On the validity of the centrality hypothesis in cross-sectional between-subject networks of psychopathology

• Published in: BMC medicine Vol. 18; no. 1; pp. 297 - 14
• Main Authors: Spiller, Tobias R, Levi, Ofir, Neria, Yuval, Suarez-Jimenez, Benjamin, Bar-Haim, Yair, Lazarov, Amit
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 12.10.2020; BioMed Central; BMC
• Subjects: Amnesia; Analysis; Causality; Centrality measures; Cross-Sectional Studies; Deactivation; Female; Humans; Hypotheses; Male; Mental disorders; Network analysis; Network approach; Networks; Nodes; Outliers (statistics); Phenomenology; Post traumatic stress disorder; Posttraumatic stress disorder; Psychological aspects; Psychological stress; Psychopathology; Psychopathology - methods; Replication; Reproducibility of Results; Signs and symptoms; Social Network Analysis; Social networks; Target recognition; Treatment; Validity; Posttraumatic stress disorder; Treatment; Centrality measures; Network analysis; Network approach
• ISSN: 1741-7015; 1741-7015
• DOI: 10.1186/s12916-020-01740-5

In the network approach to psychopathology, psychiatric disorders are considered networks of causally active symptoms (nodes), with node centrality hypothesized to reflect symptoms' causal influence within a network. Accordingly, centrality measures have been used in numerous network-based cross-sectional studies to identify specific treatment targets, based on the assumption that deactivating highly central nodes would proliferate to other nodes in the network, thereby collapsing the network structure and alleviating the overall psychopathology (i.e., the centrality hypothesis). Here, we summarize three types of evidence pertaining to the centrality hypothesis in psychopathology. First, we discuss the validity of the theoretical assumptions underlying the centrality hypothesis in psychopathology. We then summarize the methodological aspects of extant studies using centrality measures as predictors of symptom change following treatment, while delineating their main findings and several of their limitations. Finally, using a specific dataset of 710 treatment-seeking patients with posttraumatic stress disorder (PTSD) as an example, we empirically examine node centrality as a predictor of therapeutic change, replicating the approach taken by previous studies, while addressing some of their limitations. Specifically, we investigated whether three pre-treatment centrality indices (strength, predictability, and expected influence) were significantly correlated with the strength of the association between a symptom's change and the change in the severity of all other symptoms in the network from pre- to post-treatment (Δnode-Δnetwork association). Using similar analyses, we also examine the predictive validity of two simple non-causal node properties (mean symptom severity and infrequency of symptom endorsement). Of the three centrality measures, only expected influence successfully predicted how strongly changes in nodes/symptoms were associated with change in the remainder of the nodes/symptoms. Importantly, when excluding the amnesia node, a well-documented outlier in the phenomenology of PTSD, none of the tested centrality measures predicted symptom change. Conversely, both mean symptom severity and infrequency of symptom endorsement, two standard non-network-derived indices, were found to be more predictive than expected influence and remained significantly predictive also after excluding amnesia from the network analyses. The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.