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

Published online by Cambridge University Press:  14 September 2016

C. Beard*
Affiliation:
McLean Hospital/Harvard Medical School, Belmont, MA, USA
A. J. Millner
Affiliation:
McLean Hospital/Harvard Medical School, Belmont, MA, USA Department of Psychology, Harvard University, Cambridge, MA, USA
M. J. C. Forgeard
Affiliation:
McLean Hospital/Harvard Medical School, Belmont, MA, USA
E. I. Fried
Affiliation:
University of Amsterdam, Haarlem, The Netherlands
K. J. Hsu
Affiliation:
McLean Hospital/Harvard Medical School, Belmont, MA, USA Department of Psychology, University of California, Los Angeles, CA, USA
M. T. Treadway
Affiliation:
McLean Hospital/Harvard Medical School, Belmont, MA, USA Department of Psychology, Emory University, Atlanta, GA, USA
C. V. Leonard
Affiliation:
Department of Psychology, Emory University, Atlanta, GA, USA
S. J. Kertz
Affiliation:
Department of Psychology, Southern Illinois University, Carbondale, IL, USA
T. Björgvinsson
Affiliation:
McLean Hospital/Harvard Medical School, Belmont, MA, USA
*
*Address for correspondence: C. Beard, Ph.D., McLean Hospital, 115 Mill St, Mailstop 113, Belmont, MA 02478, 617.855.3557, USA. (Email: cbeard@mclean.harvard.edu)

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.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2016 

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