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Revealing the dynamic network structure of the Beck Depression Inventory-II

Published online by Cambridge University Press:  05 September 2014

L. F. Bringmann ,
L. H. J. M. Lemmens ,
M. J. H. Huibers ,
D. Borsboom  and
F. Tuerlinckx
L. F. Bringmann*
Affiliation:
Faculty of Psychology and Educational Sciences, University of Leuven, Leuven, Belgium
L. H. J. M. Lemmens
Affiliation:
Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands
M. J. H. Huibers
Affiliation:
Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands Department of Clinical Psychology, VU University of Amsterdam, Amsterdam, The Netherlands
D. Borsboom
Affiliation:
Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
F. Tuerlinckx
Affiliation:
Faculty of Psychology and Educational Sciences, University of Leuven, Leuven, Belgium
*
*Address for correspondence: L. F. Bringmann, Department Quantitative Psychology and Individual Differences, University of Leuven, Tiensestraat 102 – Box 3713, 3000 Leuven, Belgium. (Email: laura.bringmann@ppw.kuleuven.be)
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Abstract

Background

Structured interviews and questionnaires are important tools to screen for major depressive disorder. Recent research suggests that, in addition to studying the mean level of total scores, researchers should focus on the dynamic relations among depressive symptoms as they unfold over time. Using network analysis, this paper is the first to investigate these patterns of short-term (i.e. session to session) dynamics for a widely used psychological questionnaire for depression – the Beck Depression Inventory (BDI-II).

Method

With the newly developed vector autoregressive (VAR) multilevel method we estimated the network of symptom dynamics that characterizes the BDI-II, based on repeated administrations of the questionnaire to a group of depressed individuals who participated in a treatment study of an average of 14 weekly assessments. Also the centrality of symptoms and the community structure of the network were examined.

Results

The analysis showed that all BDI-II symptoms are directly or indirectly connected through patterns of temporal influence. In addition, these influences are mutually reinforcing, ‘loss of pleasure’ being the most central item in the network. Community analyses indicated that the dynamic structure of the BDI-II involves two clusters, which is consistent with earlier psychometric analyses.

Conclusion

The network approach expands the range of depression research, making it possible to investigate the dynamic architecture of depression and opening up a whole new range of questions and analyses. Regarding clinical practice, network analyses may be used to indicate which symptoms should be targeted, and in this sense may help in setting up treatment strategies.

Keywords

BDI-IIdepression symptomslongitudinal datamultilevelnetwork analysesvector autoregressive (VAR)
Type
Original Articles
Information
Psychological Medicine , Volume 45 , Issue 4 , March 2015 , pp. 747 - 757
Copyright
Copyright © Cambridge University Press 2014 

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