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Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis

Hasmi, L., Drukker, M., Guloksuz, S., Menne-Lothmann, C., Decoster, J., van Winkel, R., Collip, D., Delespaul, P., De Hert, M., Derom, C., Thiery, E., Jacobs, N., Rutten, B. P. F., Wichers, M. & van Os, J., 2-Nov-2017, In : Frontiers in Psychology. 8, 14 p., 1908.

Research output: Contribution to journalArticleAcademicpeer-review

  • Laila Hasmi
  • Marjan Drukker
  • Sinan Guloksuz
  • Claudia Menne-Lothmann
  • Jeroen Decoster
  • Ruud van Winkel
  • Dina Collip
  • Philippe Delespaul
  • Marc De Hert
  • Catherine Derom
  • Evert Thiery
  • Nele Jacobs
  • Bart P. F. Rutten
  • Marieke Wichers
  • Jim van Os

Background: The network analysis of intensive time series data collected using the Experience Sampling Method (ESM) may provide vital information in gaining insight into the link between emotion regulation and vulnerability to psychopathology. The aim of this study was to apply the network approach to investigate whether genetic liability (GL) to psychopathology and childhood trauma (CT) are associated with the network structure of the emotions "cheerful,""insecure,""relaxed,""anxious,""irritated,"and "down"-collected using the ESM method.

Methods: Using data from a population-based sample of twin pairs and siblings (704 individuals), we examined whether momentary emotion network structures differed across strata of CT and GL. GL was determined empirically using the level of psychopathology in monozygotic and dizygotic co-twins. Network models were generated using multilevel time-lagged regression analysis and were compared across three strata (low, medium, and high) of CT and GL, respectively. Permutations were utilized to calculate p values and compare regressions coefficients, density, and centrality indices. Regression coefficients were presented as connections, while variables represented the nodes in the network.

Results: In comparison to the low GL stratum, the high GL stratum had significantly denser overall (p = 0.018) and negative affect network density (p <0.001). The medium GL stratum also showed a directionally similar (in-between high and low GL strata) statistically inconclusive association with network density. In contrast to GL, the results of the CT analysis were less conclusive, with increased positive affect density (p = 0.021) and overall density (p = 0.042) in the high CT stratum compared to the medium CT stratum but not to the low CT stratum. The individual node comparisons across strata of GL and CT yielded only very few significant results, after adjusting for multiple testing.

Conclusions: The present findings demonstrate that the network approach may have some value in understanding the relation between established risk factors for mental disorders (particularly GL) and the dynamic interplay between emotions. The present finding partially replicates an earlier analysis, suggesting it may be instructive to model negative emotional dynamics as a function of genetic influence.

Original languageEnglish
Article number1908
Number of pages14
JournalFrontiers in Psychology
Volume8
Publication statusPublished - 2-Nov-2017

    Keywords

  • emotion dynamics, directed, weighted, network, time-series, genetic, psychopathology, childhood trauma, PSYCHOTIC DISORDER, DAILY-LIFE, DEPRESSION, SYMPTOMS, UNCERTAINTY, ANXIETY, CONTEXT, STRESS, MODELS, HEALTH

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