Psychopathological networks: Theory, methods and practice

Bringmann, Laura F.; Albers, Casper J.; Bockting, Claudi; Borsboom, Denny; Ceulemans, Eva; Cramer, Angélique O. J.; Epskamp, Sacha; Eronen, Markus I.; Hamaker, Ellen L.; Kuppens, Peter; Lutz, Wolfgang; McNally, Richard J.; Molenaar, Peter; Tio, Pia; Voelkle, Manuel C.; Wichers, Marieke

doi:10.1016/j.brat.2021.104011

Cited by 142 publications

(125 citation statements)

References 153 publications

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“…As they discussed, this finding indicates that when associations between symptoms are not time-invariant, interventions based on an analysis of symptom-level associations that do not take this variation over time into account might be suboptimal. Our results thus again reinforce recent calls for the use of network analysis methods that are equipped to detect variation over time (Bringmann et al, 2022). However, changes of estimates over time may be hard to grasp.…”

Section: Discussionsupporting

confidence: 81%

“…To make them useful in the latter case, the selection of an appropriate context where gradual change is to be expected is important, as well as possibly tailoring the bandwidth selection process to accommodate interpretative purposes. As time-varying techniques need a lot of data, especially to detect cross-lagged effects (Bringmann et al, 2022), the proper selection of a limited number of important variables is crucial. Finally, even if researchers intend to use stationary time series models, additionally fitting time-varying models could provide more information about the appropriateness of model selection (Bringmann et al, 2016) as they can pick up on changes in autocorrelations and covariances that are not detected by most commonly used tests for nonstationarity (Bringmann et al, 2016;Piccirillo et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Several authors have emphasized that this assumption restricts the investigation of change processes that can be of central interest for researchers and clinicians (e.g. Bringmann et al, 2022;Piccirillo et al, 2019). For example, psychotherapists using daily diary data of their patients might be interested if the association between stress at work and subsequent depressive mood changes during therapy, as their patients might be incrementally able to handle stress better.…”

Section: Introductionmentioning

confidence: 99%

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Temporal dynamics of depressive symptomatology: An idiographic time series analysis applying network models to patients with depressive disorders

Siepe¹,

Sander²,

Schultze³

et al. 2022

Preprint

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Background: As phenotypes of depressive disorders (DD) are highly heterogenous, a growing number of studies investigate person-specific associations of depressive symptoms in time series data. Most available methods for estimating applicable models rely on the assumption that the associations between variables stay constant over time, which can be unrealistic in clinical contexts. To circumvent this limitation, we used a recently developed technique to estimate time-varying vector autoregressive models. Methods: In daily diary data of 20 participants with DD with a mean length of 274 days (SD = 82.4, range = 154-539), we modeled idiographic associations between core depressive symptoms, rumination, sleep, and quantity and quality of social contacts as idiographic time-varying dynamical networks. Results: Resulting models showed marked inter- as well as intraindividual differences. For some participants, associations between variables changed fast over time, whereas for others they showed more stability. Our results further indicated nonstationarity in all time series. Discussion: Idiographic symptom networks of depression can be of interest to clinicians and researchers as they can capture changes over time and provide detailed insights into the temporal course of mental disorders. Whilst the assumption of stationarity can hinder insights into important change processes, time-varying network models are a promising approach. We discuss limitations, their possible solutions, and recommendations for further use of the modeling technique.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temporal dynamics of depressive symptomatology: An idiographic time series analysis applying network models to patients with depressive disorders

Siepe¹,

Sander²,

Schultze³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This allows one to explore and model complex sets of multivariate data, flexibly and robustly, without needing strong theoretical models [ 58 ]. Some applied examples are present in psychopathology [ 59 ], personality [ 60 ], social psychology [ 61 ], and psychometric research [ 62 , 63 ]. Therefore, a network analysis can help researchers quickly understand the relationships between variables and provide exploratory interactive models [ 64 ].…”

Section: Introductionmentioning

confidence: 99%

A Network Analysis of the Fear of COVID-19 Scale (FCV-19S): A Large-Scale Cross-Cultural Study in Iran, Bangladesh, and Norway

Lecuona

Lin

Rozgonjuk

et al. 2022

IJERPH

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The rapid spread of the coronavirus disease 2019 (COVID-19) has led to high levels of fear worldwide. Given that fear is an important factor in causing psychological distress and facilitating preventive behaviors, assessing the fear of COVID-19 is important. The seven-item Fear of COVID-19 Scale (FCV-19S) is a widely used psychometric instrument to assess this fear. However, the factor structure of the FCV-19S remains unclear according to the current evidence. Therefore, the present study used a network analysis to provide further empirical evidence for the factor structure of FCV-19S. A total of 24,429 participants from Iran (n = 10,843), Bangladesh (n = 9906), and Norway (n = 3680) completed the FCV-19S in their local language. A network analysis (via regularized partial correlation networks) was applied to investigate the seven FCV-19S items. Moreover, relationships between the FCV-19S items were compared across gender (males vs. females), age groups (18–30 years, 31–50 years, and >50 years), and countries (Iran, Bangladesh, and Norway). A two-factor structure pattern was observed (three items concerning physical factors, including clammy hands, insomnia, and heart palpitations; four items concerning psychosocial factors, including being afraid, uncomfortable, afraid of dying, and anxious about COVID-19 news). Moreover, this pattern was found to be the same among men and women, across age groups and countries. The network analysis used in the present study verified the two-factor structure for the FCV-19S. Future studies may consider using the two-factor structure of FCV-19S to assess the fear of COVID-19 during the COVID-19 era.

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“…Other models and adaptions of these models are also possible. Further, results highly depend on what (kind of) variables are included as nodes in the network (Bringmann et al, 2022). Researchers need to decide which symptoms to include and if non-symptoms such as moderating variables and/or a treatment node as proposed by Blanken et al (2019) should be included.…”

Section: Methodological and Statistical Practices Of Using Symptom Ne...mentioning

confidence: 99%

Methodological and Statistical Practices of using Symptom Networks to Evaluate Mental Health Interventions: A Systematic Review

Schumacher,

Burger,

Echterhoff

et al. 2022

Preprint

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Background: The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting a network analysis in intervention research exist, no structured guidelines have been established yet. To gain an overview and evaluate the potential of different analytic options, we conducted a systematic review on these studies. Methods: We systematically searched the literature with combining terms on network analysis, mental health problems, and intervention studies. Studies were included if they constructed a symptom network, analysed data that was collected before, during or after a treatment for mental disorders, and yielded information about the treatment effect. Information on sample characteristics, the intervention, the research design, the estimated networks, the statistical analyses, and open science practices were extracted. Results: Across the 56 included studies, network analyses varied widely. About half of the studies estimated cross-sectional networks without a treatment node, e.g., for different treatment groups separately or for a single group before, and after treatment. About 20% of studies analysed cross-sectional networks including a treatment node and a third of the studies estimated longitudinal networks. Studies differed on how networks were estimated, which network parameters were calculated, and which statistical tests were applied. Conclusion: This review highlighted that many different analytic options exist when applying the network approach to intervention research. As the potential of the network approach depends on the applied methodologies, the analytic choices need to be further investigated and structured guidelines need to be developed.

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Psychopathological networks: Theory, methods and practice

Cited by 142 publications

References 153 publications

Temporal dynamics of depressive symptomatology: An idiographic time series analysis applying network models to patients with depressive disorders

Temporal dynamics of depressive symptomatology: An idiographic time series analysis applying network models to patients with depressive disorders

A Network Analysis of the Fear of COVID-19 Scale (FCV-19S): A Large-Scale Cross-Cultural Study in Iran, Bangladesh, and Norway

Methodological and Statistical Practices of using Symptom Networks to Evaluate Mental Health Interventions: A Systematic Review

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