Network theory of psychiatric disorders

A closer look at individual symptoms and their interactions

For the past 50 years, the dominating model of mental disorders has been the “common cause” model, which stipulates that the symptoms seen in mental disorders are caused by an underlying entity: the disorder. However, specific markers are not available for mental disorders despite massive effort 1. Further, the symptoms seen in mental disorders are often non-specific and there is large within-disorder variation in symptom profiles. Among 3703 patients with depression, there were 1030 unique symptom profiles, and the most common symptom profile was only seen in 1.8% of patients! 2 The current classification system may be insufficient in capturing the complexity seen in mental disorders 3.

In recent years an alternative approach called the network theory of psychopathology has gained traction 4. Well-known applications of network theory include social networks (individuals connected through acquaintance) and neural networks in the brain (where neurons are connected through axons). In the network theory of psychopathology, the symptoms of mental disorders are not only seen as passive “output” from an underlying (yet to be identified) cause, but instead as active components that casually contribute to and reinforce other symptoms in the network 5,6. For example, certain symptoms may have many links to other symptoms, and if those links are strong those symptoms are central in the network. When two disorders have shared symptoms, the symptoms that link the two disorder networks together are typically called bridge symptoms 7. The two images below illustrate the difference in viewpoint between the traditional common cause model and the alternative network model.

Major depressive disorder in the common cause model. From Borsboom & Cramer, 2013.
Symptom networks from MDD and GAD in two individuals. From Borsboom & Cramer, 2013.

Network studies in OCD

Since the introduction of network models to describe psychopathology, there have been several studies on obsessive-compulsive disorder and comorbid conditions. A recent study investigated obsessive-compulsive symptoms in eating disorders, and found that the two symptom clusters were largely distinct with few potential bridge symptoms 8, whereas distress from obsessions has been identified as a bridge symptom between OCD and depression and thus a potentially important treatment target in patients with this common comorbidity9. In addition, a few studies have investigated the most central symptoms in OCD. Negative appraisals of intrusive thoughts (e.g., “having nasty thoughts means I am a terrible person”) and doubting/checking have emerged as potential central symptoms in adults and youth, respectively 10,11. However, whether bridge symptoms or central symptoms are useful as treatment targets in OCD remains unclear since studies to date have used cross-sectional rather than repeated measurement data 4.

Symptom networks during treatment

By analyzing symptom networks over time in what is called sequential network analysis, it is possible to investigate how the effects of treatment on symptom networks evolve and cascade over time. Blanken and colleagues 12 presented a sequential network analysis in co-occurring insomnia and depression, showing that for patients with both conditions, treatment effects on depressive symptoms are likely indirect through improvements in sleep. A recent study performed a similar sequential analysis using data from a large randomized controlled trial comparing CBT to supportive psychotherapy for body dysmorphic disorder, and concluded that CBT and supportive psychotherapy exerted their beneficial effects through distinct mechanisms 13. Interestingly, the authors also found that effects of specific symptoms followed the typical sequence in CBT (i.e., cognitive interventions first and behavioral interventions later). The sequential network analysis approach is therefore promising in being able to address questions of how psychotherapy exerts its effects as well as evaluating proposed theoretical mechanisms.

Remaining questions

Some of the challenges so far is that few studies have been designed with the intention of conducting network analyses, and they are mostly secondary analyses of already collected data. This gives rise to multiple challenges.

One challenge is that symptoms have often been measured as binary (presence/absence of symptom), but they are likely dimensional (more or less of the symptom).

The network effects may also vary in time-scale, for example seconds or minutes in the case of a panic attack, to weeks or months for other effects. How to best capture these varying time-scales is not well understood.

A third challenge for the field is how to properly synthesize results from different studies and the development of meta-analytic techniques. The methods are in rapid development and there is not yet a broad consensus about the proper methods to address our hypotheses and questions.

References

  1. Borsboom, D., Cramer, A., & Kalis, A. (2018). Brain disorders? Not really… Why network structures block reductionism in psychopathology research. Behavioral and Brain Sciences, 1–54. https://doi.org/10.1017/S0140525X17002266 

  2. 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. https://doi.org/10.1016/j.jad.2014.10.010 

  3. Boschloo, L., Borkulo, C. D. van, 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. https://doi.org/10.1371/journal.pone.0137621 

  4. Robinaugh, D. J., Hoekstra, R. H. A., Toner, E. R., & Borsboom, D. (2020). The network approach to psychopathology: A review of the literature 2008–2018 and an agenda for future research. Psychological Medicine, 50(3), 353–366. https://doi.org/10.1017/S0033291719003404  2

  5. 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. https://doi.org/10.1146/annurev-clinpsy-050212-185608 

  6. Hout van den, M. (2014). Psychiatric symptoms as pathogens. Clinical Neuropsychiatry, 11(6), 153–159. 

  7. Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5–13. https://doi.org/10.1002/wps.20375 

  8. Meier, M., Kossakowski, J. J., Jones, P. J., Kay, B., Riemann, B. C., & McNally, R. J. (2020). Obsessive–compulsive symptoms in eating disorders: A network investigation. International Journal of Eating Disorders, 53(3), 362–371. https://doi.org/10.1002/eat.23196 

  9. McNally, R. J., Mair, P., Mugno, B. L., & Riemann, B. C. (2017). Co-morbid obsessive-compulsive disorder and depression: A Bayesian network approach. Psychol. Med., 47(7), 1204–1214. https://doi.org/10.1017/S0033291716003287 

  10. Olatunji, B. O., Christian, C., Brosof, L., Tolin, D. F., & Levinson, C. A. (2019). What is at the core of OCD? A network analysis of selected obsessive-compulsive symptoms and beliefs. Journal of Affective Disorders, 257, 45–54. https://doi.org/10.1016/j.jad.2019.06.064 

  11. Cervin, M., Perrin, S., Olsson, E., Aspvall, K., Geller, D. A., Wilhelm, S., McGuire, J., Lázaro, L., Martínez-González, A. E., Barcaccia, B., Pozza, A., Goodman, W. K., Murphy, T. K., Seçer, İ., Piqueras, J. A., Rodríguez-Jiménez, T., Godoy, A., Rosa-Alcázar, A. I., Rosa-Alcázar, Á., … Mataix-Cols, D. (2020). The Centrality of Doubting and Checking in the Network Structure of Obsessive-Compulsive Symptom Dimensions in Youth. Journal of the American Academy of Child & Adolescent Psychiatry, 59(7), 880–889. https://doi.org/10.1016/j.jaac.2019.06.018 

  12. 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(1), 52–54. https://doi.org/10.1159/000495045 

  13. Bernstein, E. E., Phillips, K. A., Greenberg, J. L., Curtiss, J., Hoeppner, S. S., & Wilhelm, S. (2021). Mechanisms of cognitive-behavioral therapy effects on symptoms of body dysmorphic disorder: A network intervention analysis. Psychological Medicine, 1–9. https://doi.org/10.1017/S0033291721004451