The exploratory value of cross-sectional partial correlation networks: Predicting relationships between change trajectories in borderline personality disorder

• Published in: PloS one Vol. 16; no. 7; p. e0254496
• Main Authors: von Klipstein, Lino, Borsboom, Denny, Arntz, Arnoud
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.07.2021; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Borderline personality disorder; Borderline Personality Disorder - psychology; Computer and Information Sciences; Correlation; Cross-Sectional Studies; Female; Humans; Hypotheses; Male; Medicine and Health Sciences; Mental disorders; Methods; Networks; Parameters; Pathology; Personality; Personality disorders; Physical Sciences; Psychiatric research; Psychological manifestations of general diseases; Psychology, Pathological; Psychopathology; Research and Analysis Methods; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0254496

Within the network approach to psychopathology, cross-sectional partial correlation networks have frequently been used to estimate relationships between symptoms. The resulting relationships have been used to generate hypotheses about causal links between symptoms. In order to justify such exploratory use of partial correlation networks, one needs to assume that the between-subjects relationships in the network approximate systematic within-subjects relationships, which are in turn the results of some within-subjects causal mechanism. If this assumption holds, relationships in the network should be mirrored by relationships between symptom changes; if links in networks approximate systematic within-subject relationships, change in a symptom should relate to change in connected symptoms. To investigate this implication, we combined longitudinal data on the Borderline Personality Disorder Severity Index from four samples of borderline personality disorder patients (N = 683). We related parameters from baseline partial correlation networks of symptoms to relationships between change trajectories of these symptoms. Across multiple levels of analysis, our results showed that parameters from baseline partial correlation networks are strongly predictive of relationships between change trajectories. By confirming its implication, our results support the idea that cross-sectional partial correlation networks hold a relevant amount of information about systematic within-subjects relationships and thereby have exploratory value to generate hypotheses about the causal dynamics between symptoms.