TBI Rehabilitation

[Abstract+References] Brain Plasticity and Modern Neurorehabilitation Technologies

Kostas Pantremenos

Abstract

In recent decades, interest in studies on basic and applied aspects of how the nervous system functions has been growing rapidly around the world. The recovery of lost functions rests on processes of neuroplasticity, which is determined by the ability of the brain to transform its structures in response to injury. The effects of both routine and state-of-the-art neurorehabilitation technologies are ensured by synaptic plasticity— long-term potentiation and long-term depression, which influence learning and the preservation of new knowledge and skills obtained during rehabilitation. The introduction of new methods of neuroimaging, neurophysiology, and mathematical statistics have powerfully stimulated the development of the neuroplasticity doctrine. It has become clear that the main role in the recovery of injured functions is played by the reorganization of cortical nets and not by tissue reparation as such. The Research Center of Neurology has accumulated significant experience in the use of innovative treatment methods based on modern neurorehabilitation principles. Some of them are used for acute stroke; among other things, their effectiveness and safety have been shown with regard to patients in intensive care units (cyclic robotic mechanotherapy) and patients with severe motor deficit and an associated somatic pathology (stimulation of plantar support zones). Opportunities to assess neuroplasticity under various rehabilitation methods using fMRI and navigated transcranial magnetic stimulation (TMS) are revealed. The center also studies the fundamentals of consciousness using original neuroimaging and neurophysiological protocols for the sake of its recovery. The center is actively introducing its data into the practice of domestic clinics specializing in recovery medicine and neurorehabilitation.

References

  1. 1.
    C. H. Rankin, T. Abrams, R. J. Barry, et al., “Habituation revisited: An updated and revised description of the behavioral characteristics of habituation,” Neurobiol. Learn. Mem. 92 (2), 135–138 (2009).CrossRefGoogle Scholar
  2. 2.
    I. Jin, E. R. Kandel, and R. D. Hawkins, “Whereas short-term facilitation is presynaptic, intermediateterm facilitation involves both presynaptic and postsynaptic protein kinases and protein synthesis,” Learn. Mem. Cold Spring Harb. 18, 96–102 (2011).CrossRefGoogle Scholar
  3. 3.
    C. Lüscher, R. A. Nicoll, R. C. Malenka, and D. Muller, “Synaptic plasticity and dynamic modulation of the postsynaptic membrane,” Nat. Neurosci., No. 3, 545–550 (2000).CrossRefGoogle Scholar
  4. 4.
    M. Lenz, A. Vlachos, and N. Maggio, “Ischemic longterm-potentiation (iLTP): Perspectives to set the threshold of neural plasticity toward therapy,” Neural Regen. Res., No. 10, 1537–1539 (2015).CrossRefGoogle Scholar
  5. 5.
    N. Hardingham, J. Dachtler, and K. Fox, “The role of nitric oxide in pre-synaptic plasticity and homeostasis,” Front Cell Neurosci., No. 7, 1–19 (2013).CrossRefGoogle Scholar
  6. 6.
    S. D. Bury and T. A. Jones, “Unilateral sensorimotor cortex lesions in adult rats facilitate motor skill learning with the ‘unaffected’ forelimb and training-induced dendritic structural plasticity in the motor cortex,” J. Neurosci. Off. J. Soc. Neurosci. 22, 8597–8606 (2002).CrossRefGoogle Scholar
  7. 7.
    R. J. Nudo, “Postinfarct cortical plasticity and behavioral recovery,” Stroke 38, 840–845 (2007).CrossRefGoogle Scholar
  8. 8.
    A. Arvidsson, T. Collin, D. Kirik, et al., “Neuronal replacement from endogenous precursors in the adult brain after stroke,” Nat. Med. 8, 963–970 (2002).CrossRefGoogle Scholar
  9. 9.
    Y. Bach and P. Rita, “Central nervous system lesions: Sprouting and unmasking in rehabilitation,” Arch. Phys. Med. Rehabil. 62, 413–417 (1981).Google Scholar
  10. 10.
    W. T. Greenough, H. M. Hwang, and C. Gorman, “Evidence for active synapse formation or altered postsynaptic metabolism in visual cortex of rats reared in complex environments,” Proc. Natl. Acad. Sci. U. S. A. 82, 4549–4552 (1985).CrossRefGoogle Scholar
  11. 11.
    J. Liepert, H. Bauder, H. R. Wolfgang, et al., “Treatment-induced cortical reorganization after stroke in humans,” Stroke J. Cereb. Circ. 31, 1210–1216 (2000).CrossRefGoogle Scholar
  12. 12.
    Y. Sagi, I. Tavor, S. Hofstetter, et al., “Learning in the fast lane: New insights into neuroplasticity,” Neuron 73, 1195–1203 (2012).CrossRefGoogle Scholar
  13. 13.
    E. Auriel, B. L. Edlow, Y. D. Reijmer, et al., “Microinfarct disruption of white matter structure: A longitudinal diffusion tensor analysis,” Neurology 83, 182–188 (2014).CrossRefGoogle Scholar
  14. 14.
    L. A. Chernikova, M. A. Piradov, N. A. Suponeva, et al., “High-tech methods of neurorehabilitation in nervous system diseases,” in Neurology of the 21st Century: Diagnostic, Treatment, and Research Technologies: Manual for Doctors, Ed. by M. A. Piradov, S. N. Illarioshkin, and M. M. Tanashyan (ATMO, Moscow, 2015) [in Russian].Google Scholar
  15. 15.
    L. G. Tarasova, L. A. Chernikova, and A. S. Chubukov, “Hand motion recovery in poststroke hemiparesis patients by the method of intensive training of the paretic upper limb,” Lech. Fizkul’t. Sport. Med., No. 8, 34–39 (2008).Google Scholar
  16. 16.
    P. R. Prokazova, M. A. Piradov, Yu. V. Ryabinkina, et al., “Robotic mechanotherapy using the Motomed Letto 2 simulator in complex early stroke rehabilitation in the resuscitation and intensive care unit,” Annaly Klinich. Eksp. Nevrolog., No. 2, 11–15 (2013).Google Scholar
  17. 17.
    A. A. Belkin, I. A. Avdyunina, N. A. Varako, et al., “Intensive care rehabilitation: Clinical recommendations,” Vestn. Vosstanov. Med., No. 2, 139–143 (2017).Google Scholar
  18. 18.
    K. Ustinova, N. Epstein, L. Chernikova, et al., “Effect of robotic locomotor training in an individual with Parkinson’s disease: A case report,” Disab. Rehab.: Assist. Technol. 6 (1), 77–85 (2011).Google Scholar
  19. 19.
    S. N. Morozova, E. A. Zmeykina, R. N. Konovalov, et al., “Changes in functional connectivity of motor zones in the course of treatment with a Regent multimodal complex exoskeleton in neurorehabilitation of poststroke patients.” Hum. Physiol., No. 1, 54–60 (2016).Google Scholar
  20. 20.
    E. I. Kremneva, L. A. Chernikova, R. N. Konovalov, et al., “Assessing supraspinal control of locomotion in norm and in pathology using a passive motor fMRT paradigm,” Annaly Klinich. Eksp. Nevrol., No. 1, 31–37 (2012).Google Scholar
  21. 21.
    L. A. Chernikova, E. I. Kremneva, A. V. Chervyakov, et al., “New approaches in the study of the neuroplasticity process in patients with central nervous system lesions,” Hum. Physiol., No. 3, 272–277 (2013).CrossRefGoogle Scholar
  22. 22.
    O. V. Glebova, M. Yu. Maksimova, and L. A. Chernikova, “Mechanical stimulation of plantar support zones during acute moderate and severe stroke,” Vestn. Vosstanov. Med., No. 1, 71–75 (2014).Google Scholar
  23. 23.
    I. V. Saenko, S. N. Morozova, E. A. Zmeikina, et al., “Change in functional connectivity of motor zones using the Regent multimodal exoskeleton complex in stroke patients,” Fiziol. Chel., No. 1, 64–72 (2016).Google Scholar
  24. 24.
    M. A. Piradov, S. N. Illarioshkin, A. O. Gushcha, et al., “State-of-the-art neuromodulation technologies,” in Neurology of the 21st Century: Diagnostic, Treatment, and Research Technologies: Manual for Doctors, Ed. by M. A. Piradov, S. N. Illarioshkin, and M. M. Tanashyan (ATMO, Moscow, 2015), pp. 46–98 [in Russian].Google Scholar
  25. 25.
    N. A. Suponeva, I. S. Bakulin, A. G. Poidasheva, and M. A. Piradov, “Safety of transcranial magnetic stimulation: A review of international recommendations and new data,” Nervno-Myshech. Bol., No. 2, 21–36 (2017).Google Scholar
  26. 26.
    M. A. Piradov, M. V. Krotenkova, R. N. Konovalov, et al., “Neuroimaging technologies,” in Neurology of the 21st Century: Diagnostoc, Treatment, and Research Technologies: Manual for Doctors, Ed. by M. A. Piradov, S. N. Illarioshkin, and M. M. Tanashyan (ATMO, Moscow, 2015), pp. 11–82 [in Russian].Google Scholar
  27. 27.
    L. A. Legostaeva, E. A. Zmeikina, A. G. Poidasheva, et al., “Navigated transcranial magnetic stimulation under fMRT resting control during rehabilitation of patients with chronic consciousness disorders: Blind intervention study,” in VI Baltic Congress on Child Neurology: A Collection of Abstracts, (St. Petersburg, 2016), pp. 221–222 [in Russian].Google Scholar
  28. 28.
    O. A. Mokienko, R. K. Lyukmanov, L. A. Chernikova, et al., “Brain–computer interface: The first experience of clinical use in Russia,” Hum. Physiol., No. 1, 24–31 (2016).CrossRefGoogle Scholar
  29. 29.
    O. A. Mokienko, A. V. Chervyakov, S. Kulikova, et al., “Increased motor cortex excitability during motor imagery in brain–computer interface trained subjects,” Front. Comput. Neurosci. 7, 168 (2013).CrossRefGoogle Scholar
  30. 30.
    A. G. Poidasheva, G. A. Aziatskaya, A. Yu. Chernyavskii, et al., “Dynamics of cortical motor representation of the common digital extensor when teaching motor imaging using the brain–computer interface: A controlled study,” Zh. Vyssh. Nerv. Deyat. im. I.P. Pavlova, No. 4, 473–484 (2017).Google Scholar

via Braind Modern Neurorehabilitation Technologies | SpringerLink