Advertisement

Autism Spectrum Disorder and Deep Attractors in Neurodynamics

  • Włodzisław DuchEmail author
Chapter
Part of the Springer Series in Cognitive and Neural Systems book series (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.

Keywords

Neurodynamics RDoC Mental disorders Autism spectrum disorder (ASD) Brain fingerprints Computational modeling 

Notes

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.

References

  1. 1.
    Amit DJ (1992) Modeling brain function: the world of attractor neural networks. Cambridge University Press, CambridgeGoogle Scholar
  2. 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):30–42CrossRefGoogle Scholar
  3. 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):419–450CrossRefGoogle Scholar
  4. 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):6828CrossRefGoogle Scholar
  5. 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 15:225–230CrossRefGoogle Scholar
  6. 6.
    Diagnostic & Statistical Manual of Mental Disorders (2013, 5th ed) American Psychiatric Association, Washington, DCGoogle Scholar
  7. 7.
    Dobosz K, Mikołajewski D, Wójcik GM, Duch W (2013) Simple cyclic movements as a distinct autism feature-computational approach. Comput Sci 14(3):475–489CrossRefGoogle Scholar
  8. 8.
    Dobosz K, Duch W (2010) Understanding neurodynamical systems via fuzzy symbolic dynamics. Neural Netw 23:487–496CrossRefGoogle Scholar
  9. 9.
    Duch W, Dobosz K, Mikołajewski D (2013) Autism and ADHD–two ends of the same spectrum? Lect Notes Comput Sci 8226:623–630, 2013CrossRefGoogle Scholar
  10. 10.
    Duch W, Dobosz K (2011) Visualization for understanding of neurodynamical systems. Cogn Neurodyn 5(2):145–160CrossRefGoogle Scholar
  11. 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 13:47–61CrossRefGoogle Scholar
  12. 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 11:115CrossRefGoogle Scholar
  13. 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):189–194CrossRefGoogle Scholar
  14. 14.
    Gepner B, Féron F (2009) Autism: a world changing too fast for a miswired brain? Neurosci Biobehav Rev 33(8):1227–1242CrossRefGoogle Scholar
  15. 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):49–72CrossRefGoogle Scholar
  16. 16.
    Grossberg S, Seidman D (2006) Neural dynamics of autistic behaviors: cognitive, emotional, and timing substrates. Psychol Rev 113(3):483–525CrossRefGoogle Scholar
  17. 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 9:34CrossRefGoogle Scholar
  18. 18.
    Heine M, Ciuraszkiewicz A, Voigt A, Heck J, Bikbaev A (2016) Surface dynamics of voltage-gated ion channels. Channels 10(4):267–281CrossRefGoogle Scholar
  19. 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):1292–1313CrossRefGoogle Scholar
  20. 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 118:1464–1471CrossRefGoogle Scholar
  21. 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 97–136Google Scholar
  22. 22.
    Lai HC, Jan LY (2006) The distribution and targeting of neuronal voltage-gated ion channels. Nat Rev Neurosci 7(7):548–562CrossRefGoogle Scholar
  23. 23.
    Landry R, Bryson SE (2004) Impaired disengagement of attention in young children with autism. J Child Psychol Psychiatry 45(6):1115–1122CrossRefGoogle Scholar
  24. 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, OxfordGoogle Scholar
  25. 25.
    O’Reilly RC, Munakata Y (2000) Computational explorations in cognitive neuroscience. MIT-Press, CambridgeCrossRefGoogle Scholar
  26. 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):1255–1268CrossRefGoogle Scholar
  27. 27.
    Thelen E, Smith LB (1996) A dynamic systems approach to the development of cognition and action. MIT Press, CambridgeGoogle Scholar
  28. 28.
    Uddin LQ, Supekar K, Menon V (2013) Reconceptualizing functional brain connectivity in autism from a developmental perspective. Front Hum Neurosci 7:458CrossRefGoogle Scholar
  29. 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):e0153329CrossRefGoogle Scholar
  30. 30.
    Zimmerman AW (ed) (2008) Autism: current theories and evidence. Humana Press, TotowaGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Informatics, Faculty of Physics, Astronomy and Informatics, and Neurocognitive Laboratory, Center for Modern Interdisciplinary TechnologiesNicolaus Copernicus UniversityToruńPoland

Personalised recommendations