Network Analysis of PTSD Symptoms Following Mass Violence

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Abstract

Objective: Network analysis is a useful tool for understanding how symptoms interact with one another to influence psychopathology. However, this analytic strategy has not been fully utilized in the PTSD field. The current study utilized network analysis to examine connectedness and strength among PTSD symptoms (employing both partial correlation and regression network analyses) among a community sample of students exposed to the 2007 Virginia Tech shootings. Method: Respondents (N = 4,639) completed online surveys 3–4 months postshootings, with PTSD symptom severity measured via the Trauma Symptom Questionnaire. Results: Data were analyzed via adaptive least absolute shrinkage and selection operator (LASSO) and relative importance networks, as well as Dijkstra’s algorithm to identify the shortest path from each symptom to all other symptoms. Relative importance network analysis revealed that intrusive thoughts had the strongest influence on other symptoms (i.e., had many strong connections [highest outdegree]) while computing Dijkstra’s algorithm indicated that anger produced the shortest path to all other symptoms (i.e., the strongest connections to all other symptoms). Conclusion: Findings suggest that anger or intrusion likely play a crucial role in the development and maintenance of PTSD (i.e., are more influential within the network than are other symptoms).

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