Advertisement

Cognitive Modeling of Mindfulness Therapy by Autogenic Training

  • S. Sahand Mohammadi ZiabariEmail author
  • Jan Treur
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 863)

Abstract

In this paper, the effect of a mindfulness therapy based on a Network-Oriented Modeling approach is addressed. The considered therapy is Autogenic Training that can be used when under stress; it has as two main goals to achieve feeling heavy and warm body parts (limbs). Mantras have been used in therapies since long ago to make stressed individuals more relaxed, and they are also used in Autogenic Training. The presented cognitive temporal-causal network model addresses the modeling of Autogenic Training asking this into account. In the first phase a strong stress-inducing stimulus causes the individual to develop an extreme stressful emotion. In the second phase, the therapy with the two goals is shown to make the stressed individual relaxed. Hebbian learning is used to increase the influence of the therapy.

Keywords

Cognitive temporal-causal network model Hebbian learning Extreme emotion Mindfulness Autogenic training 

References

  1. 1.
    Sousa, N., Osborne, F.X. A.: Disconnection and reconnection: the morphological basis of (mal)adaptation to stress. Trends. Neurosci. 35(12), 742–751 (2012).  https://doi.org/10.1016/j.tins.2012.08.006. Epub 21 Sep 2012
  2. 2.
    Masand, P.S., Gupta, S.: Selective serotonin-reuptake inhibitors: an update. Harvard. Rev. Psychiatry 7, 69–84 (1999)CrossRefGoogle Scholar
  3. 3.
    Schultz, J.H., Luthe, W.: Autogenic Training. A Psychophysiologic Approach in Psychotherapy. Grune & Stratton, New York (1959)Google Scholar
  4. 4.
    Luthe, W.: Autogenic training: methods, research and application in medicine. Am. J. Psychother, 174–195 (1963). PMID: 13931814Google Scholar
  5. 5.
    Murakami, H., Katsunuma, R., Oba, K., Terasawa, K., Motomura, Y., Mishima, K., Moriguchi, Y.: Neural networks for mindfulness and emotion suppression. PloS One 17, 10(6), e0128005 (2015).  https://doi.org/10.1371/journal.pone.0128005. eCollection 2015
  6. 6.
    Limb, C.J., Braun, A.R.: Neural substrates of spontaneous musical performance: an fMRI study of jazz improvisation. PloS One.  https://doi.org/10.1371/journal.pone.0001679 (2008)
  7. 7.
    Kaixiang, Z., Minghua, B., Yu, Li., Yuman, X., Xuehua, G., Qunlin, C., Xue, D., Kangcheng, W., Dongtao, W., Huazhan, Y., Jiang, Q.: A distinction between two instruments measuring dispositional mindfulness and the correlations between those measurements and the neuroanatomical structure. Sci. Rep. 7, 652. Published online 24 July 2017.  https://doi.org/10.1038/s41598-017-06599-w, PMID: 28740242 (2017)
  8. 8.
    Spijkerman, M.P., Pots, WT., Bohlmeijer, E.T.: Effectiveness of online mindfulness-based interventions in improving mental health: a review and meta-analysis of randomized controlled trials. Clin. Psychol. Rev. 45, 102–114 (2016).  https://doi.org/10.1016/j.cpr.2016.03.009. Epub 1 Apr 2016
  9. 9.
    Treur, J.: Network-Oriented Modeling: addressing Complexity of Cognitive, Affective and Social Interactions. Springer Publishers, (2016)Google Scholar
  10. 10.
    Stetter, F., Kupper, S.: Autogenic training: a meta-analysis of clinical outcome studies. Appl. Psychophysiological. Biofeedback. 27(1), 45–98 (2002)CrossRefGoogle Scholar
  11. 11.
    Gunter, K.: Evaluation of effectiveness of autogenic training in gerontopsychology. Eur. Psychol. 1, 243–254. Hogrefe Publishing (1996).  https://doi.org/10.1027/1016-9040.1.4.243
  12. 12.
    Schultz, J.H. Luthe, W.: Autogenic methods. Autogenic therapy. In: Grune, N.Y. (ed.) Stratton, vol. 1 (1969)Google Scholar
  13. 13.
    Holzel, B.K., et al.: Mindfulness practice leads to increases in regional brain gray matter density. Psychiatry Res. 191, 36–43 (2011). [PubMed:21071182]CrossRefGoogle Scholar
  14. 14.
    Grant, J.A., Coutemanche, J., Rainville, P.: A no-elaborative mental stance and decoupling of executive and pain-related cortices predicts low pain sensitivity in Zen mediators. Pain 152, 150–156 (2011). [PubMed: 21055874]CrossRefGoogle Scholar
  15. 15.
    Schlamann, M., Naglatzki, R., de Greiff, Forsting, F., Gizewski, E.R.: Autogenic training alters cerebral activation patterns in fMRI, Int. J. Clin. Exp. Hypn, 58(4), 444–456 (2010).  https://doi.org/10.1080/00207144.2010.499347
  16. 16.
    Lutz, A., McFarlin, D.R., Perlman, D.M., Salomons, T.V., Davidson, R.J.: Altered anterior insula activation during anticipation and experience of painful stimuli in expert mediators. Neuroimage 64, 538–546 (2013). [PubMed: 23000783]CrossRefGoogle Scholar
  17. 17.
    Treur, J.: Verification of temporal-causal network models by mathematical analysis. Vietnam. J. Comput. Sci. 3, 207–221 (2016)CrossRefGoogle Scholar
  18. 18.
    Mohr, J.P., Pessin, M.S., Finkelstein, S., Funkenstein, H.H., Duncan, G.W., Davis, K.R.: Broca aphasia: pathologic and clinical. Neurology 28, 311 (1978)CrossRefGoogle Scholar
  19. 19.
    Goldin, P.R., McRae, K., Ramel, W., Gross, J.J.: The neural bases of emotion regulation: reappraisal and suppression of negative emotion. Biol. Psychiat. 63, 577–586 (2008).  https://doi.org/10.1016/j.biopsych.2007.05.031CrossRefGoogle Scholar
  20. 20.
    Craig, A.D.: How do you feel—now? The anterior insula and human awareness. Nat. Rev. Neurosci. 10, 59–70 (2009)CrossRefGoogle Scholar
  21. 21.
    Gusnard, D.A., Akbudak, E., Shulman, G.L., Raichle, M.: Medial Prefrontal Cortex and self-referential mental activity: relation to a default mode of brain function. Proc. Natl. Acad. Sci. U.S.A. 98, 4259–4264 (2001)CrossRefGoogle Scholar
  22. 22.
    Cntalpu, C., Hopkins, W.D.: Asymmetric Broca’s area in great apes. Nature 414(6863), 505 (2001).  https://doi.org/10.1038/35107134.PMC2043144.PMID11734839CrossRefGoogle Scholar
  23. 23.
    Purves, D., Augustine, G.J., Fitzpatrick, D., Katz, L.C., LaMantia, A.S., McNamara, J.O., Williams, S.M.: Neuroscience, 2nd edn, Sinauer Associates, Sunderland (MA) (2001)Google Scholar
  24. 24.
    Oosterwijk, S., Lindquist, K.A., Anderson, E., Dautoff, R., Moriguchi, Y., Barrett, L.F.: States of mind; emotions, body feelings, and thoughts share distributed neural networks. Neuroimage 62(3), 2110–2128 (2012).  https://doi.org/10.1016/j.neuroimage.2012.05.079. Epub 2012 Jun 5CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Behavioural Informatics GroupVrije Universiteit AmsterdamAmsterdamNetherlands

Personalised recommendations