Your Brain in Six Minutes: The NeuroCatch Platform for Brain Assessment
Historically, the only assessment of the brain function was a neurological/neuropsychological examination. These traditional tests were conducted through lengthy patient interviews with several, tedious, paper-pencil tests.
At a certain point, neurophysiological methods, such as electroencephalography (EEG), were added to these traditional tests, and then further enhanced through the addition of novel neuroimaging methods.
However, recent findings suggest that an interview and a test are not sufficient. Current neuroimaging methods like computerized tomography (CT), many of the various methods based on magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG) tend to be very expensive and require both intensive infrastructure and professional personnel. Thus, both Industry and Academia are on the hunt for methods that are cheaper, more reliable, and more accessible to everyday patients.
One such method is event related potentials (ERP). Currently, measuring evoked potentials is one of the most popular experimental methods in cognitive neuroscience for studying the physiological correlates of sensory and cognitive information processing to learning processes, memory and attention, overload, or even stress.
Although ERP is an established method (it was first developed in the ‘30s), implementation in daily clinical trials is still ongoing. ERPs are also used in one of the most popular protocols for noninvasive brain computer interface (BCI).
NeuroCatch is an exciting company which not only works on the clinical use of ERP (particularly on brain injuries, neurological diseases, and mental health), but also leverages ERPs in the context of brain optimization and science research.
More specifically, NeuroCatch has been developing its own methods for assessment and monitoring on a range of conditions such as Concussion and PTSD, and is already exploring broader applications for their solution.
The NeuroCatch Solution is an accessible, efficient tool for Assessment and Monitoring of the Brain
NeuroCatch’s solution is based on electroencephalography (EEG) technology, which records electrical activity of the brain noninvasively (electrodes are placed along the scalp). Such measurement is an accessible, available, low cost and portable method which can be performed in any neurologist’s office.
Its solution contains an EEG cup, headphones, computer, and a device that can make accurate measurements a reality: the Evoked Potential Input/Output (EPiO)™ adapter. Using this system, the company leverages evoked related potentials (ERP) into fast measures of brain functions.
One of the critical parameters of any ERP system is time precision, as brain potentials require many repetitions of stimulation and the brain response to these stimuli occurs tens or hundreds of milliseconds after they occur. This is why NeuroCatch developed its time synchronizer — EPiO™. Their technology allows [one] to capture brain waves with near-zero measurement inaccuracy.
NeuroCatch uses an EEG cap with a three-channel mesh with most popular AG/AgCl wet electrodes placed on the standard positions for ERP measuring — Fz (over frontal lobe), Cz (between frontal and parietal lobes, over somato-sensory cortex), & Pz (over parietal lobe), all three based on established science research.
They selected well established sensation-to-cognition ERPs: (1) the auditory sensation after about 100 ms post stimuli; (2) the auditory oddball (i.e. Basic attention) after 300 ms; and (3) the auditory speech processing (i.e. Cognitive processing) after 400 ms. Publications show that these ERPs can provide specific information across brain functioning, from low-level sensory to higher level cognitive processing and thus have a diagnostic capability. Beyond technical essential parameters, their system has on-board noise detection and correction algorithms which speeds up generation of the final assessment report to the patient or clinician. Furthermore, fully automatic software filtering signals detect ERP peaks and perform all the background calculations needed to deliver an insightful report (more on this below).
Conventional EEG has a well-established foothold in the medical community for hospital or ambulatory quantification of seizure activity, detection of epileptic foci, sleep assessments, monitoring of anesthetic states. However, the technology is generally not portable, requires extended assessment sessions (e.g. > 45 minutes, sometimes even overnight) and specialists are needed to interpret and monitor the data. Thus, conventional EEG is a slow, cumbersome, personnel-heavy method of assessment.
Moreover, conventional EEG assessments lack an automatically generated report providing insightful test results that are readily accessible by both clinicians and patients.
Overall, NeuroCatch’s platform is both quick and portable, and can provide reports which can be used in a variety of contexts, for example to assess the evolution of brain conditions following a concussion.
NeuroCatch’s Testing Procedure is markedly more Time-efficient:
Preparation for the test takes about 5 minutes, and consists in putting a cap on the patient’s head and getting relevant conductivity (impedance level <30kΩ). The patient does not need a specific preparation for this test (e.g., no fasting required).
Patients are, however, advised against consuming alcohol or drugs prior to the NeuroCatch test, due to their documented effect on brain function.
Furthermore, the patient can also report caffeine or nicotine intake to the doctor or technician, effects of which can be accounted for by NeuroCatch’s algorithms. The actual test takes about 6 mins of sound (in a variety of tones) and cognitive (spoken word pairs) stimulation. Tone stimuli elicits the N100 and P300 responses and spoken word pairs elicit the N400. Participants have to pay attention to the auditory stimuli while maintaining visual fixation on a cross presented on the monitor.
Results of the test are presented through automatically generated subject profiles composed of radar plots superimposed onto a standardized healthy range to enable quick comparisons.
Automatization speeds up the entire workflow while ensuring test standardization and objectiveness.
Overall, this report is used by professionals to identify cognitive areas for improvement for healthy individuals, but also to customize a patient’s treatment course.
A Reliable solution based on several noteworthy collaborations
From the beginning, NeuroCatch’s founders wanted to directly address the need for a rapid, objective, physiological measurement of cognitive brain function.
The needs are enormous, because brain disorders directly impact 1 in 3 North Americans, and the current set of healthcare tools available still lack sensitive instruments to measure healthy cognitive brain function versus dysfunction.
With over 7-years of R&D in the USA, and over 25 years of scientific research behind NeuroCatch’s solution, brain vital sign monitoring at Neurocatch has been underway with elite athletes at the Mayo Clinic, at the Sanford Clinic and with the Creative Artists Agency (CAA).
These research collaborations have been critical in the commercialization of the NeuroCatch® Platform (see NeuroCatch’s website for more information on the company’s journey and body of research and publications). The company’s current development plan provides a very clear product roadmap for expanding clinical indications and increasing ease-of-use.
NeuroCatch has always operated at the intersection of science, medicine, and business to ensure its solutions are both innovative and effective.
More specifically, in addition to R&D projects carried out in cooperation with North American scientists and clinicians, NeuroCatch’s commercial solution is used by scientists for automated acquisition of ERPs.
The NeuroCatch® Platform provides raw ERP data, opening a wider range of possible applications for scientists (while underlying EEG data is also accessible), but undoubtedly those advantages will be appreciated by those involved in research using ERPs.
For example, the Platform has been used by many independent groups in academia for over a decade (e.g. Mayo Clinic). It is deployed widely in North America and further expanding in Europe and Australia. The Platform is also used by some of the US’ top research centers (e.g. Cornell) who use it in research to publish independent studies.
Finally, the NeuroCatch® Platform has been deployed for use across a growing variety of leading amateur and professional organizations in sports (e.g., youth football, USA hockey, MMA), Defense (e.g., veterans research), and Space (e.g., SpaceX).
One example of a study, where NeuroCatch’s system was used to build evidence, was a research conducted on youth contact sports in Hockey.
Individuals were monitored between baseline, injury, return-to-play (RTP), and end-of- season.
ERPs were recorded at these interventions alongside administration of routine clinical concussion management protocols and existing and novel interventions. Immediately post-concussion, significant changes can be detected in all three ERP metrics, with the initial landmark study showing increases in amplitude and delays in latency.
While most of these metrics returned to baseline levels after the players had passed their RTP protocol, P300 amplitudes were still significantly higher than baseline, demonstrating enhanced sensitivity to undetected residual concussion impairment.
Further highlighting how sensitive the NeuroCatch® Platform is, similar analyses have demonstrated and replicated the ability to detect sub-concussive changes in Hockey and Football players who did not sustain diagnosed concussions. The NeuroCatch® Platform results were highly and significantly sensitive to the number of impacts these players experienced, showing a consistent pattern of changes across studies.
The genesis of NeuroCatch and their continued commitment to Research and Academia, the scarcity of reliable assessment tools, the results of NeuroCatch’s own and independent research, the portability of the platform, the automation of the assessment process, and the use of assessment results to improve the brain performance of healthy individuals or tailor therapies for neurological patients — all lead to the opinion that NeuroCatch has created a useful product for clinicians, researchers, and beyond.
Much still lies ahead for NeuroCatch, including further standardization across patient groups and age groups, and the development of assessments for a wider range of conditions. In that regard, NeuroCatch is already noticing a virtuous cycle in its approach where (i) The NeuroCatch automation algorithms already translate tests into standardized results with the scanning being independent of the person who is performing it (ii) As NeuroCatch and its partners perform additional tests, NeuroCatch can refine existing normative databases by taking into account patient specificities (e.g., age, sex, underlying condition), with scalability in mind given NeuroCatch’s short test time. As Dr. Ryan D’Arcy mentioned: “It is, to our knowledge, a notable first in terms of future wide scale ERP common standardization and open-access normative data through peer reviewed publication, something which is being pulled together under the brain vital sign framework. We think this is a very important direction for the field to move towards”.
Overall, the achievements of Dr. Ryan D’Arcy and the NeuroCatch team are noteworthy, to say the least, and the potential for implementation of the NeuroCatch® Platform should be monitored closely as the company is making notable progress on the Regulatory front, with plans to expand upon its existing clearances for clinical use in the US and Canada.
Description of the “Demo” performed by the NeuroCatch Team for NTX Services
NTX Services was able to meet (virtually) twice with the NeuroCatch team in an effort to take part in the actual user experience for both clinicians/technicians (in the context of this Product Review, the word “technician” will be used) and participants/patients.
In total, 3 procedures were performed across 2 different volunteer participants.
Each procedure started by ensuring the volunteer participant put on a headphone and an EEG cap.
With regards to the EEG Cap, the clinician ensured the right impedance was obtained (all electrodes had an impedance of about 5 kΩ, except for the reference one at 12kΩ).
In parallel, the technician conducted an interview of the participant by going through a questionnaire with questions relative to mood, stimulants, medication, etc., the whole process lasting no more than 10 minutes. The team explained that they were taking their time for demonstration purposes and highlighted that the test is often done quickly in the field, with the shortest benchmarked times commonly being 10 minutes in total.
At that point, the series of tests started, requiring complete silence for the 6 minutes and 32 seconds it took to conduct the patient test.
The report from this test was generated within 2 minutes, and ready to review by the technician for the patient.
This report includes the actual recorded ERP waveforms along with both an overview of the 3 tests performed (i.e., N100 to determine Auditory Sensation, P300 to determine Basic Attention, and N400 to determine Cognitive Processing), as well as more detailed results for each of the 3 tests with a perspective on amplitude and latency for each test compared to a sample (of the general population of all ages and sexes).
While the summary page typically shows these 6 summary values (i.e., amplitude and latency across each type of test), and whether the values obtained are “in range”, for the first participant taking part in this Demo the two P300 values were not presented in the summary page however the successfully recorded P300 was shown in the waveform result.
The absence of this summary data happens when the NeuroCatch AI peak detection algorithm has not passed established sufficient criteria to be activated. This is a design feature to avoid false positives and allow the AI algorithm to continuously ‘learn’ over time, with the actual recorded ERP waveforms always provided as the base result. While infrequent as per the NeuroCatch team, the AI peak detector may not activate due to a combination of factors, which can impact psychophysiological data, including external stimuli and/or intrapersonal factors (e.g., cognitive or physiological state of the participant during the test, lack of attention to the P300 test, etc).
As a result, the NeuroCatch algorithm did not automatically calculate the amplitude and latency of the P300 test, however a trained technician/clinician should be able to review the waveforms from the detailed report, and provide an assessment to patients regarding their P300 test nonetheless.
The procedure was also prepared for another volunteer participant.
The preparation was done in the same way, and took approximately the same amount of time as with the first participant.
In this instance, the impedance was almost the same for every electrode at ~5 kΩ.
The AI peak detection algorithm was activated for all ERP responses, so the report included all the values from the analyses, including in the summary portion of the report (with information on P300 amplitude and latency presented this time around).
The data for this healthy volunteer showed a lower P300 amplitude combined with an increased N400 latency, which the NeuroCatch technician interpreted as likely having a tired participant in this case, despite results being within the standardized normative range.
To validate the hypothesis that it is possible to optimize the results, the technician repeated the NeuroCatch scan after “waking up” the volunteer participant by serving him a double espresso. A second series of tests was then performed on the volunteer participant.
This time around, the P300 test showed a higher amplitude and the N400 test showed a shorter latency.
Once again, the AI peak detection algorithm activated and the summary report also showed amplitude and latency values for all 3 tests performed. In all cases, the actual recorded ERP waveforms were available to verify the physiological responses.
Overall, performing two series of tests (one shortly after the other) has allowed us to observe that this series of tests (and corresponding waveform results) are quite sensitive to changes in the participant’s specific situation on a given day (e.g., level of caffeine in the system).
This further supports the importance of administering the initial questionnaire to ensure that patients take tests in similar conditions (e.g., # of hours slept the night before, quantity of caffeine consumed on that day), and to allow for technicians/clinicians to properly interpret results by taking the patient’s situation into account.
Finally, performing two series of tests for the same participant outlined how the NeuroCatch report can clearly present comparative results for the same patients on the same chart, allowing for a precise comparison of performance across tests.
Authorship
Written by NTX Services in affiliation with NeuroCatch. NTX Services is currently collaborating with NeuroCatch and its parent company HealthTech Connex in the context of a broad consulting project.
NTX Services is the exclusive partner of NeuroTechX, which brings neuroscience, technology, and strategy experts together from a variety of professional and academic backgrounds to offer professional services.
NeuroCatch is the manufacturer of The NeuroCatch® Platform, an industry-leading medical device that offers an objective evaluation of cognitive function, which is delivered in minutes at the point of care.
HealthTech Connex is a biotechnology company bridging the gap between research and real-world applications in advanced brain care.
Design Contributions
Han Cat Nguyen is a neurotech enthusiast and PhD student at McGill University. Her passion is brain-computer interfaces, especially brain-controlled robots.
References
Handy, T. C. (2005). Event Related Potentials: A Methods Handbook. Cambridge, MA: Bradford/MIT Press.
Hajra, S. G. et al. (2016). Developing Brain Vital Signs: Initial Framework for Monitoring Brain Function Changes Over Time. Frontiers in Neuroscience 10, 211.
Fickling, S. D. et al. (2019). Brain vital signs detect concussion-related neurophysiological impairments in ice hockey. Brain 142, 255–262.
Fickling, S. D., et al. (2021). Subconcussive brain vital signs changes predict head-impact exposure in ice hockey players. Brain Communications 3, issue 2, 1–10.
Fickling, S. D., et al. (2022). Subconcussive changes in youth football players: objective evidence using brain vital signs and instrumented accelerometers. Brain Communications 4, issue 2, 1–10.
Carrick, F. R. et al. (2021). Brain Vital Signs in Elite Ice Hockey: Towards Characterizing Objective and Specific Neurophysiological Reference Values for Concussion Management. Frontiers in Neuroscience 15, 670563.
