Project 2: Investigating the effects of aversive interoceptive states on computations underlying avoidance behavior and their neural basis

项目 2：研究厌恶内感受状态对回避行为背后的计算及其神经基础的影响

• 批准号: 10711140
• 负责人: Ryan S Smith
• 金额: $ 31.75万
• 依托单位: LAUREATE INSTITUTE FOR BRAIN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10711140
• 关键词: Affect; Amygdaloid structure; Anterior; Anxiety; Anxiety Disorders; Arousal; Breathing; Cognitive; Computer Models; Data; Decision Making; Disease; Distal; Dorsal; Esthesia; Exposure to; Fright; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Heart; Impairment; Individual; Individual Differences; Insula of Reil; Interoception; Intervention; Learning; Link; Mental Health; Mental disorders; Modeling; Neurosciences; Outcome; Participant; Performance; Policies; Population; Process; Quality of life; Race; Rewards; Severities; System; Task Performances; Testing; Time; Uncertainty; Visceral; Work; anxiety states; anxiety symptoms; anxious; anxious individuals; avoidance behavior; design; expectation; health assessment; improved; improved outcome; indexing; innovation; motivated behavior; neural; neural correlate; neural network; neuroregulation; novel; pharmacologic; recruit; response; self-reported anxiety; symptomatic improvement; trait; treatment response

PROJECT SUMMARY: Project 2 – Smith Anxiety disorders are the most ubiquitous form of mental illness, affecting roughly 34% of the population. One major factor that maintains anxiety symptoms is the continued avoidance of feared situations that are in fact tolerable and would often improve quality of life. However, it is unknown how the aversive interoceptive states associated with high anxiety influence maladaptive avoidance behavior – or what the neural underpinnings of such influences are. To answer these questions, we propose to employ a previously validated inspiratory breathing load paradigm capable of reliably inducing aversive visceral states while individuals complete two decision-making tasks. We will use computational modeling to identify dissociable parameters underlying learning and decision-making on an individual basis. Some important individual differences that can be estimated within these models are values of parameters reflecting: (1) how much decisions are driven by seeking information vs. reward, and (2) how far into the future an individual considers when making decisions (planning horizon). Abnormal values for these parameters could drive maladaptive avoidance behavior in anxious populations, because learning to approach uncomfortable situations requires both information-seeking (to test expectations) and a sufficient planning horizon to anticipate that short-term discomfort can lead to long- term benefit. Here we hypothesize that aversive visceral states reduce information-seeking and shorten planning horizon. We further hypothesize that these effects are magnified in anxious populations. In this project will recruit 50 low- and 50 high-anxiety participants (Overall Anxiety Severity and Impairment Scale [OASIS] scores > 8) to test the above-stated hypotheses. Participants will complete two decision-making tasks widely used with computational modelling – the aversive pruning (AP) task and the horizon task. They will complete each task twice, once with and once without the unpleasant breathing load. The AP task assesses aversive decision true `pruning' – the tendency to not evaluate distal outcomes of a possible course of action if a proximal negative outcome is expected (i.e. effectively reducing planning horizon for policies that may have positive distal outcomes). The horizon task assesses goal-directed information-seeking – the tendency to strategically seek out observations to reduce uncertainty before becoming confident in the best course of action. The AP task will be completed during fMRI. We will compare information-seeking and pruning parameter values with vs. without breathing loads and correlate these parameters with state and trait anxiety levels. We will also test the hypothesis that aversive states during breathing load will amplify the known neural correlates of pruning, by increasing neural responses in subgenual cingulate, insula, and amygdala and reducing dorsal frontoparietal activity associated with future planning. Identifying the mechanisms by which aversive states promote avoidance will be an important first step toward designing new interventions to target these mechanisms and improve symptoms.

项目概要：项目2 - Smith 焦虑症是最普遍的精神疾病形式，影响大约34%的人口。一 保持焦虑症状的主要因素是持续避免恐惧的情况， 可以忍受，往往会提高生活质量。然而，目前还不清楚厌恶的内感受状态是如何 与高度焦虑相关的影响适应不良的回避行为-或者说， 这种影响是。为了回答这些问题，我们建议采用先前验证的吸气 呼吸负荷范式能够可靠地诱导厌恶的内脏状态，而个人完成两个 决策任务。我们将使用计算建模来识别潜在的可分离参数， 个人的学习和决策。一些重要的个体差异， 在这些模型中估计的参数值反映：（1）有多少决策是由 寻求信息与奖励，以及（2）个人在做出决策时考虑的未来有多远 （规划范围）。这些参数的异常值可能会导致适应不良的回避行为， 焦虑的人群，因为学习接近不舒服的情况下，既需要寻求信息， (to测试预期）和充分的规划范围，以预期短期不适可能导致长期- 长期福利在这里，我们假设，厌恶的内脏状态减少信息寻求和缩短 规划视野我们进一步假设，这些影响在焦虑人群中被放大。在这个项目中 将招募50名低焦虑和50名高焦虑参与者（总体焦虑严重程度和损害量表[OASIS]） 评分> 8）以检验上述假设。参与者将广泛完成两项决策任务 与计算建模一起使用-厌恶修剪（AP）任务和地平线任务。他们将完成 每个任务两次，一次有，一次没有令人不快的呼吸负荷。AP任务评估厌恶性 决策真“修剪”-如果一个可能的行动方案的最终结果不被评估的倾向， 预计会产生近似的负面结果（即有效地减少可能具有 积极的远端结果）。水平任务评估目标导向的信息寻求-倾向于 战略性地寻求观察，以减少不确定性，然后才能在最佳过程中充满信心。 行动上AP任务将在fMRI期间完成。我们将比较信息搜索和修剪 参数值与无呼吸负荷，并将这些参数与状态和特质焦虑相关 程度.我们还将测试呼吸负荷期间的厌恶状态会放大已知神经元的假设。 修剪的相关性，通过增加膝下扣带、杏仁核和杏仁核的神经反应， 减少与未来规划相关的背侧额顶叶活动。确定 厌恶的国家促进避免将是设计新干预措施的重要第一步， 这些机制和改善症状。