Hostname: page-component-5f745c7db-sbzbt Total loading time: 0 Render date: 2025-01-06T21:22:58.381Z Has data issue: true hasContentIssue false
  • English
  • Français

Connect: A Novel Network Approach for Investigating the Co-occurrence of Binary Psychopathological Symptoms Over Time

Published online by Cambridge University Press:  01 January 2025

Nadja Bodner Open the ORCID record for Nadja Bodner [Opens in a new window] ,
Laura Bringmann Open the ORCID record for Laura Bringmann [Opens in a new window] ,
Francis Tuerlinckx Open the ORCID record for Francis Tuerlinckx [Opens in a new window] ,
Peter de Jonge Open the ORCID record for Peter de Jonge [Opens in a new window]  and
Eva Ceulemans Open the ORCID record for Eva Ceulemans [Opens in a new window]
Nadja Bodner*
Affiliation:
KU Leuven (University of Leuven)
Laura Bringmann
Affiliation:
University of Groningen University of Groningen
Francis Tuerlinckx
Affiliation:
KU Leuven (University of Leuven)
Peter de Jonge
Affiliation:
University of Groningen University of Groningen
Eva Ceulemans
Affiliation:
Leuven (University of Leuven)
*
Correspondence should be made to Nadja Bodner, Quantitative Psychology and Individual Differences Research Group, Faculty of Psychology and Educational Studies, KU Leuven (University of Leuven), Tiensestraat 102, Box 3713, 3000 Leuven, Belgium. Email: nadja.bodner@kuleuven.be
Get access Rights & Permissions [Opens in a new window]

Abstract

Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We therefore propose ConNEcT, a network approach for binary symptom data across time. ConNEcT allows to visualize and study the prevalence of different symptoms as well as their co-occurrence, measured by means of a contingency measure in one single network picture. ConNEcT can be complemented with a significance test that accounts for the serial dependence in the data. To illustrate the usefulness of ConNEcT, we re-analyze data from a study in which patients diagnosed with major depressive disorder weekly reported the absence or presence of eight depression symptoms. We first extract ConNEcTs for all patients that provided data during at least 104 weeks, revealing strong inter-individual differences in which symptom pairs co-occur significantly. Second, to gain insight into these differences, we apply Hierarchical Classes Analysis on the co-occurrence patterns of all patients, showing that they can be grouped into meaningful clusters. Core depression symptoms (i.e., depressed mood and/or diminished interest), cognitive problems and loss of energy seem to co-occur universally, but preoccupation with death, psychomotor problems or eating problems only co-occur with other symptoms for specific patient subgroups.

Keywords

binary datanetwork analysistime seriesdepressionindividual differences
Type
Application Reviews and Case Studies
Information
Psychometrika , Volume 87 , Issue 1: Special Issue on Network Psychometrics in Action , March 2022 , pp. 107 - 132
Copyright
Copyright © 2021 The Psychometric Society

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

Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

Albert, A., &Anderson, J. A.(1984).On the existence of maximum likelihood estimates in logistic regression models.Biometrika,71(1),1–10.CrossRefGoogle Scholar
Bakeman, R.,McArthur, D., &Quera, V.(1996).Detecting group differences in sequential association using sampled permutations: Log odds, kappa, and phi compared.Behavior Research Methods, Instruments, and Computers,28(3),446–457.CrossRefGoogle Scholar
Bakeman, R., &Quera, V.(2011).Sequential analysis and observational methods for the behavioral sciences.Cambridge:Cambridge University Press.CrossRefGoogle 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(1),289–300.CrossRefGoogle Scholar
Bodner, N.,Bosmans, G.,Sannen, J.,Verhees, M., &Ceulemans, E.(2019).Unraveling middle childhood attachment-related behavior sequences using a micro-coding approach.Plos One,14(10),e0224372–CrossRefGoogle ScholarPubMed
Bodner, N.,Kuppens, P.,Allen, N. B.,Sheeber, L. B., &Ceulemans, E.(2018).Affective family interactions and their associations with adolescent depression: A dynamic network approach.Development and Psychopathology,30(4),1459–1473.CrossRefGoogle ScholarPubMed
Bodner, N., Tuerlinckx, F., Bosmans, G., & Ceulemans, E. (2020). Accounting for auto-dependency in binary dyadic time series data: A comparison of model- and permutation-based approaches for testing pairwise associations. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12222CrossRefGoogle Scholar
Borsboom, D.(2008).Psychometric perspectives on diagnostic systems.Journal of Clinical Psychology,64(9),1089–1108.CrossRefGoogle ScholarPubMed
Borsboom, D.(2017).A network theory of mental disorders.World Psychiatry,16(1),5–13.CrossRefGoogle ScholarPubMed
Borsboom, D.,Cramer, A. O. J.(2013).Network analysis: An integrative approach to the structure of psychopathology.Annual Review of Clinical Psychology,9(1),91–121.CrossRefGoogle Scholar
Boschloo, L.,van Borkulo, C. D.,Rhemtulla, M.,Keyes, K. M.,Borsboom, D., &Schoevers, R. A.(2015).The network structure of symptoms of the diagnostic and statistical manual of mental disorders.Plos One,10(9),e0137621–CrossRefGoogle Scholar
Bosley, H. G.,Sandel, D. B., &Fisher, A. J.(2020).Idiographic dynamics of positive affect in gad: Modeling emotion regulation at the person level.European Journal of Psychological Assessment,36(3),500–509.CrossRefGoogle Scholar
Brandt, M. J.,Sibley, C. G., &Osborne, D.(2019).What is central to political belief system networks?.Personality and Social Psychology Bulletin,45(9),1352–1364.CrossRefGoogle ScholarPubMed
Bringmann, L. F.,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(4),e60188–CrossRefGoogle ScholarPubMed
Brusco, M. J.,Steinley, D.,Hoffman, M.,Davis-Stober, C., &Wasserman, S.(2019).On Ising models and algorithms for the construction of symptom networks in psychopathological research.Psychological Methods,24(6),735–753.CrossRefGoogle ScholarPubMed
Bulteel, K.,Mestdagh, M.,Tuerlinckx, F., &Ceulemans, E.(2018).VAR (1) based models do not always outpredict AR(1) models in typical psychological applications.Psychological Methods,23(4),740–756.CrossRefGoogle Scholar
Bulteel, K.,Tuerlinckx, F.,Brose, A., &Ceulemans, E.(2018).Improved insight into and prediction of network dynamics by combining VAR and dimension reduction.Multivariate Behavioral Research,53(6),853–875.CrossRefGoogle ScholarPubMed
Ceulemans, E., &Van Mechelen, I.(2005).Hierarchical classes models for three-way three-mode binary data: Interrelations and model selection.Psychometrika,70(3),461–480.CrossRefGoogle Scholar
Ceulemans, E.,Van Mechelen, I., &Leenen, I.(2007).The local minima problem in hierarchical classes analysis: An evaluation of a simulated annealing algorithm and various multistart procedures.Psychometrika,72(3),377–391.CrossRefGoogle Scholar
Ceulemans, E., &Kiers, H. A. L.(2006).Selecting among three-mode principal component models of different types and complexities: A numerical convex hull based method.British Journal of Mathematical and Statistical Psychology,59(1),133–150.CrossRefGoogle ScholarPubMed
Cohen, J.(1960).A coefficient of agreement for nominal scales.Educational and Psychological Measurement,20(1),37–46.CrossRefGoogle Scholar
Cramer, A. O. J.,van Borkulo, C. D.,Giltay, E. J.,van der Maas, H. L. J.,Kendler, K. S.,Scheffer, M., &Borsboom, D.(2016).Major depression as a complex dynamic system.Plos One,11(12),e0167490–CrossRefGoogle ScholarPubMed
Cramer, A. O. J.,van der Sluis, S.,Noordhof, A.,Wichers, M.,Geschwind, N.,Aggen, S. H.,Kendler, K. S., &Borsboom, D.(2012).Dimensions of normal personality as networks in search of equilibrium: You can’t like parties if you don’t like people.European Journal of Personality,26(4),414–431.CrossRefGoogle Scholar
Cramer, A. OJ.,Waldorp, L. J.,van der Maas, H. L. J., &Borsboom, D.(2010).Comorbidity: A network perspective.Behavioral and Brain Sciences,33(2–3),137–150.CrossRefGoogle ScholarPubMed
Dalege, J.,Borsboom, D.,van Harreveld, F.,van den Berg, H.,Conner, M., &van der Maas, H. L. J.(2016).Toward a formalized account of attitudes: The Causal Attitude Network (CAN) model.Psychological Review,123(1),2–22.CrossRefGoogle Scholar
De Boeck, P., &Rosenberg, S.(1988).Hierarchical classes: Model and data analysis.Psychometrika,53(3),361–381.CrossRefGoogle Scholar
de Vos, S.,Wardenaar, K. J.,Bos, E. H.,Wit, E. C.,Bouwmans, M. E. J., &de Jonge, P.(2017).An investigation of emotion dynamics in major depressive disorder patients and healthy persons using sparse longitudinal networks.Plos One,12(6),e0178586–CrossRefGoogle ScholarPubMed
Edgington, E. S., &Onghena, P.(2007).Randomization tests,4Florida:Chapman & Hall/CRC.CrossRefGoogle Scholar
Epskamp, S.,Borsboom, D., &Fried, E. I.(2018).Estimating psychological networks and their accuracy: A tutorial paper.Behavior Research Methods,50(1),195–212.CrossRefGoogle ScholarPubMed
Epskamp, S.,Cramer, A. O. J.,Waldorp, L. J.,Schmittmann, V. D., &Borsboom, D.(2012).qgraph: Network visualizations of relationships in psychometric data.Journal of Statistical Software,48(4),1–18.CrossRefGoogle Scholar
Epskamp, S.,Kruis, J., &Marsman, M.(2017).Estimating psychopathological networks: Be careful what you wish for.Plos One,12(6),e0179891–CrossRefGoogle ScholarPubMed
Epskamp, S.,Waldorp, L. J.,Mõttus, R., &Borsboom, D.(2018).The Gaussian graphical model in cross-sectional and time-series data.Multivariate Behavioral Research,53(4),453–480.CrossRefGoogle ScholarPubMed
Firth, D.(1993).Bias reduction of maximum likelihood estimates.Biometrika,80(1),27–38.CrossRefGoogle Scholar
Fisher, A. J.,Reeves, J. W.,Lawyer, G.,Medaglia, J. D., &Rubel, J. A.(2017).Exploring the idiographic dynamics of mood and anxiety via network analysis.Journal of Abnormal Psychology,126(8),1044–1056.CrossRefGoogle ScholarPubMed
Fisher, R. A. (1949). The design of experiments (5th ed.). Oliver & Boyd.Google Scholar
Fried, E. I.,van Borkulo, C. D.,Cramer, A. OJ.,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),1–10.CrossRefGoogle ScholarPubMed
Fried, E. I.,Epskamp, S.,Nesse, R. M.,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.Journal of Affective Disorders,189 314–320.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Gelman, A.,Jakulin, A.,Pittau, M. G., &Su, Y.-S.(2008).A weakly informative default prior distribution for logistic and other regression models.The Annals of Applied Statistics,2(4),1360–1383.CrossRefGoogle Scholar
Golino, H. F.,Epskamp, S.(2017).Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research.Plos One,12(6),e0174035–CrossRefGoogle ScholarPubMed
Good, P. (2000). Permutation Tests. Springer New York. https://doi.org/10.1007/978-1-4757-3235-1CrossRefGoogle Scholar
Hamaker, E.,Asparouhov, T.,Brose, A.,Schmiedek, F., &Muthén, B.(2018).At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study.Multivariate Behavioral Research,53(6),820–841.CrossRefGoogle ScholarPubMed
Hamming, R. W.(1950).Error detecting and error correcting codes.The Bell System Technical Journal,29(2),147–160.CrossRefGoogle Scholar
Haslbeck, J. M. B., & Waldorp, L. J. (2020). mgm: Estimating time-varying mixed graphical models in high-dimensional data. ArXiv:1510.06871 [Stat].Google Scholar
Heinze, G., &Schemper, M.(2002).A solution to the problem of separation in logistic regression.Statistics in Medicine,21(16),2409–2419.CrossRefGoogle Scholar
Holloway, E. L.,Wampold, B. E., &Nelson, M. L.(1990).Use of a paradoxical intervention with a couple: An interactional analysis.Journal of Family Psychology,3(4),385–402.CrossRefGoogle Scholar
Hosenfeld, B.,Bos, E. H.,Wardenaar, K. J.,Conradi, H. J.,van der Maas, H. L. J.,Visser, I., &de Jonge, P.(2015).Major depressive disorder as a nonlinear dynamic system: Bimodality in the frequency distribution of depressive symptoms over time.BMC Psychiatry,15(1),222–CrossRefGoogle ScholarPubMed
Isvoranu, A.-M.,Borsboom, D.,van Os, J., &Guloksuz, S.(2016).A network approach to environmental impact in psychotic disorder: Brief theoretical framework.Schizophrenia Bulletin,42(4),870–873.CrossRefGoogle ScholarPubMed
Jaccard, P.(1901).Étude comparative de la distribution florale dans une portion des Alpes et des Jura.Bulletin de La Société Vaudoise Des Sciences Naturelles,37 547–579.Google Scholar
Jaccard, P.(1912).The distribution of the flora in the Alpine zone.New Phytologist,11(2),37–50.CrossRefGoogle Scholar
Lamers, F.,de Jonge, P.,Nolen, W. A.,Smit, J. H.,Zitman, F. G.,Beekman, A. T. F., &Penninx, B. W. J. H.(2010).Identifying depressive subtypes in a large Cohort study: Results from the Netherlands study of depression and anxiety (NESDA).The Journal of Clinical Psychiatry,71(12),1582–1589.CrossRefGoogle Scholar
Mansournia, M. A.,Geroldinger, A.,Greenland, S., &Heinze, G.(2018).Separation in logistic regression: Causes, consequences, and control.American Journal of Epidemiology,187(4),864–870.CrossRefGoogle ScholarPubMed
Marsman, M., Waldorp, L., & Borsboom, D. (2019). Towards an encompassing theory of network models [Preprint]. https://doi.org/10.31234/osf.io/n98qtCrossRefGoogle Scholar
Moulder, R. G.,Boker, S. M.,Ramseyer, F., &Tschacher, W.(2018).Determining synchrony between behavioral time series: An application of surrogate data generation for establishing falsifiable null-hypotheses.Psychological Methods,23(4),757–773.CrossRefGoogle ScholarPubMed
Olthof, M.,Hasselman, F.,Strunk, G.,van Rooij, M.,Aas, B.,Helmich, M. A.,Schiepek, G., &Lichtwarck-Aschoff, A.(2020).Critical fluctuations as an early-warning signal for sudden gains and losses in patients receiving psychotherapy for mood disorders.Clinical Psychological Science,8(1),25–35.CrossRefGoogle Scholar
Racine, J.(2000).Consistent cross-validatory model-selection for dependent data: Hv-block cross-validation.Journal of Econometrics,99(1),39–61.CrossRefGoogle Scholar
Salvatore, S., Rand, K. D., Grytten, I., Ferkingstad, E., Domanska, D., Holden, L., Gheorghe, M., Mathelier, A., Glad, I., & Sandve, G. K. (2019). Beware the Jaccard: The choice of metric is important and non-trivial in genomic colocalisation analysis. BioRxiv, 479253. https://doi.org/10.1101/479253.CrossRefGoogle Scholar
Scott, W. A. (1955). Reliability of content analysis: The case of nominal scale coding. The Public Opinion Quarterly, 19(3), 321–325. https://www.jstor.org/stable/2746450.CrossRefGoogle Scholar
Steegen, S.,Tuerlinckx, F.,Gelman, A., &Vanpaemel, W.(2016).Increasing transparency through a multiverse analysis.Perspectives on Psychological Science,11(5),702–712.CrossRefGoogle ScholarPubMed
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,CrossRefGoogle ScholarPubMed
van de Leemput, I. A., Wichers, M., Cramer, A. O. J., Borsboom, D., Tuerlinckx, F., Kuppens, P., et al. (2014). Critical slowing down as early warning for the onset and termination of depression. Proceedings of the National Academy of Sciences, 111(1), 87–92. https://doi.org/10.1073/pnas.1312114110.CrossRefGoogle Scholar
Van Keer, I.,Ceulemans, E.,Bodner, N.,Vandesande, S.,Van Leeuwen, K., &Maes, B.(2019).Parent-child interaction: A micro-level sequential approach in children with a significant cognitive and motor developmental delay.Research in Developmental Disabilities,85 172–186.CrossRefGoogle ScholarPubMed
Van Mechelen, I.,De Boeck, P., &Rosenberg, S.(1995).The conjunctive model of hierarchical classes.Psychometrika,60(4),505–521.CrossRefGoogle Scholar
Vermunt, J. K., &Magidson, J.Hagenaars, J., &McCutcheon, A.(2002).Latent class cluster analysis.Applied latent class analysis,Cambridge:Cambridge University Press.89–106.CrossRefGoogle Scholar
Wardenaar, K. J.,Monden, R.,Conradi, H. J., &de Jonge, P.(2015).Symptom-specific course trajectories and their determinants in primary care patients with Major Depressive Disorder: Evidence for two etiologically distinct prototypes.Journal of Affective Disorders,179 38–46.CrossRefGoogle ScholarPubMed
Wilderjans, T. F.,Ceulemans, E., &Meers, K.(2013).CHull: A generic convex-hull-based model selection method.Behavior Research Methods,45(1),1–15.CrossRefGoogle ScholarPubMed
Wright, A. G. C., &Woods, W. C.(2020).Personalized models of psychopathology.Annual Review of Clinical Psychology,16(1),49–74.CrossRefGoogle ScholarPubMed
Supplementary material: File

Bodner et.al supplementary material

Bodner et.al supplementary material
Download Bodner et.al supplementary material(File)
File 79.2 KB
Supplementary material: File

Bodner et.al supplementary material

Table S1, Table S2 and Table S3
Download Bodner et.al supplementary material(File)
File 491 KB