Centrality measures in psychological networks: A simulation study on identifying effective treatment targets

TL;DR

This study explores how centrality measures in psychological networks can help identify key symptoms for effective mental health treatments.

Contribution

The novelty lies in comparing cascade-based and normal attacks to evaluate how centrality measures influence network disruption in psychological symptom networks.

Findings

Centrality measures significantly affect network disruption in both normal and cascade attacks.

Degree centrality had the highest impact on network properties like number of components and average path length.

Abstract

The network theory of psychopathology suggests that symptoms in a disorder form a network and that identifying central symptoms within this network might be important for an effective and personalized treatment. However, recent evidence has been inconclusive. We analyzed contemporaneous idiographic networks of depression and anxiety symptoms. Two approaches were compared: a cascade-based attack where symptoms were deactivated in decreasing centrality order, and a normal attack where symptoms were deactivated based on original centrality estimates. Results showed that centrality measures significantly affected the attack’s magnitude, particularly the number of components and average path length in both normal and cascade attacks. Degree centrality consistently had the highest impact on the network properties. This study emphasizes the importance of considering centrality measures when identifying treatment targets in psychological networks. Further research is needed to better understand the causal relationships and predictive capabilities of centrality measures in personalized treatments for mental disorders.

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