Does centrality in a cross-sectional network suggest intervention targets for social anxiety disorder?

Abstract

Objective Network analysis allows us to identify the most interconnected (i.e., central) symptoms, and multiple authors have suggested that these symptoms might be important treatment targets. This is because change in central symptoms (relative to others) should have greater impact on change in all other symptoms. It has been argued that networks derived from cross-sectional data may help identify such important symptoms. We tested this hypothesis in social anxiety disorder. Method We first estimated a state-of-the-art regularized partial correlation network based on participants with social anxiety disorder (N = 910) to determine which symptoms were more central. Next, we tested whether change in these central symptoms were indeed more related to overall symptom change in a separate dataset of participants with social anxiety disorder who underwent a variety of treatments (N = 244). We also tested whether relatively superficial item properties (infrequency of endorsement and variance of items) might account for any effects shown for central symptoms. Results Centrality indices successfully predicted how strongly changes in items correlated with change in the remainder of the items. Findings were limited to the measure used in the network and did not generalize to three other measures related to social anxiety severity. In contrast, infrequency of endorsement showed associations across all measures. Conclusions The transfer of recently published results from cross-sectional network analyses to treatment data is unlikely to be straightforward.

Lack of theory building and testing impedes progress in the factor and network literature

“…For example, reductions in central symptoms predict stronger reductions in the other symptoms than reductions in peripheral (i.e. weakly connected) symptoms (Elliott et al, 2019;Robinaugh et al, 2016;Rodebaugh et al, 2018). This has been taken by some to support network theory-after all, reductions in central symptoms should deactivate numerous other symptoms and reduce overall severity more than reductions in peripheral symptoms.…”

“…on relations among variables, or on variables directly . There is some initial evidence to support interventions on central symptoms (Elliott et al, 2019;Robinaugh et al, 2016;Rodebaugh et al, 2018); however, future behavior of complex systems is difficult to predict, and it will require theoretical, methodological, and experimental work to achieve actionable progress moving forward (Henry et al, 2020).…”

Self-Dislike and Sadness are Central Symptoms of Depression in College Students: A Network Analysis

“…Thus, the final goal of this study was to examine whether depression symptom centrality measured in a cross-sectional network provided predictive validity for identifying the most central symptoms in a symptom change network in an independent sample. Although there is a dearth of research demonstrating the predictive utility of cross-sectional symptom centrality, prior work suggests that centrality in a cross-sectional network can predict future onset of depression (Boschloo et al, 2016) and out-of-sample symptom change among people with social anxiety (Rodebaugh et al, 2018). Thus, we expected centrality in the cross-sectional depression network would predict symptom centrality out-of-sample in a depression symptom change network.…”

Using network analysis to identify central eating disorder symptoms among men

“…We combined theoretical and data-driven approaches to select network nodes that assessed unique constructs, given that including nodes with very high similarity can obscure the true relations that exist among all symptoms in the network Rodebaugh et al, 2018). Theoretical decisions were made through consulting EDE-Q, MBAS, and DMS factor analytic work (Grilo, Reas, Hopwood, & Crosby, 2015;McCreary & Sasse, 2000;Tylka et al, 2005).…”