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.

项目摘要：项目 2 – 史密斯 焦虑症是最普遍的精神疾病，影响约 34% 的人口。一 维持焦虑症状的主要因素是持续回避令人恐惧的情况，而这些情况实际上是 可以忍受，并且通常会提高生活质量。然而，目前尚不清楚厌恶性内感受是如何状态的 与高度焦虑影响适应不良的回避行为有关——或者神经基础是什么 这些影响是。为了回答这些问题，我们建议采用先前验证的吸气法 呼吸负荷范式能够可靠地诱导厌恶的内脏状态，同时个体完成两个 决策任务。我们将使用计算模型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可能是 这些模型中估计的参数值反映了：（1）有多少决策是由以下因素驱动的： 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，因为学习处理不舒服的情况需要信息寻求 （测试期望）和足够的计划范围来预测短期的不适可能会导致长期的 期限利益。在这里，我们假设厌恶的本能状态会减少信息寻求并缩短 规划视野。我们进一步假设这些影响在焦虑人群中会被放大。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伤量表 [OASIS] 分数> 8）来检验上述假设。参与者将广泛完成两项决策任务 与计算建模一起使用——厌恶剪枝（AP）任务和地平线任务。他们将完成 每个任务两次，一次有令人不愉快的呼吸负荷，一次没有。 AP 任务评估厌恶 决策真正的“修剪”——如果一个可能的行动方案的最终结果不评估，则倾向于不评估 预计近期会出现负面结果（即有效缩短可能会产生负面影响的政策的规划范围） 积极的远端结果）。地平线任务评估以目标为导向的信息寻求——倾向 在对最佳方案充满信心之前，战略性地寻找观察结果以减少不确定性 行动。 AP 任务将在 fMRI 期间完成。我们将比较信息查找和修剪 有呼吸负荷与无呼吸负荷的参数值，并将这些参数与状态和特质焦虑相关联 水平。我们还将检验这样的假设：呼吸负荷期间的厌恶状态会放大已知的神经 通过增加膝下扣带回、岛叶和杏仁核的神经反应，与修剪相关 减少与未来规划相关的背额叶活动。确定机制 厌恶状态促进回避将是设计新干预措施的重要第一步 这些机制并改善症状。