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

情绪反应的尖端聚类揭示新的焦虑亚型

• 批准号: 9789940
• 负责人: Lisa M McTeague
• 金额: $ 7.61万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-21 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789940
• 关键词: Acoustics; Address; Affective; Anxiety; Anxiety Disorders; Arousal; Behavior; Biological; Biology; Classification; Clinical; Complex; Control Groups; Coupled; Data; Data Set; Development; Diagnosis; Diagnostic; Dimensions; Doctor of Philosophy; Electromyography; Elements; Emotional; Emotions; Exhibits; Face; Functional disorder; Galvanic Skin Response; Genes; Genetic; Goals; Health Status; Healthcare; Heart Rate; Heterogeneity; Imagery; Impairment; Individual; Intervention; Linear Regressions; Measures; Mental Health; Mental disorders; Methodology; Methods; Modeling; Motivation; Negative Valence; Normalcy; Participant; Patient Self-Report; Patients; Pattern; Physiological; Physiology; Positive Valence; Psychology; Psychophysiology; Psychotherapy; Publications; Reflex action; Reproducibility; Research; Research Domain Criteria; Rewards; Sampling; Structural Models; Symptoms; System; Techniques; Therapeutic Intervention; Time; Treatment Efficacy; Trees; Work; anxiety spectrum disorders; base; clinically relevant; depressed patient; emotional experience; flexibility; functional disability; functional status; high dimensionality; multimodality; negative affect; neuroimaging; novel; patient response; predict clinical outcome; psychologic; response; tool; treatment of anxiety disorders

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.

摘要 研究领域标准（RDoC）倡议促使我们考虑“维度结构整合 心理学和生物学的元素”与多个单元的分析，以更好地理解审问 精神疾病（1）。然而，这种方法，不可知的传统离散诊断分类， 而不是用规定的分析方法。相反，RDoC挑战的一部分是确定 适当的统计技术，用于识别生物学定义的替代性、有意义的亚型或聚类， 心理档案与此同时，在与联合国系统进行分组方面存在固有的方法挑战。 为RDoC方法提出了多个分析单元。这些包括：1）高度倾斜的生物学和 心理变量，2）多维，通常是高维数据集，以及3）多个统计 合理的聚类解决方案。最容易获得和最常用的传统聚类方法 在精神病学研究中不能容纳这些问题;它们的应用可能产生误导性的结果。 我们建议利用尖端的有限混合建模聚类方法，对这些问题具有鲁棒性，在一个 大样本的焦虑谱系障碍患者和匹配的对照参与者（n=518）， 心理生理学评估在叙事意象。与RDoC结构一致， 消极和积极的配价系统域，参与者想象不愉快（例如，威胁）和愉快 (e.g.,联系/奖励）以及中性叙述，而自主，面部表情，听觉惊吓反射 记录了对参与情况的回应和主观评分。在DSM的基础上，防御性超- 反应性可能是预期的1）跨焦虑症和2）跨反应通道。事实上， 低反应性和高反应性通常在不同的通道中观察到，在个体内（例如，夸张 惊吓反射、面部表情和主观唤醒，以及心率和皮肤电导变钝 响应）。这项研究的假设是，利用尖端的聚类方法将揭示新的 基于多模态、维度响应协调（或其缺乏）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， 揭示了某些渠道之间的协调程度最能预测情绪健康， 功能状态长期目标是确定定制干预措施的潜在新目标，特别是 以情绪为中心的心理治疗，以协调具体的反应渠道。短期目标是 确定分析方法，利用丰富的信息，在多模式电子邮件-适用于广泛的 一系列RDoC方法。