The network structure of paranoia in the general population.
The network structure of paranoia in the general population.
复制标题
普通人群中偏执狂的网络结构。
DOI：
10.1007/s00127-018-1487-0
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发表时间：
2018-07
影响因子：
4.4
通讯作者：
O'Driscoll C
中科院分区：
文献类型：
--
作者：
Bell V;O'Driscoll C
Bebbington和他的同事们对“普通人群中偏执狂的结构”进行了有影响力的研究，他们使用了英国国家精神病发病率调查和潜在变量分析方法的数据。网络分析是一种相对较新的精神病理学研究方法，它认为精神障碍是症状之间因果相互作用的涌现现象。这项研究重新分析了英国国家精神病发病率调查的数据，使用网络分析来检查一般人群中偏执的网络结构。我们使用图形最小绝对收缩和选择算子（glasso）方法来估计基于扩展贝叶斯信息准则的最优网络结构。采用spinglass和EGA算法识别网络子群落，并计算每个项目和每个子群落的中心性指标。我们复制了Bebbington的偏执四成分结构，确定了“人际敏感”、“不信任”、“参照观念”和“迫害观念”作为网络中的子社区。与之前的实验结果一致，担忧是网络中最核心的项目。然而，“不信任”和“参考观念”是最核心的子群体。我们认为，偏执的结构最好被认为是一种层次结构，而不是严格的层次结构，其中高中心节点和社区的激活最有可能导致稳定状态的偏执。我们还强调了本研究使用的新方法：即使用网络分析来重新检查以前由潜在变量方法确定的精神病理学群体结构。本文的在线版本（10.1007/s00127-018-1487-0）包含补充资料，仅供授权用户使用。
Bebbington and colleagues’ influential study on ‘the structure of paranoia in the general population’ used data from the British National Psychiatric Morbidity Survey and latent variable analysis methods. Network analysis is a relatively new approach in psychopathology research that considers mental disorders to be emergent phenomena from causal interactions among symptoms. This study re-analysed the British National Psychiatric Morbidity Survey data using network analysis to examine the network structure of paranoia in the general population. We used a Graphical Least Absolute Shrinkage and Selection Operator (glasso) method that estimated an optimal network structure based on the Extended Bayesian Information Criterion. Network sub-communities were identified by spinglass and EGA algorithms and centrality metrics were calculated per item and per sub-community. We replicated Bebbington’s four component structure of paranoia, identifying ‘interpersonal sensitivities’, ‘mistrust’, ‘ideas of reference’ and ‘ideas of persecution’ as sub-communities in the network. In line with previous experimental findings, worry was the most central item in the network. However, ‘mistrust’ and ‘ideas of reference’ were the most central sub-communities. Rather than a strict hierarchy, we argue that the structure of paranoia is best thought of as a heterarchy, where the activation of high-centrality nodes and communities is most likely to lead to steady state paranoia. We also highlight the novel methodological approach used by this study: namely, using network analysis to re-examine a population structure of psychopathology previously identified by latent variable approaches. The online version of this article (10.1007/s00127-018-1487-0) contains supplementary material, which is available to authorized users.
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影响因子：
4.4
作者：
Fried EI;van Borkulo CD;Cramer AO;Boschloo L;Schoevers RA;Borsboom D
通讯作者：
Borsboom D
影响因子：
6.7
作者：
Gracie, A.;Freeman, D.;Fowler, D.
通讯作者：
Fowler, D.
影响因子：
3.7
作者：
Golino, Hudson F.;Epskamp, Sacha
通讯作者：
Epskamp, Sacha
影响因子：
10.5
作者：
Bebbington, Paul E.;McBride, Orla;Freeman, Daniel
通讯作者：
Freeman, Daniel
DOI：
10.1146/annurev-clinpsy-050212-185608
发表时间：
2013-01-01
期刊：
ANNUAL REVIEW OF CLINICAL PSYCHOLOGY, VOL 9
影响因子：
--
作者：
Borsboom, Denny;Cramer, Angelique O. J.
通讯作者：
Cramer, Angelique O. J.