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

• Type of Material: Article
• Authors: Courtney Beard, McLean Hospital; Alex J. Millner, Harvard University; Marie J. C. Forgeard, McLean Hospital; Eiko I. Fried, University of Amsterdam; Kean J. Hsu, University of California; Michael T. Treadway, Emory University; Chelsea V. Leonard, Emory University; Sarah Kertz, Southern Illinois University; Thröstur Bjoegvinsson, McLean Hospital
• Language: English
• Date: 2016-12-01
• Publisher: Cambridge University Press (CUP): STM Journals
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 Cambridge University Press.
• Final Published Version (URL): http://dx.doi.org/10.1017/S0033291716002300
• Title of Journal or Parent Work: Psychological Medicine
• ISSN: 0033-2917
• Volume: 46
• Issue: 16
• Start Page: 3359
• End Page: 3369
• Grant/Funding Information: The Harvard Clinical and Translational Science Center (National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health Award UL1 TR001102) and financial contributions from Harvard University and its affiliated academic healthcare centers.; This work was conducted with support from Harvard Catalyst
• Keywords: Psychology; VALIDATION; NEUROSCIENCE; PERSPECTIVES; Life Sciences & Biomedicine; CENTRALITY; Psychology, Clinical; DISORDER; Science & Technology; GAD-7; co-morbidity; depression; SENSITIVITY; COMORBIDITY; SUICIDE; network analysis; causal systems; Social Sciences; Anxiety; Psychiatry; PSYCHOPATHOLOGY
• Research Categories: Psychology, Behavioral; Psychology, General

Abstract

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.