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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

基本信息

批准号：
10634710
负责人：
Timothy A Allen
金额：
$ 17.3万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
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/10634710
关键词：
Adult Aggressive behavior Amygdaloid structure Anger Anxiety Aversive Stimulus Behavior Behavior Control Behavioral Behavioral Paradigm Behavioral inhibition Biological Clinical Cognitive Complex Computer Models Cues Decision Making Decision Theory Depressed mood Desire for food Development Diagnosis Diagnostic Dimensions Disease Distal Distress Economic Burden Economics Emotional Environment Equation Frustration Functional Magnetic Resonance Imaging Future Goals Hostility Impairment Individual Individual Differences Interpersonal Relations 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 Symptoms System Testing Training Variant Ventral Striatum Work active control anxious behavioral response clinical development clinical predictors clinical prognosis computational neuroscience conditioning cost experimental study flexibility functional disability functional outcomes imaging modality improved indexing multilevel analysis negative affect neural neural circuit neurobehavioral neuroimaging novel psychiatric comorbidity psychologic recruit response skills social treatment response vigilance

项目摘要

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.

消极情绪的个体差异被一个相对稳定的跨诊断维度所捕捉