Dispositional Negativity and the Pavlovian Control of Active and Passive Defensive Behavior

性格消极性与主动和被动防御行为的巴甫洛夫控制

• 批准号: 10038826
• 负责人: Timothy A Allen
• 金额: $ 17.29万
• 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: 2020-07-13 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10038826
• 关键词: Adult; Aggressive behavior; Amygdaloid structure; Anger; Anxiety; Behavior; Behavior Control; Behavioral; Behavioral Paradigm; Behavioral inhibition; Biological; Clinical; Cognitive; Complex; Computer Models; Cues; Decision Theory; Depressed mood; Desire for food; Development; Diagnosis; Dimensions; Disease; Distal; Distress; Economic Burden; Economics; Emotional; Environment; Equation; Frustration; Functional Magnetic Resonance Imaging; Future; Goals; Impairment; Individual; Individual Differences; Intervention; Label; Laboratories; Learning; Link; Literature; Maintenance; Measures; Mental disorders; Mentors; Methods; Modeling; Modernization; Motivation; National Institute of Mental Health; Neurosciences; Neurotic Disorders; Operant Conditioning; Outcome; Participant; Pattern; Phenotype; Process; Protocols documentation; Psychological reinforcement; Psychometrics; Psychopathology; Punishment; Recurrence; Relapse; Research; Research Domain Criteria; Research Support; Risk Factors; Sampling; Series; Signal Transduction; Site-Directed Mutagenesis; Stimulus; Structure; Symptoms; System; Testing; Training; Variant; Ventral Striatum; Work; active control; anxious; base; behavioral response; clinical development; clinical predictors; comorbidity; computational neuroscience; conditioning; experimental study; flexibility; functional disability; functional outcomes; imaging modality; improved; indexing; multilevel analysis; negative affect; neural circuit; neurobehavioral; neuroimaging; novel; outcome forecast; psychologic; recruit; relating to nervous system; response; social; treatment response; vigilance

Individual differences in negative affect are captured by a relatively stable, transdiagnostic dimension known as dispositional negativity, which can be decomposed into two correlated subcomponents: anxious distress (AD; reflecting tendencies toward sadness and anxiety) and irritable distress (ID; reflecting tendencies toward frustration and anger). The goal of this proposal is to provide training in behavioral experiments, computational modeling, and functional magnetic resonance imaging (fMRI) methods that can be used to interrogate the learning processes that underlie the subcomponents of dispositional negativity. Understanding the neurocomputational basis of dispositional negativity is of central importance because it contributes to nearly all forms of psychopathology and is strongly related to clinical prognosis, functional impairment, and economic burden. The central hypothesis of the current proposal is that elevations in dispositional negativity reflect a predominance of an inflexible Pavlovian learning system over instrumental behavioral control in aversive contexts, with AD reflecting a Pavlovian bias to engage in passive avoidance, and ID reflecting a Pavlovian bias to engage in active defense. Specifically, the aims of this project are to 1) demonstrate that passive and active Pavlovian biases are differentially associated with individual differences in AD and ID; 2) characterize the neural circuitry underlying AD, ID, and their associated Pavlovian biases; and 3) show that biases toward active and passive defense are associated with common real-world correlates of AD and ID. Consistent with a transdiagnostic, dimensional approach informed by the Research Domain Criteria (RDoC), 200 adults representing the full spectrum of dispositional negativity and its subcomponents will complete a series of behavioral paradigms that manipulate the influence of the Pavlovian learning system on instrumental behavior in an aversive context, including a novel aversive Pavlovian-Instrumental Transfer (PIT) task. A subsample of 70 participants will complete the aversive PIT task while undergoing fMRI. Behavior will be analyzed using frequentist multi-level models and reinforcement learning (RL) models that formally quantify Pavlovian influence within a Bayesian decision theory framework. RL model-estimated prediction errors will be regressed against BOLD signal, and structural equation modeling will be used to link AD, ID, and their associated Pavlovian biases to real-world outcomes. The proposed training plan leverages a world-class research environment with a team of highly skilled mentors and consultants to provide the candidate with training in experimental learning paradigms, computational modeling, and functional neuroimaging methods. In line with NIMH’s Strategic Objectives, the proposed work will describe the neural circuitry associated with complex forms of learned defensive behavior, enable the development of clinically useful behavioral and biological indices of dispositional negativity, and identify potential targets for transdiagnostic interventions.

负面情绪的个体差异被一个相对稳定的跨诊断维度捕捉到，称为 性格负性，可分解为两个相互关联的子成分：焦虑苦恼(AD； 反映悲伤和焦虑的倾向)和易怒的痛苦(ID；反映倾向于 沮丧和愤怒)。这项提议的目标是提供行为实验方面的培训，计算 建模和功能磁共振成像(FMRI)方法，可用于询问 学习过程构成了性格消极子成分的基础。了解 倾向性负性的神经计算基础至关重要，因为它对几乎所有 精神病理学的形式，与临床预后、功能损害和经济密切相关 负担。当前建议的中心假设是，性格消极情绪的提升反映了 僵化的巴甫洛夫学习系统对工具性行为控制的优势 背景，AD反映了巴甫洛夫式的偏见，而ID反映了巴甫洛夫式的 偏向于积极防御。具体地说，这个项目的目的是1)证明被动和 活跃的巴甫洛夫偏见与AD和ID的个体差异不同；2)表征 AD、ID及其相关的巴甫洛夫偏向的神经回路；以及3)显示偏向 主动防御和被动防御与AD和ID在现实世界中的常见关联性相关。与 研究领域标准(RDoC)提供的跨诊断、维度方法，200名成人 表示性情负性及其子成分的全谱将完成一系列 操纵巴甫洛夫学习系统对工具性行为影响的行为范式 在厌恶情境下，包括一种新的厌恶巴甫洛夫-工具性迁移(PIT)任务。的一个小样本 70名参与者将在接受功能磁共振检查的同时完成厌恶坑任务。将使用以下工具分析行为 形式量化巴甫洛夫的频率多层模型和强化学习(RL)模型 在贝叶斯决策理论框架内的影响力。RL模型-估计的预测误差将被回归 针对粗体信号，将使用结构方程建模将AD、ID及其关联 巴甫洛夫式的对现实世界结果的偏见。拟议的培训计划利用了世界级的研究 拥有一支高技能的导师和顾问团队，为应聘者提供以下方面的培训 实验学习范式、计算建模和功能神经成像方法。与…一致 NIMH的战略目标，拟议的工作将描述与复杂的神经电路相关 习得的防御行为的形式，使临床上有用的行为和生物学的发展 性情负性指数，并确定跨诊断干预的潜在目标。