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The network approach to psychopathology: a review of the literature 2008–2018 and an agenda for future research

Published online by Cambridge University Press:  26 December 2019

Donald J. Robinaugh Open the ORCID record for Donald J. Robinaugh [Opens in a new window] ,
Ria H. A. Hoekstra ,
Emma R. Toner  and
Denny Borsboom
Donald J. Robinaugh*
Affiliation:
Department of Psychiatry, Massachusetts General Hospital, Boston, MA02114, USA Harvard Medical School, Boston, MA02114, USA
Ria H. A. Hoekstra
Affiliation:
University of Amsterdam, Amsterdam, The Netherlands
Emma R. Toner
Affiliation:
Department of Psychiatry, Massachusetts General Hospital, Boston, MA02114, USA
Denny Borsboom
Affiliation:
University of Amsterdam, Amsterdam, The Netherlands
*
Author for correspondence: Donald J. Robinaugh, E-mail: [email protected]
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Abstract

The network approach to psychopathology posits that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This approach, first posited in 2008, has grown substantially over the past decade and is now a full-fledged area of psychiatric research. In this article, we provide an overview and critical analysis of 363 articles produced in the first decade of this research program, with a focus on key theoretical, methodological, and empirical contributions. In addition, we turn our attention to the next decade of the network approach and propose critical avenues for future research in each of these domains. We argue that this program of research will be best served by working toward two overarching aims: (a) the identification of robust empirical phenomena and (b) the development of formal theories that can explain those phenomena. We recommend specific steps forward within this broad framework and argue that these steps are necessary if the network approach is to develop into a progressive program of research capable of producing a cumulative body of knowledge about how specific mental disorders operate as causal systems.

Keywords

Causal systemsnetwork approachnetwork psychometricsnetwork theorypsychopathologysymptom networks
Type
Review Article
Information
Psychological Medicine , Volume 50 , Issue 3 , February 2020 , pp. 353 - 366
Copyright
Copyright © Cambridge University Press 2019

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References

Anderson, G. M. (2015). Autism biomarkers: challenges, pitfalls and possibilities. Journal of Autism and Developmental Disorders, 45, 1103–1113.CrossRefGoogle ScholarPubMed
Armour, C., Fried, E. I., & Olff, M. (2017). PTSD symptomics: network analyses in the field of psychotraumatology. European Journal of Psychotraumatology, 8, 1398003.CrossRefGoogle ScholarPubMed
Barabasi, A. L. (2012). The network takeover. Nature Physics, 8, 14–16.CrossRefGoogle Scholar
Beard, C., Millner, A. J., Forgeard, M. J., Fried, E. I., Hsu, K. J., Treadway, M. T., … Bjorgvinsson, T. (2016). Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychological Medicine, 46, 3359–3369.CrossRefGoogle Scholar
Beltz, A. M., & Gates, K. M. (2017). Network mapping with GIMME. Multivariate Behavioral Research, 52, 789–804.CrossRefGoogle ScholarPubMed
Belvederi Murri, M., Amore, M., Respino, M., & Alexopoulos, G. S. (2018). The symptom network structure of depressive symptoms in late-life: results from a European population study. Molecular Psychiatry, 44, 1–10.Google Scholar
Bentall, R. P. (2014). The search for elusive structure: a promiscuous realist case for researching specific psychotic experiences such as hallucinations. Schizophrenia Bulletin 40(Suppl. 4), S198–S201.CrossRefGoogle ScholarPubMed
Blake, M. J., Trinder, J. A., & Allen, N. B. (2018). Mechanisms underlying the association between insomnia, anxiety, and depression in adolescence: implications for behavioral sleep interventions. Clinical Psychology Review, 63, 25–40.CrossRefGoogle ScholarPubMed
Blanken, T. F., Deserno, M. K., Dalege, J., Borsboom, D., Blanken, P., Kerkhof, G. A., & Cramer, A. O. J. (2018). The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks. Scientific Reports, 8, 5854.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. (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology, 64, 1089–1108.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
Borsboom, D., Cramer, A. O., Schmittmann, V. D., Epskamp, S., & Waldorp, L. J. (2011). The small world of psychopathology. PLoS One, 6, e27407.CrossRefGoogle ScholarPubMed
Borsboom, D., Fried, E. I., Epskamp, S., Waldorp, L. J., van Borkulo, C. D., van der Maas, H. L. J., & Cramer, A. O. J. (2017). False alarm? A comprehensive reanalysis of ‘Evidence that psychopathology symptom networks have limited replicability’ by Forbes, Wright, Markon, and Krueger (2017). Journal of Abnormal Psychology, 126, 989–999.CrossRefGoogle Scholar
Borsboom, D., Rhemtulla, M., Cramer, A. O. J., van der Maas, H. L. J., Scheffer, M., & Dolan, C. V. (2016). Kinds versus continua: a review of psychometric approaches to uncover the structure of psychiatric constructs. Psychological Medicine, 46, 1567–1579.CrossRefGoogle ScholarPubMed
Borsboom, D., Robinaugh, D. J., The Psychosystems Group, Rhemtulla, M., & Cramer, A. O. J. (2018). Robustness and replicability of psychopathology networks. World Psychiatry, 17, 143–144.CrossRefGoogle ScholarPubMed
Bos, E. H., & Wanders, R. B. (2016). Group-level symptom networks in depression. JAMA Psychiatry, 73, 411.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
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
Boyd, R. (1991). Realism, antifoundationalism and the enthusiasm for natural kinds. Philosophical Studies, 61, 127–148.CrossRefGoogle Scholar
Boyd, R. (1999). Homeostasis, species, and higher taxa. Cambridge, MA: A Bradford Book/MIT Press.Google Scholar
Bringmann, L. F., Elmber, T., Epskamp, S., Kraus, R. W., Schoch, D., Wichers, M., … Snippe, E. (2019). What do centrality measures measure in psychological networks. Journal of Abnormal Psychology, 128, 892–903.CrossRefGoogle ScholarPubMed
Bringmann, L. F., & Eronen, M. I. (2018). Don't blame the model: reconsidering the network approach to psychopathology. Psychological Review, 125, 606–615.CrossRefGoogle Scholar
Bringmann, L. F., Ferrer, E., Hamaker, E. L., Borsboom, D., & Tuerlinckx, F. (2018). Modeling nonstationary emotion dynamics in dyads using a time-varying vector-autoregressive model. Multivariate Behavioral Research, 53, 293–314.CrossRefGoogle ScholarPubMed
Bringmann, L. F., Pe, M. L., Vissers, N., Ceulemans, E., Borsboom, D., Vanpaemel, W., … Kuppens, P. (2016). Assessing temporal emotion dynamics using networks. Assessment, 23, 425–435.CrossRefGoogle ScholarPubMed
Bringmann, L. F., Vissers, N., Wichers, M., Geschwind, N., Kuppens, P., Peeters, F., … Tuerlinckx, F. (2013). A network approach to psychopathology: new insights into clinical longitudinal data. PLoS One, 8, e60188.CrossRefGoogle ScholarPubMed
Bryant, R. A., Creamer, M., O'Donnell, M., Forbes, D., McFarlane, A. C., Silove, D., & Hadzi-Pavlovic, D. (2017). Acute and chronic posttraumatic stress symptoms in the emergence of posttraumatic stress disorder: a network analysis. JAMA Psychiatry, 74, 135–142.CrossRefGoogle ScholarPubMed
Brzović, Z., Jurjako, M., & Šustar, P. (2017). The kindness of psychopaths. International Studies in the Philosophy of Science, 31, 189–211.CrossRefGoogle Scholar
Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016a). Clustering vector autoregressive models: capturing qualitative differences in within-person dynamics. Frontiers in Psychology, 7, 1540.CrossRefGoogle Scholar
Bulteel, K., Tuerlinckx, F., Brose, A., & Ceulemans, E. (2016b). Using raw VAR regression coefficients to build networks can be misleading. Multivariate Behavioral Research, 51, 330–344.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, 853–875.CrossRefGoogle ScholarPubMed
Caspi, A., & Moffitt, T. E. M. (2018). All for one and one for all: mental disorders in all for one and one for all: mental disorders in one dimension. The American Journal of Psychiatry, 175, 831–844.CrossRefGoogle ScholarPubMed
Chen, Y., Li, X., Liu, J., & Ying, Z. (2018). Robust measurement via a fused latent and graphical item response theory model. Psychometrika, 83, 538–562.CrossRefGoogle Scholar
Christensen, A. P., Kenett, Y. N., Aste, T., Silvia, P. J., & Kwapil, T. R. (2018). Network structure of the Wisconsin Schizotypy Scales-Short Forms: examining psychometric network filtering approaches. Behavior Research Methods, 50, 2531–2550.CrossRefGoogle ScholarPubMed
Clark, D. M. (1986). A cognitive approach to panic. Behaviour Research and Therapy, 24, 461–470.CrossRefGoogle ScholarPubMed
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., Mottus, R., Waldorp, L. J., & Cramer, A. O. J. (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., & Perugini, M. (2014). Generalization of clustering coefficients to signed correlation networks. PLoS One, 9, e88669.CrossRefGoogle ScholarPubMed
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., van Borkulo, C. D., Giltay, E. J., van der Maas, H. L., Kendler, K. S., Scheffer, M., & Borsboom, D. (2016). Major depression as a complex dynamic system. PLoS One, 11, e0167490.CrossRefGoogle ScholarPubMed
Cramer, A. O., Waldorp, L. J., van der Maas, H. L., & Borsboom, D. (2010a). Comorbidity: a network perspective. Behavioral and Brain Sciences, 33, 137–150, discussion 150-93.CrossRefGoogle Scholar
Cramer, A. O., Waldorp, L. J., van der Maas, H. L., & Borsboom, D. (2010b). Complex realities require complex theories: refining and extending the network approach to mental disorders. Behavioral and Brain Sciences, 33, 178–193.CrossRefGoogle Scholar
Curtiss, J., Ito, M., Takebayashi, Y., & Hofmann, S. G. (2018). Longitudinal network stability of the functional impairment of anxiety and depression. Clinical Psychological Science, 6, 325–334.CrossRefGoogle Scholar
Dablander, F., & Hinne, M. (2018). Node centrality measures are a poor substitute for causal inference. Scientific Reports, 9, 6846.CrossRefGoogle Scholar
de Beurs, D. (2017). Network analysis: a novel approach to understand suicidal behaviour. International Journal of Environmental Research and Public Health, 14, 219.CrossRefGoogle Scholar
de Haan-Rietdijk, S., Voelkle, M. C., Keijsers, L., & Hamaker, E. L. (2017). Discrete- vs. continuous-time modeling of unequally spaced experience sampling method data. Frontiers in Psychology, 8, 1849.CrossRefGoogle ScholarPubMed
de Jonge, P., Wardenaar, K. J., & Wichers, M. (2015). What kind of thing is depression? Epidemiology and Psychiatric Sciences, 24, 312–314.CrossRefGoogle ScholarPubMed
De Ron, J., Fried, E. I., & Epskamp, S. (2019). Psychological networks in clinical populations: a tutorial on the consequences of Berkson's bias. Retrieved from https://psyarxiv.com/5t8zw/.Google 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, e0178586.CrossRefGoogle ScholarPubMed
DeYoung, C. G., & Krueger, R. F. (2018). Understanding psychopathology: cybernetics and psychology on the boundary between order and chaos. Psychological Inquiry, 29, 165–174.CrossRefGoogle Scholar
Eaton, N. R. (2015). Latent variable and network models of comorbidity: toward an empirically derived nosology. Social Psychiatry and Psychiatric Epidemiology, 50, 845–849.CrossRefGoogle ScholarPubMed
Epskamp, S. (2019). Psychometric network models from time-series and panel data. Retrieved from https://psyarxiv.com/8ha93/.Google Scholar
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., Fried, E. I., van Borkulo, C. D., Robinaugh, D. J., Marsman, M., Dalege, J., … Cramer, A. O. J. (2018b). Investigating the utility of fixed-margin sampling in network psychometrics. Multivariate Behavioral Research, 1–15.CrossRefGoogle Scholar
Epskamp, S., Kruis, J., & Marsman, M. (2017a). Estimating psychopathological networks: be careful what you wish for. PLoS One, 12, e0179891.CrossRefGoogle Scholar
Epskamp, S., Maris, G. K. J., Waldorp, L. J., & Borsboom, D. (2016). Network psychometrics. In Irwing, P., Hughes, D. & Booth, T. (Eds.), Handbook of psychometrics (pp. 953–986). New York: Wiley.Google Scholar
Epskamp, S., Rhemtulla, M., & Borsboom, D. (2017b). Generalized network psychometrics: combining network and latent variable models. Psychometrika, 82, 904–927.CrossRefGoogle Scholar
Epskamp, S., van Borkulo, C. D., van der Veen, D. C., Servaas, M. N., Isvoranu, A. M., Riese, H., & Cramer, A. O. J. (2018c). Personalized network modeling in psychopathology: the importance of contemporaneous and temporal connections. Clinical Psychological Science, 6, 416–427.CrossRefGoogle Scholar
Epstein, J. M. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11, 12.Google Scholar
Esfahlani, F. Z., Sayama, H., Visser, K., & Strauss, G. P. (2017). Sensitivity of the positive and negative syndrome (PANSS) in detecting treatment effects via network analysis. Innovations in Clinical Neuroscience, 14, 59–67.Google ScholarPubMed
Fonseca-Pedrero, E., Ortuno, J., Debbane, M., Chan, R. C. K., Cicero, D., Zhang, L. C., … Fried, E. I. (2018). The network structure of schizotypal personality traits. Schizophrenia Bulletin, 44, S468–S479.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Forbes, M. K., Wright, A. G. C., Markon, K. E., & Krueger, R. F. (2017a). Evidence that psychopathology symptom networks have limited replicability. Journal of Abnormal Psychology, 126, 969–988.CrossRefGoogle Scholar
Forbes, M. K., Wright, A. G. C., Markon, K. E., Krueger, R. F. (2017b). Further evidence that psychopathology networks have limited replicability and utility: response to Borsboom et al. (2017) and Steinley et al. (2017). Journal of Abnormal Psychology, 126, 1011–1016.CrossRefGoogle Scholar
Frewen, P. A., Schmittmann, V. D., Bringmann, L. F., & Borsboom, D. (2013). Perceived causal relations between anxiety, posttraumatic stress and depression: extension to moderation, mediation, and network analysis. European Journal of Psychotraumatology, 4, 20656.CrossRefGoogle ScholarPubMed
Fried, E. (2015). Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward. Frontiers in Psychology, 6, 309.CrossRefGoogle Scholar
Fried, E. I. (2017). The 52 symptoms of major depression: lack of content overlap among seven common depression scales. Journal of Affective Disorders, 208, 191–197.CrossRefGoogle ScholarPubMed
Fried, E. I., Boschloo, L., van Borkulo, C. D., Schoevers, R. A., Romeijn, J., Wichers, M., … Borsboom, D. (2015). Commentary: ‘Consistent superiority of selective serotonin reuptake inhibitors over placebo in reducing depressed mood in patients with major depression’. Frontiers in Psychiatry, 6, 117.CrossRefGoogle Scholar
Fried, E. I., & Cramer, A. O. J. (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., & 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 Psychological Science, 6, 335–351.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., 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
Friston, K. J., Redish, A. D., & Gordon, J. A. (2017). Computational nosology and precision psychiatry. Computational Psychiatry, 1, 2–23.CrossRefGoogle ScholarPubMed
Goldstein, A. J., & Chambless, D. L. (1978). A reanalysis of agoraphobia. Behavior Therapy, 9, 47–59.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, e0174035.CrossRefGoogle ScholarPubMed
Granic, I. (2005). Timing is everything: developmental psychopathology from a dynamic systems perspective. Developmental Review, 25, 386–407.CrossRefGoogle Scholar
Guloksuz, S., Pries, L. K., & van Os, J. (2017). Application of network methods for understanding mental disorders: pitfalls and promise. Psychological Medicine, 47, 2743–2752.CrossRefGoogle ScholarPubMed
Guyon, H., Falissard, B., & Kop, J. L. (2017). Modeling psychological attributes in psychology – an epistemological discussion: network analysis vs. latent variables. Frontiers in Psychology, 8, 798.CrossRefGoogle ScholarPubMed
Haag, C., Robinaugh, D. J., Ehlers, A., & Kleim, B. (2017). Understanding the emergence of chronic posttraumatic stress disorder through acute stress symptom networks. JAMA Psychiatry, 74, 649–650.CrossRefGoogle ScholarPubMed
Haig, B. D. (2005). An abductive theory of scientific method. Psychological Methods, 10, 371–388.CrossRefGoogle ScholarPubMed
Haig, B. D. (2008). Precis of ‘an abductive theory of scientific method’. Journal of Clinical Psychology, 64, 1019–1022.CrossRefGoogle ScholarPubMed
Hallquist, M., Wright, A. G., & Molenaar, P. C. (2019). Problems with centrality measures in psychopathology symptom networks: why network psychometrics cannot escape psychometric theory. Multivariate Behavioral Research, 1–25.CrossRefGoogle ScholarPubMed
Hamaker, E. L., & Wichers, M. (2017). No time like the present: discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26, 10–15.CrossRefGoogle Scholar
Haslam, N., Holland, E., & Kuppens, P. (2012). Categories versus dimensions in personality and psychopathology: a quantitative review of taxometric research. Psychological Medicine, 42, 903–920.CrossRefGoogle ScholarPubMed
Haslbeck, J. M. B., & Lourens, L. J. (2016). mgm: structure estimation for time-varying mixed graphical models in high-dimensional data. Retrieved from https://arxiv.org/abs/1510.06871.Google Scholar
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
Hasmi, L., Drukker, M., Guloksuz, S., Menne-Lothmann, C., Decoster, J., van Winkel, R., … van Os, J. (2017). Network approach to understanding emotion dynamics in relation to childhood trauma and genetic liability to psychopathology: replication of a prospective experience sampling analysis. Frontiers in Psychology, 8, 1908.CrossRefGoogle ScholarPubMed
Hayes, A. M., & Strauss, J. L. (1998). Dynamic systems theory as a paradigm for the study of change in psychotherapy: an application to cognitive therapy for depression. Journal of Consulting and Clinical Psychology, 66, 939–947.CrossRefGoogle Scholar
Hayes, A. M., Yasinski, C., Ben Barnes, J., & Bockting, C. L. (2015). Network destabilization and transition in depression: new methods for studying the dynamics of therapeutic change. Clinical Psychology Review, 41, 27–39.CrossRefGoogle ScholarPubMed
Heeren, A., & McNally, R. J. (2016). A call for complexity in the study of social anxiety disorder. Commentary: the aetiology and maintenance of social anxiety disorder: a synthesis of complementary theoretical models and formulation of a new integrated model. Frontiers in Psychology, 7, 1963.CrossRefGoogle Scholar
Heeren, A., & McNally, R. J. (2018). Social anxiety disorder as a densely interconnected network of fear and avoidance for social situations. Cognitive Therapy and Research, 42, 103–113.CrossRefGoogle Scholar
Held, B. S. (2017). The distinction between psychological kinds and natural kinds revisited: can updated natural-kind theory help clinical psychological science and beyond meet psychology's philosophical challenges? Review of General Psychology, 21, 82–94.CrossRefGoogle Scholar
Hoffart, A., & Johnson, S. U. (2017). Psychodynamic and cognitive-behavioral therapies are more different than you think: conceptualizations of mental problems and consequences for studying mechanisms of change. Clinical Psychological Science, 5, 1070–1086.CrossRefGoogle Scholar
Hoffman, M., Steinley, D., Trull, T. J., & Sher, K. J. (2018). Criteria definitions and network relations: the importance of criterion thresholds. Clinical Psychological Science, 6, 506–516.CrossRefGoogle ScholarPubMed
Hofmann, S. G. (2014). Toward a cognitive-behavioral classification system for mental disorders. Behavior Therapy, 45, 576–587.CrossRefGoogle Scholar
Hofmann, S. G., & Curtiss, J. (2018). A complex network approach to clinical science. European Journal of Clinical Investigation, 48, e12986.CrossRefGoogle ScholarPubMed
Hofmann, S. G., Curtiss, J., & McNally, R. J. (2016). A complex network perspective on clinical science. Perspectives on Psychological Science, 11, 597–605.CrossRefGoogle ScholarPubMed
Hosseinichimeh, N., Wittenborn, A. K., Rick, J., Jalali, M. S., & Rahmandad, H. (2018). Modeling and estimating the feedback mechanisms among depression, rumination, and stressors in adolescents. PLoS One, 13, e0204389.CrossRefGoogle ScholarPubMed
Huys, Q. J., Maia, T. V., & Frank, M. J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19, 404–413.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, 870–873.CrossRefGoogle ScholarPubMed
Jones, P., Williams, D., & McNally, R. J. (2019). Sampling variability is not nonreplication: a Bayesian reanalysis of Forbes, Wright, Markon, & Krueger. Retrieved from https://psyarxiv.com/egwfj/.Google Scholar
Jones, P. J., Mair, P., & McNally, R. (2018). Visualizing psychological networks: a tutorial in R. Frontiers in Psychology, 9, 1742.CrossRefGoogle Scholar
Kendler, K. S. (2016). The nature of psychiatric disorders. World Psychiatry, 15, 5–12.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
Kendler, K. S., Zachar, P., & Craver, C. (2011). What kinds of things are psychiatric disorders? Psychological Medicine, 41, 1143–1150.CrossRefGoogle ScholarPubMed
Klippel, A., Viechtbauer, W., Reininghaus, U., Wigman, J., van Borkulo, C., MERGE, … Wichers, M. (2018). The cascade of stress: a network approach to explore differential dynamics in populations varying in risk for psychosis. Schizophrenia Bulletin, 44, 328–337.CrossRefGoogle ScholarPubMed
Kruis, J., & Maris, G. (2016). Three representations of the Ising model. Scientific Reports, 6, 34175.CrossRefGoogle ScholarPubMed
Kuiper, R. M., & Ryan, O. (2018). Drawing conclusions from cross-lagged relationships: re-considering the role of the time-interval. Structural Equation Modeling: A Multidisciplinary Journal, 25, 809–823.CrossRefGoogle Scholar
Levine, S. Z., & Leucht, S. (2016). Identifying a system of predominant negative symptoms: network analysis of three randomized clinical trials. Schizophrenia Research, 178, 17–22.CrossRefGoogle ScholarPubMed
Levinson, C. A., Brosof, L. C., Vanzhula, I., Christian, C., Jones, P., Rodebaugh, T. L., … Fernandez, K. C. (2018a). Social anxiety and eating disorder comorbidity and underlying vulnerabilities: using network analysis to conceptualize comorbidity. International Journal of Eating Disorders, 51, 693–709.CrossRefGoogle Scholar
Levinson, C. A., Vanzhula, I. A., Brosof, L. C., & Forbush, K. (2018b). Network analysis as an alternative approach to conceptualizing eating disorders: implications for research and treatment. Eating Disorders, 20, 67.Google Scholar
Looijestijn, J., Blom, J. D., Aleman, A., Hoek, H. W., & Goekoop, R. (2015). An integrated network model of psychotic symptoms. Neuroscience & Biobehavioral Reviews, 59, 238–250.CrossRefGoogle ScholarPubMed
Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading: Addison-Wesley.Google Scholar
Lydon-Staley, D. M., Schnoll, R. A., Hitsman, B., & Bassett, D. S. (2018). The network structure of tobacco withdrawal in a community sample of smokers treated with nicotine patch and behavioral counseling. Nicotine & Tobacco Research, 1–7.Google Scholar
Marques, D. R., & Azevedo, M. H. P. (2018). Potentialities of network analysis for sleep medicine. Journal of Psychosomatic Research, 111, 89–90.CrossRefGoogle ScholarPubMed
Marsman, M., Borsboom, D., Kruis, J., Epskamp, S., van Bork, R., Waldorp, L. J., … Maris, G. (2018). An introduction to network psychometrics: relating Ising network models to item response theory models. Multivariate Behavioral Research, 53, 15–35.CrossRefGoogle ScholarPubMed
Maung, H. H. (2016). Diagnosis and causal explanation in psychiatry. Studies in History and Philosophy of Biological and Biomedical Sciences, 60, 15–24.CrossRefGoogle Scholar
McGorry, P. D., Hartmann, J. A., Spooner, R., & Nelson, B. (2018). Beyond the ‘at risk mental state’ concept: transitioning to transdiagnostic psychiatry. World Psychiatry, 17, 133–142.CrossRefGoogle Scholar
McNally, R. J. (2012). The ontology of posttraumatic stress disorder: natural kind, social construction, or causal system? Clinical Psychology: Science and Practice, 19, 220–228.Google Scholar
McNally, R. J. (2016). Can network analysis transform psychopathology? Behaviour Research and Therapy, 86, 95–104.CrossRefGoogle ScholarPubMed
McNally, R. J. (2017). Networks and nosology in posttraumatic stress disorder. JAMA Psychiatry, 74, 124–125.CrossRefGoogle ScholarPubMed
McNally, R. J., Robinaugh, D. J., Wu, G. W. Y., Wang, L., Deserno, M. K., & Borsboom, D. (2015). Mental disorders as causal systems: a network approach to posttraumatic stress disorder. Clinical Psychological Science, 3, 836–849.CrossRefGoogle Scholar
Muchinsky, P. M. (1996). The correction for attenuation. Educational and Psychological Measurement, 56, 63–75.CrossRefGoogle Scholar
Nelson, B., McGorry, P. D., Wichers, M., Wigman, J. T. W., & Hartmann, J. A. (2017). Moving from static to dynamic models of the onset of mental disorder: a review. JAMA Psychiatry, 74, 528–534.CrossRefGoogle ScholarPubMed
Pe, M. L., Kircanski, K., Thompson, R. J., Bringmann, L. F., Tuerlinckx, F., Mestdagh, M., … Gotlib, I. H. (2015). Emotion-network density in major depressive disorder. Clinical Psychological Science, 3, 292–300.CrossRefGoogle Scholar
Pearl, J. (2009). Causality: models, reasoning, and inference. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Pincus, D., & Metten, A. (2010). Nonlinear dynamics in biopsychosocial resilience. Nonlinear Dynamics, Psychology, and Life Sciences, 14, 353–380.Google ScholarPubMed
Porter, D. (2015). Colonization by/in psychiatry: from over-medicalization to democratization. Journal of Ethics in Mental Health Open Volume, 1.Google Scholar
Radden, J. (2018). Rethinking disease in psychiatry: disease models and the medical imaginary. Journal of Evaluation in Clinical Practice, 24, 1087–1092.CrossRefGoogle ScholarPubMed
Rikkert, O., Marcel, G. M., Dakos, V., Buchman, T. G., Boer, R. D., Glass, L., … Scheffer, M. (2016). Slowing down of recovery as generic risk marker for acute severity transitions in chronic diseases. Critical Care Medicine, 44, 601–606.CrossRefGoogle Scholar
Robinaugh, D. J., Haslbeck, J., Waldorp, L., Kossakowski, J. J., Fried, E. I., Millner, A. J., … Borsboom, D. (2019). Advancing the network theory of mental disorders: a computational model of panic disorder. doi:10.31234/osf.io/km37wCrossRefGoogle 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
Rodebaugh, T. L., Tonge, N. A., Piccirillo, M. L., Fried, E., 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, 831–844.CrossRefGoogle Scholar
Rouquette, A., Pingault, J. B., Fried, E. I., Orri, M., Falissard, B., Kossakowski, J. J., … Borsboom, D. (2018). Emotional and behavioral symptom network structure in elementary school girls and association with anxiety disorders and depression in adolescence and early adulthood: a network analysis. JAMA Psychiatry, 75, 1173–1181.CrossRefGoogle ScholarPubMed
Russell, J. D., Neill, E. L., Carrion, V. G., & Weems, C. F. (2017). The network structure of posttraumatic stress symptoms in children and adolescents exposed to disasters. Journal of the American Academy of Child and Adolescent Psychiatry, 56, 669–677, e5.CrossRefGoogle Scholar
Ruzzano, L., Borsboom, D., & Geurts, H. M. (2015). Repetitive behaviors in autism and obsessive-compulsive disorder: new perspectives from a network analysis. Journal of Autism and Developmental Disorders, 45, 192–202.CrossRefGoogle ScholarPubMed
Santos, H. Jr., Fried, E. I., Asafu-Adjei, J., & Ruiz, R. J. (2017). Network structure of perinatal depressive symptoms in Latinas: relationship to stress and reproductive biomarkers. Research in Nursing & Health 40, 218–228.CrossRefGoogle ScholarPubMed
Scheffer, M. (2009). Critical transitions in nature and society. Princeton, NJ: Princeton University Press.Google Scholar
Schiepek, G. (2003). A dynamic systems approach to clinical case formulation. European Journal of Psychological Assessment, 19, 175–184.CrossRefGoogle Scholar
Schuler, M., Wittmann, M., Faller, H., & Schultz, K. (2018). The interrelations among aspects of dyspnea and symptoms of depression in COPD patients – a network analysis. Journal of Affective Disorders, 240, 33–40.CrossRefGoogle ScholarPubMed
Schuurman, N. K., Ferrer, E., de Boer-Sonnenschein, M., & Hamaker, E. L. (2016). How to compare cross-lagged associations in a multilevel autoregressive model. Psychological Methods, 21, 206–221.CrossRefGoogle Scholar
Schuurman, N. K., Houtveen, J. H., & Hamaker, E. L. (2015). Incorporating measurement error in n = 1 psychological autoregressive modeling. Frontiers in Psychology, 6, 1038.CrossRefGoogle ScholarPubMed
Schweren, L., van Borkulo, C. D., Fried, E., & Goodyer, I. M. (2018). Assessment of symptom network density as a prognostic marker of treatment response in adolescent depression. JAMA Psychiatry, 75, 98–100.CrossRefGoogle ScholarPubMed
Smith, K. E., Crosby, R. D., Wonderlich, S. A., Forbush, K. T., Mason, T. B., & Moessner, M. (2018). Network analysis: an innovative framework for understanding eating disorder psychopathology. International Journal of Eating Disorders, 51, 214–222.CrossRefGoogle ScholarPubMed
Southward, M. W., & Cheavens, J. S. (2018). Identifying core deficits in a dimensional model of borderline personality disorder features: a network analysis. Clinical Psychological Science, 6, 685–703.CrossRefGoogle Scholar
Tio, P., Epskamp, S., Noordhof, A., & Borsboom, D. (2016). Mapping the manuals of madness: comparing the ICD-10 and DSM-IV-TR using a network approach. International Journal of Methods in Psychiatric Research, 25, 267–276.CrossRefGoogle ScholarPubMed
Treadway, M. T., & Leonard, C. V. (2016). Isolating biomarkers for symptomatic states: considering symptom-substrate chronometry. Molecular Psychiatry, 21, 1180–1187.CrossRefGoogle ScholarPubMed
Tretter, F., & Loffler-Stastka, H. (2018). Steps toward an integrative clinical systems psychology. Frontiers in Psychology, 9, 1616.CrossRefGoogle ScholarPubMed
Tryon, W. W. (2018). Mediators and mechanisms. Clinical Psychological Science, 6, 619–628.CrossRefGoogle Scholar
Tsou, J. Y. (2016). Natural kinds, psychiatric classification and the history of the DSM. History of Psychiatry, 27, 406–424.CrossRefGoogle ScholarPubMed
Tzur-Bitan, D., Meiran, N., & Shahar, G. (2010). The importance of modeling comorbidity using an intra-individual, time-series approach. Behavioral and Brain Sciences, 33, 172–173.CrossRefGoogle ScholarPubMed
van Bork, R., Epskamp, S., Rhemtulla, M., Borsboom, D., & van der Maas, H. L. (2017). What is the p-factor of psychopathology? Some risks of general factor modeling. Theory & Psychology, 27, 759–773.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. D., Borsboom, D., & Schoevers, R. A. (2016). Group-level symptom networks in depression – reply. JAMA Psychiatry, 73, 411–412.CrossRefGoogle ScholarPubMed
van Borkulo, C. D., 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. J., Tio, P., Schoevers, R. A., Borsboom, D., & Waldorp, L. (2017). Comparing network structures on three aspects: a permutation test. Retrieved from https://www.researchgate.net/publication314750838_Comparing_network_structures_on_three_aspects_A_permutation_test.Google Scholar
van de Leemput, I. A., Wichers, M., Cramer, A. O. J., Borsboom, D., Tuerlinckx, F., Kuppens, P., … Scheffer, M. (2014). Critical slowing down as early warning for the onset and termination of depression. Proceedings of the National Academy of Sciences of the United States of America, 111, 87–92.CrossRefGoogle Scholar
van den Hout, M. (2014). Psychiatric symptoms as pathogens. Journal of Treatment Evaluation, 11, 153–159.Google Scholar
van Loo, H. M., & Romeijn, J. W. (2015). Psychiatric comorbidity: fact or artifact? Theoretical Medicine and Bioethics, 36, 41–60.CrossRefGoogle ScholarPubMed
van Loo, H. M., Van Borkulo, C. D., Peterson, R. E., Fried, E. I., Aggen, S. H., Borsboom, D., & Kendler, K. S. (2018). Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk. Journal of Affective Disorders, 227, 313–322.CrossRefGoogle ScholarPubMed
van Os, J., Delespaul, P., Wigman, J., Myin-Germeys, I., & Wichers, M. (2013a). Beyond DSM and ICD: introducing ‘precision diagnosis’ for psychiatry using momentary assessment technology. World Psychiatry, 12, 113–117.CrossRefGoogle Scholar
van Os, J., Delespaul, P., Wigman, J., Myin-Germeys, I., & Wichers, M. (2013b). Psychiatry beyond labels: introducing contextual precision diagnosis across stages of psychopathology. Psychological Medicine, 43, 1563–1567.CrossRefGoogle Scholar
van Rooijen, G., Isvoranu, A. M., Kruijt, O. H., van Borkulo, C. D., Meijer, C. J., Wigman, J. T. W., … investigators, G. (2018). A state-independent network of depressive, negative and positive symptoms in male patients with schizophrenia spectrum disorders. Schizophrenia Research 193, 232–239.CrossRefGoogle ScholarPubMed
Verhoeff, B. (2013). The autism puzzle: challenging a mechanistic model on conceptual and historical grounds. Philosophy, Ethics, and Humanities in Medicine, 8, 17.CrossRefGoogle ScholarPubMed
von Stockert, S. H. H., Fried, E. I., Armour, C., & Pietrzak, R. H. (2018). Evaluating the stability of DSM-5 PTSD symptom network structure in a national sample of U.S. military veterans. Journal of Affective Disorders, 229, 63–68.CrossRefGoogle Scholar
Vosgerau, G., & Soom, P. (2018). Reduction without elimination: mental disorders as causally efficacious properties. Minds & Machines, 28, 311–330.CrossRefGoogle Scholar
Waller, N. G., & Meehl, P. E. (1998). Multivariate taxometric procedures: distinguishing types from continua. Thousand Oaks: Sage.Google Scholar
Walter, H. (2013). The third wave of biological psychiatry. Frontiers in Psychology, 4, 582.CrossRefGoogle ScholarPubMed
Wardenaar, K. J., & de Jonge, P. (2013). Diagnostic heterogeneity in psychiatry: towards an empirical solution. BMC Medicine, 11, 201.CrossRefGoogle ScholarPubMed
Wass, S., & Karmiloff-Smith, A. (2010). The missing developmental dimension in the network perspective. Behavioral and Brain Sciences, 33, 175–176.CrossRefGoogle ScholarPubMed
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
Wichers, M., Wigman, J. T. W., Bringmann, L. F., & De Jonge, P. (2017). Mental disorders as networks: some cautionary reflections on a promising approach. Social Psychiatry and Psychiatric Epidemiology, 52, 143–145.CrossRefGoogle ScholarPubMed
Wichers, M., Wigman, J. T. W., & Myin-Germeys, I. (2015). Micro-level affect dynamics in psychopathology viewed from complex dynamical system theory. Emotion Review, 7, 362–367.CrossRefGoogle Scholar
Williams, D., & Rast, P. (2018). Back to the basics: rethinking partial correlation network methodology. Retrieved from https://osf.io/fndru.Google Scholar
Williams, D., Rast, P., & Mulder, J. (2019). Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection. Retrieved from https://psyarxiv.com/yt386/.Google Scholar
Wittenborn, A. K., Rahmandad, H., Rick, J., & Hosseinichimeh, N. (2016). Depression as a systemic syndrome: mapping the feedback loops of major depressive disorder. Psychological Medicine, 46, 551–562.CrossRefGoogle ScholarPubMed
Wusten, C., Schlier, B., Jaya, E. S., Genetic, R., Outcome of Psychosis, I., Fonseca-Pedrero, E., Peters, E., … Lincoln, T. M. (2018). Psychotic experiences and related distress: a cross-national comparison and network analysis based on 7141 participants from 13 countries. Schizophrenia Bulletin 44, 1185–1194.CrossRefGoogle ScholarPubMed
Yang, X., Ram, N., Gest, S. D., Lydon-Staley, D. M., Conroy, D. E., Pincus, A. L., & Molenaar, P. C. M. (2018). Socioemotional dynamics of emotion regulation and depressive symptoms: a person-specific network approach. Complexity 2018, 1–14.Google ScholarPubMed
Yordanova, J., Kolev, V., Kirov, R., & Rothenberger, A. (2010). Comorbidity in the context of neural network properties. Behavioral and Brain Sciences, 33, 176–177.CrossRefGoogle ScholarPubMed
Young, G. (2015). Causality in psychiatry: a hybrid symptom network construct model. Frontiers in Psychiatry, 6, 164.CrossRefGoogle ScholarPubMed
Zachar, P. (2015). Psychiatric disorders: natural kinds made by the world or practical kinds made by us? World Psychiatry, 14, 288–290.CrossRefGoogle ScholarPubMed
Zhao, X., Yang, B., Liu, X., & Chen, H. (2017). Statistical inference for community detection in signed networks. Physical Review E, 95, 042313.CrossRefGoogle ScholarPubMed
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