Lunansky Gabriela, Bonanno George A, Blanken Tessa F, van Borkulo Claudia D, Cramer Angélique O J, Borsboom Denny
Department of Epidemiology and Data Science, Amsterdam University Medical Centers (VUmc).
Department of Counseling and Clinical Psychology, Teachers College, Columbia University.
Psychol Rev. 2024 Oct 21. doi: 10.1037/rev0000497.
Experiencing stressful or traumatic events can lead to a range of responses, from mild disruptions to severe and persistent mental health issues. Understanding the various trajectories of response to adversity is crucial for developing effective interventions and support systems. Researchers have identified four commonly observed response trajectories to adversity, from which the resilient is the most common one. Resilience refers to the maintenance of healthy psychological functioning despite facing adversity. However, it remains an open question how to understand and anticipate resilience, due to its dynamic and multifactorial nature. This article presents a novel formalized framework to conceptualize resilience from a complex systems perspective. We use the network theory of psychopathology, which states that mental disorders are self-sustaining endpoints of direct symptom-symptom interactions organized in a network system. The internal structure of the network determines the most likely trajectory of symptom development. We introduce the resilience quadrant, which organizes the state of symptom networks on two domains: (1) healthy versus dysfunctional and (2) stable versus unstable. The quadrant captures the four commonly observed response trajectories to adversity along those dimensions: resilient trajectories in the face of adversity, as well as persistent symptoms despite treatment interventions. Subsequently, an empirical illustration, by means of a proof-of-principle, shows how simulated observations from four different network architectures lead to the four commonly observed responses to adversity. As such, we present a novel outlook on resilience by combining existing statistical symptom network models with simulation techniques. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
经历压力或创伤性事件会导致一系列反应，从轻微干扰到严重且持续的心理健康问题。了解对逆境的各种反应轨迹对于开发有效的干预措施和支持系统至关重要。研究人员已经确定了四种常见的对逆境的反应轨迹，其中恢复力强是最常见的一种。恢复力是指尽管面临逆境仍能维持健康的心理功能。然而，由于其动态性和多因素性质，如何理解和预测恢复力仍然是一个悬而未决的问题。本文提出了一个新颖的形式化框架，从复杂系统的角度来概念化恢复力。我们使用精神病理学的网络理论，该理论指出精神障碍是在网络系统中组织的直接症状 - 症状相互作用的自我维持终点。网络的内部结构决定了症状发展最可能的轨迹。我们引入了恢复力象限，它在两个维度上组织症状网络的状态：（1）健康与功能失调；（2）稳定与不稳定。该象限沿着这些维度捕捉了四种常见的对逆境的反应轨迹：面对逆境时的恢复力轨迹，以及尽管进行了治疗干预仍持续存在的症状。随后，通过一个原理验证的实证例证，展示了来自四种不同网络架构的模拟观察如何导致对逆境的四种常见反应。因此，我们通过将现有的统计症状网络模型与模拟技术相结合，对恢复力提出了一种新颖的观点。（PsycInfo数据库记录（c）2025美国心理学会，保留所有权利）
