Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Objectives: Autism Spectrum Disorders (ASD) represent developmental conditions with deficits in the cognitive, motor, communication and social domains. It is thought that imitative behaviour may be impaired in children with ASD. The Mirror Neural System (MNS) concept plays an important role in theories explaining the link between action perception, imitation and social decision-making in ASD. Methods: In this study, Emergent 7.0.1 software was used to build a computational model of the phenomenon of MNS influence on motion imitation. Seven point populations of Hodgkin-Huxley artificial neurons were used to create a simplified model. Results: The model shows pathologically altered processing in the neural network, which may reflect processes observed in ASD due to reduced stimulus attenuation. The model is considered preliminary-further research should test for a minimally significant difference between the states: normal processing and pathological processing. Conclusions: The study shows that even a simple computational model can provide insight into the mechanisms underlying the phenomena observed in experimental studies, including in children with ASD.
Czasopismo
Rocznik
Tom
Strony
95--102
Opis fizyczny
Bibliogr. 63 poz., rys., tab.
Twórcy
autor
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Lublin, Poland
autor
- Institute of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland
- Neurocognitive Laboratory, Interdisciplinary Center for Modern Technologies, Nicolaus Copernicus University, Toruń, Poland
Bibliografia
- 1. Khalil R, Tindle R, Boraud T, Moustafa AA, Karim AA. Social decision making in autism: on the impact of mirror neurons, motor control, and imitative behaviors. CNS Neurosci Ther 2018;24: 669-76.
- 2. Zimmerman AW, editor. Autism: current theories and evidence. Totowa: Humana Press; 2008.
- 3. Duch W, Nowak W, Meller J, Osiński G, Dobosz K, Mikołajewski D, et al. Computational approach to understanding autism spectrum disorders. Comput Sci 2012;13:47-61.
- 4. Duch W, Dobosz K, Mikołajewski D. Autism and ADHD - two ends of the same spectrum? Lect Notes Comput Sci 2013;8226:623-30.
- 5. Dobosz K, Mikołajewski D, Wójcik GM, Duch W. Simple cyclic movements as a distinct autism feature – computational approach. Comput Sci 2013;14:475-89.
- 6. Dobosz K, Duch W. Understanding neurodynamical systems via fuzzy symbolic dynamics. Neural Network 2010;23:487-96.
- 7. Duch W, Dobosz K. Visualization for understanding of neurodynamical systems. Cognit Neurodynamics 2011;5:145-60.
- 8. Pineda JA. The functional significance of mu rhythms: translating ‘‘seeing’’ and ‘‘hearing’’ into ‘‘doing’’. Brain Res Rev 2005;50:57-68.
- 9. Isoda K, Sueyoshi K, Ikeda Y, Nishimura Y, Hisanaga I, Orlic S, et al. Effect of the hand-omitted tool motion on mu rhythm suppression. Front Hum Neurosci 2016;10:266.
- 10. Hobson HM, Bishop DV. Mu suppression - a good measure of the human mirror neuron system? Cortex 2016;82:290-310.
- 11. Simon S, Mukamel R. Power modulation of electroencephalogram mu and beta frequency depends on perceived level of observed actions. Brain Behav 2016;6:e00494.
- 12. Yin S, Liu Y, Ding M. Amplitude of sensorimotor mu rhythm is correlated with BOLD from multiple brain regions: a simultaneous EEG-fMRI study. Front Hum Neurosci 2016;10:364.
- 13. Wrightson JG, Twomey R, Smeeton NJ. Exercise performance and corticospinal excitability during action observation. Front Hum Neurosci 2016;10:106.
- 14. Fox NA, Bakermans-Kranenburg MJ, Yoo KH, Bowman LC, Cannon EN, Vanderwert RE, et al. Assessing human mirror activity with EEG mu rhythm: a meta-analysis. Psychol Bull 2016;142:291-313.
- 15. Gonzalez SL, Reeb-Sutherland BC, Nelson EL. Quantifying motor experience in the infant brain: EEG power, coherence, and mu desynchronization. Front Psychol 2016;7:216.
- 16. Yoo KH, Cannon EN, Thorpe SG, Fox NA. Desynchronization in EEG during perception of means-end actions and relations with infants’ grasping skill. Br J Dev Psychol 2016;34:24-37.
- 17. Thorpe SG, Cannon EN, Fox NA. Spectral and source structural development of mu and alpha rhythms from infancy through adulthood. Clin Neurophysiol 2016;127:254-69.
- 18. Hudac CM, Kresse A, Aaronson B, DesChamps TD, Webb SJ, Bernier RA. Modulation of mu attenuation to social stimuli in children and adults with 16p11.2 deletions and duplications. J Neurodev Disord 2015;7:25.
- 19. Sakihara K, Inagaki M. Mu rhythm desynchronization by tongue thrust observation. Front Hum Neurosci 2015;9:501.
- 20. Perkins T, Stokes M, McGillivray J, Bittar R. Mirror neuron dysfunction in autism spectrum disorders. J Clin Neurosci 2010; 17:1239-43.
- 21. Baird AD, Scheffer IE, Wilson SJ. Mirror neuron system involvement in empathy: a critical look at the evidence. Soc Neurosci 2011;6:327-35.
- 22. Oberman LM, Ramachandran VS. Preliminary evidence for deficits in multisensory integration in autism spectrum disorders: the mirror neuron hypothesis. Soc Neurosci 2008;3:348-55.
- 23. Saffin JM, Tohid H. Walk like me, talk like me. The connection between mirror neurons and autism spectrum disorder. Neurosciences 2016;21:108-19.
- 24. Lapenta OM, Boggio PS. Motor network activation during human action observation and imagery: mu rhythm EEG evidence on typical and atypical neurodevelopment. Res Autism Spectr Disord 2014;2914:759-66.
- 25. Bernier R, Dawson G, Webb S, Murias M. EEG mu rhythm and imitation impairments in individuals with autism spectrum disorder. Brain Cognit 2007;64:228-37.
- 26. Hasegawa C, Ikeda T, Yoshimura Y, Hiraishi H, Takahashi T, Furutani N, et al. Mu rhythm suppression reflects mother-child face-to-face interactions: a pilot study with simultaneous MEG recording. Sci Rep 2016;6:34977.
- 27. Markram H. Seven challenges for neuroscience. Funct Neurol 2013;28:145-51.
- 28. O’Reilly RC, Munakata Y. Computational explorations in cognitive neuroscience. New Jersey: The MIT Press; 2000.
- 29. Gatti R, Rocca MA, Fumagalli S, Cattrysse E, Kerckhofs E, Falini A, et al. The effect of action observation/execution on mirror neuron system recruitment: an fMRI study in healthy individuals. Brain Imag Behav 2017;11:565-76.
- 30. Yates L, Hobson H. Continuing to look in the mirror: a review of neuroscientific evidence for the broken mirror hypothesis, EP-M model and STORM model of autism spectrum conditions. Autism 2020;24:1945-59.
- 31. Chan MMY, Han YMY. Differential mirror neuron system (MNS) activation during action observation with and without social-emotional components in autism: a meta-analysis of neuroimaging studies. Mol Autism 2020;11:72.
- 32. Bekkali S, Youssef GJ, Donaldson PH, Albein-Urios N, Hyde C, Enticott PG. Is the putative mirror neuron system associated with empathy? A systematic review and meta-analysis. Neuropsychol Rev 2021;31:14-57.
- 33. Krivan SJ, Caltabiano N, Cottrell D, Thomas NA. I’ll cry instead: mu suppression responses to tearful facial expressions. Neuropsychologia 2020;143:107490.
- 34. Karakale O, Moore MR, Kirk IJ. Mental simulation of facial expressions: mu suppression to the viewing of dynamic neutral face videos. Front Hum Neurosci 2019;13:34.
- 35. Moore MR, Franz EA. Mu rhythm suppression is associated with the classification of emotion in faces. Cognit Affect Behav Neurosci 2017;17:224-34.
- 36. Zapała D, Zabielska-Mendyk E, Augustynowicz P, Cudo A, Jaśkiewicz M, Szewczyk M, et al. The effects of handedness on sensorimotor rhythm desynchronization and motor-imagery BCI control. Sci Rep 2020;10:2087.
- 37. Zapała D, Francuz P, Zapała E, Kopiś N, Wierzgała P, Augustynowicz P, et al. The impact of different visual feedbacks in user training on motor imagery control in BCI. Appl Psychophysiol Biofeedback 2018;43:23-35.
- 38. Mikołajewska E, Mikołajewski D. Non-invasive EEG-based braincomputer interfaces in patients with disorders of consciousness. Mil Med Res 2014;1:1-6.
- 39. Mikołajewska E, Mikołajewski D. Ethical considerations in the use of brain-computer interfaces. Cent Eur J Med 2013;8: 720-4.
- 40. Duch W, Nowak W, Meller J, Osiński G, Dobosz K, Mikołajewski D, et al. Three-stage neurocomputational modelling using emergent and GENESIS software. In: Proceeedings of Cracow grid workshop 2010; 2011:202-11 pp.
- 41. Wierzgała P, Zapała D, Wójcik GM, Masiak J. Most popular signal processing methods in motor-imagery BCI: a review and metaanalysis. Front Neuroinf 2018;12:78.
- 42. 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 Neuroinf 2018;12:73.
- 43. 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 Neuroinf 2019;13:73.
- 44. Rojek I. Hybrid neural networks as prediction models. In: Rutkowski L, Scherer R, Tadeusiewicz R, Zadeh LA, Zurada JM, editors. Artifical intelligence and soft computing. ICAISC 2010. Lecture notes in computer science, 6114. Berlin, Heidelberg: Springer; 2010:88-5 pp.
- 45. Rojek I. Classifier models in intelligent CAPP systems. In: Cyran KA, Kozielski S, Peters JF, Stańczyk U, Wakulicz-Deja A, editors. Man-machine interactions. Advances in intelligent and soft computing. Berlin, Heidelberg: Springer; 2009, vol 59: 311-19 pp.
- 46. Rojek I. Neural networks as prediction models for water intake in water supply system. In: Rutkowski L, Tadeusiewicz R, Zadeh LA, Zurada JM, editors. Artificial intelligence and soft computing - ICAISC 2008. ICAISC 2008. Lecture notes in computer science. Berlin, Heidelberg: Springer; 2008, vol 5097:1109-19 pp.
- 47. Prokopowicz P, Czerniak J, Mikołajewski D, Apiecionek Ł, Ślęzak D, editors. 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), vol. 356. Cham, Switzerland: Springer; 2017.
- 48. Duch W. Computational models of dementia and neurological problems. Methods Mol Biol 2007;401:305-36.
- 49. Komendziński T, Mikołajewska E, Mikołajewski D, Dreszer J, Bałaj B. Cognitive robots in the development and rehabilitation of children with developmental disorders. Bio Algorithm Med Syst 2016;12:93-8.
- 50. Molinaro A, Micheletti S, Pagani F, Garofalo G, Galli J, Rossi A, et al. Action Observation Treatment in a tele-rehabilitation setting: a pilot study in children with cerebral palsy. Disabil Rehabil 2020:1-6. https://doi.org/10.1080/09638288.2020.1793009.
- 51. Beani E, Menici V, Ferrari A, Cioni G, Sgandurra G. Feasibility of a home-based action observation training for children with unilateral cerebral palsy: an explorative study. Front Neurol 2020;11:16.
- 52. Zhu MH, Zeng M, Shi MF, Gu XD, Shen F, Zheng YP, et al. Visual feedback therapy for restoration of upper limb function of stroke patients. Int J Nurs Sci 2020;7:170-8.
- 53. Mao H, Li Y, Tang L, Chen Y, Ni J, Liu L, et al. Effects of mirror neuron system-based training on rehabilitation of stroke patients. Brain Behav 2020;10:e01729.
- 54. Cuenca-Martínez F, Suso-Martí L, León-Hernández JV, La Touche R. The role of movement representation techniques in the motor learning process: a neurophysiological hypothesis and a narrative review. Brain Sci 2020;10:27.
- 55. Buccino G. Action observation treatment: a novel tool in neurorehabilitation. Phil Trans Biol Sci 2014;369:20130185.
- 56. Guillot A, Collet C. Construction of the motor imagery integrative model in sport: a review and theoretical investigation of motor imagery use. Int Rev Sport Exerc Psychol 2008;1:31-44.
- 57. Dickstein R, Deutsch JE. Motor imagery in physical therapist practice. Phys Ther 2007;87:942-53.
- 58. Decety J. The neurophysiological basis of motor imagery. Behav Brain Res 1996;77:45-52.
- 59. Isaac AR. Mental practice - does it work in the field? Sport Psychol 1992;6:192-8.
- 60. Torres E, Donnellan AM. Editorial for research topic “Autism: the movement perspective. Front Integr Neurosci 2015;9. https://doi.org/10.3389/fnint.2015.00012.
- 61. Uddin LQ, Supekar K, Menon V. Reconceptualizing functional brain connectivity in autism from a developmental perspective. Front Hum Neurosci 2013;7:458.
- 62. Vasa RA, Mostofsky SH, Ewen JB. The disrupted connectivity hypothesis of autism spectrum disorders: time for the next phase in research. Biol Psychiatr: Cognit Neurosci Neuroimaging 2016; 1:245-52.
- 63. Volkmar FR, Wolf JM. When children with autism become adults. World Psychiatr 2013;12:79-80.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4df6d4ac-21b7-4c32-9d1d-e0f8890bb803