Neurocomputational substrates of maladaptive uncertainty learning and avoidance in anxiety

焦虑中适应不良的不确定性学习和回避的神经计算基础

• 批准号: 10306402
• 负责人: Vanessa Brown
• 金额: $ 17.08万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10306402
• 关键词: Anxiety; Anxiety Disorders; Arousal; Behavior; Behavior assessment; Behavioral; Brain; Clinical; Clinical assessments; Complex; Computer Models; Dangerousness; Diagnosis; Disease; Ecological momentary assessment; Fright; Functional Magnetic Resonance Imaging; Future; Goals; Impairment; Individual Differences; Interview; Laboratories; Learning; Link; Maps; Measures; Mental disorders; Mentors; Modeling; National Institute of Mental Health; Neurobiology; Outcome; Parietal; Participant; Patient Recruitments; Performance; Persons; Play; Process; Psychiatry; Psychologist; Questionnaires; Reporting; Research; Role; Sampling; Scientist; Signal Transduction; Specificity; Stimulus; Symptoms; Task Performances; Testing; Training; Training Activity; Uncertainty; Universities; Update; Visit; anxiety treatment; anxious; avoidance behavior; base; behavior measurement; career; classical conditioning; clinically relevant; computer framework; computerized tools; depressive symptoms; effective therapy; experience; functional MRI scan; individual variation; neuroimaging; neuromechanism; novel; prevent; relating to nervous system; response; theories

This K23 application will provide the applicant, a clinical psychologist with expertise in neuroimaging and computational modeling, with training and mentored research experience towards an independent research career studying disrupted learning processes in anxiety disorders. Training activities will focus on: 1) clinically informative applications of computational modeling and neuroimaging in anxiety, 2) advanced computational modeling of uncertainty and exploration, and 3) ecological momentary assessment of behavioral avoidance. This training will be facilitated by an interdisciplinary team of experts in computational and neural approaches to understanding psychiatric disorders, neurally-informed computational modeling of uncertainty and avoidance, and ecological assessment of clinically-relevant behaviors. Training will take place at the Department of Psychiatry at the University of Pittsburgh, which has a long and successful track record of supporting junior scientists. To fulfill these training goals, the proposed research adapts approaches from basic neurocomputational studies on uncertainty and exploration to apply to anxiety. Specifically, the proposed research will test the hypotheses that anxiety, particularly anxious arousal, is related to disrupted learning about uncertain, aversive outcomes, as measured by neural and behavioral measures; that disrupted uncertainty learning leads to avoidance of uncertain options in anxiety; and that measures of uncertainty avoidance relate to real-world behavioral avoidance. Participants (n=85), oversampled for high anxiety, will complete a task assessing uncertainty learning while undergoing fMRI scanning. They will then report on real- world avoidance behaviors for two weeks. Participants’ performance on the uncertainty learning task will be fit to a computational model to measure learning from uncertainty as well as the tendency to explore versus avoid options based on uncertainty. Measures of uncertainty estimated from the computational model will be regressed against fMRI BOLD signals and behavioral choices; these effects on neural and behavioral function will be tested for differences with anxious arousal. Individual variation in uncertainty-dependent exploration will be tested for concordance with participants’ current real-world reports of behavioral avoidance and if they predict future real-world behavioral avoidance. The anticipated impact, in line with NIMH’s Strategic Objectives, will be identification of a) neural mechanisms for a complex behavior, maladaptive behavioral avoidance, b) objective assessments of anxiety and avoidance, and c) possible novel treatment targets.

这份K23申请书将为申请者提供一名临床心理学家，具有神经成像和 计算建模，具有独立研究的培训和指导研究经验 职业学习打乱了焦虑症患者的学习过程。培训活动将集中在：1)临床 计算建模和神经成像在焦虑中的信息性应用，2)高级计算 不确定性和探索性的建模；3)行为回避的生态瞬时评估。 这项培训将由计算和神经方法方面的跨学科专家团队提供协助 为了理解精神障碍，不确定性的神经信息计算模型和 临床相关行为的回避和生态评估。培训将在 匹兹堡大学精神病学系，拥有长期和成功的记录 支持初级科学家。为了实现这些培训目标，拟议的研究采用了从基础 神经计算研究不确定性和探索性应用于焦虑症。具体地说，建议的 研究将检验焦虑，尤其是焦虑唤醒与学习障碍有关的假设。 关于不确定的、令人厌恶的结果，用神经和行为测量来衡量；这扰乱了 不确定性学习导致在焦虑中避免不确定的选择；而不确定性的衡量标准 回避与现实世界的行为回避有关。参与者(n=85)，因高度焦虑而被过度抽样，将 在接受功能磁共振扫描的同时完成一项评估不确定性学习的任务。然后他们将报道真实的- 两周的世界回避行为。参与者在不确定性学习任务上的表现将是合适的 到一个计算模型，以衡量从不确定性中学习以及探索与回避的倾向 基于不确定性的期权。从计算模型估计的不确定度将是 对fMRI大胆信号和行为选择的影响；这些对神经和行为功能的影响 将在焦虑的唤醒状态下接受差异测试。依赖于不确定性的探索意志中的个体差异 接受测试，以确定与参与者当前的行为回避真实报告是否一致，如果他们 预测未来现实世界中的行为回避。预期的影响，与NIMH的战略目标一致， 将识别a)复杂行为的神经机制，不适应行为回避，b) 对焦虑和回避的客观评估，以及c)可能的新治疗目标。