Mental disorders as networks of problems: a review of recent insights.
Mental disorders as networks of problems: a review of recent insights.
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DOI：
10.1007/s00127-016-1319-z
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发表时间：
2017-01
影响因子：
4.4
通讯作者：
Borsboom D
中科院分区：
文献类型：
--
作者：
Fried EI;van Borkulo CD;Cramer AO;Boschloo L;Schoevers RA;Borsboom D
精神病理学的网络观点将精神障碍理解为相互作用的症状的复杂网络。尽管该框架最近才首次亮相，在2008年奠定了概念基础，在2010年奠定了经验基础，但在过去几年中得到了相当大的关注和认可。本文回顾了2010年至2016年期间发表的所有经验网络研究，并根据三个主题进行了讨论：合并症，预测和临床干预。关于合并症，网络方法提供了一个强大的新框架来解释为什么某些疾病可能比其他疾病更频繁地同时发生。对于预测，研究一直发现，精神障碍患者的症状网络表现出与健康个体不同的特征，初步证据表明，健康人的网络在转变为紊乱状态之前会显示出早期预警信号。对于干预，中心性-一个衡量症状在网络中的联系和临床相关性的指标-是最常研究的主题，许多研究表明，针对最中心的症状可能会提供新的治疗策略。我们勾画了未来的网络方法的临床和方法学研究的方向，并得出结论，网络分析产生了重要的见解，并可能提供一个重要的进展，通过调查个体患者的网络结构的个性化医疗。本文的在线版本（doi：10.1007/s 00127 -016-1319-z）包含补充材料，可供授权用户使用。
The network perspective on psychopathology understands mental disorders as complex networks of interacting symptoms. Despite its recent debut, with conceptual foundations in 2008 and empirical foundations in 2010, the framework has received considerable attention and recognition in the last years. This paper provides a review of all empirical network studies published between 2010 and 2016 and discusses them according to three main themes: comorbidity, prediction, and clinical intervention. Pertaining to comorbidity, the network approach provides a powerful new framework to explain why certain disorders may co-occur more often than others. For prediction, studies have consistently found that symptom networks of people with mental disorders show different characteristics than that of healthy individuals, and preliminary evidence suggests that networks of healthy people show early warning signals before shifting into disordered states. For intervention, centrality—a metric that measures how connected and clinically relevant a symptom is in a network—is the most commonly studied topic, and numerous studies have suggested that targeting the most central symptoms may offer novel therapeutic strategies. We sketch future directions for the network approach pertaining to both clinical and methodological research, and conclude that network analysis has yielded important insights and may provide an important inroad towards personalized medicine by investigating the network structures of individual patients. The online version of this article (doi:10.1007/s00127-016-1319-z) contains supplementary material, which is available to authorized users.