Six new network papers, including a review of the empirical literature

The last weeks have been pretty busy with teaching, traveling, and writing that I didn’t find the time I wanted to for blogging. To catch up, this blog post will feature several new network papers (in alphabetical order by first author). It is becoming increasingly hard to keep up with the exploding literature, so please send in papers that I missed.

  1. Armour et al. 2016: “A Network Analysis of DSM-5 posttraumatic stress disorder symptoms and clinically relevant correlates in a national sample of U.S. military veterans” (dataset available)
  2. Cramer et al. 2016: “Major depression as a complex dynamical system”
  3. Fried et al. 2016: “Mental disorders as networks of problems: a review of recent insights”
  4. McNally et al. 2016: “Comorbid obsessive-compulsive disorder and depression: A Bayesian network approach”
  5. Schiepek et al. 2016: “Systemic Case Formulation, Individualized Process Monitoring, and State Dynamics in a Case of Dissociative Identity Disorder”
  6. van Nuijten et al. 2016: “Mental disorders as complex networks: An introduction and overview of a network approach to psychopathology”

1. Network Analysis of DSM-5 PTSD symptoms and clinical covariates

The DSM-5 changed the PTSD symptoms substantially compared to the DSM-IV, and in this paper published in the Journal of Anxiety Disorders (PDF) we investigated the network structure of DSM-5 PTSD symptoms in a cross-sectional sample of 221 veterans with at least subthreshold levels of PTSD symptomatology. We also examined whether clinical covariates such as gender, anxiety, suicidal ideation, or quality of life are differentially related to the DSM-5 PTSD symptoms.

We published the dataset and syntax with the paper, so this is a nice start if you want to get into network modeling. Note that 221 people is a fairly small sample for modeling 20+ items in a network and that this paper definitely needs replication work. Below the two networks without and with covariates.

Read the full abstract here.

Objective: Recent developments in psychometrics enable the application of network models to analyze psychological disorders, such as PTSD. Instead of understanding symptoms as indicators of an underlying common cause, this approach suggests symptoms co-occur in syndromes due to causal interactions. The current study has two goals: (1) examine the network structure among the 20 DSM-5 PTSD symptoms, and (2) incorporate clinically relevant variables to the network to investigate whether PTSD symptoms exhibit differential relationships with suicidal ideation, depression, anxiety, physical functioning/quality of life (QoL), mental functioning/QoL, age, and sex. Method: We utilized a nationally representative U.S. military veteran’s sample; and analyzed the data from a subsample of 221 veterans who reported clinically significant DSM-5 PTSD symptoms. Networks were estimated using state-of-the-art regularized partial correlation models. Data and code are published along with the paper. Results: The 20-item DSM-5 PTSD network revealed that symptoms were positively connected within the network. Especially strong connections emerged between nightmares and flashbacks; blame of self or others and negative trauma-related emotions, detachment and restricted affect; and hypervigilance and exaggerated startle response. The most central symptoms were negative trauma-related emotions, flashbacks, detachment, and physiological cue reactivity. Incorporation of clinically relevant covariates into the network revealed paths between self-destructive behavior and suicidal ideation; concentration difficulties and anxiety, depression, and mental QoL; and depression and restricted affect. Conclusion: These results demonstrate the utility of a network approach in modeling the structure of DSM-5 PTSD symptoms, and suggest differential associations between specific DSM-5 PTSD symptoms and clinical outcomes in trauma survivors. Implications of these results for informing the assessment and treatment of this disorder, are discussed.
— Armour, C., Fried, E. I., Deserno, M. K., Tsai, J., Southwick, S. M., & Pietrzak, R. H. (2016). A Network Analysis of DSM-5 posttraumatic stress disorder symptoms and clinically relevant correlates in a national sample of U.S. military veterans. Journal of Anxiety Disorders, 45(44), 1–32. (PDF)

DSM-5 PTSD symptoms, Armour et al 2016

DSM-5 PTSD symptoms + clinical covariates

2. Major depression as a complex dynamical system

The new paper by Angélique Cramer and colleagues published in PLOS ONE (PDF) introduces both a conceptual and statistical model for major depression. The main contribution is that the authors show how individuals with more connected networks are more vulnerable to develop depression. These people can also transition from a healthy to a depressed state with only minimal amount of external stress (“cusp effect”), while a lot of stress reduction is needed to get that same system back into a healthy state (“hysteresis effect”). That is, if you stress a healthy system for people with highly interacting problems until it transitions into a clinical state, simply removing the stress may not be sufficient for the system to revert back. This is an exciting model that explains a number of observed phenomena in depression and now seeks further empirical validation in future studies.

Read the full abstract here.

In this paper, we characterize major depression (MD) as a complex dynamic system in which symptoms (e.g., insomnia and fatigue) are directly connected to one another in a network structure. We hypothesize that individuals can be characterized by their own network with unique architecture and resulting dynamics. With respect to architecture, we show that individuals vulnerable to developing MD are those with strong connections between symptoms: e.g., only one night of poor sleep suffices to make a particular person feel tired. Such vulnerable networks, when pushed by forces external to the system such as stress, are more likely to end up in a depressed state; whereas networks with weaker connections tend to remain in or return to a non-depressed state. We show this with a simulation in which we model the probability of a symptom becoming ‘active’ as a logistic function of the activity of its neighboring symptoms. Additionally, we show that this model potentially explains some well-known empirical phenomena such as spontaneous recovery as well as accommodates existing theories about the various subtypes of MD. To our knowledge, we offer the first intra-individual, symptom-based, process model with the potential to explain the pathogenesis and maintenance of major depression.
— Cramer, A. O. J., Borkulo, C. D. Van, Giltay, E. J., Han, L., Maas, J. Van Der, Kendler, K. S., … Borsboom, D. (2016). Major depression as a complex dynamical system. Plos ONE. (PDF)

Hysteresis effect

3. A review of the empirical literature on psychopathology networks

We were invited by Social Psychiatry and Psychiatric Epidemiology to write a review on the empirical psychopathology network literature, which was a challenge because every time we wanted to submit the paper, a few new papers were out. The paper was published last week, is a bit more concise that we wanted it to be (the word limit was rather strict with 4500 words for a review), and focuses on the topics comorbidity, prediction of psychopathology onset or relapse, and clinical intervention. The paper is open access and available here.

Below an example of how comorbidity can be conceptualized from the perspective of the network approach: disorder X consists of the eight symptoms X1–X5 and B1–B3, disorder Y consists of the eight symptoms Y1–Y5 and B1–B3, and B1–B3 are bridge symptoms that feature in both diagnoses. In this case, a person first develops X3 in response to an environmental stressor E, then symptoms of disorder X, then bridge symptoms B, and finally symptoms of disorder Y.

Read the full abstract here.

Purpose. The network perspective on psychopathology understands mental disorders as complex networks of interacting symptoms. Despite its recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in the last years. Methods This paper provides a review of all empirical network studies published between 2010 and 2016 and discusses them according to three main themes: comorbidity, prediction, and clinical intervention. Results Pertaining to comorbidity, the network approach provides a powerful new framework to explain why certain disorders may co-occur more often than others. For prediction, studies have consistently found that symptom networks of people with mental disorders show different characteristics than that of healthy individuals, and preliminary evidence suggests that networks of healthy people show early warning signals before shifting into disordered states. For intervention, centrality—a metric that measures how connected and clinically relevant a symptom is in a network—is the most commonly studied topic, and numerous studies have suggested that targeting the most central symptoms may offer novel therapeutic strategies. Conclusions We sketch future directions for the network approach pertaining to both clinical and methodological research, and conclude that network analysis has yielded important insights and may provide an important inroad towards personalized medicine by investigating the net- work structures of individual patients.
— Fried, E. I., van Borkulo, C. D., Cramer, A. O. J., Boschloo, L., Schoevers, R. A., & Borsboom, D. (2016). Mental disorders as networks of problems: a review of recent insights. Social Psychiatry and Psychiatric Epidemiology, 1, 1–32. (PDF)

Fried et al 2016 network review comorbidity

4. Modeling depression-OCD comorbidity via Bayesian networks

Richard McNally and colleagues have a new paper forthcoming in Psychological Medicine you should keep an eye out for! The paper isn’t online yet but I found it announced on a website, so I will limit myself to posting the abstract here as a teaser.

Read the full abstract here.

Background. Obsessive-compulsive disorder (OCD) is often comorbid with depression. Using the methods of network analysis, we computed two networks that disclose the potentially causal relations among symptoms of these two disorders in 408 adult patients with primary OCD and comorbid depression symptoms. Method. We examined the relation between the symptoms constituting these syndromes by computing a (regularized) partial correlation network via the graphical LASSO procedure, and a Directed Acyclic Graph (DAG) via a Bayesian hill-climbing algorithm. Results. The results suggest that the degree of interference and distress associated with obsessions, and the degree of interference associated with compulsions, are the chief drivers of comorbidity. Moreover, activation of the depression cluster appears to occur solely through distress associated with obsessions activating sadness – a key symptom that “bridges” the two syndromic clusters in the DAG. Conclusion. Bayesian analysis can expand the repertoire of network analytic approaches to psychopathology. We discuss clinical implications and limitations of our findings.
– McNally RJ, Mair P, Mugno BL, Riemann BC. Comorbid obsessive-compulsive disorder and depression: A Bayesian network approach. Psychological Medicine. Forthcoming.

5. Case-report of one patient with Dissociative Identity Disorder

Günter Schiepek and colleagues have published a case-report in Frontiers in Psychology (PDF). They estimated the network of cognitions, emotions, and behavior in a female patient diagnosed with borderline personality disorder and dissociative identity disorder. I admit that I’m not familiar with the modeling approach, but the paper features a larger number of graphs and I plan to devote some time next week trying to understand the model better.

Schiepek 2016

Read the full abstract here.

Objective: The aim of this case report is to demonstrate the feasibility of a systemic procedure (synergetic process management) including modeling of the idiographic psychological system and continuous high-frequency monitoring of change dynamics in a case of dissociative identity disorder. The psychotherapy was realized in a day treatment center with a female client diagnosed with borderline personality disorder (BPD) and dissociative identity disorder.
Methods: A three hour long co-creative session at the beginning of the treatment period allowed for modeling the systemic network of the client’s dynamics of cognitions, emotions, and behavior. The components (variables) of this idiographic system model (ISM) were used to create items for an individualized process questionnaire for the client. The questionnaire was administered daily through an internet-based monitoring tool (Synergetic Navigation System, SNS), to capture the client’s individual change process continuously throughout the therapy and after-care period. The resulting time series were reflected by therapist and client in therapeutic feedback sessions.
Results: For the client it was important to see how the personality states dominating her daily life were represented by her idiographic system model and how the transitions between each state could be explained and understood by the activating and inhibiting relations between the cognitive-emotional components of that system. Continuous monitoring of her cognitions, emotions, and behavior via SNS allowed for identification of important triggers, dynamic patterns, and psychological mechanisms behind seemingly erratic state fluctuations. These insights enabled a change in management of the dynamics and an intensified trauma-focused therapy.
Conclusion: By making use of the systemic case formulation technique and subsequent daily online monitoring, client and therapist continuously refer to detailed visualizations of the mental and behavioral network and its dynamics (e.g., order transitions). Effects on self-related information processing, on identity development, and toward a more pronounced autonomy in life (instead of feeling helpless against the chaoticity of state dynamics) were evident in the presented case and documented by the monitoring system.
– Schiepek, G. K., Stöger-Schmidinger, B., Aichhorn, W., Schöller, H., & Aas, B. (2016). Systemic Case Formulation, Individualized Process Monitoring, and State Dynamics in a Case of Dissociative Identity Disorder. Frontiers in Psychology, 7(1545), 1–11. (PDF)

6. Overview paper of mental disorders as complex networks

Update: I forgot to mention the new paper by van Nuijten et al. 2016 — so much work coming out it’s hard to keep up! The paper entitled “Mental disorders as complex networks: An introduction and overview of a network approach to psychopathology” (PDF) was published in Clinical Neuropsychiatry, and features an overview of the network approach. It makes for a great introduction and features multiple topics such as heterogeneity and comorbidity, and I see it as a concise and insightful summary of the fantastic Cramer & Borsboom (2013) that appeared in the Annual review of clinical psychology.


The paper took a while to get published, which may explain that there is only one reference to papers in 2016. Given the fast evolution of the field, some important developments are not mentioned. This caveat notwithstanding, a fantastic introductory read. Slate Star Codex wrote a blog about the network approach to psychopathology recently, featuring the paper by Nuijten et al. in more detail.

Read the full abstract here.

Mental disorders have traditionally been conceptualized as latent variables, which impact observable symptomatology. Recent alternative approaches, however, view mental disorders as systems of mutually reinforcing symptoms, and utilize network models to analyze the structure of these symptom-symptom interactions. This paper gives an introduction to and overview of the network approach to psychopathology, as it has developed over the past years.
– Nuijten, M.B., Deserno, M. K., Cramer, A. O. J., & Borsboom, D. (2016). Mental disorders as complex networks: An introduction and overview of a network approach to psychopathology. Clinical Neuropsychiatry, 13 (4/5), 68-76. (PDF)

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