Data_Sheet_1_The Mental Health Ecosystem: Extending Symptom Networks With Risk and Protective Factors.docx

Inspired by modeling approaches from the ecosystems literature, in this paper, we expand the network approach to psychopathology with risk and protective factors to arrive at an integrated analysis of resilience. We take a complexity approach to investigate the multifactorial nature of resilience and present a system in which a network of interacting psychiatric symptoms is targeted by risk and protective factors. These risk and protective factors influence symptom development patterns and thereby increase or decrease the probability that the symptom network is pulled toward a healthy or disorder state. In this way, risk and protective factors influence the resilience of the network. We take a step forward in formalizing the proposed system by implementing it in a statistical model and translating different influences from risk and protective factors to specific targets on the node and edge parameters of the symptom network. To analyze the behavior of the system under different targets, we present two novel network resilience metrics: Expected Symptom Activity (ESA, which indicates how many symptoms are active or inactive) and Symptom Activity Stability (SAS, which indicates how stable the symptom activity patterns are). These metrics follow standard practices in the resilience literature, combined with ideas from ecology and physics, and characterize resilience in terms of the stability of the system's healthy state. By discussing the advantages and limitations of our proposed system and metrics, we provide concrete suggestions for the further development of a comprehensive modeling approach to study the complex relationship between risk and protective factors and resilience.

受生态系统文献中建模方法之启发，本文拓展了心理病理学网络方法，结合风险与保护因素，旨在对韧性进行综合分析。我们采用复杂性方法，探究韧性的多因素特性，并提出一个系统，其中风险与保护因素针对相互作用的精神性状网络进行靶向。这些风险与保护因素影响症状发展模式，从而增加或降低症状网络向健康或失调状态转变的概率。以此方式，风险与保护因素影响网络的韧性。通过将所提出系统在统计模型中实现，并将风险与保护因素的影响转化为症状网络节点与边参数的具体目标，我们进一步规范了该系统。为分析系统在不同目标下的行为，我们提出了两种新颖的网络韧性指标：预期症状活动（ESA，表示活动或非活动的症状数量）和症状活动稳定性（SAS，表示症状活动模式的稳定性）。这些指标遵循韧性文献中的标准实践，结合生态学与物理学的理念，并以系统健康状态的稳定性来表征韧性。通过讨论我们提出系统与指标的优缺点，我们为研究风险与保护因素与韧性之间复杂关系的综合建模方法提供了具体建议。