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

项目摘要 焦虑症是一些最常见的和衰弱的精神病诊断， 影响全球约三分之一的人。尽管对全球福祉产生了巨大影响， 对理解这些疾病至关重要的概念在现存的文献中仍然定义不清。 文学尽管对“不确定威胁预期”的高度敏感性被视为核心， 焦虑和焦虑症的贡献者和威胁不确定性是一个核心结构， 在NIMH RDoC框架中的焦虑模型中， “不确定性”。以前的研究发现，对时间上“不确定”的威胁的预期， 与时间上的“某些”威胁相比，与自我报告的焦虑增加有关， 惊恐反应和焦虑相关脑区的大脑激活。在本提案中，我们将 测试这些结果的替代解释使用一个计算的， approach.特别是，我们认为，基于实验室的评估不确定的预期， 混淆了“不确定性”与威胁概率的变化，因为它没有 已经发生，即危险率。在这里，基于我们团队在焦虑和 决策，我们将测试的假设，它的风险率，而不是不确定性本身， 解释了自我报告的焦虑和持久性（目标1）的增加，以及 焦虑相关脑区的焦虑相关BOLD反应（目标2）。为此我们 开发了一种新的模式，允许2x2的设计，我们可以操纵不确定性， (high/低）和危害率（高-低）。与现有理论相反，现有理论预测一个主要的 不确定性的影响，我们预测的主要影响的危险率的所有相关措施。相关 测量包括自我报告的焦虑，在威胁环境中的持续性，以及功能磁共振成像。 大脑活动的指标我们将把这个范例与以前的范例进行对比， 混杂不确定性和危险率，以提供对现存 调查结果。这种理论驱动的计算方法来理解不确定的预期， 为该领域提供“正确的课程”的潜力，完善我们对焦虑的理解， 为新疗法的发展提供信息。

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.