Deconstructing and Reconstructing Resilience: A Dynamic Network Approach. (September 2019)

• Record Type: Journal Article
• Authors: Kalisch, Raffael; Cramer, Angélique O. J.; Binder, Harald; Fritz, Jessica; Leertouwer, IJsbrand; Lunansky, Gabriela; Meyer, Benjamin; Timmer, Jens; Veer, Ilya M.; van Harmelen, Anne-Laura
• Is Part Of: Perspectives on psychological science. Volume 14:Number 5(2019)
• Page Start: 765
• Page End: 777
• Publication Date: 2019-09
• Subjects: resilience -- stress -- mental health -- dynamic system -- symptom network -- emotion regulation; Psychology -- Periodicals; 150
• DOI: 10.1177/1745691619855637
• Languages: English
• ISSNs: 1745-6916

Abstract

Resilience is still often viewed as a unitary personality construct that, as a kind of antinosological entity, protects individuals against stress-related mental problems. However, increasing evidence indicates that maintaining mental health in the face of adversity results from complex and dynamic processes of adaptation to stressors that involve the activation of several separable protective factors. Such resilience factors can reside at biological, psychological, and social levels and may include stable predispositions (such as genotype or personality traits) and malleable properties, skills, capacities, or external circumstances (such as gene-expression patterns, emotion-regulation abilities, appraisal styles, or social support). We abandon the notion of resilience as an entity here. Starting from a conceptualization of psychiatric disorders as dynamic networks of interacting symptoms that may be driven by stressors into stable maladaptive states of disease, we deconstruct the maintenance of mental health during stressor exposure into time-variant dampening influences of resilience factors onto these symptom networks. Resilience factors are separate additional network nodes that weaken symptom–symptom interconnections or symptom autoconnections, thereby preventing maladaptive system transitions. We argue that these hybrid symptom-and-resilience-factor networks provide a promising new way of unraveling the complex dynamics of mental health.