The VoxNeuro Advantage
VoxNeuro’s Cognitive Health Assessments™ provide objective data of a patient’s actively-engaged, real-time electrical output of brain function in an easy to use patient report. This report empowers clinicians to tailor rehab specific to a patient’s unique needs, optimize treatment, track recovery, and ultimately confirm when a patient is ready to return to regular activity or inform long-term accommodations that may be required.
- Backed by 25+ years of globally funded research
- Health Canada cleared lab developed service
- Patented protocol and proprietary algorithms
Our proprietary methods include adapted industry-validated and novel neuropsychological tests with simultaneous electroencephalography (EEG) recording. By leveraging widely used and validated assessment methods and pairing them with EEG, we transform what would be qualitative results from traditional testing into objective, quantifiable measurements of brain function that support informed clinical decision making.
This is achieved through a 60-minute test, with a series of tasks that trigger event related potentials (ERPs) in a patient’s brain, captured by EEG. Different ERPs are indicative of a patient’s performance in their core cognitive functions including: executive function, language comprehension, working memory, automatic attention, reactive attention, concentration, information processing, auditory processing, and visual processing. The integrity of these functions are essential for a patient’s well-being and quality of life.
Our reports target the patient’s performance, captured through their brain’s electrical activity (example brain wave recording seen in Figure 1), in each core function by comparing their response strength and timing to our healthy control database.
Comparing methods of assessing brain injury
When an individual suffers a brain injury, whether mild or severe, two types of neurological damage are to be assessed: structural and functional. Structural damage refers to physical damage to the skull and brain; all other symptoms or effects resulting from the injury fall under the category of functional damage. It is important to note that the presence of structural damage does not necessarily imply functional damage, and vice-versa.
Healthcare providers commonly use the following tools to help assess and diagnose brain injuries:
1. Behavioural and neuropsychological tests: used to measure symptoms, mood and behavioural changes in a patient following an injury.
2. Traditional neuroimaging: magnetic resonance imaging (MRI) and computed tomography (CT) scans are useful in identifying structural injuries associated with the brain. For example, injuries such as skull fractures, brain tumors, and lesions.
Functional MRI (fMRI) is not yet clinically available, but is starting to be offered through private services. It measures blood flow in the brain and can identify abnormal structural integrity and functional connectivity.
VoxNeuro’s Cognitive Health Assessments™ are non-invasive and are used to identify abnormal brain function, objectively confirming the presence of a concussion or brain injury, and pinpointing function that has been impacted by an injury. This empowers clinicians to deliver customized rehabilitation for executive function, language comprehension, working memory, automatic attention, reactive attention, concentration, information processing, auditory processing, and visual processing.
How Cognitive Health Assessments™ are different from traditional EEG
You may have had experience with EEG in the past. Traditional EEG is commonly used, with qEEG becoming more common, to assess seizures and sleep disorders. In some cases, they may also be used to help assess severe brain injuries. EEG testing available in most clinics and hospitals is capable of capturing ongoing brain-signals and oscillations passively (e.g., alpha and beta brain waves), and is commonly known as “resting state”. Clinicians typically ‘read’ traditional EEG scans free-form. qEEG is an improvement on traditional EEG in the way it analyses a patient’s data, statistically comparing their results to healthy controls.
VoxNeuro’s Cognitive Health Assessments™ use world leading FDA and Health Canada approved EEG equipment. These systems vastly expand the range of measurable brain activity by enabling direct measurement of the brain’s responses to stimulation and cognitive tasks. We track a patient’s active responses to a range of tasks and stimuli instead of resting state. This active engagement with stimuli throughout testing, which can be thought of as a stress-test or performance-test for the brain, is how VoxNeuro is able to provide objective data on specific brain functions that require rehabilitation. This is in contrast to generalizing the higher-level brain function of a patient at resting state to see if, generally, their brain function looks normal or injured. General information on whether a brain is functioning normally or not is valuable in making an initial diagnosis within the initial days post-injury, but does not allow clinicians to objectively inform targeted cognitive rehabilitation plans. VoxNeuro’s assessment can be run at any time, whether an injury happened 2 days ago or 20 years ago, to quantify the functional issues and objectively inform a treatment path.
Dr. John F. Connolly is the Chief Science Officer and co-founder of VoxNeuro. He holds the Senator William McMaster Chair in Cognitive Neuroscience, is the founding director of the ARiEAL Research Centre, and co-directs the Language, Memory and Brain Lab. He is an author on over 330 articles, chapters, and presentations, most of which involve EEG/ERP, MEG and MRI studies of cognition in health and brain pathology.
The scientific breakthrough in brain assessment.
In 1999, Dr. Connolly was the first person in the world to demonstrate that a patient (mis)diagnosed as being in a vegetative state (now referred to as Unresponsive Wakefulness Syndrome) was actually in a locked-in state, meaning they were fully functioning cognitively but experiencing near-complete physical paralysis. After 9 months of successful cognitive rehabilitation efforts, the patient had regained complete motor control and was discharged, walking himself out of the hospital. This landmark case led to the development of EEG-based methods of assessing conscious states and mental functions in patient populations including autism, Unresponsive Wakefulness Syndrome (vegetative state), coma, and concussion. This research forms the foundation of VoxNeuro’s Cognitive Health Assessments™.
Concussion is no longer the ‘invisible injury’.
Since 2015, a dedicated focus has been on the application of VoxNeuro’s technology to assess concussions. Founded in research on one of the largest studies of living ex-football players to date, VoxNeuro’s Cognitive Health Assessments™ were proven alongside traditional methods of assessments with the ability to provide objective insights on cognitive function otherwise impossible to obtain.
The following collection of publications and peer reviewed journals document the evidence based research behind the methods performed in VoxNeuro’s Cognitive Health Assessments™ (CHA).
Neurophysiological Correlates of Concussion: Deep Learning for Clinical Assessment
Boshra, Ruiter, DeMatteo, Reilly, Connolly
Nature: Scientific Reports, November 2019
From Group-Level Statistics to Single-Subject Prediction: Machine Learning Detection of Concussion in Retired Athletes
Boshra, Dhindsa, Boursalie, Ruiter, Sonnadara, Samavi, Doyle, Reilly, Connolly
IEEE, June 2019
Disruption of Function: Neurophysiological markers of cognitive deficits in retired football players
Ruiter, Boshra, Doughty, Noseworthy, Connolly, 2018
Science Direct, January 2019
Disorders of Consciousness
Global Aphasia: An Innovative Assessment Approach
Connolly et al., 1999 Science Direct, January 2019
American Congress of Rehabilitation Medicine and The American Academy of Physical Medicine & Rehabilitation,
Linking neurophysiological and neuropsychological measures for aphasia assessment
Clinical Neurophysiology, November 2002
Development of a point of care system for automated coma prognosis: a prospective cohort study protocol
Connolly JF, Reilly JP, Fox-Robichaud A, et al
BMJ, June 2019
Automatic and continuous assessment of ERPs for mismatch negativity detection
Institute of Electrical and Electronics Engineers, August 2016
Pediatric Communication Impairments
Assessment of children’s receptive vocabulary using event related potentials; Development of clinically validated test
Joseph M. Byrne, Christopher A. Dywan & John F. Connolly (1995)
Child Neuropsychology, August 1995
Assessing adult receptive vocabulary with event related potentials; An investigation of cross-modal and cross-form priming
John F. Connolly, Joseph M. Byrne, & Christopher A. Dywan (1995)
Journal of Clinical and Experimental Neuropsychology, Issue 4 1995
An innovative method to assess the receptive vocabulary of children with cerebral palsy using event-related potentials
Joseph M. Byrne, Christopher A. Dywan & John F. Connolly (1995)
Journal of Clinical and Experimental Neuropsychology, Issue 1 1995
Brain activity and language assessment using event-related potentials: development of a clinical protocol
Joseph M Byrne, John F Connolly, Shannon E MacLean, Joseph M Dooley, Kevin E Gordon, & Tricia L Beattie
Published in Developmental Medicine & Clinical Neurology, April 1999
Brain Activity and Cognitive Status in Pediatric Patients: Development of a Clinical Assessment Protocol
Joseph M. Byrne, John F. Connolly, Shannon E. MacLean, Tricia L. Beattie, Joseph M. Dooley, & Kevin E. Gordon
Journal of Child Neurology, May 2001
Electrophysiological evidence for the integral nature of tone in Mandarin spoken word recognition
Ho, Boshra, Schmidtke, Oralova, Moro, Service, Connolly, 2019
Science Direct, August 2019
Electroencephalography & Event-Related Potential Literature
1. Byrne, J.M., Connolly, J.F., McLean, S.E., Beattie, T.L., Dooley, J.M., & Gordon, K.E. (2001). Brain activity and cognitive status in pediatric patients: Development of a clinical assessment protocol. Journal of Child Neurology, 16, 325-332. DOI: 10.1177/088307380101600504
2. Connolly, J.F., D’Arcy, R.C.N., Newman, R.L., & Kemps, R. (2000). The application of cognitive event-related brain potentials (ERPs) in language- impaired individuals: Review and case studies (Invited Review: Ed. Dr. J. Polich). International Journal of Psychophysiology, 38, 55-70.
3. Connolly, J.F. & D’Arcy, R.C.N. (2000). Innovations in neuropsychological assessment using event-related brain potentials (Invited Review). International Journal of Psychophysiology, 37, 31-47. DOI: 10.1016/S0167-8760(00)00093-3
4. D’Arcy, R.C.N. & Connolly, J.F. (1999). An event-related brain potential study of receptive speech comprehension using a modified Token Test. Neuropsychologia, 37, 1477-1489. DOI: 0.1016/S0028-3932(99)00057-3
5. D’Arcy, R.C.N., Connolly, J.F., & Eskes, G. A. (2000). Evaluation of reading comprehension with neuropsychological and event-related brain potential (ERP) methods. Journal of the International Neuropsychological Society, 6, 556-567. LINK: researchgate.net
6. Duncan CC, Barry RJ, Connolly JF, et al. Event-related potentials in clinical research: guidelines for eliciting, recording, and quantifying mismatch negativity, P300, and N400. Clin Neurophysiol. 2009;120(11):1883–1908. DOI: 10.1016/j.clinph.2009.07.045
7. Frishkoff, G., Sydes, J., Mueller, K., Frank, R., Curran, T., Connolly, J., Kilborn, K., Molfese, D., Perfetti, C., & Malony, A. (2011). Minimal Information for Neural Electromagnetic Ontologies (MINEMO): A standards- compliant method for analysis and integration of event-related potentials (ERP) data. Standards in Genomic Sciences, 5(2): 211–223. DOI: 10.4056/sigs.2025347
8. Folstein JR, Van Petten C. Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology. 2008;45(1):152–170.
9. Harker, K.T. & Connolly, J.F. (2007). Assessment of visual working memory using event-related potentials. Clinical Neurophysiology, 118, 2479–2488. DOI: 10.1016/j.clinph.2007.07.026
10. Heil M, Osman A, Wiegelmann J, Rolke B, Hennighausen E. N200 in the Eriksen-task: Inhibitory executive process? J Psychophysiol. 2000;14(4):218–218.
11. Kok A. On the utility of P3 amplitude as a measure of processing capacity. Psychophysiology. 2001;38(3):557–577.
12. Kutas M, McCarthy G, Donchin E. Augmenting mental chronometry: the P300 as a measure of stimulus evaluation time. Science. 1977;197(4305):792–795.
13. Lefebvre, C.D., Marchand, Y., Eskes, G.A., & Connolly, J.F. (2005). Assessment of working memory abilities using an event-related brain potential (ERP)-compatible digit span backward task. Clinical Neurophysiology, 116, 1665-1680. DOI: 10.1016/j.clinph.2005.03.015
14. Light GA, Swerdlow NR, Braff DL. Preattentive sensory processing as indexed by the MMN and P3a brain responses is associated with cognitive and psychosocial functioning in healthy adults. J Cogn Neurosci. 2007;19(10):1624–1632.
15. Lim CL, Gordon E, Rennie C, et al. Dynamics of SCR, EEG, and ERP activity in an oddball paradigm with short interstimulus intervals. Psychophysiology. 1999;36(5):543–551.
16. Marchand, Y., Lefebvre, C., & Connolly, J.F. (2006). Correlating digit span performance and event-related potentials to assess working memory. International Journal of Psychophysiology, 62, 280-289. DOI: 10.1016/j.ijpsycho.2006.05.007
17. Näätänen R, Gaillard AWK. 5 The orienting reflex and the N2 deflection of the event-related potential (ERP). 1983 Epub.
18. Näätänen R, Gaillard AWK, Mäntysalo S. Early selective-attention effect on evoked potential reinterpreted. Acta Psychol (Amst). 1978;42(4):313–329.
19. Näätänen R, Paavilainen P, Rinne T, Alho K. The mismatch negativity (MMN) in basic research of central auditory processing: a review. Clin Neurophysiol. 2007;118(12):2544–2590.
20. Näätänen R, Picton T. The N1 wave of the human electric and magnetic response to sound: a review and an analysis of the component structure. Psychophysiology. 1987;24(4):375–425.
21. Patel SH, Azzam PN. Characterization of N200 and P300: selected studies of the event-related potential. Int J Med Sci. 2005;2(4):147–147.
22. Polich J. Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol. 2007;118(10):2128–2148.
23. Polich J. Task difficulty, probability, and inter-stimulus interval as determinants of P300 from auditory stimuli. Electroencephalogr Clin Neurophysiol Potentials Sect. 1987;68(4):311–320.
24. Polich J. Normal variation of P300 from auditory stimuli. Electroencephalogr Clin Neurophysiol Potentials Sect. 1986;65(3):236–240.
25. Polich J, Howard L, Starr A. P300 latency correlates with digit span. Psychophysiology. 1983;20(6):665–669.26. Reinvang, I. (1999). Cognitive event-related potentials in neuropsychological assessment. Neuropsychol. Rev. 9, 231–248.
26. Reinvang, I. (1999). Cognitive event-related potentials in neuropsychological assessment. Neuropsychol. Rev. 9, 231–248.
Concussion Event-Related Potential Literature
1. Baillargeon A, Lassonde M, Leclerc S, Ellemberg D. Neuropsychological and neurophysiological assessment of sport concussion in children, adolescents and adults. Brain Inj. 2012;26(3):211–220.
2. Broglio SP, Moore RD, Hillman CH. A history of sport-related concussion on event-related brain potential correlates of cognition. Int J Psychophysiol. 2011;82(1):16–23.
3. Broglio SP, Pontifex MB, O’Connor P, Hillman CH. The persistent effects of concussion on neuroelectric indices of attention. J Neurotrauma. 2009;26(9):1463–1470.
4. De Beaumont L, Lassonde M, Leclerc S, Théoret H. Long-term and cumulative effects of sports concussion on motor cortex inhibition. Neurosurgery. 2007;61(2):329–337.
5. De Beaumont L, Theoret H, Mongeon D, et al. Brain function decline in healthy retired athletes who sustained their last sports concussion in early adulthood. Brain. 2009;132(3):695–708.
6. De Beaumont L, Beauchemin M, Beaulieu C, Jolicoeur P. Long-term attenuated electrophysiological response to errors following multiple sports concussions. Journal of clinical and experimental neuropsychology. 2013 Jul 1;35(6):596-607.
7. Dupuis F, Johnston KM, Lavoie M, Lepore F, Lassonde M. Concussions in athletes produce brain dysfunction as revealed by event-related potentials. Neuroreport. 2000;11(18):4087–4092.
8. Ellemberg D, Henry LC, Macciocchi SN, Guskiewicz KM, Broglio SP. Advances in sport concussion assessment: from behavioral to brain imaging measures. Journal of neurotrauma. 2009 Dec 1;26(12):2365-82.
9. Gaetz M, Goodman D, Weinberg H. Electrophysiological evidence for the cumulative effects of concussion. Brain Inj. 2000;14(12):1077–1088.
10. Gawryluk, J.R., D’Arcy, R.C.N., Connolly, J.F., & Weaver, D.F. (2010). Improving the clinical assessment of consciousness with advances in electrophysiological and neuroimaging techniques. BMC Neurology, 10:11. DOI: 10.1186/1471-2377-10-11
11. Gosselin N, Bottari C, Chen J-K, et al. Evaluating the cognitive consequences of mild traumatic brain injury and concussion by using electrophysiology. Neurosurg Focus. 2012;33(6):E7–E7.
12. Johnston KM. New investigative tools in concussion. British Journal of Sports Medicine. 2001 Oct 1;35(5):371-.
13. Harrison, A. & Connolly, J.F. (2013). Finding a way in: a review and practical evaluation of fMRI and EEG in disorders of consciousness. Neuroscience and Biobehavioral Reviews, 37(8):1403-1419. DOI: 10.1016/j.neubiorev.2013.05.004.
14. Henry LC, Tremblay S, De Beaumont L. Long-term effects of sports concussions: bridging the neurocognitive repercussions of the injury with the newest neuroimaging data. The Neuroscientist. 2017 Oct;23(5):567-78.
15. Hudac CM, Cortesa CS, Ledwidge PS, Molfese DL. History of concussion impacts electrophysiological correlates of working memory. International journal of psychophysiology. 2017 Oct 10.
16. Ledwidge PS, Molfese DL. Long-term effects of concussion on electrophysiological indices of attention in varsity college athletes: An event-related potential and standardized low-resolution brain electromagnetic tomography approach. Journal of neurotrauma. 2016 Dec 1;33(23):2081-90.
17. Lovell, M. R., Collins, M. W., Iverson, G. L., Field, M., Maroon, J. C., Cantu, R., … & Fu, F. H. (2003). Recovery from mild concussion in high school athletes. Journal of neurosurgery, 98(2), 296-301.
18. Mizrahi, E. M., & Kellaway, P. (1984). Cerebral concussion in children: assessment of injury by electroencephalography. Pediatrics, 73(4), 419-425.
19. Pratap‐Chand, R., Sinniah, M., & Salem, F. A. (1988). Cognitive evoked potential (P300): a metric for cerebral concussion. Acta Neurologica Scandinavica, 78(3), 185-189.
20. Moore DR, Pindus DM, Raine LB, Drollette ES, Scudder MR, Ellemberg D, Hillman CH. The persistent influence of concussion on attention, executive control and neuroelectric function in preadolescent children. International Journal of Psychophysiology. 2016 Jan 1;99:85-95.
21. Ozen, L. J., Itier, R. J., Preston, F. F., & Fernandes, M. A. (2013). Long-term working memory deficits after concussion: electrophysiological evidence. Brain injury, 27(11), 1244-1255.22.
22. Thériault M, De Beaumont L, Tremblay S, Lassonde M, Jolicoeur P. Cumulative effects of concussions in athletes revealed by electrophysiological abnormalities on visual working memory. Journal of clinical and experimental neuropsychology. 2011 Jan 5;33(1):30-41.
Disorders of Consciousness & Coma Event-Related Potential Literature
1. ArmanFard, N., Komeili, M., Reilly, J. & Connolly, J. (2018). A machine learning framework for automatic and continuous MMN detection with preliminary results for coma outcome prediction. IEEE Journal of Biomedical and Health Informatics [Epud ahead of print]. DOI: 10.1109/JBHI.2018.2877738.
2. Blain-Moraes, S., Boshra, R., Ma, H.K., Mah, R., Ruiter, K., Avidan, M., Connolly, J.F. and Mashour, G.A. (2016). Normal brain response to propofol in advance of recovery from unresponsive wakefulness syndrome. Frontiers in Human Neuroscience. 2016 June 2; 10:248. DOI: 10.3389/fnhum.2016.00248
3. Connolly, J.F., Mate-Kole, C.C., & Joyce, B.M. (1999). Global aphasia: An innovative assessment approach. Archives of Physical Medicine and Rehabilitation, 80, 1309-1315. LINK: archives.pmr.org
4. Connolly, J.F., Major, A., Allen, S., & D’Arcy, R.C.N. (1999). Performance on WISC-III and WAIS-R NI vocabulary subtests assessed with event-related brain potentials: An innovative method of assessment. Journal of Clinical and Experimental Neuropsychology, 21, 444-464. DOI: 10.1076/jcen.21.4.444.879
5. Connolly, J.F, Marchand, Y., Major, A., & D’Arcy, R.C.N. (2006). Event- related brain potentials as a measure of performance on WISC-III and WAIS- R NI Similarities sub-tests. Journal of Clinical & Experimental Neuropsychology, 28, 1327-1345. DOI: 10.1080/13803390500428484
6. Fischer C, Luaute J, Morlet D. Event-related potentials (MMN and novelty P3) in permanent vegetative or minimally conscious states. Clin Neurophysiol. 2010;121(7):1032–1042.
7. Mah, R., & Connolly, J. (2018). A framework for the extended monitoring of levels of cognitive function in unresponsive patients. PLoS ONE, 13(7): e0200793. DOI: 10.1371/journal.pone.0200793
8. Morlet D, Fischer C. MMN and novelty P3 in coma and other altered states of consciousness: a review. Brain Topogr. 2014;27(4):467–479
9. Wang, J.T., Young, G.B., & Connolly, J.F. (2004). Prognostic value of evoked responses and event-related brain potentials in coma. Canadian Journal of Neurological Sciences, 31, 438- 450. DOI: 10.1017/S0317167100003619
10. Young, G.B., Wang, J.T., & Connolly, J.F. (2004). Prognostic determination in anoxic-ischemic and traumatic encephalopathies. Journal of Clinical Neurophysiology, 21, 379-390. Link: europepmc.org/abstract/med/15592010
Stroke Event-Related Potential Literature
1. D’Arcy, R.C.N., Marchand, Y., Eskes, G.A., Harrison, E.R., Phillips, S. J., Major, A., & Connolly, J.F. (2003). Electrophysiological assessment of language function following stroke. Clinical Neurophysiology, 114, 662-672. DOI: 10.1016/S1388-2457(03)00007-5
Pain Event-Related Potential Literature
1. Dick, B.D., Connolly, J.F., McGrath, P.J., Finley, G.A., Stroink, G., Houlihan, M.E., & Clark, A.J. (2003). The disruptive effect of chronic pain on mismatch negativity. Clinical Neurophysiology, 114, 1497-1506. DOI: 10.1016/S1388-2457(03)00133-0
2. Dick, B.D., Connolly, J.F., Houlihan, M.E., McGrath, P.J., Finley, G.A., Stroink, G., & Clark, A. J. (2006). Effects of experimental pain on Mismatch Negativity. Journal of Psychophysiology, 20(1), 21-31. DOI: 10.1027/0269-8803.20.1.21
3. Houlihan, M.E., McGrath, P.J., Connolly, J.F., Stroink, G., Finley G.A., Dick, B., & Phi, T.T. (2004). Assessing the effect of pain on demands for attentional resources using ERPs. International Journal of Psychophysiology, 51, 181-187. DOI: 10.1016/j.ijpsycho.2003.08.001
4. Troche, S.J., Houlihan, M.E., Connolly, J.F., Dick, B.D., McGrath, P.J., Finley, G.A. and Stroink, G. (2014). The effect of pain on involuntary and voluntary capture of attention. European Journal of Pain, Article first published online: 23 JUL 2014 | DOI: 10.1002/ejp.553