Network Analysis of Depression and Anxiety Symptom Relations in a Psychiatric Sample

By C. Beard, Alexander J. Millner, M. Forgeard, Eiko I. Fried, Kean J. Hsu, Michael T. Treadway, Chelsea V. Leonard, S. Kertz, T. Björgvinsson. Psychological Medicine, 2016

738 citations recorded by the source corpus.

Background Researchers have studied psychological disorders extensively from a common cause perspective, in which symptoms are treated as independent indicators of an underlying disease. In contrast, the causal systems perspective seeks to understand the importance of individual symptoms and symptom-to-symptom relationships. In the current study, we used network analysis to examine the relationships between and among depression and anxiety symptoms from the causal systems perspective. Method We utilized data from a large psychiatric sample at admission and discharge from a partial hospital program (N = 1029, mean treatment duration = 8 days). We investigated features of the depression/anxiety network including topology, network centrality, stability of the network at admission and discharge, as well as change in the network over the course of treatment. Results Individual symptoms of depression and anxiety were more related to other symptoms within each disorder than to symptoms between disorders. Sad mood and worry were among the most central symptoms in the network. The network structure was stable both at admission and between admission and discharge, although the overall strength of symptom relationships increased as symptom severity decreased over the course of treatment. Conclusions Examining depression and anxiety symptoms as dynamic systems may provide novel insights into the maintenance of these mental health problems.

Subfields: Clinical Psychology, Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/d5a1ebd97e69901b69c3bc766d57dd2462a4a0a3

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Citations in the corpus

Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology (2017, background): “In recent years, more network research has focused on comorbidity among two or more disorders (e.g., Afzali et al., 2017; Beard et al., 2016; McNally, Mair, Mugno, & Riemann, 2017; Robinaugh, Leblanc, Vuletich, & McNally, 2014), and several papers have looked into the network structures of psychopat”
Bridge Centrality: A Network Approach to Understanding Comorbidity (2019, background): “Psychometric researchers in psychopathology have relied on visual inspection of networks to identify bridge symptoms (Beard et al., 2016; Jones et al., 2017; Levinson et al., 2017; McNally, Mair, Mugno, & Riemann, 2017).”
Network analysis: a brief overview and tutorial (2018, methodology): “The EBIC has been widely used in psychology networks (e.g. Beard et al., 2016; Isvoranu et al., 2017) and it enhances both the accuracy and interpretability of networks produced (Tibshirani, 1996).”
How well do network models predict observations? On the importance of predictability in network models (2016, background): “See [14] for a discussion of this problem.”
Emotion Regulation Difficulties Related to Depression and Anxiety: A Network Approach to Model Relations Among Symptoms, Positive Reappraisal, and Repetitive Negative Thinking (2019, background): “For example, of the connections between symptoms of depression and generalized anxiety disorder, a study found that guilty feelings were related to worryrelated symptoms and sadness was related to nervousness (Beard et al., 2016).”