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2012 | 26 | 1 | 51-58
Tytuł artykułu

Neuroplasticity in rehabilitation after central nervous system damages – computational models

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Increasing survival rates in severe illnesses and traumatic injuries can lead to an increase in the number of disabled people with central nervous system (CNS) damages. Motor training after CNS damage is an important part of neurorehabilitation. It can partially reverse the loss of cortical representation after lesion thanks to neuroplasticity. Patients may regain some motor functions in the months following damage due both to spontaneous recovery and physical therapy interventions targeted at further improvement of function. The neural correlates of motor training after CNS damage have been investigated in animals with motor cortex lesions and in humans using fMRI, TMS, etc. However it is hard to fully explain all mechanisms of neuroplasticity. One of ways to increase knowledge and clinical experience is developing of computational models. To refine a lot of hypotheses existing in the area of CNS neuroplasticity there are useful computational models of lesions and following recovery due to neurorehabilitation. The models based on artificial neural networks are novel solution, but in some cases can provide effectivity and biological plausibility. This article aims at investigating the extent to which the available opportunities are being exploited, including models as a first step in the development of adaptive and cost-effective rehabilitation methods tailored to individuals with CNS deficits.
Słowa kluczowe
Wydawca

Rocznik
Tom
26
Numer
1
Strony
51-58
Opis fizyczny
Daty
wydano
2012-03-01
online
2013-08-31
Twórcy
  • Klinika Rehabilitacji, 10 Wojskowy Szpital Kliniczny z Polikliniką SP ZOZ w Bydgoszczy
  • Katedra Informatyki Stosowanej, Wydział Fizyki, Astronomii i Informatyki Stosowanej, Uniwersytet Mikołaja Kopernika w Toruniu
Bibliografia
  • 1. Konorski J. Conditioned reflexes and neuron organization. Cambridge: Cambridge Univ Press; 1948.
  • 2. Konorski J. Integracyjna działalność mózgu. Warszawa: PWN; 1969.
  • 3. Kossut M. Brain plasticity. Neurol Neurochir Pol 2000; 34(6):1091-1099.[PubMed]
  • 4. Siucińska E. Neuroprzekaźnik hamujący w plastyczności kory mózgu. Kosmos Problemy Nauk Biologicznych 2005; 2-3(267-268):195-212.
  • 5. Mikołajewska E. Metoda NDT-Bobath w neurorehabilitacji osób dorosłych. Warszawa: Wydawnictwo Lekarskie PZWL; 2011.
  • 6. Dayan E, Cohen LG. Neuroplasticity subserving motor skill learning. Neuron 2011; 72(3): 443-454.[Crossref][PubMed][WoS]
  • 7. Martino G, Pluchino S, Bonfanti L, Schwartz M. Brain regeneration in physiology and pathology: the immune signature driving therapeutic plasticity of neural stem cells. Physiol Rev 2011; 91(4): 1281-1304.[Crossref][PubMed][WoS]
  • 8. Bonfanti L, Peretto P. Adult neurogenesis in mammals - a theme with many variations. Eur J Neurosci 2011; 34(6): 930-950.[Crossref][WoS][PubMed]
  • 9. Dancause N, Nudo RJ. Shaping plasticity to enhance recovery after injury. Prog Brain Res 2011; 192: 273-295.[WoS][PubMed][Crossref]
  • 10. Mikołajewska E., Mikołajewski D. Wybrane zastosowania modeli komputerowych w medycynie. Ann Acade Med Siles 2011; 1-2: 78-88.
  • 11. Tadeusiewicz R. (red.) Neurocybernetyka teoretyczna. Warszawa: Wydawnictwo Uniwersytetu Warszawskiego; 2009.
  • 12. PubMed (U.S. National Library of Medicine) http://www.ncbi.nlm.nih.gov/pubmed/ - data pobrania 20.01.2012r.
  • 13. Kleim JA, Jones TA. Principles of experience-dependent neural plasticity: implications for rehabilitation. J Speech Lang Hear Res 2008; 51(1):225-239.
  • 14. Adkins DL, Boychuk J, Remple MS et al. Motor training induces experience-specific patterns of plasticity across motor cortex and spinal cord. J Appl Physiol 2006; 101(6):1776-1782. [PubMed]
  • 15. Ward NS, Newton JM, Swayne OB et al. Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain 2006; 129(3):809-819.[Crossref]
  • 16. Monfils MH, Plautz EJ, Kleim JA. In search of the motor engram: motor map plasticity as a mechanism for encoding motor experience. Neuroscientist 2005; 11(5):471-483. [Crossref][PubMed]
  • 17. Nudo RJ. Adaptive plasticity in motor cortex: implications for rehabilitation after brain injury. J Rehabil Med 2003; (41 Suppl.):7-10.[Crossref][PubMed]
  • 18. Rossini PM, Dal Forno G. Integrated technology for evaluation of brain function and neural plasticity. Phys Med Rehabil Clin N Am 2004; 15(1):263-306. [PubMed][Crossref]
  • 19. Rossini PM, Altamura C, Ferreri F et al. Neuroimaging experimental studies on brain plasticity in recovery from stroke. Eura Medicophys 2007; 43(2):241-54.[PubMed]
  • 20. Momjian S, Seghier M, Seeck M i wsp. Mapping of the neuronal networks of human cortical brain functions. Adv Tech Stand Neurosurg 2003; 28:91-142.
  • 21. Turing A. Maszyna licząca a inteligencja. W: Chwedeńczuk B, redakcja. Filozofia umysłu. Warszawa: Aletheia; 1995.
  • 22. Putnam H. Minds and Machines. In: Hook S, editor. Dimensions of Mind. New York: New York University Press; 1960. p. 148-180.
  • 23. Putnam H. Representation and Reality. Cambridge: MIT Press; 1988.
  • 24. Kuhn TS. Dwa bieguny. Tradycja i nowatorstwo w badaniach naukowych. Warszawa: PIW; 1985. s. 406-439.
  • 25. Mikołajewska E, Mikołajewski D. Role of brainstem within human body systems - computational approach. J Health Sci 2012; (2)1: 95-106.
  • 26. Searle JR. Mózg, umysł i nauka. Warszawa Wydawnictwo Naukowe: PWN; 1995.
  • 27. Koch C, Segev I. Methods in Neural Modeling. From Ions to Networks. Wyd. 2 poprawione. Cambridge: MIT Press; 1998.
  • 28. Bower JD, Beeman D. The Book of GENESIS. Wyd. 2. New York: Springer Verlag; 1998.
  • 29. O’Reilly R, Munakata Y. Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. Cambridge: MIT Press; 2000.
  • 30. Reinkensmeyer DJ, Iobbi MG, Kahn LE et al. Modeling reaching impairment after stroke using a population vector model of movement control that incorporates neural firing-rate variability. Neural Comput 2003; 15:2619-2642.[PubMed][Crossref]
  • 31. Scheidt RA, Stoeckmann T. Reach adaptation and final position control amid environmental uncertainty after stroke. J Neurophysiol 2007; 97:2824-2836.[PubMed][Crossref][WoS]
  • 32. Reggia J, Goodall S, Chen Y et al. Modeling post-stroke cortical map reorganization. In: Reggia JA, Ruppin E, Berndt RS, editors. Neural Modeling of Brain and Cognitive Disorders. New York: World Scientific; 1996. p. 283-302.
  • 33. Goodall S, Reggia JA, Chen Y et al. A computational model of acute focal cortical lesions. Stroke 1997; 28:101-109.[Crossref][PubMed]
  • 34. Han CE, Arbib MA, Schweighofer N. Stroke rehabilitation reaches a threshold. PLoS Comput Biol 2008; 4(8):e1000133.[WoS][Crossref]
  • 35. Crystal H, Finkel L. Computational Approaches to Neurological Disease. In: Reggia J A, Ruppin E, Berndt RS, editors. Neural Modeling of Brain and Cognitive Disorders. New York: World Scientific; 1996. p. 251-272.
  • 36. Duch W, Dobosz K. Attractors in neurodynamical systems. In: Wang R, Gu F, editors. Advances in cognitive neurodynamics II. ICCN; 2011. p.157-161.
  • 37. Dobosz K, Duch W. Visualization for understanding of neurodynamical systems. Cognitive Neurodynamics 2011; 5(2):145-160.[PubMed][WoS]
  • 38. Dobosz K, Duch W. Understanding neurodynamical systems via Fuzzy Symbolic Dynamics. Neural Networks 2010; 23: 487-496.[Crossref][WoS]
  • 39. Simpson HD, Mortimer D, Goodhill GJ. Theoretical models of neural circuit development. Curr Top Dev Biol 2009; 87: 1-51.[PubMed]
  • 40. Wojcik GM. Self-organising criticality in the simulated models of the rat cortical microcircuits. Neurocomputing 2012; 79: 61-67.[Crossref][WoS]
  • 41. Wojcik GM. Electrical parameters influence on the dynamics of the hodgkin-huxley liquid state machine. Neurocomputing 2011; 79: 68-78.[WoS]
  • 42. Grzyb BJ, Chinellato E, Wojcik GM, Kaminski WA. Which model to use for the liquid state machine? IJCNN, IEEE; 2010. p. 1018-1024.
  • 43. Kaminski WA, Wojcik GM. Liquid state machine built of hodgkin-huxley neurons. Informatica 2004; 15(1): 39-44.
  • 44. Wojcik GM, Kaminski WA. Liquid state machine and its separation ability as function of electrical parameters of cell. Neurocomputing 2007; 70(13-15): 2593-2697.[Crossref][WoS]
  • 45. Rubinov M, McIntosh AR, Valenzuela MJ, Breakspear M. Simulation of neuronal death and network recovery in a computational model of distributed cortical activity. Am J Geriatr Psychiatry 2009; 17(3): 210-217.[Crossref][WoS]
  • 46. Wojtowicz JM. Adult neurogenesis. From circuits to models. Behav Brain Res 2012 Feb 14;227(2):490-6.[WoS]
  • 47. Mikołajewska E, Mikołajewski D. Interfejsy mózg-komputer - zastosowania cywilne i wojskowe. Kwartalnik Bellona 2011; 2: 123-133.
  • 48. Duch W, Nowak W, Meller J, Osiński G, Dobosz K, Mikołajewski D, Wójcik GM. Consciousness and attention in autism spectrum disorders. Proceedings of Cracow Grid Workshop 2010. p. 202-211.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.-psjd-doi-10_2478_rehab-2013-0029
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