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: 10518399
• 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/10518399
• 关键词: Anxiety; Anxiety Disorders; Arousal; Association Learning; Behavior; Behavior assessment; Behavioral; Brain; Clinical; Complex; Computer Models; Dangerousness; Diagnosis; Diagnostic; 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; 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; behavior measurement; career; clinical predictors; clinically relevant; computer framework; depressive symptoms; effective therapy; experience; functional MRI scan; improved; individual variation; neural; neuroimaging; neuromechanism; novel; prevent; remediation; response; theories; tool

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.

这个K23应用程序将提供申请人，临床心理学家与神经成像的专业知识， 计算建模，培训和指导的研究经验，对一个独立的研究 职业学习扰乱焦虑症患者的学习过程。培训活动将侧重于：1）临床 计算建模和神经成像在焦虑中的信息应用，2）高级计算 不确定性和探索的建模，以及3）行为回避的生态瞬时评估。 这项培训将由一个跨学科的计算和神经方法专家小组提供便利 了解精神疾病，神经信息的不确定性计算模型， 回避和临床相关行为的生态评估。培训将在 匹兹堡大学精神病学系，该系有着长期而成功的记录， 支持年轻科学家为了实现这些培训目标，拟议的研究采用了基本的方法， 神经计算研究的不确定性和探索适用于焦虑。具体而言，建议 研究将检验焦虑，特别是焦虑的激发，与中断的学习有关的假设 关于不确定的，令人厌恶的结果，通过神经和行为测量;这破坏了 不确定性学习导致在焦虑中避免不确定的选择;不确定性的测量 回避与现实世界行为回避有关。参与者（n=85），高焦虑过度抽样，将 在接受功能磁共振成像扫描的同时完成评估不确定性学习的任务。他们会报道真实的- 两周的回避行为参与者在不确定性学习任务上的表现将适合 到一个计算模型来衡量从不确定性中学习以及探索与避免的倾向， 基于不确定性的选择。根据计算模型估计的不确定性度量将为 对fMRI BOLD信号和行为选择进行回归;这些对神经和行为功能的影响 将被测试焦虑唤醒的差异。不确定性依赖探索中的个体差异将 与参与者当前真实世界的行为回避报告进行一致性测试， 预测未来现实世界的行为回避。根据NIMH的战略目标， 将是识别a）复杂行为的神经机制，适应不良的行为回避，B） 焦虑和回避的客观评估，和c）可能的新治疗靶点。