Modelling effects of consciousness disorders in brainstem computational model - Preliminary findings

• Autorzy: Duch Włodzisław , Mikołajewski Dariusz

Identyfikatory

DOI

10.1515/bams-2020-0018

• Języki publikacji: EN

Abstrakty

EN

Objectives: Disorders of consciousness are very big medical and social problem. Their variability, problems in precise definition and proper diagnosis make difficult assessing their causes and effectiveness of the therapy. In the paper we present our point of view to a problem of consciousness and its most common disorders. Methods: For this moment scientists do not know exactly, if these disorders can be a result of simple but general mechanism, or a complex set of mechanisms, both on neural, molecular or system level. Presented in the paper simulations using neural network models, including biologically relevant consciousness’ modelling, help assess influence of specified causes. Results: Nonmotoric brain activity can play important role within diagnostic process as a supplementary method for motor capabilities. Simple brain sensory (e.g. visual) processing of both healthy subject and people with consciousness disorders help checking hypotheses in the area of consciousness’ disorders’ mechanisms, including associations between consciousness and its neural correlates. Conclusions: The results are promising. Project announced herein will be developed and its next result will be presented in subsequent articles.

Słowa kluczowe

EN

alterations in arousal; brainstem; computational model; disorders of consciousness; neural network

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2020
• Tom: Vol. 16, no. 2
• Strony: art. no. 20200018
• Opis fizyczny: Bibliogr. 41 poz., rys., tab.

Twórcy

autor

Duch Włodzisław

• Department of Informatics, Nicolaus Copernicus University, Toruń, Poland; Neurocognitive Laboratory, Center for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland

autor

Mikołajewski Dariusz

• Department of Teleinformatics and Electronic Devices, Institute of Informatics, Kazimierz Wielki University, Bydgoszcz, Poland
• Neurocognitive Laboratory, Center for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland
• Institute of Informatics, Kazimierz Wielki University, Bydgoszcz, Poland

Bibliografia

• 1. Seth AK, Dienes Z, Cleeremans A, Overgaart M, Pessoa L. Measuring consciousness: relating behavioural and neurophysiological approaches. Trends Cognit Sci 2008;12:314-21.
• 2. Zeman A. Consciousness Brain 2001;124:1263-89.
• 3. Vanhaudenhuyse A, Demertzi A, Schabus M, Noirhomme Q, Bredart S, Boly M, et al. Two distinct neuronal networks mediate the awareness of environment and of self. J Cognit Neurosci 2011; 23:570-8.
• 4. Laureys S, Owen AM, Schiff ND. Brain function in coma, vegetative state, and related disorders. Lancet Neurol 2004;3:537-46.
• 5. Giacino JT, Ashwal S, Childs N, Cranford R, Jennett B, Katz DI, et al. The minimally conscious state: definition and diagnostic criteria. Neurology 2002;58:349-53.
• 6. Henry GL, Little N, Jagoda A, Pellegrino TR, Quint D. Neurologic emergencies. 3rd ed. New York: McGraw-Hill; 2010.
• 7. Beamont JG, Kenealy PM. Incidence and prevalence of the vegetative and minimally conscious states. Neuropsychol Rehabil 2005;15:184-9.
• 8. Laureys S, Antoine S, Boly M, Elincx S, Faymonville ME, Berré J, et al. Brain function in the vegetative state. Acta Neurol Belg 2002;102:177-85.
• 9. Province C. The vegetative state: promoting greater clarity and improved treatment. Neuropsychol Rehabil 2005;15:264-71.
• 10. Coleman MR, Davis MH, Rodd JM, Robson T, Ali A, Owen AM, et al. Towards the routine use of brain imaging to aid the clinical diagnosis of disorders of consciousness. Brain 2009; 132:2541-52.
• 11. Cruse D, Owen AM. Consciousness revealed: new ingsights into the vegetative and minimally conscious states. Curr Opin Neurol 2010;23:656-60.
• 12. Giancino JT, Kalmar K. Diagnostic and prognostic guidelines for the vegetative and minimalny conscious states. Neuropsychol Rehabil 2005;15:166-74.
• 13. Jennet B. Definitions, diagnosis, prevalence and ethics. Neuropsychol Rehabil 2005;15:163-5.
• 14. Bradley WG,Daroff RB, FenichelGM,JankovicJ. Neurologyin clinical practice. 5th ed. Oxford-Waltham: Butterworth-Heinemann; 2008.
• 15. Mazur R, Książkiewicz B, Nyka WM, Świerkocka-Miastkowska M. (eds.) Pień mózgu - oś życia. Brain stem - axis of the life. Gdańsk: via Medica Gdańsk, 2007 [in Polish)].
• 16. Monti MM, Owen AM. The behavior in the brain: using functional neuroimaging to assess residual cognition and awareness after severe brain injury. J Psychophysiol 2010;24:76-2.
• 17. Boly M, Garrido MI, Gosseries O, Bruno MA, Boveraux P, Schnakers C, et al. Preserved feedforward but impaired top-down processes in the vegetative state. Science 2011;332:858-62.
• 18. Laureys S, Faymonville ME, De Tiege X, Peigneux P, Berre J, Moonen G, et al. Brain function in the vegetative state. In: Machado CD, Shewmon DA. ed Brain death and disorders of consciousness. New York: Springer, 2004; 229-38 pp.
• 19. Monti MM, Laureys S, Owen AM. Vegetative state. Br Med J 2010; 341:292-6.
• 20. Fernandez-Espejo D, Bekinschtein T, Monti MM, Pickard JD, Junque C, Coleman MR, et al. Diffusion weighted imaging distinguishes the vegetative state from the minimally conscious state. Neuroimage 2011;54:103-12.
• 21. Laureys S, Owen AM, Schiff N. Brain function in coma, vegetative state and related disorders. Lancet Neurol 2004;3:537-46.
• 22. Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 1952;117:500-44.
• 23. O’Reilly RC, Munakata Y. Computational explorations in cognitive neuroscience. Cambridge: MIT Press; 2000.
• 24. Faisal AA, Selen LPJ, Wolpert DM. Noise in the nervous system. Nat Rev Neurosci 2008;9:292-3.
• 25. Duch W. Computational models of dementia and neurological problems. Methods Mol Biol 2007;401:305-36.
• 26. Li L, Xia Y, Jelfs B, Cao J, Mandic DP. Modelling of brain consciousness based on collaborative adaptive filters. Neurocomputing 2012;76:36-3.
• 27. Dobosz K, Duch W. Understanding neurodynamical systems via fuzzy symbolic dynamics. Neural Netw 2010;23:487-96.
• 28. Tononi G, Sporns O. Measuring information integration. BMC Neurosci 2003;4:31.
• 29. Balduzzi D, Tononi G. Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Comput Biol 2008;4:e1000091.
• 30. Gamez D, Aleksander I. Accuracy and performance of the statebased Φ and liveliness measures of information integration. Conscious Cogn 2011;20:1403-24.
• 31. Duch W, Mikołajewski D. Brain stem modelling on system level - chances and limitations. Bio Algorithm Med Syst 2018; 14: 20180015.
• 32. Duch W, Mikołajewski D. Brain stem – from general view to computational model based on switchboard rules of operation. Bio Algorithm Med Syst 2020; 16:20190059.
• 33. Demertzi A, Schnakers C, Soddu A, Bruno MA, Gosseries O, Vanhaudenhuyse A, et al. Neural plasticity lessons form disorders of consciousness. Front Psychol 2011;1:1-7.
• 34. Rojek I. Neural networks as prediction models for water intake in water supply system. In: Rutkowski, L, Tadeusiewicz, R, Zadeh, LA, Zurada, JM. (eds) Artificial Intelligence and Soft computing - ICAISC 2008. ICAISC 2008. Lecture Notes in computer science, 5097. Springer, Berlin, Heidelberg, 1109-19 pp.
• 35. Rojek I. Hybrid Neural Networks as Prediction Models. In: Rutkowski L, Scherer R, Tadeusiewicz, R, Zadeh LA, Zurada JM. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science, 6114. Springer, Berlin, Heidelberg, 2010;88-5. 36.
• Rojek I. Classifier models in intelligent CAPP systems. In: Cyran KA, Kozielski S, Peters JF, Stańczyk U, Wakulicz-Deja A. (eds) Man-machine interactions. Advances in Intelligent and Soft computing, 59. Springer, Berlin, Heidelberg, 2009, 311-19.
• 37. Prokopowicz P, Czerniak J, Mikołajewski D, Apiecionek Ł, Ślęzak D. Theory and applications of ordered Fuzzy numbers A tribute to Professor Witold Kosiński. Part of the studies in Fuzziness and Soft computing book series (STUDFUZZ), 356, Springer 2017.
• 38. Wójcik GM, Masiak J, Kawiak A, Kwaśniewicz Ł, Schneider P, Polak N, et al. Mapping the human brain in frequency band analysis of brain cortex electroencephalographic activity for selected psychiatric disorders. Front Neuroinform 2018; 12:73.
• 39. Wójcik GM, Masiak J, Kawiak A, Schneider P, Kwaśniewicz Ł, Polak N, et al. New protocol for quantitative analysis of brain cortex electroencephalographic activity in patients with psychiatric disorders. Front Neuroinform 2018;12:27.
• 40. Wierzgała P, Zapała D, Wójcik GM, Masiak J. Most popular signal processing methods in motor-imagery BCI: a review and metaanalysis. Front Neuroinform 2018; 12:78.
• 41. Wójcik GM, Masiak J, Kawiak A, Kwaśniewicz Ł, Schneider P, Postępski F, et al. Analysis of decision-making process using methods of quantitative electroencephalography and machine learning tools. Front Neuroinform 2019;13:73.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).