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跨诊断症状群与心境、焦虑和创伤障碍中的大脑、行为和日常功能的关联。

Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders.

机构信息

Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California.

Sierra-Pacific Mental Illness Research, Education, and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California.

出版信息

JAMA Psychiatry. 2018 Feb 1;75(2):201-209. doi: 10.1001/jamapsychiatry.2017.3951.

Abstract

IMPORTANCE

The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices.

OBJECTIVE

To propose and demonstrate 1 approach for identifying subtypes within a transdiagnostic sample.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study analyzed data from the Brain Research and Integrative Neuroscience Network Foundation Database that had been collected at the University of Sydney and University of Adelaide between 2006 and 2010 and replicated at Stanford University between 2013 and 2017. The study included 420 individuals with a primary diagnosis of major depressive disorder (n = 100), panic disorder (n = 53), posttraumatic stress disorder (n = 47), or no disorder (healthy control participants) (n = 220). Data were analyzed between October 2016 and October 2017.

MAIN OUTCOMES AND MEASURES

We followed a data-driven approach to achieve the primary study outcome of identifying transdiagnostic subtypes. First, machine learning with a hierarchical clustering algorithm was implemented to classify participants based on self-reported negative mood, anxiety, and stress symptoms. Second, the robustness and generalizability of the subtypes were tested in an independent sample. Third, we assessed whether symptom subtypes were expressed at behavioral and physiological levels of functioning. Fourth, we evaluated the clinically meaningful differences in functional capacity of the subtypes. Findings were interpreted relative to a complementary diagnostic frame of reference.

RESULTS

Four hundred twenty participants with a mean (SD) age of 39.8 (14.1) years were included in the final analysis; 256 (61.0%) were female. We identified 6 distinct subtypes characterized by tension (n=81; 19%), anxious arousal (n=55; 13%), general anxiety (n=38; 9%), anhedonia (n=29; 7%), melancholia (n=37; 9%), and normative mood (n=180; 43%), and these subtypes were replicated in an independent sample. Subtypes were expressed through differences in cognitive control (F5,383 = 5.13, P < .001, ηp2 = 0.063), working memory (F5,401 = 3.29, P = .006, ηp2 = 0.039), electroencephalography-recorded β power in a resting paradigm (F5,357 = 3.84, P = .002, ηp2 = 0.051), electroencephalography-recorded β power in an emotional paradigm (F5,365 = 3.56, P = .004, ηp2 = 0.047), social functional capacity (F5,414 = 21.33, P < .001, ηp2 = 0.205), and emotional resilience (F5,376 = 15.10, P < .001, ηp2 = 0.171).

CONCLUSIONS AND RELEVANCE

These findings offer a data-driven framework for identifying robust subtypes that signify specific, coherent, meaningful associations between symptoms, behavior, brain function, and observable real-world function, and that cut across DSM-IV-defined diagnoses of major depressive disorder, panic disorder, and posttraumatic stress disorder.

摘要

重要性

定义情绪、焦虑和创伤障碍的症状在障碍之间高度重叠,在障碍内也存在异质性。目前还不清楚是否存在连贯的亚型,这些亚型跨越多种诊断,并在功能上(潜在认知和大脑功能)和临床上(日常功能)表达。识别连贯的亚型将有助于理清我们目前诊断中的症状重叠,并作为定制治疗选择的工具。

目的

提出并展示一种在跨诊断样本中识别亚型的方法。

设计、地点和参与者:这项横断面研究分析了悉尼大学和阿德莱德大学于 2006 年至 2010 年期间以及斯坦福大学于 2013 年至 2017 年期间收集的大脑研究和综合神经科学网络基金会数据库的数据。研究包括 420 名患有主要抑郁障碍(n=100)、惊恐障碍(n=53)、创伤后应激障碍(n=47)或无障碍(健康对照组)(n=220)的个体。数据于 2016 年 10 月至 2017 年 10 月间进行分析。

主要结果和测量

我们采用数据驱动的方法来实现主要的研究结果,即识别跨诊断的亚型。首先,使用层次聚类算法的机器学习被用来根据自我报告的负面情绪、焦虑和压力症状对参与者进行分类。其次,在独立样本中测试了亚型的稳健性和可推广性。第三,我们评估了症状亚型在行为和生理功能水平上的表达。第四,我们评估了亚型在功能能力方面的临床有意义的差异。研究结果相对于互补的诊断参考框架进行了解释。

结论和相关性

这些发现提供了一个数据驱动的框架,用于识别稳健的亚型,这些亚型表示症状、行为、大脑功能和可观察的现实世界功能之间的特定、连贯和有意义的关联,跨越 DSM-IV 定义的重度抑郁障碍、惊恐障碍和创伤后应激障碍的诊断。

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