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Major Depression as a Complex Dynamic System

Author

Listed:
  • Angélique O J Cramer
  • Claudia D van Borkulo
  • Erik J Giltay
  • Han L J van der Maas
  • Kenneth S Kendler
  • Marten Scheffer
  • Denny Borsboom

Abstract

In this paper, we characterize major depression (MD) as a complex dynamic system in which symptoms (e.g., insomnia and fatigue) are directly connected to one another in a network structure. We hypothesize that individuals can be characterized by their own network with unique architecture and resulting dynamics. With respect to architecture, we show that individuals vulnerable to developing MD are those with strong connections between symptoms: e.g., only one night of poor sleep suffices to make a particular person feel tired. Such vulnerable networks, when pushed by forces external to the system such as stress, are more likely to end up in a depressed state; whereas networks with weaker connections tend to remain in or return to a non-depressed state. We show this with a simulation in which we model the probability of a symptom becoming ‘active’ as a logistic function of the activity of its neighboring symptoms. Additionally, we show that this model potentially explains some well-known empirical phenomena such as spontaneous recovery as well as accommodates existing theories about the various subtypes of MD. To our knowledge, we offer the first intra-individual, symptom-based, process model with the potential to explain the pathogenesis and maintenance of major depression.

Suggested Citation

  • Angélique O J Cramer & Claudia D van Borkulo & Erik J Giltay & Han L J van der Maas & Kenneth S Kendler & Marten Scheffer & Denny Borsboom, 2016. "Major Depression as a Complex Dynamic System," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-20, December.
    • Handle: RePEc:plo:pone00:0167490
    DOI: 10.1371/journal.pone.0167490
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    References listed on IDEAS

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    Cited by:

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    2. Inken Höller & Dajana Schreiber & Fionneke Bos & Thomas Forkmann & Tobias Teismann & Jürgen Margraf, 2022. "The Mereology of Depression—Networks of Depressive Symptoms during the Course of Psychotherapy," IJERPH, MDPI, vol. 19(12), pages 1-13, June.
    3. Savi, Alexander O. & Marsman, Maarten & van der Maas, Han L.J., 2021. "Evolving networks of human intelligence," Intelligence, Elsevier, vol. 88(C).
    4. Miguel Ángel Castellanos & Berta Ausín & Sara Bestea & Clara González-Sanguino & Manuel Muñoz, 2020. "A Network Analysis of Major Depressive Disorder Symptoms and Age- and Gender-Related Differences in People over 65 in a Madrid Community Sample (Spain)," IJERPH, MDPI, vol. 17(23), pages 1-13, December.
    5. Zhou, Jianhua & Zhang, Lulu & Gong, Xue, 2023. "Longitudinal network relations between symptoms of problematic internet game use and internalizing and externalizing problems among Chinese early adolescents," Social Science & Medicine, Elsevier, vol. 333(C).
    6. Srebrenka Letina & Tessa F. Blanken & Marie K. Deserno & Denny Borsboom, 2019. "Expanding Network Analysis Tools in Psychological Networks: Minimal Spanning Trees, Participation Coefficients, and Motif Analysis Applied to a Network of 26 Psychological Attributes," Complexity, Hindawi, vol. 2019, pages 1-27, February.
    7. Protzko, John & Colom, Roberto, 2021. "A new beginning of intelligence research. Designing the playground," Intelligence, Elsevier, vol. 87(C).
    8. Nadja Bodner & Laura Bringmann & Francis Tuerlinckx & Peter Jonge & Eva Ceulemans, 2022. "ConNEcT: A Novel Network Approach for Investigating the Co-occurrence of Binary Psychopathological Symptoms Over Time," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 107-132, March.
    9. María Guillot-Valdés & Alejandro Guillén-Riquelme & Juan Carlos Sierra & Gualberto Buela-Casal, 2022. "Network and Exploratory Factorial Analysis of the Depression Clinical Evaluation Test," IJERPH, MDPI, vol. 19(17), pages 1-26, August.
    10. Abolfazl Ramezanpour & Alireza Mashaghi, 2020. "Disease evolution in reaction networks: Implications for a diagnostic problem," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-17, June.

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