Cutting- Edge Clustering of Emotional Reactivity to Reveal Novel Anxiety Subtypes

摘要 研究领域标准(Research Domain Criteria，RDoC)倡议促使我们考虑将维度结构集成 《心理学和生物学的要素》通过多个分析单元进行审问，以便更好地理解 精神疾病(1)。然而，这种与传统离散诊断分类无关的方法确实 而不是使用规定的分析方法。相反，RDoC面临的部分挑战是确定 适当的统计技术，用于识别由BIONAL定义的可选的、有意义的亚型或簇 心理侧写。同时，使用 为RDoC方法提出了多个分析单元。这些问题包括1)高度扭曲的生物学和 心理变量，2)多维数据集，并且通常是高维数据集，以及3)多个统计数据 看似合理的集群解决方案。最易于访问和使用最频繁的传统集群方法 在精神病学研究中不能容纳这些问题；它们的应用可能会产生误导性的结果。 我们建议利用前沿的有限混合建模聚类方法，对这些问题具有健壮性，在 焦虑谱系障碍患者和匹配的对照组参与者(n=518)的大样本 叙事表象过程中的心理生理评估。与RDoC结构保持一致 负价和正价系统域，参与者想象不愉快(如威胁)和愉快 (例如，隶属/奖励)以及中性叙述，而自主神经、面部表情、声学惊吓反射 回答和主观参与度评分被记录下来。在需求侧管理的基础上，防御性超- 反应性可能是1)跨焦虑症和2)跨反应渠道的。事实上，防御性的 低反应性和高反应性经常在不同的渠道观察到，在个体内(例如，夸大 惊吓反射、面部表情和主观唤醒，并伴有钝性心率和皮肤电导 正在响应)。这项研究的假设是，利用尖端聚类方法将揭示新的 基于多模式、维度反应协调(或缺乏协调)的跨诊断亚型 情感投入。这些亚型有望跨越传统的诊断界限，而 揭示某些渠道之间的协调程度最能预测情绪健康和 功能状态。长期目标是确定调整干预措施的潜在新目标，特别是 以情绪为中心的心理治疗，以协调特定反应渠道。短期目标是 确定利用多模式数据集中丰富信息的分析方法-适用于广泛的 RDoC方法的范围。

Abstract The Research Domain Criteria (RDoC) initiative prompts us to consider “dimensional constructs integrating elements of psychology and biology” interrogated with multiple units of analysis toward better understanding mental illness (1). However, such an approach, agnostic to traditional discrete diagnostic classification, does not come with prescribed analytical approaches. Rather, part of the RDoC challenge is to determine appropriate statistical techniques for identifying alternative, meaningful subtypes or clusters defined by bio- psychological profiles. At the same time, there are inherent methodological challenges to clustering with the multiple units of analysis proposed for RDoC approaches. These include 1) highly skewed biological and psychological variables, 2) multi-dimensional, and often high-dimensional datasets, and 3) multiple statistically plausible clustering solutions. Traditional clustering methods most readily accessible and most frequently used in psychiatric research cannot accommodate these issues; their application can produce misleading findings. We propose to utilize cutting-edge finite mixture modeling clustering approaches, robust to these issues, in a large sample of anxiety spectrum disorder patients and matched control participants (n=518) who completed psychophysiological assessment during narrative imagery. Consistent with RDoC constructs within both the Negative and Positive Valence System Domains, participants imagined unpleasant (e.g., threat) and pleasant (e.g., affiliation/reward) as well as neutral narratives while autonomic, facial expressivity, acoustic startle reflex responding and subjective ratings of engagement were recorded. On the basis of the DSM, defensive hyper- reactivity might be expected 1) across anxiety disorders and 2) across response channels. In fact, defensive hypo- and hyper-reactivity were often observed in different channels, within individuals (e.g., exaggerated startle reflex, facial expressivity, and subjective arousal coupled with blunted heart rate and skin conductance responding). The hypothesis of this study is that utilizing cutting-edge clustering approaches will reveal novel transdiagnostic subtypes based on multimodal, dimensional response coordination (or lack thereof) during emotional engagement. These subtypes are expected to cut across traditional diagnostic boundaries, while revealing that the extent of coordination among certain channels is most predictive of emotional health and functional status. The long-term goal is to identify potential novel targets for tailoring interventions, particularly emotion-focused psychotherapy, to coordination among specific response channels. The shorter-term goal is to identify analytical approaches that leverage the rich information in multimodal datasets—applicable to a wide range of RDoC approaches.