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Review
´¡±è°ù¾±±ôÌý3, 2024

A Dynamical Systems View of Psychiatric Disorders—Theory: A Review

Author Affiliations
  • 1Wageningen University, Wageningen, the Netherlands
  • 2Amsterdam UMC, Amsterdam, the Netherlands
  • 3University of Amsterdam, Amsterdam, the Netherlands
  • 4Donders Institute, Nijmegen, the Netherlands
  • 5University of Melbourne, Melbourne, Victoria, Australia
  • 6Virginia Commonwealth University, Richmond
  • 7Johns Hopkins University, Baltimore, Maryland
  • 8Orygen, Parkville, Victoria, Australia
JAMA Psychiatry. Published online April 3, 2024. doi:10.1001/jamapsychiatry.2024.0215
Abstract

ImportanceÌý Psychiatric disorders may come and go with symptoms changing over a lifetime. This suggests the need for a paradigm shift in diagnosis and treatment. Here we present a fresh look inspired by dynamical systems theory. This theory is used widely to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems.

ObservationsÌý In the dynamical systems view, we propose the healthy state has a basin of attraction representing its resilience, while disorders are alternative attractors in which the system can become trapped. Rather than an immutable trait, resilience in this approach is a dynamical property. Recent work has demonstrated the universality of generic dynamical indicators of resilience that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforests and tipping elements of the climate system. Other dynamical systems tools are used in ecology and climate science to infer causality from time series. Moreover, experiences in ecological restoration confirm the theoretical prediction that under some conditions, short interventions may invoke long-term success when they flip the system into an alternative basin of attraction. All this implies practical applications for psychiatry, as are discussed in part 2 of this article.

Conclusions and RelevanceÌý Work in the field of dynamical systems points to novel ways of inferring causality and quantifying resilience from time series. Those approaches have now been tried and tested in a range of complex systems. The same tools may help monitoring and managing resilience of the healthy state as well as psychiatric disorders.

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