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

用统计学习方法重新思考不确定威胁预期的神经关联

• 批准号: 10426704
• 负责人: Andrew S Fox
• 金额: $ 18.67万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10426704
• 关键词: Address; Affect; Amygdaloid structure; Anxiety; Anxiety Disorders; Aversive Stimulus; Behavior; Biological Process; Brain; Brain region; Cell Nucleus; Computer Models; Conflict (Psychology); Cues; Data; Decision Making; Development; Disease; Environment; Feeling; Foundations; Functional Magnetic Resonance Imaging; Individual; Influentials; Learning; Light; Literature; Magnetic Resonance Imaging; Measures; Mental disorders; Modeling; National Institute of Mental Health; Neurobiology; Participant; Patients; Personal Satisfaction; Persons; Phase; Prevention; Probability; Psychiatric Diagnosis; Psychiatry; Research; Research Domain Criteria; Research Personnel; Series; Shock; Startle Reaction; Stimulus; Structure of terminal stria nuclei of preoptic region; Testing; Theoretical model; Time; Training; Uncertainty; Work; anxious; base; blood oxygenation level dependent response; design; effective therapy; experimental study; hazard; improved; neural circuit; neural correlate; novel; patient population; self-reported anxiety; statistical learning; statistics; theories

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.

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。