Abstract
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.
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Research funding: None declared.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Competing interest: The funding organisation(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.
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Ethical Approval: The conducted research is not related to either human or animal use.
References
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. https://doi.org/10.1111/cns.13001.Search in Google Scholar PubMed PubMed Central
2. Zimmerman, AW, editor. Autism: current theories and evidence. Totowa: Humana Press; 2008.10.1007/978-1-60327-489-0Search in Google Scholar
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. https://doi.org/10.7494/csci.2012.13.2.47.Search in Google Scholar
4. Duch, W, Dobosz, K, Mikołajewski, D. Autism and ADHD – two ends of the same spectrum? Lect Notes Comput Sci 2013;8226:623–30. https://doi.org/10.1007/978-3-642-42054-2_78.Search in Google Scholar
5. Dobosz, K, Mikołajewski, D, Wójcik, GM, Duch, W. Simple cyclic movements as a distinct autism feature – computational approach. Comput Sci 2013;14:475–89.10.7494/csci.2013.14.3.475Search in Google Scholar
6. Dobosz, K, Duch, W. Understanding neurodynamical systems via fuzzy symbolic dynamics. Neural Network 2010;23:487–96. https://doi.org/10.1016/j.neunet.2009.12.005.Search in Google Scholar PubMed
7. Duch, W, Dobosz, K. Visualization for understanding of neurodynamical systems. Cognit Neurodynamics 2011;5:145–60. https://doi.org/10.1007/s11571-011-9153-1.Search in Google Scholar PubMed PubMed Central
8. Pineda, JA. The functional significance of mu rhythms: translating ‘‘seeing’’ and ‘‘hearing’’ into ‘‘doing’’. Brain Res Rev 2005;50:57–68. https://doi.org/10.1016/j.brainresrev.2005.04.005.Search in Google Scholar PubMed
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. https://doi.org/10.3389/fnhum.2016.00266.Search in Google Scholar PubMed PubMed Central
10. Hobson, HM, Bishop, DV. Mu suppression – a good measure of the human mirror neuron system? Cortex 2016;82:290–310. https://doi.org/10.1016/j.cortex.2016.03.019.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1002/brb3.494.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.3389/fnhum.2016.00364.Search in Google Scholar PubMed PubMed Central
13. Wrightson, JG, Twomey, R, Smeeton, NJ. Exercise performance and corticospinal excitability during action observation. Front Hum Neurosci 2016;10:106. https://doi.org/10.3389/fnhum.2016.00106.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1037/bul0000031.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.3389/fpsyg.2016.00216.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1111/bjdp.12115.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1016/j.clinph.2015.03.004.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1186/s11689-015-9118-5.Search in Google Scholar PubMed PubMed Central
19. Sakihara, K, Inagaki, M. Mu rhythm desynchronization by tongue thrust observation. Front Hum Neurosci 2015;9:501. https://doi.org/10.3389/fnhum.2015.00501.Search in Google Scholar PubMed PubMed Central
20. Perkins, T, Stokes, M, McGillivray, J, Bittar, R. Mirror neuron dysfunction in autism spectrum disorders. J Clin Neurosci 2010;17:1239–43. https://doi.org/10.1016/j.jocn.2010.01.026.Search in Google Scholar PubMed
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. https://doi.org/10.1080/17470919.2010.547085.Search in Google Scholar PubMed
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. https://doi.org/10.1080/17470910701563681.Search in Google Scholar PubMed
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. https://doi.org/10.17712/nsj.2016.2.20150472.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1016/j.rasd.2014.03.019.Search in Google Scholar
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. https://doi.org/10.1016/j.bandc.2007.03.004.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1038/srep34977.Search in Google Scholar PubMed PubMed Central
27. Markram, H. Seven challenges for neuroscience. Funct Neurol 2013;28:145–51. https://doi.org/10.11138/FNeur/2013.28.3.144.Search in Google Scholar PubMed PubMed Central
28. O’Reilly, RC, Munakata, Y. Computational explorations in cognitive neuroscience. New Jersey: The MIT Press; 2000.10.7551/mitpress/2014.001.0001Search in Google Scholar
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. https://doi.org/10.1007/s11682-016-9536-3.Search in Google Scholar PubMed
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. https://doi.org/10.1177/1362361320936945.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1186/s13229-020-00374-x.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1007/s11065-020-09452-6.Search in Google Scholar PubMed
33. Krivan, SJ, Caltabiano, N, Cottrell, D, Thomas, NA. I’ll cry instead: mu suppression responses to tearful facial expressions. Neuropsychologia 2020;143:107490. https://doi.org/10.1016/j.neuropsychologia.2020.107490.Search in Google Scholar PubMed
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. https://doi.org/10.3389/fnhum.2019.00034.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.3758/s13415-016-0476-6.Search in Google Scholar PubMed
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. https://doi.org/10.1038/s41598-020-59222-w.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1007/s10484-017-9383-z.Search in Google Scholar PubMed PubMed Central
38. Mikołajewska, E, Mikołajewski, D. Non-invasive EEG-based brain-computer interfaces in patients with disorders of consciousness. Mil Med Res 2014;1:1–6. https://doi.org/10.1186/2054-9369-1-14.Search in Google Scholar PubMed PubMed Central
39. Mikołajewska, E, Mikołajewski, D. Ethical considerations in the use of brain-computer interfaces. Cent Eur J Med 2013;8:720–4.10.2478/s11536-013-0210-5Search in Google Scholar
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.Search in Google Scholar
41. Wierzgała, P, Zapała, D, Wójcik, GM, Masiak, J. Most popular signal processing methods in motor-imagery BCI: a review and meta-analysis. Front Neuroinf 2018;12:78. https://doi.org/10.3389/fninf.2018.00078.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.3389/fninf.2018.00073.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.3389/fninf.2019.00073.Search in Google Scholar PubMed PubMed Central
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.10.1007/978-3-642-13232-2_12Search in Google Scholar
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.10.1007/978-3-642-00563-3_32Search in Google Scholar
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.10.1007/978-3-540-69731-2_104Search in Google Scholar
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; 2017.10.1007/978-3-319-59614-3Search in Google Scholar
48. 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 PubMed
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;12:93–8.10.1515/bams-2016-0010Search in Google Scholar
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.Search in Google Scholar PubMed
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. https://doi.org/10.3389/fneur.2020.00016.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1016/j.ijnss.2020.04.004.Search in Google Scholar
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. https://doi.org/10.1002/brb3.1729.Search in Google Scholar
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. https://doi.org/10.3390/brainsci10010027.Search in Google Scholar
55. Buccino, G. Action observation treatment: a novel tool in neurorehabilitation. Phil Trans Biol Sci 2014;369:20130185. https://doi.org/10.1098/rstb.2013.0185.Search in Google Scholar
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. https://doi.org/10.1080/17509840701823139.Search in Google Scholar
57. Dickstein, R, Deutsch, JE. Motor imagery in physical therapist practice. Phys Ther 2007;87:942–53. https://doi.org/10.2522/ptj.20060331.Search in Google Scholar
58. Decety, J. The neurophysiological basis of motor imagery. Behav Brain Res 1996;77:45–52. https://doi.org/10.1016/0166-4328(95)00225-1.Search in Google Scholar
59. Isaac, AR. Mental practice – does it work in the field? Sport Psychol 1992;6:192–8. https://doi.org/10.1123/tsp.6.2.192.Search in Google Scholar
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.Search in Google Scholar PubMed PubMed Central
61. Uddin, LQ, Supekar, K, Menon, V. Reconceptualizing functional brain connectivity in autism from a developmental perspective. Front Hum Neurosci 2013;7:458. https://doi.org/10.3389/fnhum.2013.00458.Search in Google Scholar PubMed PubMed Central
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. https://doi.org/10.1016/j.bpsc.2016.02.003.Search in Google Scholar PubMed PubMed Central
63. Volkmar, FR, Wolf, JM. When children with autism become adults. World Psychiatr 2013;12:79–80. https://doi.org/10.1002/wps.20020.Search in Google Scholar PubMed PubMed Central
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