Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

ACC 2024 Congress

Catch up on all the top stories and latest evidence in cardiology research with our ACC 2024 Congress hub page.
ADVANCED SEARCH
Log in
Top
Social Psychiatry and Psychiatric Epidemiology
Published in: Social Psychiatry and Psychiatric Epidemiology 6/2018

Open Access 01-06-2018 | Original Paper

Cross-sectional networks of depressive symptoms before and after antidepressant medication treatment

Authors: Fionneke M. Bos, Eiko I. Fried, Steven D. Hollon, Laura F. Bringmann, Sona Dimidjian, Robert J. DeRubeis, Claudi L. H. Bockting

Published in: Social Psychiatry and Psychiatric Epidemiology | Issue 6/2018

Login to get access

Abstract

Purpose

Recent reviews have questioned the efficacy of selective serotonin reuptake inhibitors (SSRIs) above placebo response, and their working mechanisms remain unclear. New approaches to understanding the effects of SSRIs are necessary to enhance their efficacy. The aim of this study was to explore the possibilities of using cross-sectional network analysis to increase our understanding of symptom connectivity before and after SSRI treatment.

Methods

In two randomized controlled trials (total N = 178), we estimated Gaussian graphical models among 20 symptoms of the Beck Depression Inventory-II before and after 8 weeks of treatment with the SSRI paroxetine. Networks were compared on connectivity, community structure, predictability (proportion explained variance), and strength centrality (i.e., connectedness to other symptoms in the network).

Results

Symptom severity for all individual BDI-II symptoms significantly decreased over 8 weeks of SSRI treatment, whereas interconnectivity and predictability of the symptoms significantly increased. At baseline, three communities were detected; five communities were detected at week 8.

Conclusions

Findings suggest the effects of SSRIs can be studied using the network approach. The increased connectivity, predictability, and communities at week 8 may be explained by the decrease in depressive symptoms rather than specific effects of SSRIs. Future studies with larger samples and placebo controls are needed to offer insight into the effects of SSRIs.
Appendix
Available only for authorised users
Literature
1.
2.
American Psychiatric Association (2010) Practice guideline for the treatment of patients with major depressive disorder. American Psychiatric Association
3.
4.
Kirsch I, Deacon BJ, Huedo-Medina TB, Scoboria A, Moore TJ, Johnson BT (2008) Initial severity and antidepressant benefits: a meta-analysis of data submitted to the Food and Drug Administration. PLoS medicine 5:e45CrossRefPubMedPubMedCentral
5.
Turner EH, Matthews AM, Linardatos E, Tell RA, Rosenthal R (2008) Selective publication of antidepressant trials and its influence on apparent efficacy. N Engl J Med 358:252–260CrossRefPubMed
6.
Pigott HE, Leventhal AM, Alter GS, Boren JJ (2010) Efficacy and effectiveness of antidepressants: current status of research. Psychother Psychosom 79:267–279CrossRefPubMed
7.
8.
Cramer AOJ, Waldorp LJ, van der Maas HLJ, Borsboom D (2010) Comorbidity: A network perspective. Behav Brain Sci 33:137–150CrossRefPubMed
9.
10.
Opsahl T, Agneessens F, Skvoretz J (2010) Node centrality in weighted networks: generalizing degree and shortest paths. Social Networks 32:245–251CrossRef
11.
van Borkulo CD, Borsboom D, Epskamp S, Blanken TF, Boschloo L, Schoevers RA, Waldorp LJ (2014) A new method for constructing networks from binary data. Sci Rep 4:5918CrossRefPubMedPubMedCentral
12.
Fried EI, Epskamp S, Nesse RM, Tuerlinckx F, Borsboom D (2016) What are ‘good’ depression symptoms? Comparing the centrality of DSM and non-DSM symptoms of depression in a network analysis. J Affect Disord 189:314–320CrossRefPubMed
13.
Bringmann LF, Lemmens LHJM., Huibers MJH, Borsboom D, Tuerlinckx F (2015) Revealing the dynamic network structure of the Beck Depression Inventory-II. Psychol Med 45:747–757CrossRefPubMed
14.
van Borkulo C, Boschloo L, Borsboom D, Penninx BW, Waldorp LJ, Schoevers RA (2015) Association of symptom network structure with the course of depression. JAMA Psychiatry 72
15.
Pe ML, Kircanski K, Thompson RJ, Bringmann LF, Tuerlinckx F, Mestdagh M, Mata J, Jaeggi SM, Buschkuehl M, Jonides J, Kuppens P, Gotlib IH (2015) Emotion-network density in major depressive disorder. Clin Psychol Sci 3:292–300CrossRef
16.
Fried E, van Borkulo C, Epskamp S, Schoevers R, Tuerlinckx F, Borsboom D (2016) Measuring depression over time… Or not?. Psychol Assess 28
17.
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. Sci Rep 7:46523CrossRefPubMedPubMedCentral
18.
Madhoo M, Levine SZ (2016) Network analysis of the quick inventory of depressive symptomatology: reanalysis of the STARD clinical trial. Eur Neuropsychopharmacol 26:1768–1774CrossRefPubMed
19.
Dimidjian S, Hollon SD, Dobson KS, Schmaling KB, Kohlenberg RJ, Addis ME, Gallop R, McGlinchey JB, Markley DK, Gollan JK (2006) Randomized trial of behavioral activation, cognitive therapy, and antidepressant medication in the acute treatment of adults with major depression. J Consult Clin Psychol 74:658CrossRefPubMed
20.
DeRubeis RJ, Hollon SD, Amsterdam JD, Shelton RC, Young PR, Salomon RM, O’Reardon JP, Lovett ML, Gladis MM, Brown LL (2005) Cognitive therapy vs medications in the treatment of moderate to severe depression. Arch Gen Psychiatry 62:409–416CrossRefPubMed
21.
First MB, Gibbon M, Spitzer RL, Williams JB (1996) User’s guide for the structured clinical interview for DSM-IV axis I Disorders—Research version. Biometrics Research Department, New York State Psychiatric Institute, New York
22.
23.
Beck AT, Steer RA, Brown GK (1996) Manual for the BDI-II. Psychological Corporation, San Antonio
24.
Fawcett J, Epstein P, Fiester SJ, Elkin I, Autry JH (1987) Clinical management: imipramine/placebo administration manual. Psychopharmacol Bull 23:309–324PubMed
25.
Beck AT, Steer RA, Garbin MG (1988) Psychometric properties of the Beck Depression Inventory: twenty-five years of evaluation. Clin Psychol Rev 8:77–100CrossRef
26.
Epskamp S, Fried EI (2018) A tutorial on regularized partial correlation networks. Psychol Methods
27.
Santos H, Fried EI, Asafu-Adjei J, Ruiz RJ (2017) Network structure of perinatal depressive symptoms in Latinas: relationship to stress and reproductive biomarkers. Res Nurs Health 40:218–228CrossRefPubMedPubMedCentral
28.
Epskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D (2012) qgraph: network visualizations of relationships in psychometric data. J Stat Softw 48:1–18CrossRef
29.
Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodological):267–288
30.
Foygel R, Drton M (2010) Extended Bayesian information criteria for Gaussian graphical models 604–612
31.
Fruchterman TM, Reingold EM (1991) Graph drawing by force-directed placement. Softw: Pract Exp 21:1129–1164
32.
Terluin B, de Boer MR, de Vet HC (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:e0155205CrossRefPubMedPubMedCentral
33.
Golino HF, Epskamp S (2017) Exploratory graph analysis: a new approach for estimating the number of dimensions in psychological research. PloS One 12:e0174035CrossRefPubMedPubMedCentral
34.
Van Borkulo CD, Boschloo L, Kossakowski J, Tio P, Schoevers R, Borsboom D, Waldorp LJ (2016) Comparing network structures on three aspects: a permutation test. Manuscript submitted for publication
35.
Epskamp S, Borsboom D, Fried EI (2017) Estimating psychological networks and their accuracy: a tutorial paper. Behavior Research Methods:1–18
36.
Haslbeck J, Fried EI (2017) How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets. Psychol Med:1–10
37.
Haslbeck J, Waldorp LJ (2015) Structure estimation for mixed graphical models in high-dimensional data. arXiv preprint. arXiv:​1510.​05677
38.
Fokkema M, Smits N, Kelderman H, Cuijpers P (2013) Response shifts in mental health interventions: an illustration of longitudinal measurement invariance. Psychol Assess 25:520CrossRefPubMed
39.
Quilty LC, Robinson JJ, Rolland J, Fruyt FD, Rouillon F, Bagby RM (2013) The structure of the Montgomery–Åsberg depression rating scale over the course of treatment for depression. Int J Methods Psychiatric Res 22:175–184
40.
Steer RA, Ball R, Ranieri WF (1999) Dimensions of the Beck Depression Inventory-II in clinically depressed outpatients. J Clin Psychol 55:117–128CrossRefPubMed
41.
Huang C, Chen J (2015) Meta-analysis of the factor structures of the Beck Depression Inventory-II. Assessment 22:459–472CrossRefPubMed
42.
Boschloo L, van Borkulo CD, Borsboom D, Schoevers RA (2016) A prospective study on how symptoms in a network predict the onset of depression. Psychother Psychosom 85:183–184CrossRefPubMed
43.
Andrews PW, Bharwani A, Lee KR, Fox M, Thomson JA (2015) Is serotonin an upper or a downer? The evolution of the serotonergic system and its role in depression and the antidepressant response. Neurosci Biobehav Rev 51:164–188CrossRefPubMed
44.
Durisko Z, Mulsant BH, Andrews PW (2015) An adaptationist perspective on the etiology of depression. J Affect Disord 172:315–323CrossRefPubMed
45.
Kroeze R, Van Veen D, Servaas MN, Bastiaansen JA, Oude Voshaar R, Borsboom D, Riese H (2016) Personalized feedback on symptom dynamics of psychopathology: a proof-of-principle study. J Person-Orient Res
46.
Fried EI, Cramer AO (2017) Moving forward: challenges and directions for psychopathological network theory and methodology. Perspect Psychol Sci 1–22
47.
McMakin DL, Olino TM, Porta G, Dietz LJ, Emslie G, Clarke G, Wagner KD, Asarnow JR, Ryan ND, Birmaher B (2012) Anhedonia predicts poorer recovery among youth with selective serotonin reuptake inhibitor treatment-resistant depression. J Am Acad Child Adolescent Psychiatry 51:404–411CrossRef
48.
Yee A, Chin SC, Hashim AHB, Harbajan Singh M, Kaur A, Loh HS, Sulaiman AH, Ng CG (2015) Anhedonia in depressed patients on treatment with selective serotonin reuptake inhibitor anti-depressant—a two-centered study in Malaysia. Int J Psychiatry Clin Pract 19:1–6CrossRef
49.
Vrieze E, Demyttenaere K, Bruffaerts R, Hermans D, Pizzagalli DA, Sienaert P, Hompes T, de Boer P, Schmidt M, Claes S (2014) Dimensions in major depressive disorder and their relevance for treatment outcome. J Affect Disord 155:35–41CrossRefPubMed
50.
Hyman SE, Nestler EJ (1996) Initiation and adaptation: a paradigm for understanding psychotropic drug action. Am J Psychiatry 153:151CrossRefPubMed
51.
Korte SM, Prins J, Krajnc AM, Hendriksen H, Oosting RS, Westphal KG, Korte-Bouws GA, Olivier B (2015) The many different faces of major depression: it is time for personalized medicine. Eur J Pharmacol 753:88–104CrossRefPubMed
52.
Muthén BO (1989) Factor structure in groups selected on observed scores. Br J Math Stat Psychol 42:81–90CrossRef
53.
Forbes MK, Wright AG, Markon KE, Krueger RF (2017) Evidence that psychopathology symptom networks have limited replicability. J Abnorm Psychol 126:969CrossRefPubMed
54.
Borsboom D, Fried EI, Epskamp S, Waldorp LJ, van Borkulo CD, van der Maas, Han LJ, Cramer AO (2017) False alarm? A comprehensive reanalysis of “Evidence that psychopathology symptom networks have limited replicability” by Forbes., Wright (2017)
55.
Fried EI, Eidhof MB, Palic S., Costantini G, Huisman-van Dijk HM, Bockting CLH, Engelhard I, Armour C, Nielsen ABS, Karstoft KI. (2018) Replicability and generalizability of PTSD networks: a cross-cultural multisite study of PTSD symptoms in four trauma patient samples. Clin Psychol Sci
56.
Bos FM, Snippe E, de Vos S, Hartmann JA, Simons CJP, van der Krieke L, de Jonge P, Wichers M (2017) Can we jump from cross-sectional to dynamic interpretations of networks? Implications for the network perspective in psychiatry. Psychother Psychosom 86:175–177CrossRefPubMedPubMedCentral
57.
Bos EH, Wanders RBK (2016) Group-level symptom networks in depression. JAMA Psychiatry 73:411–411CrossRefPubMed
58.
Wichers M (2014) The dynamic nature of depression: a new micro-level perspective of mental disorder that meets current challenges. Psychol Med 44:1349–1360CrossRefPubMed
Metadata
Title
Cross-sectional networks of depressive symptoms before and after antidepressant medication treatment
Authors
Fionneke M. Bos
Eiko I. Fried
Steven D. Hollon
Laura F. Bringmann
Sona Dimidjian
Robert J. DeRubeis
Claudi L. H. Bockting
Publication date
01-06-2018
Publisher
Springer Berlin Heidelberg
Published in
Social Psychiatry and Psychiatric Epidemiology / Issue 6/2018
Print ISSN: 0933-7954
Electronic ISSN: 1433-9285
DOI
https://doi.org/10.1007/s00127-018-1506-1

Other articles of this Issue 6/2018

Social Psychiatry and Psychiatric Epidemiology 6/2018 Go to the issue

Original Paper

A typology of satisfaction with mental health services based on Andersen’s behavioral model

Original Paper

Prevalence and predictors of no lifetime utilization of mental health treatment among people with mental disorders in France: findings from the ‘Mental Health in General Population’ (MHGP) survey

Invited Reviews

Vantage sensitivity: a framework for individual differences in response to psychological intervention

Original Paper

The relation between obesity and depressed mood in a multi-ethnic population. The HELIUS study

Original Paper

Validity of police contacts as a performance indicator for the public mental health care system in Amsterdam: an open cohort study

Original Paper

The utility of outpatient commitment: acute medical care access and protecting health