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一种新的精神障碍科学:使用个性化、跨诊断、动力系统来理解、建模、诊断和治疗精神病理学。

A new science of mental disorders: Using personalised, transdiagnostic, dynamical systems to understand, model, diagnose and treat psychopathology.

机构信息

Department of Clinical Psychological Science, Maastricht University, the Netherlands.

Clinical Psychology Unit, Leiden University, the Netherlands.

出版信息

Behav Res Ther. 2022 Jun;153:104096. doi: 10.1016/j.brat.2022.104096. Epub 2022 Apr 14.

Abstract

The core ideas of a 10-year research program 'New Science of Mental Disorders' are outlined. This research program moves away from the disorder-based 'one-model-fits-all' approach to treating mental disorders, and adopts the network approach to psychopathology as its foundation of research. Its core assumption is that dynamically interacting symptoms constitute the disorder. Our goal is to further develop the network approach by studying (1) dynamic networks of symptoms and other variables (i.e., elements) in a large number of individuals with a wide range of mental disorders from a transdiagnostic perspective (network-based diagnosis; mapping), including both Ecological Momentary Assessment (EMA) and digital phenotyping, (2) the transdiagnostic mechanisms reflecting potential causal relations among elements of the networks by performing experimental (pre-)clinical studies (zooming), and (3) the effectiveness of personalised network-informed interventions (targeting). Challenges to overcome in this research program are discussed, which relate to data collection (e.g., selection of EMA variables) and data analyses (e.g., power considerations), the development and application of network-informed diagnoses and network-informed interventions (e.g., what characteristic(s) of the network to target in interventions), and the implementation in clinical practice (e.g., train therapists in the use of networks in therapy).

摘要

“精神障碍新科学”十年研究计划的核心思想概述如下。该研究计划摒弃了基于障碍的“一刀切”治疗精神障碍的方法,采用网络方法作为其精神病理学研究的基础。其核心假设是,动态相互作用的症状构成了障碍。我们的目标是通过研究(1)从跨诊断角度研究大量患有各种精神障碍的个体中症状和其他变量(即元素)的动态网络(基于网络的诊断;映射),包括生态瞬时评估(EMA)和数字表型,(2)通过进行实验(临床前)研究来反映网络元素之间潜在因果关系的跨诊断机制(放大),以及(3)个性化网络知情干预的有效性(靶向)。讨论了该研究计划中需要克服的挑战,这些挑战涉及数据收集(例如,选择 EMA 变量)和数据分析(例如,考虑功效)、网络知情诊断和网络知情干预的发展和应用(例如,在干预中针对网络的什么特征),以及在临床实践中的实施(例如,培训治疗师在治疗中使用网络)。

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