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

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

doi:10.3389/fpsyg.2017.01908

Abstract

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) but 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.

Highlights

There is a growing interest in understanding the role of dailylife emotion dynamics underlying psychopathology (van Os et al, 2017)
Individuals who were registered in the East Flanders Prospective Twin Survey (EFPTS) and who fulfilled the inclusion criteria were invited to participate in the TwinssCan project, a longitudinal study collecting data on adolescents and young adults between the ages of 15 and 35 years, including twins, their siblings, and parents
childhood trauma (CT) analyses were performed with 688 individuals

Summary

Introduction

There is a growing interest in understanding the role of dailylife emotion dynamics underlying psychopathology (van Os et al, 2017). From a complex dynamic system theory perspective, alterations in personal emotion dynamics may serve as an early warning sign for a tipping point signaling a transition from a subthreshold state to a clinical state—akin to an electrical signal in epilepsy that is monitored to detect the tipping point before a convulsion (Wichers et al, 2015; Nelson et al, 2017) In this regard, the network approach provides a useful analytical strategy to gain insight into modeling interactive emotion dynamics, and identifying highly connected emotions that are critical in predicting transition to a more severe state. 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

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