Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter July 21, 2020

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

  • Włodzisław Duch ORCID logo and Dariusz Mikołajewski ORCID logo EMAIL logo

Abstract

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.


Corresponding author: Dariusz Mikołajewski, PhD Eng. Assoc. Prof. 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, E-mail:

  1. Research funding: None declared.

  2. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  3. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

  4. Ethical approval: The conducted research is not related to either human or animal use.

  5. Honorarium: None declared.

References

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. https://doi.org/10.1016/j.tics.2008.04.008.Search in Google Scholar

2. Zeman, A . Consciousness Brain 2001;124:1263–89.10.1093/brain/124.7.1263Search in Google Scholar

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. https://doi.org/10.1162/jocn.2010.21488.Search in Google Scholar

4. Laureys, S, Owen, AM, Schiff, ND . Brain function in coma, vegetative state, and related disorders. Lancet Neurol 2004;3:537–46. https://doi.org/10.1016/s1474-4422(04)00852-x.Search in Google Scholar

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. https://doi.org/10.1212/wnl.58.3.506.Search in Google Scholar

6. Henry, GL, Little, N, Jagoda, A, Pellegrino, TR, Quint, D . Neurologic emergencies. 3rd ed. New York: McGraw-Hill; 2010.Search in Google Scholar

7. Beamont, JG, Kenealy, PM . Incidence and prevalence of the vegetative and minimally conscious states. Neuropsychol Rehabil 2005;15:184–9. https://doi.org/10.1080/09602010443000489.Search in Google Scholar

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. https://doi.org/10.1093/med/9780199204854.003.240506.Search in Google Scholar

9. Province, C . The vegetative state: promoting greater clarity and improved treatment. Neuropsychol Rehabil 2005;15:264–71. https://doi.org/10.1080/09602010443000623.Search in Google Scholar

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. https://doi.org/10.1093/brain/awp183.Search in Google Scholar

11. Cruse, D, Owen, AM . Consciousness revealed: new ingsights into the vegetative and minimally conscious states. Curr Opin Neurol 2010;23:656–60. https://doi.org/10.1097/wco.0b013e32833fd4e7.Search in Google Scholar

12. Giancino, JT, Kalmar, K . Diagnostic and prognostic guidelines for the vegetative and minimalny conscious states. Neuropsychol Rehabil 2005;15:166–74. https://doi.org/10.1080/09602010443000498.Search in Google Scholar

13. Jennet, B . Definitions, diagnosis, prevalence and ethics. Neuropsychol Rehabil 2005;15:163–5. https://doi.org/10.1080/09602010443000632.Search in Google Scholar

14. Bradley, WG, Daroff, RB, Fenichel, GM, Jankovic, J . Neurology in clinical practice. 5th ed. Oxford-Waltham: Butterworth-Heinemann; 2008.Search in Google Scholar

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)].Search in Google Scholar

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. https://doi.org/10.1027/0269-8803/a000016.Search in Google Scholar

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. https://doi.org/10.1126/science.1202043.Search in Google Scholar

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. https://doi.org/10.1093/med/9780199204854.003.240506.Search in Google Scholar

19. Monti, MM, Laureys, S, Owen, AM . Vegetative state. Br Med J 2010;341:292–6.10.1136/bmj.c3765Search in Google Scholar

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. https://doi.org/10.1016/j.neuroimage.2010.08.035.Search in Google Scholar

21. Laureys, S, Owen, AM, Schiff, N . Brain function in coma, vegetative state and related disorders. Lancet Neurol 2004;3:537–46. https://doi.org/10.1016/s1474-4422(04)00852-x.Search in Google Scholar

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. https://doi.org/10.1113/jphysiol.1952.sp004764.Search in Google Scholar

23. O’Reilly, RC, Munakata, Y . Computational explorations in cognitive neuroscience. Cambridge: MIT Press; 2000.10.7551/mitpress/2014.001.0001Search in Google Scholar

24. Faisal, AA, Selen, LPJ, Wolpert, DM . Noise in the nervous system. Nat Rev Neurosci 2008;9:292–3.10.1038/nrn2258Search in Google Scholar PubMed PubMed Central

25. Duch, W . Computational models of dementia and neurological problems. Methods Mol Biol 2007;401:305–36. https://doi.org/10.1007/978-1-59745-520-6_17.Search in Google Scholar

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. https://doi.org/10.1016/j.neucom.2011.05.038.Search in Google Scholar

27. Dobosz, K, Duch, W . Understanding neurodynamical systems via fuzzy symbolic dynamics. Neural Netw 2010;23:487–96. https://doi.org/10.1016/j.neunet.2009.12.005.Search in Google Scholar

28. Tononi, G, Sporns, O . Measuring information integration. BMC Neurosci 2003;4:31.10.1186/1471-2202-4-31Search in Google Scholar PubMed PubMed Central

29. Balduzzi, D, Tononi, G . Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Comput Biol 2008;4:e1000091. https://doi.org/10.1371/journal.pcbi.1000091.Search in Google Scholar

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. https://doi.org/10.1016/j.concog.2011.05.016.Search in Google Scholar

31. Duch, W, Mikołajewski, D . Brain stem modelling on system level – chances and limitations. Bio Algorithm Med Syst 2018; 14:20180015. https://doi.org/10.1515/bams-2018-0015.Search in Google Scholar

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. https://doi.org/10.1515/bams-2019-0059.Search in Google Scholar

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. https://doi.org/10.3389/fpsyg.2010.00245.Search in Google Scholar

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. https://doi.org/10.1007/978-3-540-69731-2_104.Search in Google Scholar

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. https://doi.org/10.1007/978-3-642-13232-2_12.Search in Google Scholar

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. https://doi.org/10.1007/978-3-642-00563-3_32.Search in Google Scholar

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 2017.10.1007/978-3-319-59614-3Search in Google Scholar

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. https://doi.org/10.3389/fninf.2018.00073.Search in Google Scholar

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. https://doi.org/10.3389/fninf.2018.00027.Search in Google Scholar

40. Wierzgała, P, Zapała, D, Wójcik, GM, Masiak, J . Most popular signal processing methods in motor-imagery BCI: a review and meta-analysis. Front Neuroinform 2018; 12:78. https://doi.org/10.3389/fninf.2018.00078.Search in Google Scholar

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. https://doi.org/10.3389/fninf.2019.00073.Search in Google Scholar

Received: 2020-03-21
Accepted: 2020-04-29
Published Online: 2020-07-21

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 25.4.2024 from https://www.degruyter.com/document/doi/10.1515/bams-2020-0018/html
Scroll to top button