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Autism Spectrum Disorder and Deep Attractors in Neurodynamics

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Part of the book series: Springer Series in Cognitive and Neural Systems ((SSCNS,volume 13))

Abstract

Behavior may be analyzed at many levels, from genes to psychological constructs characterizing mental events. Neurodynamics is at the middle level. It can be related to biophysical properties of neurons that depend on lower-level molecular properties and genetics and used to characterize high-level processes correlated with behavior and mental states. A good strategy that should help to find causal relations between different levels of analysis, showing how psychological constructs used in neuropsychiatry emerge from biology, is to identify biophysical parameters of neurons required for normal neural network activity and explore all changes that may lead to abnormal functions, behavioral symptoms, cognitive phenotypes, and psychiatric syndromes. Neural network computational simulations, as well as analysis of real brain signals, show importance of attractor states, providing language that can be used to explain many features of mental disorders. Computational simulations of neurodynamics may generate hypothesis for experimental verification and help to create mechanistic explanation of observed behavior. Autism spectrum disorder is used as an example of the usefulness of such approach, showing how deep attractors resulting from ion channel dysfunctions slow down attention shifts, influence connectivity, and lead to diverse developmental problems.

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References

  1. Amit DJ (1992) Modeling brain function: the world of attractor neural networks. Cambridge University Press, Cambridge

    Google Scholar 

  2. Bilder RM, Sabb FW, Cannon TD, London ED, Jentsch JD, Parker DS et al (2009a) Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience 164(1):30–42

    Article  CAS  Google Scholar 

  3. Bilder RM, Sabb FW, Parker DS, Kalar D, Chu WW, Fox J et al (2009b) Cognitive ontologies for neuropsychiatric phenomics research. Cogn Neuropsychiatry 14(4–5):419–450

    Article  Google Scholar 

  4. Bosl WJ, Tager-Flusberg H, Nelson CA (2018) EEG analytics for early detection of autism Spectrum disorder: a data-driven approach. Sci Rep 8(1):6828

    Article  Google Scholar 

  5. Courchesne E, Pierce K (2005) Why the frontal cortex in autism might be talking only to itself: local over-connectivity but long-distance disconnection. Curr Opin Neurobiol 15:225–230

    Article  CAS  Google Scholar 

  6. Diagnostic & Statistical Manual of Mental Disorders (2013, 5th ed) American Psychiatric Association, Washington, DC

    Google Scholar 

  7. Dobosz K, Mikołajewski D, Wójcik GM, Duch W (2013) Simple cyclic movements as a distinct autism feature-computational approach. Comput Sci 14(3):475–489

    Article  Google Scholar 

  8. Dobosz K, Duch W (2010) Understanding neurodynamical systems via fuzzy symbolic dynamics. Neural Netw 23:487–496

    Article  Google Scholar 

  9. Duch W, Dobosz K, Mikołajewski D (2013) Autism and ADHD–two ends of the same spectrum? Lect Notes Comput Sci 8226:623–630, 2013

    Article  Google Scholar 

  10. Duch W, Dobosz K (2011) Visualization for understanding of neurodynamical systems. Cogn Neurodyn 5(2):145–160

    Article  Google Scholar 

  11. Duch W, Nowak W, Meller J, Osiński G, Dobosz K, Mikołajewski D, Wójcik GM (2012) Computational approach to understanding autism spectrum disorders. Comput Sci 13:47–61

    Article  Google Scholar 

  12. Duménieu M, Oulé M, Kreutz MR, Lopez-Rojas J (2017) The segregated expression of voltage-gated potassium and sodium channels in neuronal membranes: functional implications and regulatory mechanisms. Front Cell Neurosci 11:115

    Article  Google Scholar 

  13. Elsabbagh M, Fernandes J, Jane Webb S, Dawson G, Charman T, Johnson MH (2013) Disengagement of visual attention in infancy is associated with emerging autism in toddlerhood. Biol Psychiatry 74(3):189–194

    Article  Google Scholar 

  14. Gepner B, Féron F (2009) Autism: a world changing too fast for a miswired brain? Neurosci Biobehav Rev 33(8):1227–1242

    Article  Google Scholar 

  15. Gravier A, Quek C, Duch W, Wahab A, Gravier-Rymaszewska J (2016) Neural network modelling of the influence of channelopathies on reflex visual attention. Cogn Neurodyn 10(1):49–72

    Article  Google Scholar 

  16. Grossberg S, Seidman D (2006) Neural dynamics of autistic behaviors: cognitive, emotional, and timing substrates. Psychol Rev 113(3):483–525

    Article  Google Scholar 

  17. Guglielmi L, Servettini I, Caramia M, Catacuzzeno L, Franciolini F, D’Adamo MC, Pessia M (2015) Update on the implication of potassium channels in autism: K+ channel autism spectrum disorder. Front Cell Neurosci 9:34

    Article  Google Scholar 

  18. Heine M, Ciuraszkiewicz A, Voigt A, Heck J, Bikbaev A (2016) Surface dynamics of voltage-gated ion channels. Channels 10(4):267–281

    Article  Google Scholar 

  19. Just MA, Keller TA, Malave VL, Kana RK, Varma S (2012) Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity. Neurosci Biobehav Rev 36(4):1292–1313

    Article  Google Scholar 

  20. Kawakubo Y, Kasai K, Okazaki S, Hosokawa-Kakurai M, Watanabe K, Kuwabara H, Ishijima M, Yamasue H, Iwanami A, Kato N, Maekawa H (2007) Electrophysiological abnormalities of spatial attention in adults with autism during the gap overlap task. Clin Neurophysiol 118:1464–1471

    Article  Google Scholar 

  21. Kumar P, Kumar D, Jha SK, Jha NK, Ambasta RK (2016) Ion channels in neurological disorders. In: Donev R (ed) Advances in protein chemistry and structural biology, vol 103. Academic, Waltham, pp 97–136

    Google Scholar 

  22. Lai HC, Jan LY (2006) The distribution and targeting of neuronal voltage-gated ion channels. Nat Rev Neurosci 7(7):548–562

    Article  CAS  Google Scholar 

  23. Landry R, Bryson SE (2004) Impaired disengagement of attention in young children with autism. J Child Psychol Psychiatry 45(6):1115–1122

    Article  Google Scholar 

  24. O’Reilly RC, Hazy TE, Herd SA (2016) The Leabra cognitive architecture: how to play 20 principles with nature and win! In: Chipman S (ed) Oxford handbook of cognitive science. Oxford University Press, Oxford

    Google Scholar 

  25. O’Reilly RC, Munakata Y (2000) Computational explorations in cognitive neuroscience. MIT-Press, Cambridge

    Book  Google Scholar 

  26. Rogers SJ, Ozonoff S (2005) Annotation: what do we know about sensory dysfunction in autism? A critical review of the empirical evidence. J Child Psychol Psychiatry 46(12):1255–1268

    Article  Google Scholar 

  27. Thelen E, Smith LB (1996) A dynamic systems approach to the development of cognition and action. MIT Press, Cambridge

    Google Scholar 

  28. Uddin LQ, Supekar K, Menon V (2013) Reconceptualizing functional brain connectivity in autism from a developmental perspective. Front Hum Neurosci 7:458

    Article  Google Scholar 

  29. Wen Y, Alshikho MJ, Herbert MR (2016) Pathway network analyses for autism reveal multisystem involvement, major overlaps with other diseases and convergence upon MAPK and calcium signaling. PLoS One 11(4):e0153329

    Article  Google Scholar 

  30. Zimmerman AW (ed) (2008) Autism: current theories and evidence. Humana Press, Totowa

    Google Scholar 

Download references

Acknowledgments

This research was supported by the National Science Center, Poland, UMO-2016/20/W/NZ4/00354. Visualizations of trajectories have been made using VISER Toolbox developed by Krzysztof Dobosz in our laboratory.

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Correspondence to Włodzisław Duch .

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Duch, W. (2019). Autism Spectrum Disorder and Deep Attractors in Neurodynamics. In: Cutsuridis, V. (eds) Multiscale Models of Brain Disorders. Springer Series in Cognitive and Neural Systems, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-18830-6_13

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