Rethinking the Neural Correlates of Uncertain Threat Anticipation with a Statistical Learning Approach

项目摘要 焦虑症是最常见和最令人衰弱的精神病诊断之一， 全球每3人中就有1人受到影响。尽管这对全球福祉产生了巨大影响，但关键是 对理解这些疾病至关重要的概念在现有的定义中仍然很模糊。 文学。尽管对“不确定威胁预期”的高度敏感被认为是 焦虑和焦虑症的诱因以及威胁的不确定性是一个核心结构，它削减了 在NIMH RDoC框架中的焦虑模型中，没有关于 “不确定性”。先前的研究发现，对暂时“不确定”威胁的预期，如 与暂时的“某些”威胁相比，是与自我报告的焦虑增加相关的， 惊吓反应，以及焦虑相关脑区的大脑激活。在这项提案中，我们将 使用计算、统计学习来检验这些结果的另一种解释 接近。特别是，我们建议，基于实验室的不确定预期评估具有 将“不确定性”与威胁概率的变化混淆在一起，因为它没有 已经发生，即危险比率。在这里，基于我们团队在焦虑和 在决策过程中，我们将检验这一假设，即风险比率，而不是不确定性本身， 解释了自我报告的焦虑和坚持(目标1)的增加，以及 焦虑相关脑区的焦虑相关大胆反应(目标2)。为此，我们有 开发了一种新的范例，允许我们操纵不确定性的2x2设计 (高/低)和危险比率(高-低)。与现有的理论相反，现有的理论预测了主要的 在不确定性的影响下，我们预测了危险率对所有相关措施的主要影响。相关 测量包括自我报告的焦虑，在威胁环境中的坚持性，以及功能磁共振成像 脑部活动的测量。我们将把这个范例与以前的范例进行对比 混淆的不确定性和危险率，以提供对现有的更准确的解释 调查结果。这种理论驱动的计算方法来理解不确定的预期 有可能为这一领域提供一条正确的课程，完善我们对焦虑的理解，以及 告知新治疗方法的发展。

PROJECT ABSTRACT Anxiety disorders are some of the most common and debilitating psychiatric diagnoses, affecting ~1 in 3 people worldwide. Despite the enormous impact on global well-being, key concepts that are critical to understanding these disorders remain poorly defined in the extant literature. Although heightened sensitivity to "uncertain threat anticipation" is regarded as a core contributor to anxiety and anxiety disorders and Threat Uncertainty is a core construct that cuts across models of anxiety in the NIMH RDoC framework, there is no ground-truth definition of "uncertainty". Previous studies have found that anticipation of temporally “uncertain” threats, as compared to temporally “certain” threats, are associated with increases self-reported anxiety, startle-responses, and brain activation in anxiety-related brain regions. In this proposal, we will test an alternate explanation for these results using a computational, statistical-learning approach. In particular, we suggest that lab-based assessments of uncertain anticipation have confounded “uncertainty” with alterations in the probability of a threat given that it has not already occurred, i.e. the hazard-rate. Here, building on our team’s expertise in anxiety and decision-making, we will test the hypothesis that it hazard-rate, and not uncertainty per se, accounts for increases in self-reported anxiety and persistence (Aim 1), as well as alterations in anxiety-related BOLD responses in anxiety-relevant brain regions (Aim 2). To this end, we have developed a novel paradigm that allows for a 2x2 design where we manipulate uncertainty (high/low) and hazard-rate (high-low). In contrast to existing theories, which predict a main effect of uncertainty, we predict a main effect of hazard-rate on all relevant measures. Relevant measures include self-reported anxiety, persistence in a threatening environment, and fMRI measures of brain activation. We will contrast this paradigm with previous paradigms that have confounded uncertainty and hazard-rate to provide a more precise interpretation of extant findings. This theory-driven computational approach to understanding uncertain anticipation has the potential to provide a “course correct” for the field, refine our understanding of anxiety, and inform the development of new treatments.