Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-10T05:21:32.084Z Has data issue: false hasContentIssue false
  • English
  • Français

The network structure of core depressive symptom-domains in major depressive disorder following antidepressant treatment: a randomized clinical trial

Published online by Cambridge University Press:  21 April 2020

Marcelo T. Berlim ,
Stephane Richard-Devantoy ,
Nicole Rodrigues dos Santos  and
Gustavo Turecki
Marcelo T. Berlim*
Affiliation:
Depressive Disorders Program & McGill Group for Suicide Studies, McGill University & Douglas Mental Health University Institute, Montréal, Québec, Canada
Stephane Richard-Devantoy
Affiliation:
Depressive Disorders Program & McGill Group for Suicide Studies, McGill University & Douglas Mental Health University Institute, Montréal, Québec, Canada
Nicole Rodrigues dos Santos
Affiliation:
Depressive Disorders Program & McGill Group for Suicide Studies, McGill University & Douglas Mental Health University Institute, Montréal, Québec, Canada
Gustavo Turecki
Affiliation:
Depressive Disorders Program & McGill Group for Suicide Studies, McGill University & Douglas Mental Health University Institute, Montréal, Québec, Canada
*
Authors for correspondence: Marcelo T. Berlim, E-mail: marcelo.berlim@mcgill.ca; Gustavo Turecki, E-mail: gustavo.turecki@mcgill.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Network analysis (NA) conceptualizes psychiatric disorders as complex dynamic systems of mutually interacting symptoms. Major depressive disorder (MDD) is a heterogeneous clinical condition, and very few studies to date have assessed putative changes in its psychopathological network structure in response to antidepressant (AD) treatment.

Methods

In this randomized trial with adult depressed outpatients (n = 151), we estimated Gaussian graphical models among nine core MDD symptom-domains before and after 8 weeks of treatment with either escitalopram or desvenlafaxine. Networks were examined with the measures of cross-sectional and longitudinal structure and connectivity, centrality and predictability as well as stability and accuracy.

Results

At baseline, the most connected MDD symptom-domains were fatigue–cognitive disturbance, whereas at week 8 they were depressed mood–suicidality. Overall, the most central MDD symptom-domains at baseline and week 8 were, respectively, fatigue and depressed mood; in contrast, the most peripheral symptom-domain across both timepoints was appetite/weight disturbance. Furthermore, the psychopathological network at week 8 was significantly more interconnected than at baseline, and they were also structurally dissimilar.

Conclusion

Our findings highlight the utility of focusing on the dynamic interaction between depressive symptoms to better understand how the treatment with ADs unfolds over time. In addition, depressed mood, fatigue, and cognitive/psychomotor disturbance seem to be central MDD symptoms that may be viable targets for novel, focused therapeutic interventions.

Keywords

Antidepressant treatmentdepressive symptomsmajor depressionnetwork analysis
Type
Original Article
Information
Psychological Medicine , Volume 51 , Issue 14 , October 2021 , pp. 2399 - 2413
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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.)

Footnotes

*

Co-corresponding authors.

References

Abacioglu, C. S., Isvoranu, A. M., Verkuyten, M., Thijs, J., & Epskamp, S. (2019). Exploring multicultural classroom dynamics: A network analysis. Journal of School Psychology, 74, 90–105.CrossRefGoogle ScholarPubMed
American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders (DSM-IV-TR). Washington, DC: American Psychiatric Association Publishing.Google Scholar
Belvederi Murri, M., Amore, M., Respino, M., & Alexopoulos, G. S. (in press). The symptom network structure of depressive symptoms in late-life: Results from a European population study. Molecular Psychiatry, 1–10.Google Scholar
Berlim, M. T., & Turecki, G. (2007). Definition, assessment, and staging of treatment-resistant refractory major depression: A review of current concepts and methods. Canadian Journal of Psychiatry, 52, 46–54.CrossRefGoogle ScholarPubMed
Blanken, T. F., Van Der Zweerde, T., Van Straten, A., Van Someren, E. J. W., Borsboom, D., & Lancee, J. (2019). Introducing network intervention analysis to investigate sequential, symptom-specific treatment effects: A demonstration in co-occurring insomnia and depression. Psychotherapy and Psychosomatics, 88, 52–54.CrossRefGoogle ScholarPubMed
Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16, 5–13.CrossRefGoogle ScholarPubMed
Borsboom, D., & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121.CrossRefGoogle ScholarPubMed
Bos, F. M., Fried, E. I., Hollon, S. D., Bringmann, L. F., Dimidjian, S., DeRubeis, R. J., & Bockting, C. L. H. (2018). Cross-sectional networks of depressive symptoms before and after antidepressant medication treatment. Social Psychiatry and Psychiatric Epidemiology, 53, 617–627.CrossRefGoogle ScholarPubMed
Bos, F. M., Snippe, E., de Vos, S., Hartmann, J. A., Simons, C. J. P., van der Krieke, L., & Wichers, M. (2017). Can we jump from cross-sectional to dynamic interpretations of networks? Implications for the network perspective in psychiatry. Psychotherapy and Psychosomatics, 86, 175–177.CrossRefGoogle Scholar
Boschloo, L., van Borkulo, C. D., Borsboom, D., & Schoevers, R. A. (2016). A prospective study on how symptoms in a network predict the onset of depression. Psychotherapy and Psychosomatics, 85, 183–184.CrossRefGoogle Scholar
Bringmann, L. F., Lemmens, L. H., Huibers, M. J., Borsboom, D., & Tuerlinckx, F. (2015). Revealing the dynamic network structure of the Beck Depression Inventory-II. Psychological Medicine, 45, 747–757.CrossRefGoogle ScholarPubMed
Cao, B., Zhu, J., Zuckerman, H., Rosenblat, J. D., Brietzke, E., Pan, Z., & McIntyre, R. S. (2019). Pharmacological interventions targeting anhedonia in patients with major depressive disorder: A systematic review. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 92, 109–117.CrossRefGoogle ScholarPubMed
Charness, G., Gneezy, U., & Kuhn, M. A. (2012). Experimental methods: Between-subject and within-subject design. Journal of Economic Behavior & Organization, 81, 1–8.CrossRefGoogle Scholar
Chen, J., & Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika, 95, 759–771.CrossRefGoogle Scholar
Contreras, A., Nieto, I., Valiente, C., Espinosa, R., & Vazquez, C. (2019). The study of psychopathology from the network analysis perspective: A systematic review. Psychotherapy and Psychosomatics, 88, 71–83.CrossRefGoogle Scholar
Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L. J., & Cramer, A. O. (2015). State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality, 54, 13–29.CrossRefGoogle Scholar
Costantini, G., Richetin, J., Preti, E., Casini, E., Epskamp, S., & Perugini, M. (2019). Stability and variability of personality networks. A tutorial on recent developments in network psychometrics. Personality and Individual Differences, 136, 68–78.CrossRefGoogle Scholar
Cramer, A. O., Borsboom, D., Aggen, S. H., & Kendler, K. S. (2012). The pathoplasticity of dysphoric episodes: Differential impact of stressful life events on the pattern of depressive symptom inter-correlations. Psychological Medicine, 42, 957–965.CrossRefGoogle ScholarPubMed
Cramer, A. O., Waldorp, L. J., van der Maas, H. L., & Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and Brain Sciences, 33, 137–150, discussion 150–93.CrossRefGoogle ScholarPubMed
Danaher, P., Wang, P., & Witten, D. M. (2014). The joint graphical lasso for inverse covariance estimation across multiple classes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 76, 373–397.CrossRefGoogle ScholarPubMed
de Ron, J, Fried, EI, & Epskamp, S (2019) Psychological networks in clinical populations: Investigating the consequences of Berkson's bias. Psychological Medicine, 1–9.Google ScholarPubMed
Epskamp, S., Borsboom, D., & Fried, E. I. (2018a). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50, 195–212.CrossRefGoogle Scholar
Epskamp, S., Cramer, A. O., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). Qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1–18.CrossRefGoogle Scholar
Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23, 617–634.CrossRefGoogle ScholarPubMed
Epskamp, S., Waldorp, L. J., Mottus, R., & Borsboom, D. (2018b). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53, 453–480.CrossRefGoogle Scholar
Fawcett, J., Epstein, P., Fiester, S. J., Elkin, I., & Autry, J. H. (1987). Clinical management – imipramine/placebo administration manual. NIMH Treatment of Depression Collaborative Research Program. Psychopharmacology Bulletin, 23, 309–324.Google ScholarPubMed
Fisher, A. J., Medaglia, J. D., & Jeronimus, B. F. (2018). Lack of group-to-individual generalizability is a threat to human subjects research. Proceedings of the National Academy of Sciences of the United States of America, 115, E6106–E6115.CrossRefGoogle ScholarPubMed
Fonseca-Pedrero, E. (2017). Network analysis: A new way of understanding psychopathology? Revista de Psiquiatría y Salud Mental, 10, 206–215.CrossRefGoogle ScholarPubMed
Forbes, M. K., Wright, A. G., Markon, K. E., & Krueger, R. F. (2017). Evidence that psychopathology symptom networks have limited replicability. Journal of Abnormal Psychology, 126, 969–988.CrossRefGoogle ScholarPubMed
Forbes, M. K., Wright, A. G., Markon, K. E., & Krueger, R. F. (2019). Quantifying the reliability and replicability of psychopathology network characteristics. Multivariate Behavioral Research, 1–19.Google ScholarPubMed
Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. Advances in Neural Information Processing Systems, 23, 2020–2028.Google Scholar
Fried, E. I., & Cramer, A. O. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science, 12, 999–1020.CrossRefGoogle ScholarPubMed
Fried, E. I., Eidhof, M. B., Palic, S., Costantini, G., Huisman-van Dijk, H. M., Bockting, C. L. H., & Karstoft, K. I. (2018). Replicability and generalizability of posttraumatic stress disorder (PTSD) networks: A cross-cultural multisite study of PTSD symptoms in four trauma patient samples. Clinical Psychology Science, 6, 335–351.CrossRefGoogle ScholarPubMed
Fried, E. I., Epskamp, S., Nesse, R. M., Tuerlinckx, F., & Borsboom, D. (2016a). 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, 314–320.CrossRefGoogle Scholar
Fried, E. I., & Nesse, R. M. (2015). Depression sum-scores don't add up: Why analyzing specific depression symptoms is essential. BMC Medicine, 13, 72.CrossRefGoogle ScholarPubMed
Fried, E. I., Nesse, R. M., Zivin, K., Guille, C., & Sen, S. (2014). Depression is more than the sum score of its parts: Individual DSM symptoms have different risk factors. Psychological Medicine, 44, 2067–2076.CrossRefGoogle Scholar
Fried, E. I., van Borkulo, C. D., Cramer, A. O., Boschloo, L., Schoevers, R. A., & Borsboom, D. (2017). Mental disorders as networks of problems: A review of recent insights. Social Psychiatry and Psychiatric Epidemiology, 52, 1–10.CrossRefGoogle ScholarPubMed
Fried, E. I., van Borkulo, C. D., Epskamp, S., Schoevers, R. A., Tuerlinckx, F., & Borsboom, D. (2016b). Measuring depression over time … or not? Lack of unidimensionality and longitudinal measurement invariance in four common rating scales of depression. Psychological Assessment, 28, 1354–1367.CrossRefGoogle Scholar
Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics (Oxford, England), 9, 432–441.CrossRefGoogle ScholarPubMed
Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21, 1129–1164.Google Scholar
Hamaker, E. L. (2012). Why researchers should think ‘within-person’: A paradigmatic rationale. In Mehl, M. R. & Conner, T. S. (Eds.), Handbook of research methods for studying daily life (pp. 43–61). The Guilford Press: New York.Google Scholar
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23, 56–62.CrossRefGoogle ScholarPubMed
Haslbeck, J. M. B., & Waldorp, L. J. (2018). How well do network models predict observations? On the importance of predictability in network models. Behavior Research Methods, 50, 853–861.CrossRefGoogle ScholarPubMed
Heeren, A., Jones, P. J., & McNally, R. J. (2018). Mapping network connectivity among symptoms of social anxiety and comorbid depression in people with social anxiety disorder. Journal of Affective Disorders, 228, 75–82.CrossRefGoogle ScholarPubMed
Isvoranu, A. M., van Borkulo, C. D., Boyette, L. L., Wigman, J. T., Vinkers, C. H., & Borsboom, D. (2017). A network approach to psychosis: Pathways between childhood trauma and psychotic symptoms. Schizophrenia Bulletin, 43, 187–196.CrossRefGoogle ScholarPubMed
Kendler, K. S., Aggen, S. H., Flint, J., Borsboom, D., & Fried, E. I. (2018). The centrality of DSM and non-DSM depressive symptoms in Han Chinese women with major depression. Journal of Affective Disorders, 227, 739–744.CrossRefGoogle Scholar
Kessler, R. C., & Bromet, E. J. (2013). The epidemiology of depression across cultures. Annual Review of Public Health, 34, 119–138.CrossRefGoogle ScholarPubMed
Madhoo, M., & Levine, S. Z. (2016). Network analysis of the Quick Inventory of Depressive Symptomatology: Reanalysis of the STAR*D clinical trial. European Neuropsychopharmacology, 26, 1768–1774.CrossRefGoogle ScholarPubMed
Malhi, G. S., & Mann, J. J. (2018). Depression. Lancet (London, England), 392, 2299–2312.CrossRefGoogle ScholarPubMed
Moher, D., Hopewell, S., Schulz, K. F., Montori, V., Gotzsche, P. C., Devereaux, P. J., & Altman, D. G. (2010). CONSORT 2010 Explanation and elaboration: Updated guidelines for reporting parallel group randomised trials. British Medical Journal, 340, c869.CrossRefGoogle ScholarPubMed
Montazeri, F., de Bildt, A., Dekker, V., & Anderson, G. M. (in press). Network analysis of behaviors in the depression and autism realms: Inter-relationships and clinical implications. Journal of Autism and Developmental Disorders.Google Scholar
Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32, 245–251.CrossRefGoogle Scholar
Papageorgiou, K. A., Benini, E., Bilello, D., Gianniou, F. M., Clough, P. J., & Costantini, G. (2019). Bridging the gap: A network approach to dark triad, mental toughness, the big five, and perceived stress. Journal of Personality, 87, 1250–1263.CrossRefGoogle ScholarPubMed
Pe, L. M., Kircanski, K., Thompson, R. J., Bringmann, L. F., Tuerlinckx, F., Mestdagh, M., & Gotlib, I. H. (2014). Emotion-network density in major depressive disorder. Clinical Psychological Science, 3, 292–300.CrossRefGoogle Scholar
Pigott, H. E., Leventhal, A. M., Alter, G. S., & Boren, J. J. (2010). Efficacy and effectiveness of antidepressants: Current status of research. Psychotherapy and Psychosomatics, 79, 267–279.CrossRefGoogle ScholarPubMed
R Development Core Team. (2008). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Robinaugh, D. J., Hoekstra, R. H. A., Toner, E. R., & Borsboom, D. (2019). The network approach to psychopathology: A review of the literature 2008–2018 and an agenda for future research. Psychological Medicine, 1–14.Google Scholar
Robinaugh, D. J., Millner, A. J., & McNally, R. J. (2016). Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology, 125, 747–757.CrossRefGoogle ScholarPubMed
Rubel, J. A., Fisher, A. J., Husen, K., & Lutz, W. (2018). Translating person-specific network models into personalized treatments: Development and demonstration of the dynamic assessment treatment algorithm for individual networks (DATA-IN). Psychotherapy and Psychosomatics, 87, 249–251.CrossRefGoogle Scholar
Rush, A. J., Kraemer, H. C., Sackeim, H. A., Fava, M., Trivedi, M. H., Frank, E., & Schatzberg, A. F. (2006a). Report by the ACNP Task Force on response and remission in major depressive disorder. Neuropsychopharmacology, 31, 1841–1853.CrossRefGoogle Scholar
Rush, A. J., Trivedi, M. H., Ibrahim, H. M., Carmody, T. J., Arnow, B., Klein, D. N., & Keller, M. B. (2003). The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): A psychometric evaluation in patients with chronic major depression. Biological Psychiatry, 54, 573–583.CrossRefGoogle ScholarPubMed
Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., & Fava, M. (2006b). Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: A STAR*D report. American Journal of Psychiatry, 163, 1905–1917.CrossRefGoogle Scholar
Santos, H. P. Jr, Kossakowski, J. J., Schwartz, T. A., Beeber, L., & Fried, E. I. (2018). Longitudinal network structure of depression symptoms and self-efficacy in low-income mothers. PLoS One, 13, e0191675.CrossRefGoogle ScholarPubMed
Snippe, E., Viechtbauer, W., Geschwind, N., Klippel, A., de Jonge, P., & Wichers, M. (2017). The impact of treatments for depression on the dynamic network structure of mental states: Two randomized controlled trials. Scientific Reports, 7, 46523.CrossRefGoogle ScholarPubMed
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2, 53–55.CrossRefGoogle ScholarPubMed
Telford, C., McCarthy-Jones, S., Corcoran, R., & Rowse, G. (2012). Experience sampling methodology studies of depression: The state of the art. Psychological Medicine, 42, 1119–1129.CrossRefGoogle ScholarPubMed
Terluin, B., de Boer, M. R., & de Vet, H. C. (2016). Differences in connection strength between mental symptoms might be explained by differences in variance: Reanalysis of network data did not confirm staging. PLoS One, 11, e0155205.CrossRefGoogle Scholar
van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A., & Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports, 4, 5918.CrossRefGoogle ScholarPubMed
van Borkulo, C., Boschloo, L., Borsboom, D., Penninx, B. W., Waldorp, L. J., & Schoevers, R. A. (2015). Association of symptom network structure with the course of depression. JAMA Psychiatry, 72, 1219–1226.CrossRefGoogle Scholar
van Borkulo, C. D., Boschloo, L., Kossakowski, J., Tio, P., Schoevers, R., Borsboom, D., & Waldorp, L. J. (in press). Comparing network structures on three aspects: A permutation test.Google Scholar
Vargha, A., & Delaney, H. D. (2000). A critique and improvement of the ‘CL’ common language effect size statistics of McGraw and Wong. Journal of Educational and Behavioral Statistics, 25, 101–132.Google 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.CrossRefGoogle ScholarPubMed
Williams, D. R., Rhemtulla, M., Wysocki, A. C., & Rast, P. (2019). On nonregularized estimation of psychological networks. Multivariate Behavioral Research, 1–23.Google ScholarPubMed
Woolley, S. B., Cardoni, A. A., & Goethe, J. W. (2009). Last-observation-carried-forward imputation method in clinical efficacy trials: Review of 352 antidepressant studies. Pharmacotherapy, 29, 1408–1416.CrossRefGoogle ScholarPubMed
Supplementary material: PDF

Berlim et al. Supplementary Materials

Berlim et al. Supplementary Materials

Download Berlim et al. Supplementary Materials(PDF)
PDF 5.4 MB