Quantifying the Stability Landscapes of Psychological Networks

The network theory of psychopathology proposes that mental disorders can be represented as networks of interacting psychiatric symptoms. These direct symptom-symptom interactions can create a vicious cycle of symptom activation, pushing the network to a self-sustaining dysfunctional phase of psychopathology: a mental disorder. Symptom network models can be estimated from empirical data through statistical models. Although simulation studies have established a relation between the structure of these symptom network models and the probability they end up in a self-sustaining dysfunctional phase, the general stability of the system is left implicit. The general stability includes both the stability of the dysfunctional phase, indicating vulnerability, and the stability of the healthy phase, indicating resilience. In this paper we present a novel method to quantify the stability landscapes of network models through potential landscapes. Our method is based on the Hamiltonian of the microstates of Ising models and can be used to show the stability of estimated Ising network models. Compared to simulation-based methods, our approach is computationally more efficient and quantifies the stability of all possible system states. Furthermore, we propose a set of stability metrics to quantify the stability of the healthy and dysfunctional phases, introducing a novel stability metric to assess resilience. To demonstrate the method’s utility, we apply it to an empirical data set and show how it can be used to compare the stability of phases and resilience between groups. The presented method is implemented in a freely available R package, Isinglandr.