Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-23T20:22:28.809Z Has data issue: false hasContentIssue false

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: [email protected])

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 

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 (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th edn. American Psychological Association: Washington, DC.Google Scholar
Avenevoli, S, Stolar, M, Li, J, Dierker, L, Ries Merikangas, K (2001). Comorbidity of depression in children and adolescents: models and evidence from a prospective high-risk family study. Biological Psychiatry 49, 10711081.CrossRefGoogle ScholarPubMed
Baglioni, C, Battagliese, G, Feige, B, Spiegelhalder, K, Nissen, C, Voderholzer, U, Riemann, D (2011). Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. Journal of Affective Disorders 135, 1019.CrossRefGoogle ScholarPubMed
Barrat, A, Barthélemy, M, Vespignani, A (2012). Dynamical Processes on Complex Networks, Reprint edition. Cambridge University Press: Cambridge.Google Scholar
Beard, C, Björgvinsson, T (2013). Commentary on psychological vulnerability: an integrative approach. Journal of Integrative Psychotherapy 23, 281283.CrossRefGoogle Scholar
Beard, C, Björgvinsson, T (2014). Beyond generalized anxiety disorder: psychometric properties of the GAD-7 in a heterogeneous psychiatric sample. Journal of Anxiety Disorders 28, 547552.Google Scholar
Beard, C, Hsu, KJ, Rifkin, LS, Busch, A, Björgvinsson, T (2016). Validation of the PHQ-9 in a psychiatric sample. Journal of Affective Disorders 193, 267273.CrossRefGoogle Scholar
Beck, AT, Brown, G, Berchick, RJ, Stewart, BL, Steer, RA (1990). Relationship between hopelessness and ultimate suicide: a replication with psychiatric outpatients. American Journal of Psychiatry 147, 190195.Google ScholarPubMed
Björgvinsson, T, Kertz, SJ, Bigda-Peyton, J, Rosmarin, D, Aderka, I, Neuhaus, E (2014). Effectiveness of cognitive behavior therapy for severe mood disorders in an acute naturalistic setting: a benchmarking study. Cognitive Behaviour Therapy 43, 209220.CrossRefGoogle Scholar
Borsboom, D (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology 64, 10891108.Google Scholar
Borsboom, D, Cramer, AO (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology 9, 91121.CrossRefGoogle Scholar
Bringmann, LF, Lemmens, LH, Huibers, MJ, Borsboom, D, Tuerlinckx, F (2015). Revealing the dynamic network structure of the beck depression inventory-II. Psychological Medicine 45, 747757.CrossRefGoogle ScholarPubMed
Caspi, A, Houts, RM, Belsky, DW, Goldman-Mellor, SJ, Harrington, H, Israel, S, Meier, MH, Ramrakha, S, Shalev, I, Poulton, R, Moffitt, TE (2014). The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clinical Psychological Science 2, 119137.Google Scholar
Cramer, AO, Waldorp, LJ, van der Maas, HL, Borsboom, D (2010). Comorbidity: a network perspective. Behavioral and Brain Sciences 33, 137150.CrossRefGoogle ScholarPubMed
Craske, MG, Barlow, DH (2006). Mastery of Your Anxiety and Worry: Workbook (Treatments That Work). Oxford University Press: New York, NY.CrossRefGoogle Scholar
Davidson, RJ, Pizzagalli, D, Nitschke, JB, Putnam, K (2002). Depression: perspectives from affective neuroscience. Annual Review of Psychology 53, 545574.CrossRefGoogle ScholarPubMed
Davis, RN, Nolen-Hoeksema, S (2000). Cognitive inflexibility among ruminators and nonruminators. Cognitive Therapy and Research 24, 699711.CrossRefGoogle Scholar
Durmer, JS, Dinges, DF (2005). Neurocognitive consequences of sleep deprivation. Seminars in Neurology 25, 117129.CrossRefGoogle ScholarPubMed
Epskamp, S, Borsboom, D, Fried, EI (2016). Estimating psychological networks and their stability: a tutorial paper. ArXiv E-Prints, 1604, arXiv:1604.08462. Accessed 14 June 2016, from the arXiv database.Google Scholar
Epskamp, S, Cramer, AO, Waldorp, LJ, Schmittmann, VD, Borsboom, D (2012). Qgraph: network visualizations of relationships in psychometric data. Journal of Statistical Software 48, 118.CrossRefGoogle Scholar
Epskamp, S, Fried, EI (2016). A Primer on estimating regularized psychological networks. arXiv:1607.01367. Retrieved from http://arxiv.org/abs/1607.01367. Accessed 6 July 2016, from the arXiv database.Google Scholar
Epskamp, S, Maris, G, Waldorp, LJ, Borsboom, D (in press). Network psychometrics. In Handbook of Psychometrics (ed. Irwing, P., Hughes, D. and Booth, T.). Wiley: New York.Google Scholar
Fawcett, J, Scheftner, WA, Fogg, L, Clark, DC, Young, MA, Hedeker, D, Gibbons, R (1990). Time-related predictors of suicide in major affective disorder. American Journal of Psychiatry 147, 11891194.Google Scholar
Ferentinos, PP, Kontaxakis, VP, Havaki-Kontaxaki, BJ, Dikeos, DG, Papadimitriou, GN (2009). Fatigue and somatic anxiety in patients with major depression. Psychiatriki 20, 312318.Google Scholar
Foygel, R, Drton, M (2010). Extended bayesian information criteria for gaussian graphical models. In Advances in Neural Information Processing Systems 23, 604612. arXiv:1011.6640.Google Scholar
Fried, EI, Epskamp, S, Nesse, RM, Tuerlinckx, F, Borsboom, D (2016 a). What are ‘Good’ Depression Symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. Journal of Affective Disorders 189, 314320.Google Scholar
Fried, EI, Nesse, RM (2014). The impact of individual depressive symptoms on impairment of psychosocial functioning. PLoS ONE 9.CrossRefGoogle ScholarPubMed
Fried, EI, Nesse, RM (2015 a). Depression sum scores don't add up: why analyzing individual depression symptoms is essential. BMC Medicine 13, 72.CrossRefGoogle ScholarPubMed
Fried, EI (2015). Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward. Frontiers in Psychology 6, 309.CrossRefGoogle Scholar
Fried, EI, Nesse, RM (2015 b). Depression is not a consistent syndrome: an investigation of unique symptom patterns in the STAR*D study. Journal of Affective Disorders 172, 96102.CrossRefGoogle Scholar
Fried, EI, van Borkulo, CD, Epskamp, S, Schoevers, RA, Tuerlinckx, F, Borsboom, F (2016 b). Measuring depression over time … or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression. Psychological Assessment. Published online: 28 January 2016. doi:10.1037/pas0000275 Google Scholar
Friedman, J, Hastie, T, Tibshirani, R (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432441.CrossRefGoogle ScholarPubMed
Friedman, J, Hastie, T, Tibshirani, R (2014). glasso: Graphical lasso – estimation of Gaussian graphical models (Version 1.8). (https://rcan.r-project.org/web/packages/glasso/index.html).Google Scholar
Fruchterman, TMJ, Reingold, EM (1991). Graph drawing by force-directed placement. Software: Practice and Experience 21, 11291164.Google Scholar
Hamaker, EL (2012). Why researchers should think within-person: A paradigmatic view. In Handbook of Research Methods for Studying Daily Life (ed. Mehl, M. R. and Conner, T. S.), pp. 4361, Guilford Publications: New York.Google Scholar
Harris, PA, Taylor, R, Thielke, R, Payne, J, Gonzalez, J, Conde, D (2009). Research electronic data capture (REDCap) - A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics 42, 377381.CrossRefGoogle ScholarPubMed
Joormann, J, Gotlib, IH (2008). Updating the contents of working memory in depression: interference from irrelevant negative material. Journal of Abnormal Psychology 117, 182192.Google Scholar
Kapur, S, Phillips, AG, Insel, TR (2012). Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Molecular Psychiatry 17, 11741179.CrossRefGoogle Scholar
Kaufman, J, Charney, D (2000). Comorbidity of mood and anxiety disorders. Depression and Anxiety 12, 6976.Google Scholar
Kolaczyk, ED, Csárdi, G (2014). Statistical Analysis of Network Data with R, vol. 65. Springer New York: New York, NY.CrossRefGoogle Scholar
Kroenke, K, Spitzer, RL, Williams, JB (2001). The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine 9, 606613.CrossRefGoogle Scholar
Kroenke, K, Spitzer, RL, Williams, JB, Monohan, PO, Löwe, B (2007). Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection. Annuals of Internal Medicine 146, 317325.Google Scholar
Lauritzen, SL (1996). Graphical Models. Clarendon Press: New York.Google Scholar
Löwe, B, Decker, O, Müller, S, Brähler, E, Schellberg, D, Herzog, W, Herzberg, PY (2008). Validation and standardization of the Generalized Anxiety Disorder Screener (GAD-7) in the general population. Medical Care 46, 266274.Google Scholar
McNally, RJ, Robinaugh, DJ, Wu, GW, Wang, L, Deserno, MK and Borsboom, D. (2015). Mental disorders as causal systems: a network approach to posttraumatic stress disorder. Clinical Psychological Science 3, 836849.CrossRefGoogle Scholar
Moffitt, TE, Harrington, H, Caspi, A, Kim-Cohen, J, Goldberg, D, Gregory, AM, Poulton, R (2007). Depression and generalized anxiety disorder: cumulative and sequential comorbidity in a birth cohort followed prospectively to age 32 years. Archives of General Psychiatry 64, 651660.CrossRefGoogle Scholar
Molenaar, PCM (2010). Latent variable models are network models. Behavioral and Brain Sciences 33, 166166.CrossRefGoogle ScholarPubMed
Neckelmann, D, Mykletun, A, Dahl, AA (2007). Chronic insomnia as a risk factor for developing anxiety and depression. Sleep 30, 873880.CrossRefGoogle ScholarPubMed
Novati, A, Roman, V, Cetin, T, Hagewoud, R, den Boer, JA, Luiten, PG, Meerlo, P (2008). Chronically restricted sleep leads to depression-like changes in neurotransmitter receptor sensitivity and neuroendocrine stress reactivity in rats. Sleep 31, 15791585.CrossRefGoogle ScholarPubMed
Opsahl, T, Agneessens, F, Skvoretz, J (2010). Node centrality in weighted networks: generalizing degree and shortest paths. Social Networks 32, 245251.CrossRefGoogle 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
Robinaugh, DJ, LeBlanc, NJ, Vuletich, HA, McNally, RJ (2014). Network analysis of persistent complex bereavement disorder in conjugally bereaved adults. Journal of Abnormal Psychology 123, 510522.Google Scholar
Robinaugh, DJ, Millner, AJ, McNally, RJ (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology 125, 747757.CrossRefGoogle ScholarPubMed
Schmittmann, VD, Cramer, AO, Waldorp, LJ, Epskamp, S, Kievit, RA, Borsboom, D (2013). Deconstructing the construct: a network perspective on psychological phenomena. New Ideas in Psychology 31, 4353.CrossRefGoogle Scholar
Sheehan, DV, Lecrubier, Y, Sheehan, HK, Amorim, P, Janavs, J, Weiller, E, Hergueta, T, Baker, R, Dunbar, GC (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry 59, 2233.Google ScholarPubMed
Spitzer, RL, Kroenke, K, Williams, JB, Löwe, B (2006). A brief measure for assessing generalized anxiety disorder: the GAD-7. Archives of Internal Medicine 166, 10921097.CrossRefGoogle ScholarPubMed
Stefanopoulou, E, Hirsch, CR, Hayes, S, Adlam, A, Coker, S (2014). Are attentional control resources reduced by worry in generalized anxiety disorder? Journal of Abnormal Psychology 123, 330335.CrossRefGoogle ScholarPubMed
Tibshirani, R (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58, 267288.Google Scholar
Treadway, MT, Zald, DH (2011). Reconsidering anhedonia in depression: lessons from translational neuroscience. Neuroscience and Biobehavioral Reviews 35, 537555.CrossRefGoogle ScholarPubMed
van Borkulo, C, Boschloo, L, Borsboom, D, Penninx, BH, Waldorp, LJ, Schoevers, RA (2015). Association of symptom network structure with the course of longitudinal depression. Journal of the American Medical Association (JAMA) Psychiatry, 12191226.Google Scholar
Wittchen, HU, Kessler, RC, Pfister, H, Lieb, M (2000). Why do people with anxiety disorders become depressed? A prospective-longitudinal community study. Acta Psychiatrica Scandinavica (Suppl.) 406, 1423.Google Scholar
Zavos, HMS, Rijsdijk, FV, Eley, TC (2012). A longitudinal, genetically informative, study of associations between anxiety sensitivity, anxiety, and depression. Behavior Genetics 42, 592602.CrossRefGoogle ScholarPubMed
Zhang, B, Horvath, S (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology 4(1), doi:10.2202/1544-6115.1128.Google Scholar
Supplementary material: File

Beard supplementary material

Beard supplementary material 1

Download Beard supplementary material(File)
File 652.7 KB