The Differential Role of Central and Bridge Symptoms in Deactivating Psychopathological Networks

Abstract

The network model of psychopathology suggests that central and bridge symptoms represent promising treatment targets because they may accelerate the deactivation of the network of interactions between the symptoms of mental disorders. However, the evidence confirming this hypothesis is scarce. This study re-analyzed a convenience sample of 51 cross-sectional psychopathological networks published in previous studies addressing diverse mental disorders or clinically relevant problems. In order to address the hypothesis that central and bridge symptoms are valuable treatment targets, this study simulated five distinct attack conditions on the psychopathological networks by deactivating symptoms based on two characteristics of central symptoms (degree and strength), two characteristics of bridge symptoms (overlap and bridgeness), and at random. The differential impact of the characteristics of these symptoms was assessed in terms of the magnitude and the extent of the attack required to achieve a maximum impact on the number of components, average path length, and connectivity. Only moderate evidence was obtained to sustain the hypothesis that central and bridge symptoms constitute preferential treatment targets. The results suggest that the degree, strength, and bridgeness attack conditions are more effective than the random attack condition only in increasing the number of components of the psychopathological networks. The degree attack condition seemed to perform better than the strength, bridgeness, and overlap attack conditions. Overlapping symptoms evidenced limited impact on the psychopathological networks. The need to address the basic mechanisms underlying the structure and dynamics of psychopathological networks through the expansion of the current methodological framework and its consolidation in more robust theories is stressed.

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

“…Here, too, opposite result patterns were reached [55,56]. Some have tried to address the centrality hypothesis by using simulation-aided procedures, showing that the removal of central nodes from a given network has no larger effect on the resultant network structure compared to removing nodes at random [57]. However, simulation studies can provide only indirect evidence with regard to the centrality hypothesis.…”

“…Taken together, prior experimental investigation of the validity of centrality measures as signaling symptom change has produced some mixed findings, with different methodologies, centrality measures, and effects used and examined across studies [33,57,58]. While the three studies using the Robinaugh et al procedure, more directly examining the validity of central nodes in predicting treatment change, did show that centrality was partially successful in doing so, this was limited to the measure used to construct the network, not generalizing to other measures of the same examined psychopathology, which should be expected under the centrality hypothesis [18].…”

On Formalizing Theoretical Expectations: Bayesian Testing of Central Structures in Psychological Networks

“…For instance, there is theoretical interest in characterizing central structures involving bridge nodes, or nodes that connect to multiple communities (i.e., clusters) within a network (c.f., Castro et al, 2019;Cramer et al, 2010aCramer et al, , 2010bJones et al, 2019). These nodes can be identified through visualizing a network (e.g., Beard et al, 2016) or bridge centrality metrics (Jones et al, 2019).…”

Why Overfitting is Not (Usually) a Problem in Partial Correlation Networks

“…Furthermore, because the advantage of those approaches is fleeting, it makes sense to use inferential methods from the start. This also allows for seamless integration of more focused theoretical predictions (Haslbeck, Ryan, Robinaugh, Waldorp, & Borsboom, 2019), say, testing hypothesized bridge symptoms in psychopathology networks (Castro et al, 2019;Jones, Ma, & McNally, 2019).…”