Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis
Network Approach to Understanding Emotion Dynamics in Relation to Childhood Trauma and Genetic Liability to Psychopathology: Replication of a Prospective Experience Sampling Analysis
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DOI：
10.3389/fpsyg.2017.01908
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发表时间：
2017-11-02
影响因子：
3.8
通讯作者：
van Os, Jim
中科院分区：
文献类型：
--
作者：
Hasmi, Laila;Drukker, Marjan;van Os, Jim
背景资料：使用经验抽样方法（ESM）收集的密集的时间序列数据的网络分析，可以提供重要的信息，深入了解情绪调节和精神病理脆弱性之间的联系。本研究的目的是应用网络方法来调查精神病理学和儿童创伤（CT）的遗传易感性（GL）是否与使用ESM方法收集的情绪“快乐"、“不安全"、“放松"、“焦虑"、“烦躁“和“沮丧”的网络结构相关。使用来自双胞胎对和兄弟姐妹（704人）的基于人口的样本的数据，我们研究了CT和GL层之间的瞬时情绪网络结构是否不同。GL经验性地确定使用单卵和双卵双胞胎的精神病理学水平。使用多级时滞回归分析生成网络模型，并分别在CT和GL的三个分层（低、中和高）之间进行比较。利用排列计算p值并比较回归系数、密度和中心性指数。回归系数表示为连接，而变量表示网络中的节点。结果：与低GL层相比，高GL层具有显着的整体密度（p = 0.018）和负面影响网络密度（p < 0.001）。中等GL层也显示出与网络密度的方向相似（在高GL层和低GL层之间）的统计学上不确定的关联。与GL相比，CT分析的结果不太确定，与中等CT分层相比，高CT分层的积极情感密度（p = 0.021）和总体密度（p = 0.042）增加，但与低CT分层相比则没有增加。个别节点比较跨层的GL和CT只产生了非常少的显着结果，调整后的多重testing.Conclusions：目前的研究结果表明，网络的方法可能有一定的价值，在了解之间的关系建立的精神障碍的危险因素（特别是GL）和情绪之间的动态相互作用。目前的发现部分复制了早期的分析，这表明将负面情绪动态建模为遗传影响的函数可能具有指导意义。
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.