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抑郁症诊断异质性的原因及后果:发现新型生物性抑郁症亚型的途径

Causes and Consequences of Diagnostic Heterogeneity in Depression: Paths to Discovering Novel Biological Depression Subtypes.

作者信息

Lynch Charles J, Gunning Faith M, Liston Conor

机构信息

Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York.

Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York.

出版信息

Biol Psychiatry. 2020 Jul 1;88(1):83-94. doi: 10.1016/j.biopsych.2020.01.012. Epub 2020 Jan 28.

Abstract

Depression is a highly heterogeneous syndrome that bears only modest correlations with its biological substrates, motivating a renewed interest in rethinking our approach to diagnosing depression for research purposes and new efforts to discover subtypes of depression anchored in biology. Here, we review the major causes of diagnostic heterogeneity in depression, with consideration of both clinical symptoms and behaviors (symptomatology and trajectory of depressive episodes) and biology (genetics and sexually dimorphic factors). Next, we discuss the promise of using data-driven strategies to discover novel subtypes of depression based on functional neuroimaging measures, including dimensional, categorical, and hybrid approaches to parsing diagnostic heterogeneity and understanding its biological basis. The merits of using resting-state functional magnetic resonance imaging functional connectivity techniques for subtyping are considered along with a set of technical challenges and potential solutions. We conclude by identifying promising future directions for defining neurobiologically informed depression subtypes and leveraging them in the future for predicting treatment outcomes and informing clinical decision making.

摘要

抑郁症是一种高度异质性的综合征,与其生物学基础仅有适度的相关性,这促使人们重新关注为研究目的重新思考我们诊断抑郁症的方法,并重新努力发现基于生物学的抑郁症亚型。在此,我们回顾了抑郁症诊断异质性的主要原因,同时考虑了临床症状和行为(抑郁发作的症状学和病程)以及生物学因素(遗传学和性别差异因素)。接下来,我们讨论了使用数据驱动策略基于功能神经影像学测量发现抑郁症新亚型的前景,包括解析诊断异质性和理解其生物学基础的维度、分类和混合方法。我们考虑了使用静息态功能磁共振成像功能连接技术进行亚型分类的优点,以及一系列技术挑战和潜在解决方案。我们通过确定定义基于神经生物学的抑郁症亚型并在未来利用它们预测治疗结果和为临床决策提供信息的有前景的未来方向来得出结论。

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