Identifying Highly Influential Nodes in the Complicated Grief Network

By D. Robinaugh, Alexander J. Millner, Richard J. McNally. Journal of Abnormal Psychology, 2016

1,511 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/af03cdacecc7924c24623731033eb998e8b1cafa

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In Cognitive Neuroscience

1. Ruofan Ma
2. Payton J. Jones
3. Jutta Joormann
4. Jonas Everaert
5. Richard J. McNally
6. Ian H. Gotlib
7. Michael Vanderlind
8. Catherine D’Avanzato

In Developmental and Educational Psychology

1. Ruofan Ma
2. Payton J. Jones
3. Richard J. McNally
4. Jutta Joormann

In Experimental and Cognitive Psychology

1. Jonas Everaert
2. Ruofan Ma
3. Payton J. Jones
4. Jutta Joormann
5. Richard J. McNally
6. Ian H. Gotlib
7. J. LeMoult
8. Michael Vanderlind

Citations in the corpus

• Bridge Centrality: A Network Approach to Understanding Comorbidity (2019, methodology): “This is especially important when clinical researchers aim to target certain symptoms for therapeutic deactivation (Robinaugh et al., 2016).”
• Emotion Regulation Difficulties Related to Depression and Anxiety: A Network Approach to Model Relations Among Symptoms, Positive Reappraisal, and Repetitive Negative Thinking (2019, background): “, strength centrality) in cases in which networks contain both positive and negative edges (Robinaugh et al., 2016).”