Identifying highly influential nodes in the complicated grief network.

Abstract

The network approach to psychopathology conceptualizes mental disorders as networks of mutually reinforcing nodes (i.e., symptoms). Researchers adopting this approach have suggested that network topology can be used to identify influential nodes, with nodes central to the network having the greatest influence on the development and maintenance of the disorder. However, because commonly used centrality indices do not distinguish between positive and negative edges, they may not adequately assess the nature and strength of a node’s influence within the network. To address this limitation, we developed two indices of a node’s expected influence (EI) that account for the presence of negative edges. To evaluate centrality and EI indices, we simulated single-node interventions on randomly generated networks. In networks with exclusively positive edges, centrality and EI were both strongly associated with observed node influence. In networks with negative edges, EI was more strongly associated with observed influence than was centrality. We then used data from a longitudinal study of bereavement to examine the association between (a) a node’s centrality and EI in the complicated grief (CG) network and (b) the strength of association between change in that node and change in the remainder of the CG network from 6 to 18-months post-loss. Centrality and EI were both correlated with the strength of the association between node change and network change. These findings suggest high-EI nodes, such as emotional pain and feeling of emptiness, may be especially important to the etiology and treatment of CG.

Network analysis of depression and anxiety symptom relationships in a psychiatric sample

“…From the common cause perspective, future studies should explore the biological correlates of latent factors, such as the p-factor (Caspi, et al 2014). From the casual systems perspective, important next steps are testing whether, compared with lower centrality nodes, nodes with higher centrality are better prospective predictors of overall network activation (Robinaugh et al, in press) and whether targeting more central nodes in treatment is more efficient and effective at reducing overall network activation compared with targeting peripheral nodes. Finally, it should be emphasized that these two conceptual frameworks are not mutually exclusive.…”

Replicability and generalizability of PTSD networks: A cross-cultural multisite study of PTSD symptoms in four trauma patient samples

“…Another possibility is that a symptom is indeed highly central and causally impacts on many other nodes in the network, but might be very difficult to target in interventions. As discussed in Robinaugh et al (Robinaugh et al, 2016), "nodes may vary in the extent to which they are amenable to change" (p. 755). In cognitive behavioral therapy, for example, clinicians usually try to reduce negative emotions indirectly by intervening on cognitions and behavior (Barlow, 2007).…”

A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse

“…Indeed, much of the early work applying network analysis to stress-related syndromes characterized them as potentially causal systems of interacting symptoms (e.g. PTSD in earthquake survivors, McNally et al, 2015; complicated grief following spousal bereavement, Robinaugh, LeBlanc, Vuletich, & McNally, 2014; Robinaugh, Millner, & McNally, 2016). …”