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Network modeling of major depressive disorder symptoms in adult women

Published online by Cambridge University Press:  25 August 2022

Sheida Moradi Open the ORCID record for Sheida Moradi [Opens in a new window] ,
Mohammad Reza Falsafinejad ,
Ali Delavar ,
Vahid Rezaeitabar ,
Ahmad Borj'ali ,
Steven H. Aggen  and
Kenneth S. Kendler
Sheida Moradi*
Affiliation:
Department of Psychometrics, Allameh Tabataba'i University, Tehran, Iran
Mohammad Reza Falsafinejad
Affiliation:
Department of Psychometrics, Allameh Tabataba'i University, Tehran, Iran
Ali Delavar
Affiliation:
Department of Psychometrics, Allameh Tabataba'i University, Tehran, Iran
Vahid Rezaeitabar
Affiliation:
Department of Statistics, Allameh Tabataba'i University, Tehran, Iran
Ahmad Borj'ali
Affiliation:
Department of Clinical Psychology, Allameh Tabataba'i University, Tehran, Iran
Steven H. Aggen
Affiliation:
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA Department of Psychiatry, Virginia Commonwealth University, Richmond VA, USA
Kenneth S. Kendler
Affiliation:
Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA Department of Psychiatry, Virginia Commonwealth University, Richmond VA, USA
*
Author for correspondence: Sheida Moradi, E-mail: sheidamoradi2016@gmail.com
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Abstract

Background

Major depressive disorder (MDD) is one of the growing human mental health challenges facing the global health care system. In this study, the structural connectivity between symptoms of MDD is explored using two different network modeling approaches.

Methods

Data are from ‘the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD)’. A cohort of N = 2163 American Caucasian female-female twins was assessed as part of the VATSPSUD study. MDD symptoms were assessed using personal structured clinical interviews. Two network analyses were conducted. First, an undirected network model was estimated to explore the connectivity between the MDD symptoms. Then, using a Bayesian network, we computed a directed acyclic graph (DAG) to investigate possible directional relationships between symptoms.

Results

Based on the results of the undirected network, the depressed mood symptom had the highest centrality value, indicating its importance in the overall network of MDD symptoms. Bayesian network analysis indicated that depressed mood emerged as a plausible driving symptom for activating other symptoms. These results are consistent with DSM-5 guidelines for MDD. Also, somatic weight and appetite symptoms appeared as the strongest connections in both networks.

Conclusions

We discuss how the findings of our study might help future research to detect clinically relevant symptoms and possible directional relationships between MDD symptoms defining major depression episodes, which would help identify potential tailored interventions. This is the first study to investigate the network structure of VATSPSUD data using both undirected and directed network models.

Keywords

Bayesian networksmajor depressive disordernetwork modelingundirected network models
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 12 , September 2023 , pp. 5449 - 5458
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Benfer, N., Bardeen, J. R., Spitzer, E. G., & Rogers, T. A. (2021). A network analysis of two conceptual approaches to the etiology of PTSD. Journal of Anxiety Disorders, 84, 102479. doi: 10.1016/j.janxdis.2021.102479CrossRefGoogle Scholar
Berlim, M. T., Richard-Devantoy, S., Dos Santos, N. R., & Turecki, G. (2020). The network structure of core depressive symptom-domains in major depressive disorder following antidepressant treatment: A randomized clinical trial. Psychological Medicine, 51(14), 2399–2413. doi: 10.1017/S0033291720001002CrossRefGoogle Scholar
Bi, Y., Wang, L., Cao, C., Fang, R., Li, G., Liu, P., … Hall, B. J. (2021). The factor structure of major depressive symptoms in a sample of Chinese earthquake survivors. BMC Psychiatry, 21(1), 59. doi: 10.1186/s12888-020-02993-3CrossRefGoogle Scholar
Bird, J. C., Evans, R., Waite, F., Loe, B. S., & Freeman, D. (2018). Adolescent paranoia: Prevalence, structure, and causal mechanisms. Schizophrenia Bulletin, 45(5), 1134–1142. doi: 10.1093/schbul/sby180CrossRefGoogle Scholar
Black, D. W., & Grant, J. E. (2014). DSM-5 guidebook (in Persian). Tehran: Arjoman book.Google Scholar
Borsboom, D. (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology, 64(9), 1089–1108. doi: 10.1002/jclp.20503CrossRefGoogle ScholarPubMed
Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. The Annual Reviews of Clinical Psychology, 9, 91–121. doi: 10.1146/annurev-clinpsy-050212-185608CrossRefGoogle Scholar
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., … Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1, 58. doi: 10.1038/s43586-021-00055-wCrossRefGoogle Scholar
Borsboom, D., Robinaugh, D. J., Psychosystems Group, Rhemtulla, M., & Cramer, A. O. J. (2018). Robustness and replicability of psychopatology networks. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 17(2), 143–144. doi: 10.1002/wps.20515CrossRefGoogle Scholar
Briganti, G., Scutari, M., & Linkowski, P. (2021). Network structure of symptoms from the Zung depression scale. Psychological Reports, 124(4), 1897–1911. doi: 10.1177/0033294120942116CrossRefGoogle ScholarPubMed
Bringmann, L. F. (2016). Dynamical networks in psychology: More than a pretty picture? (Doctoral dissertation). Retrieved from https://www.researchgate.net/publication/308874807_Dynamical_networks_in_psychology_More_than_a_pretty_picture.Google Scholar
Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., … Snippe, E. (2019). What do centrality measures measure in psychological networks? Journal of Abnormal Psychology, 128(8), 892–903. doi: 10.1037/abn0000446CrossRefGoogle ScholarPubMed
Bringmann, L. F., Lemmens, L. H. J. M., Huibers, M. J. H., Borsboom, D., & Tuerlinckx, F. (2015). Revealing the dynamic structure of the Beck Depression Inventory – II. Psychological Medicine, 45(4), 747–757. doi: 10.1017/S0033291714001809CrossRefGoogle ScholarPubMed
Castellanos, M. A., Ausín, B., Bestea, S., González -Sanguino, C., & Muñoz, M. (2020). A network analysis of major depressive disorder symptoms and age- and gender-related differences in people over 65 in a Madrid community sample (Spain). International Journal of Environmental Research and Public Health, 17(23), 8934. doi: 10.3390/ijerph17238934CrossRefGoogle Scholar
Cernis, E., Evars, R., Ehlers, A., & Freeman, D. (2021). Dissociation in relation to other mental health conditions: An exploration using network analysis. Journal of Psychiatric Research, 136, 460–467. doi: 10.1016/j.jpsychires.2020.08.023CrossRefGoogle ScholarPubMed
Cramer, A. O. J., 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(5), 957–965. doi: 10.1017/S003329171100211XCrossRefGoogle ScholarPubMed
Cramer, A. O. J., Waldrop, L. J., Van Der Maas, H. L. J., & Borsboom, D. (2010). Comorbidity: A network perspective. The Behavioral and Brain Sciences, 33(2-3), 137–193. doi: 10.1017/S0140525X09991567CrossRefGoogle ScholarPubMed
Dalege, J., Borsboom, D., Harreveld, F. V., & Van Der Mass, H. L. J. (2017). Network analysis on attitudes: A brief tutorial. Social Psychological and Personality Science, 8(5), 528–537. doi: 10.1177/1948550617709827CrossRefGoogle ScholarPubMed
Epskamp, S. (2017). Network psychometrics (Doctoral dissertation). Retrieved from https://dare.uva.nl/search?identifier=a76273c6-6abc-4cc7-a2e9-3b5f1ae3c29e.Google Scholar
Epskamp, S. (2020). Psychometric network models from time-series and panel data. Psychometrika, 85(1), 206–231. doi: 10.1007/s11336-020-09697-3CrossRefGoogle ScholarPubMed
Epskamp, S., Borsboom, D., & Fried, E. I. (2018a). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. doi: 10.3758/s13428-017-0862-1CrossRefGoogle ScholarPubMed
Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualization of relationships in psychometric data. Journal of Statistical Software, 48(4), 1–18. doi: 10.18637/jss.v048.i04CrossRefGoogle Scholar
Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018b). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453–480. doi: 10.1080/00273171.2018.1454823CrossRefGoogle ScholarPubMed
Fried, E. I., & Nesse, R. M. (2015). Depression is not a consistent syndrome: An investigation of unique symptom patterns in the STAR*D study. Journal of Affective Disorders, 172, 96–102. doi: 10.1016/j.jad.2014.10.010CrossRefGoogle Scholar
Greden, J. F. (2001). The burden of recurrent depression: Causes, consequences, and future prospects. Journal of Clinical Psychiatry, 62(Suppl 22), 5–9. Retrieved from https://pubmed.ncbi.nlm.nih.gov/11599650/.Google ScholarPubMed
Haslbeck, J. M. B., & Fried, E. I. (2017). How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets. Psychological Medicine, 47(16), 2767–2776. doi: 10.1017/S0033291717001258CrossRefGoogle ScholarPubMed
Hevey, D. (2018). Network analysis: A brief overview and tutorial. Health Psychology and Behavioral Medicine, 6(1), 301–328. doi: 10.1080/21642850.2018.1521283CrossRefGoogle ScholarPubMed
Huey, N. S., Guan, N. C., Gill, J. S., Hui, K. O., Sulaiman, A. H., & Kanagasundram, S. (2018). Core symptoms of major depressive disorder among palliative care patients. International Journal of Environmental Research and Public Health, 15(8), 1758. doi: 10.3390/ijerph15081758CrossRefGoogle ScholarPubMed
Kamenov, K., Cabello, M., Neito, M., Bernard, R., Kohls, E., Rumme-Kluge, C., & Ayuso-Mareos, J. L. (2017). Research recommendations for improving measurement of treatment effectiveness in depression. Frontiers in Psychology, 8, 356. doi: 10.3389/fpsyg.2017.00356CrossRefGoogle ScholarPubMed
Kendler, K. S., & Prescott, C. A. (2006). Genes, environment, and psychopathology: Understanding the causes of psychiatric and substance use disorders. New York: The Guilford Press.Google Scholar
Kennedy, S. H. (2008). Core symptoms of major depressive disorder: Relevance to diagnosis and treatment. Dialogues in Clinical Neuroscience, 10(3), 271–277. doi: 10.31887/DCNS.2008.10.3/shkennedyCrossRefGoogle ScholarPubMed
Kossakowski, J. J., Gordijn, M. C. M., Riese, H., & Waldorp, L. J. (2019). Applying a dynamical systems model and network theory to major depressive disorder. Frontiers in Psychology, 10, 1762. doi: 10.3389/fpsyg.2019.01762CrossRefGoogle ScholarPubMed
Kuipers, J., Moffa, G., Kuipers, E., Freeman, D., & Bebbington, P. (2019). Links between psychotic and neurotic symptoms in the general population: An analysis of longitudinal British national survey data using directed acyclic graphs. Psychological Medicine, 49(3), 388–395. doi: 10.1017/S0033291718000879CrossRefGoogle ScholarPubMed
Kupferberg, A., Bicks, L., & Hasler, G. (2016). Social functioning in major depressive disorder. Neuroscience and Biobehavioral Reviews, 69, 313–332. doi: 10.1016/j.neubiorev.2016.07.002CrossRefGoogle ScholarPubMed
Margaroli, M., Calderon, A., & Bonanno, G. A. (2021). Networks of major depressive disorder: A systematic review. Clinical Psychology Review, 85, 102000. doi: 10.1016/j.cpr.2021.102000CrossRefGoogle Scholar
McNally, R. J., Heeren, A., & Robinaugh, D. J. (2017a). A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse. European Journal of Psychotraumatology, 8(sup3), 1341276. doi: 10.1080/20008198.2017.1341276CrossRefGoogle ScholarPubMed
McNally, R. J., Mair, P., Mungo, B. L., & Reimann, B. C. (2017b). Co-morbid obsessive-compulsive disorder and depression: A Bayesian network approach. Psychological Medicine, 47(7), 1204–1214. doi: 10.1017/S0033291716003287CrossRefGoogle ScholarPubMed
Moffa, G., Catone, G., Kuipers, J., Kuipers, E., Freeman, D., Marwaha, S., … Bebbington, P. (2017). Using directed acyclic graphs in epidemiological research in psychosis: An analysis of the role of bullying in psychosis. Schizophrenia Bulletin, 43(6), 1273–1279. doi: 10.1093/schbul/sbx013CrossRefGoogle ScholarPubMed
Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge: Cambridge University Press.Google Scholar
Prescott, C. A., Aggen, S. H., & Kendler, K. S. (2000). Sex-specific genetic influences on the comorbidity of alcoholism and major depression in a population-based sample of US twins. Archives of General Psychiatry, 57(8), 803–811. doi: 10.1001/archpsyc.57.8.803CrossRefGoogle Scholar
Reddy, M. S. (2010). Depression: The disorder and the burden. Indian Journal of Psychological Medicine, 32(1), 1–2. doi: 10.4103/0253-7176.70510CrossRefGoogle ScholarPubMed
Robinaugh, D. J., Hoekstra, R. H. A., Toner, E. R., & Boorsboom, D. (2019). The network approach to psychopathology: A review of the literature 2008-2018 and an agenda for future research. Psychological Medicine, 50(3), 353–366. doi: 10.1017/S0033291719003404CrossRefGoogle 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(6), 747–757. doi: 10.1037/abn0000181CrossRefGoogle ScholarPubMed
Rodebaugh, T. L., Tonge, N. A., Piccirillo, M. L., Fried, E. I., Horenstein, A., Morrison, A. S., … Heimberg, R. G. (2018). Does centrality in a cross-sectional network suggest intervention targets for social anxiety disorder? Journal of Consulting and Clinical Psychology, 86(10), 831–844. doi: 10.1037/ccp0000336CrossRefGoogle 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(1), e0191675. doi: 10.1371/journal.pone.0191675CrossRefGoogle ScholarPubMed
Schmittmann, V. D., Cramer, A. O. J., Waldrop, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31(1), 43–53. doi: 10.1016/j.newideapsych.2011.02.007CrossRefGoogle Scholar
Scutari, M. (2010). Learning Bayesian networks with the bnlearn R package. Journal of Statistical Software, 35(3), 1–22. doi: 10.18637/jss.v035.i03CrossRefGoogle Scholar
Scutari, M., & Nagarajan, R. (2013). Identifying significant edges in graphical models of molecular networks. Artificial Intelligence in Medicine, 57(3), 207–217. doi: 10.1016/j.artmed.2012.12.006CrossRefGoogle ScholarPubMed
Tolentino, J. C., & Schmidt, S. L. (2018). DSM-5 criteria and depression severity: Implications for clinical practice. Frontiers in Psychiatry, 9, 450. doi: 10.3389/fpsyt.2018.00450CrossRefGoogle ScholarPubMed
Ucak, S. (2021). A network analysis on mental health symptoms to identify possible intervention points in a university environment (Master's thesis). Retrieved from https://scholarworks.unist.ac.kr/handle/201301/53675.Google Scholar
Van Borkulo, C. D. (2016). Package IsingFit. Retrieved from http://cran.r-project.org/web/packages/IsingFit/IsingFit.pdf.Google Scholar
Van Borkulo, C. D. (2018). Symptom network models in depression research: From methodological exploration to clinical application (Doctoral dissertation). Retrieved from https://www.researchgate.net/publication/323389094_Symptom_network_models_in_depression_research_From_methodological_exploration_to_clinical_application.Google 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. doi: 10.1038/srep05918CrossRefGoogle ScholarPubMed
Whiteford, H. A., Ferrari, A. J., Degenhardt, L., Feigin, V., & Vos, T. (2015). The global burden of mental, neurological and substance use disorders: An analysis from the Global Burden of Disease Study 2010. PLoS ONE, 10(2), e0116820. doi: 10.1371/journal.pone.0116820CrossRefGoogle ScholarPubMed
World Health Organization. (2003). The world health report 2003: Shaping the future. Geneva: World Health Organization. Retrieved from https://apps.who.int/iris/handle/10665/42789.Google Scholar
World Health Organization. (2008). mhGAP: Mental health gap action programme: Scaling up care for mental, neurological and substance use disorders. Geneva: World Health Organization. Retrieved from https://apps.who.int/iris/handle/10665/43809.Google Scholar
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