There have been numerous reports of neurological assessments of post-concussed athletes and many deploy some type of reaction time assessment. However, most of the assessment tools currently deployed rely on consumer-grade computer systems to collect this data. In a previous report, we used robotic testing to demonstrate the inaccuracies introduced by typical consumer-grade computer systems (Holden et al, 2020). In that report, we described the accuracy of a tactile based reaction time test (administered with the Brain Gauge) of approximately 0.3 msec and discussed the shortcoming of other methods for collecting reaction time.The consumer-grade systems introduced latencies as high as 400 msec and variabilities as high as 80 msec, which greatly exceeds the control values reported for reaction time (200-220msec) and the control values for reaction time variability (10-20 msec). In this report, we examined the reaction time and reaction time variability from 396 concussed individuals and found that there were significant differences in the reaction time metrics obtained from concussed and non-concussed individuals for 14-21 days post-concussion. A survey of the literature did not reveal comparable sensitivity in reaction time testing in concussion studies using alternative methods. This finding was consistent with the prediction put forth by Holden and colleagues with robotics testing of the consumer grade computer systems that are commonly utilized by researchers conducting reaction time testing on concussed individuals. The significant difference in fidelity between the methods commonly used by concussion researchers is attributed to the differences in accuracy of the measures deployed and/or the increases in biological fidelity introduced by tactile based reaction times over visually administered reaction time tests. Additionally, while most of the commonly used computerized testing assessment tools require a pre-injury baseline test to predict a neurological insult, the tactile based methods reported in this paper did not utilize any baselines for comparisons. The reaction time data reported was one test of a battery of tests administered to the population studied, and this is the first of a series of papers that will examine each of those tests independently.
Tommerdahl M, Francisco E, Holden J, Lensch R, Tommerdahl A, Kirsch B, Dennis R, Favorov O. (2020). An Accurate Measure of Reaction Time can Provide Objective Metrics of Concussion. The Journal of Science and Medicine; 2(2): 1-12.
Currently, there is no standard, reliable, cost-effective paradigm or methodology for assessing the degree to which the central nervous system (CNS) is impacted by neurological disorders. One of these disorders or systemic central alterations due to trauma is concussion, or mild traumatic brain injury (mTBI). Although awareness of concussion and mTBI is significantly growing in the general public, there is still no standardized, quantitative, biologically based methodology that is effective for assessing the impact of mild neuro-trauma. Current existing methods and products for this need are expensive, extremely slow, and in many cases fail to definitively and quantitatively diagnose the problem. For example, clinical grade medical imaging technologies—though they are able to discern differences in subjects with traumatic brain injury —show few or no differences for mTBI or concussion, are costly (about $1K per scan), are not portable, and are not practical for getting a quick assessment. In fact, no modern medical imaging techniques are as sensitive to subtle alterations in cortical information processing as those detected by sensory percept.While it is unlikely that there will be any medical imaging technologies able to provide such high resolution in the near future, it is even more improbable that such a technology could be widely distributed and used pragmatically and cost effectively on a regular basis.
One of the greatest issues with concussion, or mTBI, is determination of return-to-duty status for the military or return to-play status for athletes at multiple levels of competition (secondary school, college/university, and professional level). Because injury from secondary concussions can be much more serious, if not fatal, during the critical post-concussion recovery period, it is imperative that methods for this determination be developed. Many years ago, we proposed to design and fabricate a noninvasive, portable, sensory-based diagnostic system using state-of-the-art technology to investigate cortical information processing. The first prototype of this device was reported by Tannan et al, 2005  and a second prototype of reported by Holden et al, 2012 . Since that time, we have developed a number of tactile sensory based protocols that target different mechanisms of CNS information processing. Previous reports have demonstrated sensitivity of the methods to concussion. Tommerdahl et al  showed that mathematically combining the results from the different measures yielded a unique CNS profile that demonstrated 99% confidence levels for differentiating concussed from non-concussed student-athletes. Additionally, the metric extracted from this CNS profile co-varied consistently with the concussed individual’s symptom score. Expanding on that method, Favorov et al , in a study of college students, reported ROC curves for each of the multiple metrics that, although they varied extensively in their ability to assess concussed status, when combined demonstrated very good sensitivity and specificity. The results of that study predicted that the method could prove to be good for tracking an individual’s recovery and could be used as a good quantitative indicator of central nervous system health. Additional reports [6-9] demonstrated the prognostic utility of the method. Demonstrating the sensitivity of the method, Pearce et al [10,11] showed information processing differences between 3 groups of individuals: healthy controls, and symptomatic vs. asymptomatic individuals who had been concussed 3-12 months prior to testing. Taken together, the evidence that has accumulated strongly suggests that the methodology is successful in differentiating concussed vs. non-concussed individuals and more importantly, can be used to track recovery of individuals from concussed to non-concussed status . While the authors view the combination of metrics as critical to obtaining a broader view of overall CNS health, it is important to review each of the measures and the contributions that they make to that profile. In this report, although a battery of tests was administered to the study subjects, only the results from the reaction time test are reported. Subsequent reports will describe the results from each of the other tests that are commonly administered with the Brain Gauge in the sports concussion environment.
Sensory assessments were performed on 793 healthy controls aged 18-22 years old and 440 concussed individuals in the same age range. All participants in the study were student-athletes. All concussed athletes were diagnosed with mTBI in the form of a concussion by a certified athletic trainer and the team physician with the help of the Sport Concussion Assessment Tool 2 (SCAT-2), ImPACT (Immediate Post-Concussion Assessment and Cognitive Test), and, in some cases, the BESS (Balance Error Scoring System). BESS was dropped from several of the participating programs because the health professionals using it deemed it as an ineffective tool for evaluating concussion. Healthy controls had no prior history of concussion or other diagnosed mental health conditions with symptoms similar to concussion. The reported assessments were obtained from concussed individuals at one or more time points ranging from a few hours after concussion to 9 months after concussion. Baseline measures were also collected on each participant prior to beginning the sports season and were used as healthy control data. The experimental procedures were reviewed and approved in advance by an institutional review board.
Somatosensory assessment was performed using a portable vibrotactile stimulation device (Brain Gauge, Cortical Metrics, Carrboro, NC). The device, of similar size and shape as a computer mouse, contains independent, computer-controlled probe tips which can deliver a wide range of sinusoidal vibrotactile stimuli of varying amplitudes and frequencies. All protocols used were originally conducted on a four-site mechanical stimulator (CM4; Cortical Metrics Model #4) which is functionally identical for 2 digits and was previously described in Holden et al.  and has been utilized to assess multiple sensory information processing characteristics in a number of subject populations [2,4,5,9,12-21]. Not only do these protocols demonstrate significant sensitivity to alterations in CNS processing, but they are independent of detection thresholds or skin sensitivity [20,22].
During the evaluation session, subjects were seated comfortably in a chair with the right hand resting on the vibrotactile stimulator. In this study, vibrotactile flutter stimulation (25Hz) was delivered via 5mm Delrin probes to the glabrous tips of either, or both, the second (index, D2) and/or the third (middle, D3) digits of the right hand. These digits were chosen as test sites for convenience and comfort and also because of the wealth of neurophysiological data that has provided observations of the associated somatotopic regions in the non-human primate cerebral cortex. The tips of the Brain Gauge device are used both to deliver vibratory stimuli and record subject response. The subject was instructed to press down on the tip to register a response after the vibration had been delivered.
A computer monitor provided visual cueing during each of the experimental runs. The cues indicated when the experimental stimuli would be delivered and when subjects were to respond. Training trials conducted prior to each task familiarized subjects with the test; correct responses on three consecutive training trials were required before the start of each assessment.
A series of sensory perceptual measures were employed to assess tactile information processing ability. In sum, these tests lasted approximately twenty minutes and consisted of sequential evaluations of reaction time (RT), amplitude discrimination (AD), temporal order judgment (TOJ), duration discrimination (DD) and then a second RT task. In this report, only the RT task is described.
Tactile Reaction Time
For the simple tactile reaction time task, a single tap (300μm, 40ms) was delivered to D3 and subjects were instructed to respond by pressing down with D2 as soon as the tap was perceived. A randomized delay ranging from 2 to 7s separated the trials. Response times were recorded for each of the 10 trials. This method was first reported in Zhang et al. and has been reported many times since then [5,10,11,23-38]. The standard deviation of the 10 reaction times was used as a measure of reaction time variability. The reaction time task was completed once at the beginning of the 20-minute battery of testing, and once again at the end.
In an ongoing sports concussion study, observations have been collected from over 400 individuals that were concussed and approximately 800 healthy controls (n for each measure in table below). Reaction times for concussed individuals were significantly slower (i.e., longer) than reaction times healthy controls, and similarly, reaction time variabilities for concussed individuals were higher than were variabilities for non-concussed individuals. Note that p values for comparisons of healthy controls (Contr.) vs. concussed individuals (Conc.) were less than 10-15 for reaction time and reaction variability, and this indicates a very significant difference in the data derived from the reaction time task from the two populations.
|RT1 Contr.||RT1 Conc.||RT1 var Contr.||RT1 var Conc.|
The results demonstrate that reaction time and reaction time variability, when collected with an accurate methodology, can be used to significantly differentiate concussed from non-concussed individuals without the use of baseline testing. Although the majority of studies that have investigated sports concussion commonly rely on reaction time as one of their metrics, very few of those studies use accurate reaction time testing methods. Rather, they rely on inferior commercially available online testing methods that are not laboratory grade and are inadequate for any clinical application. Additionally, most of these methods require the use of baseline testing to try to improve the test performance. Baseline testing is the practice of individuals taking a performance-based test before they are injured and subsequently, in the event of an injury, test performance post-injury is compared to the baseline test that was obtained pre-injury. There are obvious disadvantages to relying on baseline testing - not all individuals that become injured and need to be evaluated have the luxury of being part of a sports program that might require baseline testing. Additionally, baselines in collegiate sports programs are typically only collected during participant’s freshman year, and this baseline is expected to not change for 3-4 years (a fairly large assumption for any science-based assessment of brain function).
Of particular interest is that while reaction time is typically collected by many assessment tools used to evaluate cognitive status, very few reports demonstrate the degree of difference between concussed and non-concussed individuals as described in the methods of this report. Perhaps the best description of the utility of some of the online cognitive testing methods without baseline testing was provided by Nelson and colleagues  (Nelson et al, 2017). In that report, three commonly used online cognitive assessment tools (ANAM, DANA and ImPACT) were used to evaluate concussions in an emergency room setting. Since the individuals under study were first observed in the emergency room, there were no pre-injury baseline tests, and the results of that study demonstrated that there was no difference in reaction time detected. In fact, none of the methods demonstrated overall scores obtained from the injured individuals that were significantly different from healthy controls. If an assessment tool is to be effective, then it should not depend on knowing the performance metric of an individual before they are injured. This problem of depending on baseline testing appears to be resolved when using accurate testing methods such as described in this study. The above-mentioned cognitive assessment tools or online computerized tests that rely on consumer-grade instrumentation introduce significant timing errors to measures such as reaction time (Holden et al, 2020), and this is most likely the reason that differences in reaction time could not be resolved in prior studies without the use of baseline testing.
This is not the first report that demonstrates accurate tactile based reaction time metrics to be sensitive to neurological insult – and more specifically – to concussion. Several studies have reported that accurate tactile based reaction time, collected with the Brain Gauge, successfully differentiates concussed individuals from non-concussed individuals. . Favorov and colleagues demonstrated a 91% and 69% area under the ROC curve for differentiating concussed vs. non-concussed individuals with reaction time variability and reaction time, respectively . Pearce et al  showed significant differences between the reaction time and reaction time variabilities of three populations: healthy controls, individuals that had recovered from concussion and individuals that were still asymptomatic. Similar results were also demonstrated in a military population .
One of the reasons that there have been so few successful studies that accurately demonstrate differences between concussion and healthy controls without the use of a baseline is because the method commonly used to collect reaction time with commercial methods, such as those in the above-described Nelson study, is a visual reaction time task that relies on inferior consumer-grade equipment. Computer operating systems and hardware introduce significant delays and variability  and these delays and variability lead to significant errors. Countless publications have used these visual reaction time tasks, most likely because they are used by commercial systems that rely on inferior methods, and the researchers simply trusted those methods because they were commercially available. Some recent studies have directly compared the visual reaction time tasks that rely on consumer-grade systems with the more accurate Brain Gauge system. Note that, based on robotics testing, without a human element, the accuracy of the visual systems used introduces errors on the order of 80 - 400 msec while the error introduced by the Brain Gauge is approximately 0.3 msec . Pearce and colleagues  directly compared reaction time collected with the Brain Gauge and CogState, an online program that administers a visual reaction time task but depends on consumer grade technology. In that study, individuals with post-concussive symptoms (PCS) were evaluated with both the Brain Gauge and CogState. The study demonstrated that individuals with PCS had significantly higher tactile based reaction times and reaction time variabilities, administered with the Brain Gauge, than did healthy controls. The same two groups of individuals were also administered the visually based reaction time task with CogState, and there was no difference observed between the reaction times observed between the two groups with those measures. Additionally, the visually-based reaction time task for the healthy controls was approximately 85 msec slower than the reaction time obtained with the Brain Gauge for the same group of individuals. Since this phase lag is very close to the reaction time measured robotically (i.e., no human timing error) for the same type of computer system used in that study (80 msec; Holden et al ), the difference could be considered to be predominantly attributed to a technical instrumentation deficiency and not a biological difference. A subsequent independent study examined differences in reaction time obtained with the Brain Gauge versus a visual task and obtained the same difference, with the visual-based task resulting in the slower reaction time and delayed approximately the same amount of time as in the Pearce study . This further strengthens the argument that the delays observed between visually-based reaction time and the Brain Gauge are technological and not biological. Long before these measures were directly made, Steve Hsiao made the argument that there should not be a significant difference between tasks such as reaction time for visual vs. tactile tasks . Each task (visual vs. tactile) performs object recognition and transmits information to the decision center in the brain that leads to execution of the response task. Thus, both visual- and tactile-based tasks are predicted to take approximately the same amount of time, and any significant difference in visual vs. tactile reaction time would most likely be attributed to technical deficiencies in one or both of the test devices.
Reaction time gets significantly impacted under a number of conditions, and the task has a significant presence in the literature. There are reports on a wide range of topics over many years, such as concussion or TBI [42-55], aging [56-61], sleep deprivation , schizophrenia , Parkinson’s [64,65], acute alcohol use , pharmaceuticals [67,68], Tourette’s , ADD/ADHD [28,70-72], reading disabilities in children , and diabetes [74,75]. It is interesting to note that while reaction time has been used to examine a large number of topics, its accuracy has decreased over the past 50 years  and this has led to a significant change in the average measured reaction time drifting up 100-200% over that time period [1,76]. In other words, although a wide range of research uses the reaction time task, it is becoming increasingly inaccurate, to the point of being scientifically and clinically useless, because of the expansion of poor research techniques and tools that appear to be becoming more widespread and have come to dominate mainstream research in this area. The ease of using any of a number of commercially available online cognitive testing systems appears to have led to a degree of complacency in some researchers (particularly those without technical backgrounds) to assume that anything that is over-the-counter or involves a “computer” or tablet must have some level of fidelity. The pervasive use of inadequate consumer-grade toys by poorly-trained researchers with no technical background and no understanding of their research tools has the combined effects of (1) delaying and disempowering the use of basic but accurate measures of cognitive function for diagnosis and guidance of treatment of brain injury, and (2) undermining the belief and support of the educated tax-paying public in the enterprise of academic science through waste, and an increasingly self-evident academic scientific culture dominated by a pervasive, ineffective, feckless incompetence.
The use of accurate reaction time methods (e.g., the methods described in this report) can give clear and objective results for assessments of individuals with concussion. Consumer-grade testing devices simply cannot do so. When measured properly, reaction time variability is a more accurate indicator of concussion, and this measure requires high resolution timing accuracy that is only available in laboratory grade equipment and the Brain Gauge.
Author’s note: The results of this study will be continually updated after initial publication with additional analyses and/or data.
Editor’s note: The Journal of Science and Medicine considers all publications as living documents, and updates are not only allowed, but encouraged.
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