When we decide to move, neural signals are sent to our body from the motor cortex. The motor cortex is located across the top surface of the brain. Each side of the brain controls the opposite side of the body.
The motor cortex is roughly organized like a map of the body — from controlling lower limb actions in the middle, to upper limb actions further out, before finally becoming more involved in facial muscles and such activities as swallowing.
When we practice a movement skill this map can change.1,2 For instance, if one practices a musical instrument long enough, the part of the brain involved in performing will become larger.
While fNIRS has existed since the late 1970’s,3 it is now getting more attention in neuroscience due to technological progress offering several advantages over other imaging technologies. It has greater spatial resolution than electroencephalography (EEG), and greater temporal resolution than fMRI. More importantly, it is capable of taking measurements during movement — which makes it more suitable to real world applications.5,6,7
Why would measurement of movement related brain activity be useful? One of the most promising applications may be neurological rehabilitation, particularly stroke recovery.8
Individuals with an acquired brain injury (such as a stroke) often have mobility impairments,9 requiring intensive physical rehabilitation.10 Rehabilitation promotes recovery by leveraging neuroplasticity — the brains ability to change. 11,12,13
Research suggests that brain activity metrics can be used to predict recovery, track progress, and compare the effects of different exercises.14 This might allow clinicians to better tailor therapy to individual patients, potentially improving outcomes or shortening the length of stay at a hospital.15 What’s more, brain activity feedback can be provided to patients while performing their exercises, which early research has indicated can improve rehabilitation outcomes.16
Despite the potential value, brain activity is not typically measured in the rehabilitation setting due to inaccessibility of the equipment.
Axem extends the utility of fNIRS by making it easier than ever to set up, requiring no neuroimaging expertise. Our patent-pending design allows for measurement through hair without requiring the use of gels (as is the case with EEG) or physically tying hair in place.
The Axem software makes data collection easy, and automates state of the art processing methods to ensure the best signal possible by default. Results are presented in an intuitive manner that can be understood with no more background knowledge than that provided on this website.
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1. Nudo, R. J., Milliken, G. W., Jenkins, M. W., & Merzenich, M. M. (1996). Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. Journal of Neuroscience, 16(2), 785–807.
2. Monfils, M., Plautz, E., & Kleim, J. (2005). In Search of the Motor Engram: Motor Map Plasticity as a Mechanism for Encoding Motor Experience. The Neuroscientist, 11(5), 471–83.
3. Jöbsis, F. F. (1977). Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science (New York, N.Y.), 198(4323), 1264–7. doi:10.1126/science.929199
4. Scholkmann, F., Kleiser, S., Metz, A. J., Zimmermann, R., Mata Pavia, J., Wolf, U., & Wolf, M. (2014). A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology. NeuroImage, 85 Pt 1, 6–27.
5. Leff, D. R., Orihuela-Espina, F., Elwell, C. E., Athanasiou, T., Delpy, D. T., Darzi, A. W., & Yang, G.-Z. Z. (2011). Assessment of the cerebral cortex during motor task behaviours in adults: a systematic review of functional near infrared spectroscopy (fNIRS) studies. NeuroImage, 54(4), 2922–36.
6. Kenville, R., Maudrich, T., Carius, D., & Ragert, P. (2017). Hemodynamic Response Alterations in Sensorimotor Areas as a Function of Barbell Load Levels during Squatting: An fNIRS Study. Frontiers in Human Neuroscience, 11, 241.
7. Carius, D., Andrä, C., Clauß, M., Ragert, P., Bunk, M., & Mehnert, J. (2016). Hemodynamic Response Alteration As a Function of Task Complexity and Expertise—An fNIRS Study in Jugglers. Frontiers in Human Neuroscience, 10, 126.
8. Mihara, M., & Miyai, I. (2016). Review of functional near-infrared spectroscopy in neurorehabilitation. Neurophotonics, 3(3), 031414.
9. Mayo, N. E., Wood-Dauphinee, S., Ahmed, S., Gordon, C., Higgins, J., McEwen, S., & Salbach, N. (1999). Disablement following stroke. Disability and rehabilitation, 21(5-6), 258–68
10. French, B., Thomas, L. H., Coupe, J., McMahon, N. E., Connell, L., Harrison, J., … Watkins, C. L. (2016). Repetitive task training for improving functional ability after stroke. The Cochrane database of systematic reviews, 11, CD006073.
11. Nudo, R. J., Wise, B. M., SiFuentes, F., & Milliken, G. W. (1996). Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science, 272(5269), 1791–1794.
12. Cramer, S. C. (2008). Repairing the human brain after stroke. II. Restorative therapies. Annals of neurology, 63(5), 549–60.
13. Murphy, T. H., & Corbett, D. (2009). Plasticity during stroke recovery: from synapse to behaviour. Nature reviews. Neuroscience, 10(12), 861–72.
14. Boyd, L. A., Hayward, K. S., Ward, N. S., Stinear, C. M., Rosso, C., Fisher, R. J., … Cramer, S. C. (2017). Biomarkers of stroke recovery: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable. International journal of stroke : official journal of the International Stroke Society, 12(5), 480–493.
15. Stinear, C. M., Byblow, W. D., Ackerley, S. J., Barber, P. A., & Smith, M.-C. C. (2017). Predicting Recovery Potential for Individual Stroke Patients Increases Rehabilitation Efficiency. Stroke, 48(4), 1011–1019
16. Mihara, M., Hattori, N., Hatakenaka, M., Yagura, H., Kawano, T., Hino, T., & Miyai, I. (2013). Near-infrared Spectroscopy–mediated Neurofeedback Enhances Efficacy of Motor Imagery–based Training in Poststroke Victims. Stroke, 44(4), 1091–1098.