Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections
Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections
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DOI：
10.1177/2167702617744325
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发表时间：
2018-05-01
影响因子：
4.8
通讯作者：
Cramer, Angelique O. J.
中科院分区：
文献类型：
--
作者：
Epskamp, Sacha;van Borkulo, Claudia D.;Cramer, Angelique O. J.
最近的文献介绍了（a）网络的角度来看，心理学和（B）收集的时间序列数据，以捕捉症状波动和其他时变因素在日常生活中。结合这些趋势，可以估计个体内的网络结构。我们认为，这些网络可以直接应用于临床研究和实践的假设生成结构。可以计算两个网络：时间网络，其中一个调查症状（或其他相关变量）是否随着时间的推移相互预测，以及同期网络，其中一个调查症状是否在同一测量窗口中相互预测。同期网络是一种偏相关网络，它在截面数据的分析中出现，但尚未用于时间序列数据的分析。我们解释了部分相关网络的重要性，并对一个精神病患者的时间序列数据进行了网络结构分析。
Recent literature has introduced (a) the network perspective to psychology and (b) collection of time series data to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intraindividual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time series data. We explain the importance of partial correlation networks and exemplify the network structures on time series data of a psychiatric patient.