How perceived causal networks can complement case conceptualization, diagnostic classification, and data-based networks An introduction to a method for constructing personalized networks

The personalization of psychopathology through the use of personalized symptom networks appears to be a promising approach for gaining deeper insights into the development and maintenance of mental disorders. One way to create such networks is by using the perceived causal networks (PECAN) method. In this method, respondents are systematically asked to quantify how their symptoms are causally linked. Answers are then visualized, either for the individual or aggregated for a group, as a directed network. PECAN can represent causal relations irrespective of their timescales and requires no data-hungry estimations. The following guidelines are intended to assist clinicians and researchers in the creation of personalized networks using the PECAN method. These networks can facilitate case conceptualization and personalization of treatments for individual patients and the description of groups of patients, revealing recurring feedback loops and central symptoms. Additionally, recommendations are provided regarding the procedures to be employed in the selection of nodes, assessment of edges, and visualization of the data. Furthermore, the potential for evaluating the reliability, validity, and clinical usefulness, as well as strengths, limitations, and future challenges of PECAN, is discussed. We conclude with an overview of the challenges of PECAN and a research agenda that highlights opportunities to improve the still very young method and implement it in clinical research and practice.