Identifying Treatment Targets in Idiographic Psychopathological Networks: A Simulation Study

Abstract Following current trends for precision medicine, psychology has in recent years turned its attention to treatment personalization. Several technological and methodological advances have contributed to this renewed focus on psychology. One of these methodological advances at the center of this push toward precision medicine is network analysis. One of the core propellers of the network theory of psychopathology is the proposal that highly influential symptoms of the network (i.e., central symptoms) might be promising treatment targets. However, evidence for this proposal remains inconclusive, with evidence from idiographic longitudinal assessments lacking. In this study, we explore the impact of symptom deactivation in individual networks (named idiographic networks). We tested two types of symptom deactivation: normal and cascade attack, and five measures for highly influential symptom identification (degree, strength, eigenvector, expected influence, and random attack condition). We find that a cascade degree-based attack is more effective at deactivating the symptom network, we next discuss the implications for treatment personalization and precision psychology.