Dynamic symptom networks across different at-risk stages for psychosis: An individual and transdiagnostic perspective

Tuin, Sara van der; Balafas, Spyros; Oldehinkel, Albertine J.; Wit, Ernst; Booij, Sanne H.; Wigman, Johanna T. W.

doi:10.1016/j.schres.2021.11.018

Cited by 9 publications

(4 citation statements)

References 46 publications

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“…The contemporaneous network reflects the unique bidirectional associations among variables that occur after time effects have been taking into account. In previous work, we found that most symptom associations in this dataset occur within days (i.e., contemporaneous) rather than between days (i.e., temporal) [20,30]. This is in line with other research on diary studies that assess mental states once a day [31].…”

Section: Statistical Analysessupporting

confidence: 91%

“…A second consideration to keep in mind is that symptoms were measured only once a day, while the processes of symptom development may unfold within a shorter timeframe. In previous studies on the same dataset [20,30], we observed that most relationships between variables appeared within days rather than over days. Therefore, we looked exclusively at contemporaneous relationships between variables, which due to the design of our study reflect the associations within a day.…”

Section: Plos Onementioning

confidence: 61%

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Relating stability of individual dynamical networks to change in psychopathology

van der Tuin,

Hoekstra,

Booij

et al. 2023

PLoS ONE

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One hypothesis flowing from the network theory of psychopathology is that symptom network structure is associated with psychopathology severity and in turn, one may expect that individual network structure changes with the level of psychopathology severity. However, this expectation has rarely been addressed directly. This study aims to examine (1) the stability of individual contemporaneous symptom networks over a one-year period and (2) whether network stability is associated with a change in psychopathology. We used daily diary data of n = 66 individuals, located along the psychosis severity continuum, from two separate 90-day periods, one year apart (t = 180). Based on the newly developed Individual Network Invariance Test (INIT) to assess symptom-network stability, participants were divided into two groups with stable and unstable networks and we tested whether these groups differed in their absolute change in psychopathology severity. The majority of the sample (n = 51, 77.3%) showed a stable network over time while most individuals showed a decrease in psychopathological severity. We found no significant association between a change in psychopathology severity and individual network stability. Our results call for further critical evaluation of the association between networks and psychopathology to optimize the implementation of clinical applications based on current methods.

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Section: Statistical Analysessupporting

confidence: 91%

Section: Plos Onementioning

confidence: 61%

Relating stability of individual dynamical networks to change in psychopathology

van der Tuin,

Hoekstra,

Booij

et al. 2023

PLoS ONE

Self Cite

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“…In the empirical network condition, the network structures from two outpatients diagnosed with bipolar disorder were used as the data-generating mechanism; see Figure 3 show relatively dense connected contemporaneous and spare temporal networks (e.g., Epskamp, van Borkulo, et al, 2018;Fisher et al, 2017;Kroeze et al, 2017;van der Tuin et al, 2022). For both patients, their estimated network structure adheres to these properties.…”

Section: Empirical Networkmentioning

confidence: 93%

Testing similarity in longitudinal networks: The Individual Network Invariance Test (INIT)

Hoekstra,

Epskamp,

Nierenberg

et al. 2023

Preprint

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The comparison of idiographic network structures in order to determine the presence of heterogeneity is a challenging endeavor in many applied settings. Previously, researchers eyeballed idiographic networks, computed correlations between idiographic networks, as well as used techniques that make use of the multilevel structure of the data (e.g., GIMME and mlVAR) to investigate individual differences. However, these methods do not allow for testing the equality of idiographic network structures directly. In this paper, we propose the Individual Network Invariance Test (INIT). INIT extends common model comparison practices in Structural Equation Modeling (SEM) to idiographic network structures to test for (in)equality between idiographic network structures. In a simulation study, we evaluated the performance of INIT on both saturated and pruned idiographic network structures by inspecting the rejection rate of the χ2 difference test and of model selection criteria such as the AIC and BIC. Results show INIT performs adequately when 100 or more data points are available per individual. When applying INIT on saturated networks, AIC performed best as a model selection criteria, while BIC showed better results when applying INIT on pruned networks. In an empirical example, we highlight the possibilities of this new technique, illustrating how INIT provides researchers with a means of testing for (in)equality between idiographic network structures, and within idiographic network structures over time. To conclude, recommendations for empirical researchers are provided.

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“…Estimated GVAR networks based on empirical data often show relatively dense connected contemporaneous and spare temporal networks (e.g., Epskamp, van Borkulo, et al, 2018; A. J. Fisher et al, 2017; Kroeze et al, 2017; Levinson et al, 2022; van der Tuin et al, 2022). For both patients, their estimated network structure adheres to these properties.…”

Section: Simulation Studymentioning

confidence: 99%

Testing similarity in longitudinal networks: The Individual Network Invariance Test.

Hoekstra,

Epskamp,

Nierenberg

et al. 2024

Psychological Methods

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Dynamic symptom networks across different at-risk stages for psychosis: An individual and transdiagnostic perspective

Cited by 9 publications

References 46 publications

Relating stability of individual dynamical networks to change in psychopathology

Relating stability of individual dynamical networks to change in psychopathology

Testing similarity in longitudinal networks: The Individual Network Invariance Test (INIT)

Testing similarity in longitudinal networks: The Individual Network Invariance Test.

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