K, Ohno K. PET molecular imaging to investigate higher brain dysfunction in patients with neurotrauma. Acta Neurochir Suppl. 2013;118:251–254.
98. Byrnes KR, Wilson CM, Brabazon F, von Leden R, Jurgens JS, Oakes TR, Selwyn RG. FDG-PET imaging in mild traumatic brain injury: a critical review. Front Neuroenergetics. 2014;5:13.
99. Barr WB, Prichep LS, Chabot R, Powell MR, McCrea M. Measuring brain electrical activity to track recovery from sport-related concussion. Brain Inj. 2012;26:58–66.
100. Gosselin N, Bottari C, Chen JK, Huntgeburth SC, De Beaumont L, Petrides M, Cheung B, Ptito A. Evaluating the cognitive consequences of mild traumatic brain injury and concussion by using electrophysiology. Neurosurg Focus. 2012;33:E7: 1–7.
101. De Beaumont L, Beauchemin M, Beaulieu C, Jolicoeur P. Long-term attenuated electrophysiological response to errors following multiple sports concussions. J Clin Exp Neuropsychol. 2013;35:596–607.
102. Mohan K, Kecova H, Hernandez-Merino E, Kardon RH, Harper MM. Retinal ganglion cell damage in an experimental rodent model of blast-mediated traumatic brain injury. Invest Ophthalmol Vis Sci. 2013;54:3440–3450.
103. Harpham JA, Mihalik JP, Littleton AC, Frank BS, Guskiewicz KM. The effect of visual and sensory performance on head impact biomechanics in college football players. Ann Biomed Eng. 2014;42:1–10.

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