Dynamical systems study follows 1 psychotic patient for 1 full year

When we recently submitted a review paper on empirical network studies in psychopathology, we realized that the majority of prior papers had focused on the analyses of groups, e.g. depressed patients, psychotic patients, or people with autism. Fewer studies had looked into the question of the dynamic character of symptoms or problems, and even fewer papers had investigated these dynamics on a personalized level. However, such idiographic analyses likely reveal insights that cannot be gained from the analyses of groups, especially in time-series data.

To address this gap of literature, Bak et al. just published their new study “An n=1 Clinical Network Analysis of Symptoms and Treatment in Psychosis” in PLOS ONE. The authors looked into the dynamic system of one psychotic patient. The patient was followed over the course of 1 year, with about 40 assessments per week via the so-called Experience Sampling Method (ESM).

Here you can see the severity of symptoms over the course of the year:


They found that the severity of the patient’s problems, and the interactions among problems in a network, changed across time (for the three periods ‘stable’, ‘impending transition’, and ‘relapse’). While this makes sense clinically, it is important to keep in mind that the most commonly used methods at the moment — the vector autoregressive model (VAR) and its multilevel extensions for the case of n>1 — assume that the process is stable across time. The study by Bak et al. thus supports recent efforts to allow for modeling changing dynamics over time. There are two different versions to estimate time-varying VAR models I am aware of at present: one was recently developed by Jonas Haslbeck (preprint; presentation), the other has been published in Psychological Methods by Laura Bringmann (paper).

I would love to see more papers on idiographic aspects of psychopathology, and I am also very curious to see how large between-person differences are regarding such dynamical aspects of mental illness.


Introduction: Dynamic relationships between the symptoms of psychosis can be shown in individual networks of psychopathology. In a single patient, data collected with the Experience Sampling Method (ESM–a method to construct intensive time series of experience and context) can be used to study lagged associations between symptoms in relation to illness severity and pharmacological treatment.
Method: The patient completed, over the course of 1 year, for 4 days per week, 10 daily assessments scheduled randomly between 10 minutes and 3 hours apart. Five a priori selected symptoms were analysed: ‘hearing voices’, ‘down’, ‘relaxed’, ‘paranoia’ and ‘loss of control’. Regression analysis was performed including current level of one symptom as the dependent variable and all symptoms at the previous assessment (lag) as the independent variables. Resulting regression coefficients were printed in graphs representing a network of symptoms. Network graphs were generated for different levels of severity: stable, impending relapse and full relapse.
Results: ESM data showed that symptoms varied intensely from moment to moment. Network representations showed meaningful relations between symptoms, e.g. ‘down’ and ‘paranoia’ fuelling each other, and ‘paranoia’ negatively impacting ‘relaxed’. During relapse, symptom levels as well as the level of clustering between symptoms markedly increased, indicating qualitative changes in the network. While ‘hearing voices’ was the most prominent symptom subjectively, the data suggested that a strategic focus on ‘paranoia’, as the most central symptom, had the potential to bring about changes affecting the whole network.
Conclusion: Construction of intensive ESM time series in a single patient is feasible and informative, particularly if represented as a network, showing both quantitative and qualitative changes as a function of relapse.

— Bak, M., Drukker, M., Hasmi, L., van Os, J. (2016) An n=1 Clinical Network Analysis of Symptoms and Treatment in Psychosis. PLoS ONE 11(9): e0162811. DOI: 10.1371/journal.pone.0162811

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