On the control of psychological networks
The combination of network theory and network psychometric methods have opened up a variety of new ways to conceptualize and study psychological disorders. The idea of psychological disorders as dynamic systems has sparked interest in developing interventions based on results of network analytic tools. However, estimating a network model is not sufficient for determining which symptoms might be most effective to intervene upon, and is not sufficient for determining the potential efficacy of any given intervention. In this paper, we attempt to remedy this gap by introducing fundamental concepts in control theory to both methodologists and applied psychologists. We show how two controllability measures, average and modal controllability, can be used to select the best set of intervention targets. We provide a statistical testing procedure for determining if the dynamical systems of different people have the same optimal intervention targets. Following that, we show how intervention scientists can probe the effects of both theoretical and empirical interventions on networks derived from real data; demonstrate how simulations can be used to account for intervention cost and the desire to reduce specific symptoms; introduce a metric for evaluating intervention efficacy, the intervention efficacy ratio (IER); and showcase how between subject heterogeneity in intervention response can be evaluated. Every step is illustrated using rich clinical EMA data from a sample of subjects undergoing treatment for complicated grief, with a focus on the outcome `Suicidal Ideation'. All methods are implemented in an open-source R package ''netcontrol'', and complete code for replicating the analyses in this manuscript is available at https://osf.io/f268v/.