Abstract
Mental disorders are more and more seen as based on complex networks of symptoms and predispositions that create the disorder as an emergent behaviour of the network’s dynamics. This paper aims to provide a computational model reflecting the adaptive causal relations between anxiety, stress and physical exercise based on a network-oriented modelling approach. The model was evaluated by executing several simulations and validated through an examination of its emergent properties and their cross-reference to the available literature. The created model offers the possibility of simulating different treatments, and offers a basis to develop a virtual patient model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adorjan, P., Schwabe, L., Wenning, G., Obermayer, K.: Rapid adaptation to internal states as a coding strategy in visual cortex? NeuroReport 13(3), 337–342 (2002)
Appendix: Linked Data (2021). https://www.researchgate.net/publication/350049733
Barlow, D.H., Ellard, K.K., Sauer-Zavala, S., Bullis, J.R., Carl, J.R.: The origins of neuroticism. Perspect. Psychol. Sci. 9(5), 481–496 (2014)
Borsboom, D.: A network theory of mental disorders. World Psychiatry 16(1), 5–13 (2017)
Chandra, N., Barkai, E.: A non-synaptic mechanism of complex learning: modulation of intrinsic neuronal excitability. Neurobiol. Learn. Mem. 154, 30–36 (2018)
Cramer, A.O., Borsboom, D.: Problems attract problems: a network perspective on mental disorders. Emerg. Trends Soc. Behav. Sci. Interdiscip. Searchable Linkable Resour., 1–15 (2015)
Debanne, D., Inglebert, Y., Russier, M.: Plasticity of intrinsic neuronal excitability. Curr. Opin. Neurobiol. 54, 73–82 (2019)
Ekkekakis, P., Hall, E., Petruzzello, S.: The relationship between exercise intensity and affective responses demystified: to crack the 40-year-old nut, replace the 40-year-old nutcracker! Ann. Behav. Med. 35(2), 136–149 (2008)
Greenwood, B.N., Fleshner, M.: Exercise, stress resistance, and central serotonergic systems. Exerc. Sport Sci. Rev. 39(3), 140–149 (2011). https://doi.org/10.1097/jes.0b013e31821f7e45
Hofmann, S.G., Curtiss, J., McNally, R.J.: A complex network perspective on clinical science. Perspect. Psychol. Sci. J. Assoc. Psychol. Sci. 11(5), 597–605 (2016)
Konstantopoulou, G., Iliou, T., Karaivazoglou, K., Iconomou, G., Assimakopoulos, K., Alexopoulos, P.: Associations between (sub) clinical stress- and anxiety symptoms in mentally healthy individuals and in major depression: a cross-sectional clinical study. BMC Psychiatry 20(1) (2020)
Kurebayashi, L.F.S., Do Prado, J.M., Da Silva, M.J.P.: Correlations between stress and anxiety levels in nursing students. J. Nurs. Educ. Pract. 2(3), 128 (2012)
Matta Mello Portugal, E., et al.: Neuroscience of exercise: from neurobiology mechanisms to mental health. Neuropsychobiology 68(1), 1–14 (2013). https://doi.org/10.1159/000350946
Rebar, A.L., Stanton, R., Geard, D., Short, C., Duncan, M.J., Vandelanotte, C.: A metameta-analysis of the effect of physical activity on depression and anxiety in non-clinical adult populations. Health Psychol. Rev. 9(3), 366–378 (2015). https://doi.org/10.1080/17437199.2015.1022901
Robinaugh, D.J., Hoekstra, R.H., Toner, E.R., Borsboom, D.: The network approach to psychopathology: a review of the literature 2008–2018 and an agenda for future research. Psychol. Med. 50(3), 353 (2020)
Robinson, B.L., Harper, N.S., McAlpine, D.: Meta-adaptation in the auditory midbrain under cortical influence. Nat. Commun. 7(1), 1–8 (2016)
Salari, N., et al.: Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Glob. Health 16(1), 1–11 (2020)
Shatz, C.J.: The develo** brain. Sci. Am. 267(3), 60–67 (1992)
Sinha, R., Lacadie, C.M., Constable, R.T., Seo, D.: Dynamic neural activity during stress signals resilient co**. Proc. Natl Acad. Sci. U. S. Am. 113(31), 8837–8842 (2016). https://doi.org/10.1073/pnas.1600965113
Stranahan, A.M., Lee, K., Mattson, M.P.: Central mechanisms of HPA axis regulation by voluntary exercise. NeuroMol. Med. 10(2), 118–127 (2008). https://doi.org/10.1007/s12017-008-8027-0
Sylvia, L.G., Ametrano, R.M., Nierenberg, A.A.: Exercise treatment for bipolar disorder: potential mechanisms of action mediated through increased neurogenesis and decreased allostatic load. Psychother. Psychosom. 79(2), 87–96 (2010). https://doi.org/10.1159/000270916
Treur, J.: Dynamic modeling based on a temporal–causal network modeling approach. Biol. Inspir. Cognit. Archit. 16, 131–168 (2016)
Treur, J.: Adaptive networks at the crossroad of AI and formal, biological, medical and social sciences. In: Rezaei, N. (ed.) Integrated Science - Science without Borders, vol. 1. Springer, Cham (2021)
Treur, J.: Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models. SSDC, vol. 251. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-31445-3
Wegner, M., Helmich, I., Machado, S., Nardi, A., Arias-Carrion, O., Budde, H.: Effects of exercise on anxiety and depression disorders: review of meta-analyses and neurobiological mechanisms. CNS Neurol. Disord. Drug Targets (Former. Curr. Drug Targets-CNS Neurol. Disord.) 13(6), 1002–1014 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Rass, L., Treur, J. (2021). A Computational Model for the Second-Order Adaptive Causal Relationships Between Anxiety, Stress and Physical Exercise. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-79150-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79149-0
Online ISBN: 978-3-030-79150-6
eBook Packages: Computer ScienceComputer Science (R0)