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Major Depression as a Complex Dynamic System
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 resulti...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5145163/ https://www.ncbi.nlm.nih.gov/pubmed/27930698 http://dx.doi.org/10.1371/journal.pone.0167490 |
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author | Cramer, Angélique O. J. van Borkulo, Claudia D. Giltay, Erik J. van der Maas, Han L. J. Kendler, Kenneth S. Scheffer, Marten Borsboom, Denny |
author_facet | Cramer, Angélique O. J. van Borkulo, Claudia D. Giltay, Erik J. van der Maas, Han L. J. Kendler, Kenneth S. Scheffer, Marten Borsboom, Denny |
author_sort | Cramer, Angélique O. J. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5145163 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-51451632016-12-22 Major Depression as a Complex Dynamic System Cramer, Angélique O. J. van Borkulo, Claudia D. Giltay, Erik J. van der Maas, Han L. J. Kendler, Kenneth S. Scheffer, Marten Borsboom, Denny PLoS One Research Article 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. Public Library of Science 2016-12-08 /pmc/articles/PMC5145163/ /pubmed/27930698 http://dx.doi.org/10.1371/journal.pone.0167490 Text en © 2016 Cramer et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Cramer, Angélique O. J. van Borkulo, Claudia D. Giltay, Erik J. van der Maas, Han L. J. Kendler, Kenneth S. Scheffer, Marten Borsboom, Denny Major Depression as a Complex Dynamic System |
title | Major Depression as a Complex Dynamic System |
title_full | Major Depression as a Complex Dynamic System |
title_fullStr | Major Depression as a Complex Dynamic System |
title_full_unstemmed | Major Depression as a Complex Dynamic System |
title_short | Major Depression as a Complex Dynamic System |
title_sort | major depression as a complex dynamic system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5145163/ https://www.ncbi.nlm.nih.gov/pubmed/27930698 http://dx.doi.org/10.1371/journal.pone.0167490 |
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