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

Abstract

Background 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). Methods 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). Results 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. Conclusions The centrality hypothesis in its current form is ill-defined, showing no consistent supporting evidence in the context of cross-sectional, between-subject networks.

The network structure of stress-related states, depression, and anxiety symptoms during the COVID-19 lockdown

“…However, this measure does not capture indirect associations between nodes. Empirically, the implication of network theory that strength centrality in cross-sectional networks predicts 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 has received mixed support (Spiller et al, 2020). A fourth limitation is the cross-sectional design, which impairs the ability to draw conclusions about temporal precedence and greater insight into causal direction of the relationships obtained.…”

Excessive Worrying as a Central Feature of Anxiety during the First COVID-19 Lockdown-Phase in Belgium: Insights from a Network Approach

“…Network models of psychopathology posit that highly central nodes (i.e., symptoms) are critical to the course of the disorder since, according to the network perspective, if a central symptom is activated, it is more likely to trigger and influence other symptoms (Borsboom, 2017;McNally, 2016; for a discussion, see Blanchard & Heeren, 2022). And, although the very causal involvement of central nodes in determining the network topology remains to be experimentally proven (for discussion, see Blanchard & Heeren, 2022;Bringmann et al, 2019), early results have confirmed the highly predictive nature of highly central nodes in determining the onset, course, and recovery of psychological disorders (e.g., Boschloo et al, 2016;Elliott, Jones, & Schmidt, 2020;Papini, Rubin, Telch, Smits, & Hien, 2020;Spiller et al, 2020). If this holds true for the models investigated in this study, an intervention that would specifically "turn off" excessive worry should then lead to downstream improvement by rendering the entire network less active.…”

Intervening on psychopathology networks: Evaluating intervention targets through simulations

“…To assess the relative importance of symptoms in psychopathology networks estimated from observational data, the concept of node centrality was received with high hopes [23]. Centrality indices stem from the domain of social networks, in which the most central node in the network has the largest number of edges with neighboring nodes and the most substantial edges [24].…”

“…The concept was translated to psychology [25], where the centrality hypothesis states that the most central nodes are the best intervention targets, as they are thought to represent the most influential nodes in a network [26]. Therefore, centrality metrices are used in psychopathology networks to identify possible intervention targets [2], [20], [23], [27], [28]. However, several researchers have raised doubts regarding the suitability of centrality indices in psychological networks [23], [29]- [33].…”

“…Therefore, centrality metrices are used in psychopathology networks to identify possible intervention targets [2], [20], [23], [27], [28]. However, several researchers have raised doubts regarding the suitability of centrality indices in psychological networks [23], [29]- [33]. Centrality indices are based on the structure of the psychological network (i.e., the presence and strength of edges), but do not explicitly consider the dynamics of the network.…”