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Neurophysiological evidence of motor imagery training in Parkinson’s disease: a case series study

Published online by Cambridge University Press:  05 April 2021

Kathryn J. M. Lambert
Affiliation:
Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Alberta, Canada
Anthony Singhal
Affiliation:
Department of Psychology, Faculty of Science, University of Alberta, Edmonton, Alberta, Canada Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
Ada W. S. Leung*
Affiliation:
Department of Occupational Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Alberta, Canada Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada
*
*Corresponding author. Email: [email protected]
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Abstract

Background:

Motor imagery (MI) has become an increasingly popular rehabilitation tool for individuals with motor impairments. However, it has been proposed that individuals with Parkinson’s Disease (PKD) may not benefit from MI due to impairments in motor learning.

Objective:

This case series study investigated the effects of a 4-week MI training protocol on MI ability in three male individuals with PKD, with an emphasis on examining changes in brain responses.

Methods:

Training was completed primarily at home, via audio recordings, and emphasized the imagination of functional tasks. MI ability was assessed pre and post-training using subjective and objective imagery questionnaires, alongside an electroencephalographic (EEG) recording of a functional MI task. EEG analysis focused on the mu rhythm, as it has been proposed that suppression in the mu rhythm may reflect MI success and motor learning. Previous research has indicated that mu suppression is impaired in individuals with PKD, and may contribute to the disease’s associated deficits in motor learning.

Results:

Following training, all three participants improved in MI accuracy, but reported no notable improvements in MI vividness. Greater suppression in the mu rhythm was also exhibited by all three participants post-training.

Conclusion:

These results suggest the participants learned from the training protocol and that individuals with PKD are responsive to MI training. Further research on a larger scale is needed to verify the findings and determine if this learning translates to improvements in motor function.

Type
Brief Report
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Australasian Society for the Study of Brain Impairment

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References

Abraham, A., Hart, A., Andrade, I., & Hackney, M. E. (2018). Dynamic neuro-cognitive imagery improves mental imagery ability, disease severity, and motor and cognitive functions in people with Parkinson’s Disease. Neural Plasticity, 2018, 6168507. doi: 10.1155/2018/6168507 CrossRefGoogle ScholarPubMed
Bovend’Eerdt, T. J. H., Dawes, H., Sackley, C., & Wade, D. T. (2012). Practical research-based guidance for motor imagery practice in neurorehabilitation. Disability and Rehabilitation, 34, 21922200. doi: 10.3109/09638288.2012.676703 CrossRefGoogle ScholarPubMed
Braun, S. M., Beurskens, A., Borm, B. J., Schack, T., & Wade, D. T. (2006). The effects of mental practice in stroke rehabilitation: A systematic review. Archives of Physical Medicine and Rehabilitation, 87, 842852. doi: 10.1016/j.apmr.2006.02.034 CrossRefGoogle ScholarPubMed
Caligiore, D., Mustile, M., Spallette, G., & Baldassarre, G. (2017). Action observation and motor imagery for rehabilitation in Parkinson’s disease: A systematic review and an integrative hypothesis. Neuroscience and Biobehavioral Reviews, 72, 210222. doi: 10.1016/j.neubiorev.2016.11.005 CrossRefGoogle Scholar
Campos, A., & Pérez-Fabello, M. (2009). Psychometric quality of a revised vividness of visual imagery questionnaire. Perceptual and Motor Skills, 108, 798802. doi: 10.2466/PMS.108.3.798-802 CrossRefGoogle ScholarPubMed
Carey, T. S., & Boden, S. D. (2003). A critical guide to case series reports. SPINE, 28, 16311634. doi: 10.1097/01.BRS.0000083174.84050.E5 CrossRefGoogle ScholarPubMed
Carrasco, D. G. & Cantalapiedra, J. A. (2016). Effectiveness of motor imagery or mental practice for functional recovery after stroke: A systematic review. Neurologia, 31, 4352. doi: 10.1016/j.nrl.2013.02.003 Google Scholar
Chen, Y. Y., Lambert, K., Madan, C. R., & Singhal, A. (2020). Mu oscillations and motor imagery performance: A reflection of success, not ability. BioRxiv. doi: 10.1101/2020.09.21.291492 Google Scholar
Di Renzio, F., Collet, C., Guillot, A., & Hoyek, N.(2014). Impact of neurologic deficits on motor imagery: A systematic review of clinical evaluations. Neuropsychology Review, 24, 116147. doi: 10.1007/s11065-014-9257-6 CrossRefGoogle Scholar
Dickstein, R., & Tamir, R. (2010). Motor imagery practice in individuals with Parkinson’s disease. In Guillot, A. & Collet, C. (Eds.), The Neurophysiological Foundations of Motor Imagery (pp. 177–187). Oxford, UK: Oxford University Press.Google Scholar
Dijkerman, C. H., Ietswaart, M., & Johnston, M. (2010). Motor imagery and the rehabilitation of movement disorders: an overview. In Guillot, A. & Collet, C. (Eds.), The Neurophysiological Foundations of Motor Imagery (pp. 127–143). Oxford, UK: Oxford University Press.Google Scholar
Formaggio, E., Storti, S. F., Cerini, R., Fiaschi, A. & Manganotti, P. (2010). Brain oscillatory activity during motor imagery in EGG-fMRI coregistration. Magnetic Resonance Imaging, 28, 14031412. doi: 10.1016/j.mri.2010.06.030 CrossRefGoogle ScholarPubMed
Gregg, M., Hall, C., & Butler, A. (2010). The MIQ-RS: A suitable option for examining movement imagery ability. eCAM, 7, 249257. doi: 10.1093/ecam/nem170 Google ScholarPubMed
Guillot, A., Louis, M., & Collet, C. (2010). Neurophysiological substrates of motor imagery ability. In Guillot, A. & Collet, C. (Eds.), The Neurophysiological Foundations of Motor Imagery (pp. 109–123). Oxford, UK: Oxford University Press.CrossRefGoogle Scholar
Hallsson, H. (2013). Is relaxation prior to imagery really beneficial: effects on imagery vividness, and concentration, and performance. Unpublished Master’s thesis, Miami University, Oxford.Google Scholar
Hariz, G. M., & Forsgren, L. (2010). Activities of daily living and quality of life in individuals diagnosed with Parkinson’s Disease according to subtype of disease, and in comparison to healthy controls. Neurologica, 123, 2027. doi: 10.1111/j.1600-0404.2010.01344.x Google Scholar
Heida, T., Poppe, N. R., de Vos, C. C., van Putten, M. J. A. M., & van Vugt, J. P. P. (2014). Event-related mu-rhythm desynchronization during movement observation is impaired in Parkinson’s Disease. Clinical Neurophysiology, 125, 18191825. doi: 10.1016/j.clinph.2014.01.016 CrossRefGoogle ScholarPubMed
Heremans, E., Feys, P., Nieuwboer, A., Vercruysse, S., Vandenberghe, W., Sharma, N., & Helsen, W. (2011). Motor imagery ability in patients with early and mid stage Parkinson’s disease. Neurorehabilitation & Neural Repair, 25, 168177. doi: 10.01177/1545968310370750 CrossRefGoogle Scholar
Heremans, E., Nieuwboer, A., Feys, P., Vercruysse, S., Vandenberghe, W., Sharma, N., & Helsen, W. F. (2012). External cueing improves motor imagery quality in patients with Parkinson’s Disease. Neurorehabilitation & Neural Repair, 26, 2735. doi: 10.1177/1545968311411055 CrossRefGoogle Scholar
Hétu, S., Gregoire, M., Saimpont, A., Coll, M. P., Eugene, F., Michon, P.-E., & Jackson, P. L. (2013). The neural network of motor imagery: An ALE meta-analysis. Neuroscience & Biobehavioral Reviews, 37, 930949. doi: 10.1016/j.neubiorev.2013.03.017 CrossRefGoogle ScholarPubMed
Hobson, H. M., & Bishop, D. V. M. (2016). Mu suppression: A good measure of the human mirror neuron system? Cortex, 82, 290310. doi: 10.1016/j.cortex.2016.03.019 CrossRefGoogle ScholarPubMed
Holmes, P. S., Cumming, J., & Edwards, M. G. (2010). Movement imagery, observation, and skill. In Guillot, A. & Collet, C. (Eds.), The Neurophysiological Foundations of Motor Imagery (pp. 253–269). Oxford, UK: Oxford University Press.Google Scholar
Jankovic, J. (2008). Parkinson’s disease: Clinical features and diagnosis. Journal of Neurology, Neurosurgery & Psychiatry, 79, 368376. doi: 10.1136/jnnp.2007.131045 CrossRefGoogle ScholarPubMed
Jenkinson, C., Clarke, C., Gray, R., Hewitson, P., Ives, N., Morley, D.Williams, A. (2015). Comparing results from long and short form versions of the Parkinson’s disease questionnaire in a longitudinal study. Parkinsonism & Related Disorders, 21, 13121316. doi: 10.1016/j.parkreldis.2015.09.008 CrossRefGoogle ScholarPubMed
Kiefer, A. W., Cremades, J. G. & Myer, G. D. (2014). Train the brain: Novel electroencephalography data indicate links between motor learning and brain adaptions. Journal of Novel Physiotherapies, 4, 198. doi: 10.4172/2165-7025.1000198 Google Scholar
Lanciego, J. L., Luquin, N., & Obeso, J. A. (2012). Functional neuroanatomy of the basal ganglia. Cold Spring Harbor Perspectives in Medicine, 2, 120. doi: 10.1101/cshperspect.a009621 CrossRefGoogle ScholarPubMed
Liu, K. P., Chan, C. C. H., Wong, R. S. M., Kwan, I. W. L., Yau, C. S. F., Li, L. S. W., & Lee, T. M. C. (2009). A randomized controlled trial of mental imagery augment generalization of learning in acute post stroke patients. Stroke, 40, 22222225. doi: 10.1161/STROKEAHA.108.540997 CrossRefGoogle Scholar
Madan, C. R., & Singhal, A. (2013). Introducing TAMI: An objective test of ability in movement imagery. Journal of Motor Behavior, 45, 153166. doi: 10.1080/00222895.2013.763764 CrossRefGoogle ScholarPubMed
Malouin, F., Jackson, P. L., & Richards, C. L. (2013). The integration of mental practice in rehabilitation: A critical review. Frontiers in Human Neuroscience, 7, 576. doi: 10.3389/fnhum.2013.00576 CrossRefGoogle ScholarPubMed
Matsumoto, J., Fujiwara, T., Takahashi, O., Lieu, M., Kimura, A., & Ushiba, A. (2010). Modulation of mu rhythm desynchronization during motor imagery by transcranial direct stimulation. Journal of Neuroengineering & Rehabilitation, 7, 27. doi: 10.1186/1743-0003-7-27 CrossRefGoogle Scholar
McFarland, D. J., Minar, L. A., Vaughan, T. M., & Wolpaw, J. R. (2000). Mu and beta rhythm topographies during imagined and actual movements. Brain Topography, 12, 177186. doi: 10.1023/A:1023437823106 CrossRefGoogle ScholarPubMed
Muller, V., Lutzenberger, W., Pulvermuller, F., Mohr, B., & Birbaumer, N. (2000). Investigation of brain dynamics in Parkinson’s Disease by methods derived from nonlinear dynamics. Experimental Brain Research, 137, 103110. doi: 10.1007/s002210000638 Google Scholar
Nakano, H., Osumi, M., Ueta, K., Kodama, T., & Morioka, S. (2013). Changes in electroencephalographic activity during observation, preparation, and execution of a motor learning task. International Journal of Neuroscience, 123, 866875. doi: 10.3109/00207454.2013.813509 CrossRefGoogle ScholarPubMed
Neuper, C., & Pfurtscheller, G. (2010). Electroencephalographic characteristics during motor imagery. In Guillot, A. & Collet, C. (Eds.), The Neurophysiological Foundations of Motor Imagery (pp. 65–81). Oxford, UK: Oxford University Press.Google Scholar
Nieuwboer, A., Rochester, L., Muncks, L., & Swinnen, S. P. (2009). Motor learning in Parkinson’s disease: Limitation and potential for rehabilitation. Parkinsonism & Related Disorders, 15, S53S58. doi: 10.1016/S1353-8020(09)70781-3 CrossRefGoogle Scholar
Ono, T., Shindo, K., Kawashima, K., Ota, N., Ito, M., Ota, T., … Ushiba, J. (2014). Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Frontiers in Neuroengineering, 7, 14. doi: 10.3389/fneng.2014.00019 CrossRefGoogle ScholarPubMed
Page, S. J. (2010). An overview of the effectiveness of motor imagery after stroke: A neuroimaging approach. In Guillot, A. & Collet, C. (Eds.), The Neurophysiological Foundations of Motor Imagery (pp. 145–159). Oxford, UK: Oxford University Press.Google Scholar
Pfurtscheller, G., Neuper, C., Flotzinger, D., & Pregenzer, M. (1997). EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology, 103, 642651. doi: 10.1016/S1350-4533(00)00051-6 CrossRefGoogle ScholarPubMed
Pichiorri, F., Morone, G., Petti, M., Toppi, J., Pisotta, I., Molinari, M., … Mattia, D. (2015). Brain-computer interface boosts motor imagery practice during stroke recovery. Annals of Neurology, 77, 851865. doi: 10.1002/ana.24390 CrossRefGoogle ScholarPubMed
Poliakoff, E., & Smith-Spark, J. H. (2008). Everyday cognitive failures and memory problems in Parkinson’s patients without dementia. Brain and Cognition, 67, 340350. doi: 10.1016/j.bandc.2008.02.004 CrossRefGoogle ScholarPubMed
Ruffino, C., Papaxanthis, C., & Lebon, F. (2017). Neural plasticity during motor learning with motor imagery practice: Review and perspectives. Neuroscience, 341, 6178. doi: 10.1016/j.neuroscience.2016.11.023 CrossRefGoogle ScholarPubMed
Schuster-Amft, C., Hilfiker, R., Amft, O., Scheidhauer, A., Andrews, B., Butler, J., … Ettlin, T. (2011). Best practice for motor imagery: A systematic literature review on motor imagery training elements in five different disciplines. BMC Medicine, 9, 75. doi: 10.1186/1741-7015-9-75 CrossRefGoogle Scholar
Szamietat, A. J., Shen, S., & Sterr, A. (2007). Motor imagery of complex everyday movements. An fMRI study. Neuroimage, 34, 702713.CrossRefGoogle Scholar
Tangwiriyasakul, C., Verhagen, R., van Putten, M. J. A. M., & Rutten, W. L. C. (2013). Importance of baseline in event-related desynchronization during a combination task of motor imagery and motor observation. Journal of Neural Engineering, 10, 19. doi: 10.1088/1741-2560/10/2/026009 CrossRefGoogle ScholarPubMed
Whitten, T. A., Hughes, A. M., Dickson, C. T., & Caplan, J. B. (2011). A better oscillation detection method robustly extracts EEG rhythms across brain state changes: The human alpha rhythm as a test case. Neuroimage, 54, 860874. doi: 10.1016/j.neuroimage.2010.08.064 CrossRefGoogle ScholarPubMed
Wondrusch, C., & Schuster-Amft, C. (2013). A standardised motor imagery introduction program (MIIP) for patients with sensorimotor impairments: Development and evaluation. Frontiers in Human Neuroscience, 7, 477. doi: 10.3389/fnhum.2013.00477 CrossRefGoogle Scholar
Yaguez, L., Canavan, A. G., Lange, H. W., & Homberg, V. (1999). Motor learning by imagery is differentially affected in Parkinson’s and Huntington’s diseases. Behavioural Brain Research, 102, 115127. doi: 10.1016/s0166-4328(99)00005-4 CrossRefGoogle ScholarPubMed
Zimmermann-Schlatter, A., Schuster, C., Puhan, M. A., Siekierka, E., & Steurer, J. (2008). Efficacy of motor imagery in post-stroke rehabilitation: A systematic review. Journal of NeuroEngineering and Rehabilitation, 5, 8. doi: 10.1186/1743-0003-5-8 CrossRefGoogle ScholarPubMed