ImportancePsychiatric 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.ObservationsIn 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 RelevanceWork 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.

中文翻译：

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精神疾病的动力系统观——理论

重要性精神疾病可能会随着一生中症状的变化而出现和消失。这表明诊断和治疗需要进行范式转变。在这里，我们展示了受动力系统理论启发的全新外观。该理论广泛用于解释从气候到生态系统等复杂系统中的临界点、循环和混沌。观察在动力系统观点中，我们提出健康状态有一个代表其弹性的吸引盆，而紊乱是替代吸引子系统可能会陷入困境。这种方法中的弹性不是一种一成不变的特征，而是一种动态属性。最近的工作证明了复原力通用动态指标的普遍性，这些指标目前在全球范围内用于监测复杂系统崩溃的风险，例如热带雨林和气候系统的倾斜要素。其他动力系统工具用于生态学和气候科学，以从时间序列推断因果关系。此外，生态恢复的经验证实了理论预测，即在某些条件下，短期干预可能会在将系统转变为替代吸引力盆地时带来长期成功。所有这些都意味着精神病学的实际应用，正如本文第二部分所讨论的那样。结论和相关性动力系统领域的工作指出了从时间序列推断因果关系和量化弹性的新方法。这些方法现已在一系列复杂系统中进行了尝试和测试。同样的工具可以帮助监测和管理健康状态以及精神疾病的恢复力。