How perceived causal networks can complement case conceptualization, diagnostic classification, and data-based...

Area of Science:

• Psychiatry and Psychology
• Network Science

Background:

• Personalized symptom networks offer a novel approach to understanding psychopathology.
• The perceived causal networks (PECAN) method provides a framework for creating these networks.

Purpose of the Study:

• To provide guidelines for clinicians and researchers on using the PECAN method.
• To facilitate case conceptualization and personalized treatment planning.
• To describe patient groups and identify central symptoms and feedback loops.

Main Methods:

• Systematically asking respondents to quantify causal links between symptoms.
• Visualizing quantified causal links as directed networks for individuals or groups.
• Representing causal relations across timescales without data-hungry estimations.

Main Results:

• PECAN networks can reveal recurring feedback loops and central symptoms in individuals and groups.
• The method supports the personalization of treatments and case conceptualization.
• Guidelines are provided for node selection, edge assessment, and data visualization.

Conclusions:

• The PECAN method is a promising tool for advancing personalized mental healthcare.
• Further research is needed to evaluate reliability, validity, and clinical utility.
• Implementation in clinical research and practice presents future opportunities and challenges.