Hostname: page-component-8448b6f56d-c4f8m Total loading time: 0 Render date: 2024-04-15T05:49:33.185Z Has data issue: false hasContentIssue false
  • English
  • Français

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)
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

APA (2000). Diagnostic and Statistical Manual of Mental Disorders, 4th edn, Text Revision. American Psychiatric Association: Washington, DC.Google Scholar
Arnau, RC, Meagher, MW, Norris, MP, Bramson, R (2001). Psychometric evaluation of the Beck Depression Inventory-II with primary care medical patients. Health Psychology 20, 112–119.10.1037/0278-6133.20.2.112Google Scholar
Beck, AT (1964). Thinking and depression: II. Theory and therapy. Archives of General Psychiatry 10, 561–571.10.1001/archpsyc.1964.01720240015003Google Scholar
Beck, AT, Rush, AJ, Shaw, BF, Emery, G (1979). Cognitive Therapy of Depression. Guilford: New York.Google Scholar
Beck, AT, Steer, RA, Ball, R, Ranieri, W (1996 a). Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. Journal of Personality Assessment 67, 588–597.Google Scholar
Beck, AT, Steer, RA, Brown, GK (1996 b) Manual for the Beck Depression Inventory, 2nd edn. Psychological Corporation: San Antonio, TX.Google Scholar
Beck, AT, Ward, CH, Mendelson, M, Mock, J, Erbaugh, J (1961). An inventory for measuring depression. Archives of General Psychiatry 4, 561–571.10.1001/archpsyc.1961.01710120031004Google Scholar
Benjamini, Y, Hochberg, Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B Methodological 57, 289–300.Google Scholar
Benjamini, Y, Krieger, AM, Yekutieli, D (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93, 491–507.10.1093/biomet/93.3.491Google Scholar
Bhar, SS, Gelfand, LA, Schmid, SP, Gallop, R, DeRubeis, RJ, Hollon, SD, Amsterdam, JD, Shelton, RC, Beck, AT (2008). Sequence of improvement in depressive symptoms across cognitive therapy and pharmacotherapy. Journal of Affective Disorders 110, 161–166.Google Scholar
Boccaletti, S, Latora, V, Moreno, Y, Chavez, M, Hwang, D-U (2006). Complex networks: structure and dynamics. Physics Reports 424, 175–308.Google Scholar
Boker, SM, Molenaar, P, Nesselroade, JR (2009). Issues in intraindividual variability: individual differences in equilibria and dynamics over multiple time scales. Psychology and Aging 24, 858–862.10.1037/a0017912Google Scholar
Borsboom, D (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology 64, 1089–1108.10.1002/jclp.20503Google Scholar
Box, JEP, Jenkins, GM, Reinsel, GC (1994). Time Series Analysis: Forecasting and Control. Prentice Hall: Englewood Cliffs, NJ.Google Scholar
Bringmann, LF, Vissers, N, Wichers, M, Geschwind, N, Kuppens, P, Peeters, F, Borsboom, D, Tuerlinckx, F (2013). A network approach to psychopathology: new insights into clinical longitudinal data. PLoS ONE 8, e60188.10.1371/journal.pone.0060188Google Scholar
Cramer, AOJ, Borsboom, D, Aggen, SH, Kendler, KS (2012). The pathoplasticity of dysphoric episodes: differential impact of stressful life events on the patterns of depressive symptom inter-correlations. Psychological Medicine 42, 957–965.10.1017/S003329171100211XGoogle Scholar
Cramer, AOJ, Waldorp, LJ, van der Maas, HLJ, Borsboom, D (2010). Comorbidity: a network perspective. Behavioral and Brain Sciences 33, 137–193.10.1017/S0140525X09991567Google Scholar
Cuijpers, P, Geraedts, AS, van Oppen, P, Andersson, G, Markowitz, JC, van Straten, A (2011). Interpersonal psychotherapy for depression: a meta-analysis. American Journal of Psychiatry 168, 581–592.10.1176/appi.ajp.2010.10101411Google Scholar
Cuijpers, P, van Straten, A, Andersson, G, van Oppen, P (2008). Psychotherapy for depression in adults: a meta-analysis of comparative outcome studies. Journal of Consulting and Clinical Psychology 76, 909–922.10.1037/a0013075Google Scholar
Epskamp, S, Cramer, AOJ, Waldorp, LJ, Schmittmann, VD, Borsboom, D (2012). Qgraph: network representations of relationships in data. R package version 0.4.10 (http://CRAN.R-project.org/package=qgraph).Google Scholar
Fournier, JC, DeRubeis, RJ, Hollon, SD, Gallop, R, Shelton, RC, Amsterdam, JD (2013). Differential change in specific depressive symptoms during antidepressant medication or cognitive therapy. Behaviour Research and Therapy 51, 392–398.10.1016/j.brat.2013.03.010Google Scholar
Fried, EI, Nesse, RM, Zivin, K, Guille, C, Sen, S (2013). Depression is more than the sum score of its parts: individual DSM symptoms have different risk factors. Psychological Medicine. Published online: 2 December 2013. doi:10.1017/S0033291713002900.Google Scholar
Fruchterman, TMJ, Reingold, EM (1991). Graph drawing by force-directed placement. Software – Practice and Experience 21, 1129–1164.10.1002/spe.4380211102Google Scholar
Girvan, M, Newman, M (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 7821–7826.10.1073/pnas.122653799Google Scholar
Granger, CWJ (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438.10.2307/1912791Google Scholar
Hamaker, EL, Dolan, CV (2009). Idiographic data analysis: quantitative methods – from simple to advanced. In Dynamic Process Methodology in the Social and Developmental Sciences, pp. 191–216. Springer: New York.10.1007/978-0-387-95922-1_9Google Scholar
Hamilton, M (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry 23, 56–62.Google Scholar
Hardeveld, F, Spijker, J, De Graaf, R, Nolen, WA, Beekman, ATF (2010). Prevalence and predictors of recurrence of major depressive disorder in the adult population. Acta Psychiatrica Scandinavica 122, 184–191.10.1111/j.1600-0447.2009.01519.xGoogle Scholar
Hollon, SD, Ponniah, K (2010). A review of empirically supported psychological therapies for mood disorders in adults. Depression and Anxiety 27, 891–932.10.1002/da.20741Google Scholar
Hollon, SD, Thase, ME, Markowitz, JC (2002). Treatment and prevention of depression. Psychological Science in the Public Interest 3, 39–77.10.1111/1529-1006.00008Google Scholar
Kendler, KS, Zachar, P, Craver, C (2011). What kinds of things are psychiatric disorders? Psychological Medicine 41, 1143–1150.10.1017/S0033291710001844Google Scholar
Kessler, RC, Berglund, P, Demler, O, Jin, R, Koretz, D, Merikangas, KR, Rush, AJ, Walters, EE, Wang, PS (2003). The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). Journal of the American Medical Association 289, 3095–3105.10.1001/jama.289.23.3095Google Scholar
Klerman, GL, Weissman, MM, Rounsaville, BJ, Chevron, ES (1984). Interpersonal Psychotherapy for Depression. Basic Books: New York.Google Scholar
Lamers, F, de Jonge, P, Nolen, WA, Smit, JH, Zitman, FG, Beekman, AT, Penninx, BW (2010). Identifying depressive subtypes in a large cohort study: results from the Netherlands Study of Depression and Anxiety (NESDA). Journal of Clinical Psychiatry 71, 1582–1589.10.4088/JCP.09m05398bluGoogle Scholar
Lamers, F, Rhebergen, D, Merikangas, KR, de Jonge, P, Beekman, ATF, Penninx, BWJH (2012). Stability and transitions of depressive subtypes over a 2-year follow-up. Psychological Medicine 17, 1–11.Google Scholar
Lemmens, LH, Arntz, A, Peeters, FP, Hollon, SD, Roefs, A, Huibers, MJ (2011). Effectiveness, relapse prevention and mechanisms of change of cognitive therapy vs. interpersonal therapy for depression: study protocol for a randomised controlled trial. Trials 12, 150.10.1186/1745-6215-12-150Google Scholar
Lütkepohl, H (2005). New Introduction to Multiple Time Series Analysis. Springer: Berlin.10.1007/978-3-540-27752-1Google Scholar
Molenaar, PC, Campbell, CG (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science 18, 112–117.10.1111/j.1467-8721.2009.01619.xGoogle Scholar
Opsahl, T, Agneessens, F, Skvoretz, F (2010). Node centrality in weighted networks: generalizing degree and shortest paths. Social Networks 32, 245–251.10.1016/j.socnet.2010.03.006Google Scholar
Pons, P, Latapy, M (2005). Computing communities in large networks using random walks. In Computer and Information Sciences-ISCIS, pp. 284–293. Springer: Berlin.Google Scholar
R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria (http://www.R-project.org/).Google Scholar
Rush, AJ, Kovacs, M, Beck, AT, Weissenburger, J, Hollon, SD (1981). Differential effects of cognitive therapy and pharmacotherapy on depressive symptoms. Journal of Affective Disorders 3, 221–229.Google Scholar
Schwarz, G (1978). Estimating the dimension of a model. Annals of Statistics 6, 461–464.10.1214/aos/1176344136Google Scholar
Snijders, T, Bosker, R (2012). Multilevel Analysis: an Introduction to Basic and Advanced Multilevel Modeling. Sage Publications: London.Google Scholar
Steer, RA, Ball, R, Ranieri, WF, Beck, AT (1999). Dimensions of the Beck Depression Inventory-II in clinically depressed outpatients. Journal of Clinical Psychology 55, 117–128.10.1002/(SICI)1097-4679(199901)55:1<117::AID-JCLP12>3.0.CO;2-A3.0.CO;2-A>Google Scholar
Stewart, JG, Harkness, KL (2012). Symptom specificity in the acute treatment of major depressive disorder: a re-analysis of the treatment of depression collaborative research program. Journal of Affective Disorders 137, 87–97.10.1016/j.jad.2011.12.015Google Scholar
Tschacher, W, Zorn, P, Ramseyer, F (2012). Change mechanisms of schema-centered group psychotherapy with personality disorder patients. PLoS ONE 7, e39687.10.1371/journal.pone.0039687Google Scholar
Van der Does, AJW (2002). Manual of the Dutch Version of the Beck Depression Inventory (BDI-II-NL). Harcourt Publishers: Amsterdam.Google Scholar
Walls, TA, Schafer, JL (2006). Models for Intensive Longitudinal Data. Oxford University Press: Oxford.10.1093/acprof:oso/9780195173444.001.0001Google Scholar
Wichers, M (2014). The dynamic nature of depression: a new micro-level perspective of mental disorder that meets current challenges. Psychological Medicine 44, 1349–1360.10.1017/S0033291713001979Google Scholar
WHO (2001). The World Health Report. Mental Health: New Understanding, New Hope. World Health Organization: Geneva.Google Scholar
WHO (2008). ICD-10: International Statistical Classification of Diseases and Related Health Problems, 10th Rev. edn. Author: New York, NY.Google Scholar