Noise as a useful signal within the nervous system in neurorehabilitation

• Autorzy: Mikołajewska E. , Mikołajewski D.
• Języki publikacji: EN

Abstrakty

EN

The nervous system is one of the most complex known dynamical systems; thus, its disorders are among the most severe. Scientists and clinicians look for the best possible methods allowing for comprehensive understanding and for reliable assessment and treatment of human nervous system disorders. Noise may be perceived as a useful control signal for particular nervous system functions, including further development of neurorehabilitation and clinical applications of brain-computer interfaces (BCIs), neuroprostheses (NPs), deep brain stimulation (DBS), etc. The awareness of associated chances and limitations allow for the wise planning and management of further clinical practice, especially in the area of long-term neurorehabilitation and care. This article aims at investigating the extent to which the available knowledge and experience may be identified and utilized, including potential future applications.

Słowa kluczowe

EN

assistive technology; brain computer interface; disabled people; neuroprosthesis; physiotherapy; rehabilitation

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2013
• Tom: Vol. 9, no. 4
• Strony: 209--213
• Opis fizyczny: Bibliogr. 30 poz.

Twórcy

autor

Mikołajewska E.

• Rehabilitation Clinic, The 10th Military Clinical Hospital with Polyclinic, Powstańców Warszawy 5, 85-681 Bydgoszcz, Poland

autor

Mikołajewski D.

• Institute of Mechanics and Applied Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland
• Division of Applied Informatics, Department of Physics, Astronomy and Applied Informatics, Nicolaus Copernicus University, Toruń, Poland

Bibliografia

• 1. Duch W, Nowak W, Meller J, Osiński G, Dobosz K. Computational approach to understanding autism spectrum disorders. Comput Sci J 2012;14:47-61.
• 2. Duch W, Nowak W, Meller J, Osiński G, Dobosz K, Mikołajewski D, et al. Consciousness and attention in autism spectrum disorders. Cracow: Proceedings of Cracow Grid Workshop 2011;2010:202-ll.
• 3. Dominici N, Keller U, Vallery H, Friedli L, van den Brand R, Starkey ML, et al. Versatile robotic interface to evaluate, enable and train locomotion and balance after neuromotor disorders. Nat Med 2012;18:1142-7.
• 4. van den Brand R, Heutschi J, Barraud Q, DiGiovanna J, Bartholdi K, Huerlimann M, et al. Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science 2012;336:1182-5.
• 5. Harat M, Radziszewski K, Rudaś M, Okoń M, Galanda M. Clinical evaluation of deep cerebellar stimulation for spasticity in patients with cerebral palsy. Neurol Neurochir Pol 2009;43: 36-44.
• 6. Sokal P, Harat M, Paczkowski D, Rudaś M, Birski M, Litwinowicz A. Results of neuromodulation for the management of chronic pain. Neurol Neurochir Pol 2011;45:445-51.
• 7. Faisal AA, Selen LP, Wolpert DM. Noise in the nervous system. Nat Rev Neurosci 2008;9;292-303.
• 8. Faisal AA. Noise in neurons and other constraints. In: Le Novére N, editor. Computational systems neurobiology. New York: Springer, 2012:227-57.
• 9. Qureshi IA, Mehler MF. Towards a 'systems'-level understanding of the nervous system and its disorders. Trends Neurosci 2013. pii: S0166-2236(13)00132-X. doi: 10.1016/j.tins.2013.07.003.
• 10. McIntosh AR, Kovacevic N, Lippe S, Garrett D, Grady C, Jirsa V. The development of a noisy brain. Arch Ital Biol 2010;148: 323-37.
• 11. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. Mapping the structural core of human cerebral cortex. PLoS Biol 2008;6:el59.
• 12. Honey CJ, Sporns O. Dynamical consequences of lesions in cortical networks. Hum Brain Mapp 2008;29:802-9.
• 13. Woods HA, Wilson JK. An information hypothesis for the evolution of homeostasis. Trends Ecol Evol 2013;28:283-9.
• 14. Spiridon M, Gerstner W. Noise spectrum and signal transmission through a population of spiking neurons. Network 1999;10:257-72.
• 15. Patriarca M, Postnova S, Braun HA, Hernández-García E, Toral R. Diversity and noise effects in a model of homeostatic regulation of the sleep-wake cycle. PLoS Comput Biol 2012;8:el002650.
• 16. Xue G, Dong Q, Chen C, Lu Z, Mumford JA, Poldrack RA. Greater neural pattern similarity across repetitions is associated with better memory. Science 2010;330:97-101.
• 17. Balduzzi D, Tononi G. Integrated information in discrete dynamical systems: motivation and theoretical framework. PloS Comput Biol 2008;4:el000091.
• 18. Tononi G, Sporns O, Edelman GM. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc Natl Acad Sci USA 1994;91:5033-7.
• 19. Seth AK, Izhikevich E, Reeke GN, Edelman GM. Theories and measures of consciousness: an extended framework. Proc Natl Acad Sci 2006;103:10799-804.
• 20. Garrett DD, Kovacevic N, McIntosh AR, Grady CH. The importance of being variable. J Neurosci 2011;31:4496-503.
• 21. Mikołajewska E, Mikołajewski D. Clinical significance of computational brain models in neurorehabilitation. Med Biol Sci 2013;27:19-26.
• 22. Dobosz K, Duch W. Understanding neurodynamical systems via fuzzy symbolic dynamics. Neural Networks 2010;23:487-96.
• 23. Duch W, Dobosz K. Visualization for understanding of neurodynamical systems. Cognit Neurodyn 2011;5:145-60.
• 24. Mikołajewska E, Mikołajewski D. Role of brainstem within human body systems - computational approach. J Health Sci 2012;2:95-106.
• 25. Mikołajewska E, Mikołajewski D. Consciousness disorders as the possible effect of brainstem activity failure – computational approach.) Health Sci 2012;2:7-18.
• 26. Mikołajewska E, Mikołajewski D. Computational approach to neural plasticity of nervous system on system level. J Health Sci 2012;2:39-47.
• 27. Griffin R, Westbury CH. Infant EEG activity as a biomarker for autism: a promising approach or a false promise? BMC Med 201l;9:61.
• 28. Bosl W, Tierney A, Tager-Flusberg H, Nelson CH. EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med 2011:9:18.
• 29. Catarino A, Churches O, Baron-Cohen S, Andrade A, Ring H. Atypical EEG complexity in autism spectrum conditions: a multiscale entropy analysis. Clin Neurophysiol 2011;122:2375-83.
• 30. Gamez D, Aleksander I. Accuracy and performance of the state-based Φ and liveliness measures of information integration. Conscious Cogn 2011;20:1403-24.