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精神病理学中的个体特异性网络:过去、现在与未来。

Person-specific networks in psychopathology: Past, present, and future.

作者信息

Bringmann Laura F

机构信息

University of Groningen, University Medical Center Groningen, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), P.O. Box 30.001 (CC72), 9700 RB, Groningen, the Netherlands; University of Groningen, Faculty of Behavioural and Social Sciences, Department of Psychometrics and Statistics, Grote Kruisstraat 2/1, 9712 TS, Groningen, the Netherlands.

出版信息

Curr Opin Psychol. 2021 Oct;41:59-64. doi: 10.1016/j.copsyc.2021.03.004. Epub 2021 Mar 19.

Abstract

In the psychological network approach, mental disorders such as major depressive disorder are conceptualized as networks. The network approach focuses on the symptom structure or the connections between symptoms instead of the severity (i.e., mean level) of a symptom. To infer a person-specific network for a patient, time-series data are needed. By far the most common model to statistically model the person-specific interactions between symptoms or momentary states has been the vector autoregressive (VAR) model. Although the VAR model helps to bring psychological network theory into clinical research and closer to clinical practice, several discrepancies arise when we map the psychological network theory onto the VAR-based network models. These challenges and possible solutions are discussed in this review.

摘要

在心理网络方法中,诸如重度抑郁症等精神障碍被概念化为网络。网络方法关注症状结构或症状之间的联系,而非症状的严重程度(即平均水平)。为了推断患者特定的网络,需要时间序列数据。到目前为止,用于对症状或瞬时状态之间的个体特定相互作用进行统计建模的最常见模型是向量自回归(VAR)模型。尽管VAR模型有助于将心理网络理论引入临床研究并使其更贴近临床实践,但当我们将心理网络理论应用于基于VAR的网络模型时,会出现一些差异。本综述讨论了这些挑战及可能的解决方案。

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