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Reorganization of Bioelectrical Activity in the Neocortex after Stroke by Rehabilitation Using a Brain–Computer Interface Controlling a Wrist Exoskeleton

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The process of the functional rearrangement of the motor cortex of the brain after stroke is due to neuroplasticity, and this underlies motor recovery. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are currently recognized as the most informative methods for studying these processes. The course of the neuroplastic process can be evaluated from the power levels of EEG rhythms during imagination of movements in the paralyzed arm in right-handed patients after stroke in the left hemisphere monitored at different times – before and after courses of neurorehabilitation using a brain–computer interface controlling a wrist exoskeleton. Powerful excitatory interactions in the primary motor cortex and frontoparietal areas in the lesioned and “intact” hemispheres are initially seen, and these probably reflect reorganization of neural networks. Rehabilitation courses were followed by restoration of bioelectrical activity in the primary motor cortex due to recovery of efficient connections with the premotor and superior parietal zones and decreases in the pathological influences of the contralateral hemisphere.

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References

  • Ang, K. K., Chua, K. S., Phua, K. S., et al., “A randomized controlled trial of EEG-based motor imagery brain–computer interface robotic rehabilitation for stroke,” Clin. EEG Neurosci, 46, No. 4, 310–320 (2015), https://doi.org/https://doi.org/10.1177/1550059414522229.

  • Asadi-Pooya, A. A., Dlugos, D., Skidmore, C., and Sperling, M. R., “Atlas of Electroencephalography, 3rd edition,” Epileptic Disord., 19, No. 3, 384 (2017), https://doi.org/https://doi.org/10.1684/epd.2017.0934.

  • Bach-y-Rita, P., “Brain plasticity as a basis for recovery of function in humans,” Neuropsychologia, 28, No. 6, 547–554 (1990).

    Article  CAS  Google Scholar 

  • Benali, A., Weiler, E., Benali, Y., et al., “Excitation and inhibition jointly regulate cortical reorganization in adult rats,” J. Neurosci., 28, No. 47, 12284–12293 (2008), https://doi.org/https://doi.org/10.1523/jneurosci.1952-08.2008.

  • Berthier, M. L., García-Casares, N., Walsh, S. F., et al., “Recovery from post-stroke aphasia: lessons from brain imaging and implications for rehabilitation and biological treatments,” Discov. Med., 12, No. 65, 275–289 (2011).

    PubMed  Google Scholar 

  • Bobrov, P. D., Korshakov, A. V., Roshchin, V. Yu., and Frolov, A. A., “A Bayesian approach to brain–computer interfaces based on motor imagery,” Zh. Vyssh. Nerv. Deyat., 62, No. 1, 89–99 (2012).

    CAS  Google Scholar 

  • Buzsáki, G. and Wang, X. J., “Mechanisms of gamma oscillations,” Annu. Rev. Neurosci., 35, 203–225 (2012), https://doi.org/https://doi.org/10.1146/annurevneuro-062111-150444.

  • Cervera, M. A., Soekadar, S. R., Ushiba, J., et al., “Brain–computer interfaces for post-stroke motor rehabilitation: a meta-analysis,” Ann. Clin. Transl. Neurol., 5, No. 5, 651–663 (2018), https://doi.org/https://doi.org/10.1002/acn3.544.

  • Chen, C. C., Lee, S. H., Wang, W. J., et al., “EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity,” PLoS One, 12, No. 6, e0178822 (2017), https://doi.org/https://doi.org/10.1371/journal.pone.0178822.eCollection.

  • Chollet, F., “Pharmacologic approaches to cerebral aging and neuroplasticity: insights from the stroke model,” Dialogues Clin. Neurosci., 15, No. 1, 67–76 (2013).

    Article  Google Scholar 

  • Cunningham, D. A., Machado, A., Janini, D., et al., “Assessment of inter- hemispheric imbalance using imaging and noninvasive brain stimulation in patients with chronic stroke,” Arch. Phys. Med. Rehabil., 96, No. 4, Supplement, S94–103 (2015), https://doi.org/https://doi.org/10.1016/j.apmr.2014.07.419.

  • Damulin, I. V. and Ekusheva, E. V., “Neuroplasticity processes after stroke,” Nevrol. Neiropsikh. Psikhosom., 3, 69–74 (2014), https://doi.org/https://doi.org/10.14412/2074-2711-2014-3-69-74.

  • Fries, P., “Rhythms for cognition: communication through coherence,” Neuron, 88, No. 1, 220–235 (2015).

    Article  CAS  Google Scholar 

  • Frolov, A. A., Mokienko, O. A., Lyukmanov, R. Kh., et al., “Preliminary results of a controlled study of the efficacy of hand BCI-exoskeleton technology in poststroke paralysis of the hand,” Vestn. Ross. Gos. Med. Univ., 2, 17–25 (2016).

    Google Scholar 

  • Frolov, A., Mokienko, O., Lukmanov, R., et al., “Post-stroke rehabilitation training with a motor-imagery-based brain–computer interface (BCI)-controlled hand exoskeleton: A randomized controlled multicenter trial,” Front. Neurosci., 11, No. 400, 1–11 (2017).

    Google Scholar 

  • Gazzola, V. and Keysers, C., “The observation and execution of actions share motor and somatosensory voxels in all tested subjects: single-subject analyses of unsmoothed fMRI data,” Cereb. Cortex, 19, No. 6, 1239–1255 (2009), https://doi.org/https://doi.org/10.1093/cercor/bhn181.

  • Jeon, Y., Nam, C. S., Kim, Y.-J., and Whang, M. C., “Event-related (de) synchronization (ERD/ERS) during motor imagery tasks: Implications for brain–computer interfaces,” Int. J. Industr. Ergon., 41, 428–436 (2011).

    Article  Google Scholar 

  • Kahana, M. J., Seelig, D., and Madsen, J. R., “Theta returns,” Curr. Opin. Neurobiol., 11, No. 6, 739–744 (2001), https://doi.org/https://doi.org/10.1016/S095964388(01)0027861.

  • Klimesch, W., “Alpha-band oscillations, attention, and controlled access to stored information,” Trends Cogn. Sci., 16, No. 12, 606–617 (2012), https://doi.org/https://doi.org/10.1016/j.tics.2012.10.007.

  • Klimesch, W., Doppelmayr, M., Pachinger, T., and Russegger, H., “Eventrelated desynchronization in the alpha band and the processing of semantic information,” Brain Res. Cogn. Brain Res., 6, No. 2, 83–94 (1997), https://doi.org/https://doi.org/10.1016/S092666410(97)0001869.

  • Klimesch, W., Doppelmayr, M., Russegger, H., and Pachinger, T., “Theta band power in the human scalp EEG and the encoding of new information,” Neuroreport, 7, No. 7, 1235–1240 (1996).

    Article  CAS  Google Scholar 

  • Landau, A. N. and Fries, P., “Attention samples stimuli rhythmically,” Curr. Biol., 22, 1000–1004 (2012), https://doi.org/https://doi.org/10.1016/j.cub.2012.03.054.

  • Lotze, M., Beutling, W., Loibl, M., et al., “Contralesional motor cortex activation depends on ipsilesional corticospinal tract integrity in well-recovered subcortical stroke patients,” Neurorehabil. Neural Repair, 26, No. 6, 594–603 (2012), https://doi.org/https://doi.org/10.1177/1545968311427706.

  • Mitchell, D. J., McNaughton, N., Flanagan, D., and Kirk, I. J., “Frontalmidline theta from the perspective of hippocampal ‘theta,” Prog. Neurobiol., 86, No. 3, 156–185 (2008), https://doi.org/https://doi.org/10.1016/j.pneurobio.2008.09.005.

  • Monge-Pereira, E., Ibañez-Pereda, J., Alguacil-Diego, I. M., et al., “Use of electroencephalography brain–computer interface systems as a rehabilitative approach for upper limb function after a stroke: A systematic review,” PM R, 9, No. 9, 918–932 (2017).

    Article  Google Scholar 

  • Mukhin, K. Yu., Petrukhin, A. S., and Glukhova, L. Yu., Epilepsy. An Atlas of Electroclinical Diagnosis, Alvares Publishing, Moscow (2004).

  • Murta, T., Leite, M., Carmichael, D. W., et al., “Electrophysiological correlates of the BOLD signal for EEG-informed fMRI,” Hum. Brain Mapp., 36, No. 1, 391–414 (2015), https://doi.org/https://doi.org/10.1002/hbm.22623.

  • Neuper, C., Scherer, R., Reiner, M., and Pfurtscheller, G., “Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG,” Cogn. Brain Res., 25, No. 3, 668 (2005).

    Article  Google Scholar 

  • Nicolo, P., Rizk, S., Magnin, C., et al., “Coherent neural oscillations predict future motor and language improvement after stroke,” Brain, 138, No. 10, 3048–3060 (2015), https://doi.org/https://doi.org/10.1093/brain/awv200.

  • Ono, T., Shindo, K., Kawashima, K., et al., “Brain–computer interface with somatosensory feedback improves functional recovery from severehemiplegia due to chronic stroke,” Front. Neuroeng., 7, 19, eCollection 2014 (2014), https://doi.org/https://doi.org/10.3389/fneng.2014.00019.

  • Pascual-Leone, A., Amedi, A., Fregni, F., and Merabet, L. B., “The plastic human brain cortex,” Annu. Rev. Neurosci., 28, 377–401 (2005).

    Article  CAS  Google Scholar 

  • Pfurtscheller, G. and Lopes da Silva, F. H., “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clin. Neurophysiol., 110, 1842–1857 (1999).

    Article  CAS  Google Scholar 

  • Pichiorri, F., Petti, M., Caschera, S., et al., “An EEG index of sensorimotor interhemispheric coupling after unilateral stroke: clinical and neurophysiological study,” Eur. J. Neurosci., 47, No. 2, 158–163 (2018), https://doi.org/https://doi.org/10.1111/ejn.13797.

  • Prabhakaran, S., Zarahn, E., Riley, C., et al., “Inter-individual variability in the capacity for motor recovery after ischemic stroke,” Neurorehabil. Neural Repair, 22, No. 1, 64–71 (2008).

    Article  Google Scholar 

  • Rehme, A. K., Eickhoff, S. B., Wang, L. E., et al., “Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke,” Neuroimage, 55, No. 3, 1147–1158 (2011), https://doi.org/https://doi.org/10.1016/j.neuroimage.2011.01.014.

  • Remsik, A., Young, B., Vermilyea, R., et al., “A review of the progression and future implications of brain–computer interface therapies for restoration of distal upper extremity motor function after stroke,” Expert Rev. Med. Devices, 13, No. 5, 445–454 (2016), https://doi.org/https://doi.org/10.1080/17434440.2016.1174572.

  • Rizzolatti, G. and Sinigaglia, C., “The functional role of the parieto-frontal mirror circuit: interpretations and misinterpretations,” Nat. Rev. Neurosci., 11, No. 4, 264–274 (2010), https://doi.org/https://doi.org/10.1038/nrn2805.

  • Rossiter, H. E., Davis, E. M., Clark, E. V., et al., “Beta oscillations reflect changes in motor cortex inhibition in healthy ageing,” NeuroImage, 91, 360–365 (2014).

    Article  Google Scholar 

  • Sato, J. R., Rondinoni, C., Sturzbecher, M., et al., “From EEG to BOLD: brain mapping and estimating transfer functions in simultaneous EEG-fMRI acquisitions,” Neuroimage, 50, No. 4, 1416–1426 (2010), https://doi.org/https://doi.org/10.1016/j.neuroimage.2010.01.075.

  • Sauseng, P., Griesmayr, B., Freunberger, R., and Klimesch, W., “Control mechanisms in working memory: a possible function of EEG theta oscillations,” Neurosci. Biobehav. Rev., 34, No. 7, 1015–1022 (2010), https://doi.org/https://doi.org/10.1016/j.neubiorev.2009.12.006.

  • Stinear, C. M. and Ward, N. S., “How useful is imaging in predicting outcomes in stroke rehabilitation?” Int. J. Stroke, 8, No. 1, 33–37 (2013), https://doi.org/https://doi.org/10.1111/j.1747-4949.2012.00970.x.

  • Stinear, C. M., “Prediction of motor recovery after stroke: advances in biomarkers,” Lancet Neurol., 16, No. 10, 826–836 (2017), https://doi.org/https://doi.org/10.1016/S1474-4422(17)30283-1.

  • Stroganova, T. A., Orekhova, E. V., and Posikera, I. N., “The theta rhythm of the infant EEG and the development of the mechanisms of voluntary control of attention in the 2nd half of the first year of life,” Zh. Vyssh. Nerv. Deyat., 48, No. 6, 945–964 (1998).

    CAS  Google Scholar 

  • Suffczynski, P., Kalitzin, S., Pfurtscheller, G., and Lopes da Silva, F. H., “Computational model of thalamo-cortical networks: dynamical control of alpha rhythms in relation to focal attention,” Int. J. Psychophysiol., 43, No. 1, 25–40 (2001), https://doi.org/https://doi.org/10.1016/S016768760(01)0017765.

  • Takeuchi, N., Oouchida, Y., and Izumi, S., “Motor control and neural plasticity through interhemispheric interactions,” Neural Plast., Art. 823285 (2012), https://doi.org/https://doi.org/10.1155/2012/823285.

  • Vinogradova, O. S., Kitchigina, V. F., and Zenchenko, C. I., “Pacemaker neurons of the forebrain medical septal area and theta rhythm of the hippocampus,” Membr. Cell Biol., 11, No. 6, 715–725 (1998).

    CAS  PubMed  Google Scholar 

  • Yamawaki, N., Stanford, I. M., Hall, S. D., and Woodhall, G. L., “Pharmacologically induced and stimulus evoked rhythmic neuronal oscillatory activity in the primary motor cortex in vitro,” Neuroscience, 151, No. 2, 386–395 (2008).

    Article  CAS  Google Scholar 

  • Zenkov, L. R., Clinical Epileptology (with elements of neurophysiology): Guidelines for Doctors, MIA, Moscow (2010), 2nd ed.

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Correspondence to S. V. Kotov.

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Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 70, No. 2, pp. 217–230, March–April, 2020.

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Kotov, S.V., Romanova, M.V., Kondur, A.A. et al. Reorganization of Bioelectrical Activity in the Neocortex after Stroke by Rehabilitation Using a Brain–Computer Interface Controlling a Wrist Exoskeleton. Neurosci Behav Physi 50, 1146–1154 (2020). https://doi.org/10.1007/s11055-020-01017-7

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