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Computational Biases of Learning and Decision-Making in PTSD

PTSD 中学习和决策的计算偏差

基本信息

批准号：
10451045
负责人：
Joshua M Cisler
金额：
$ 60.68万
依托单位：
UNIVERSITY OF TEXAS AT AUSTIN
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10451045
关键词：
Address Adult Anxiety Disorders Behavior Behavioral Brain Characteristics Clinical Computational Science Computer Models Corpus striatum structure Data Decision Making Detection Development Disease Disease remission Distress Extinction (Psychology)Fright Functional Magnetic Resonance Imaging Functional disorder Goals Hippocampus (Brain)Individual Interpersonal Violence Intervention Knowledge Learning Literature Mediating Mediator of activation protein Memory Mental disorders Methods Modeling Morbidity - disease rate Outcome Participant Pharmacology Post-Traumatic Stress Disorders Process Psychiatry Psychological reinforcement Psychopathology Psychophysiology Quality of life Research Resolution Retrieval Rewards Route Stimulus Structure Symptoms Techniques Testing Therapeutic Trauma Woman Work anxious approach avoidance behavior behavioral pharmacology clinically relevant computational neuroscience conditioned fear conflict resolution design expectation learning extinction memory retrieval men neural circuit novel relating to nervous system reward processing social stress related disorder therapy development trauma exposure violence exposure

项目摘要

Project Summary The goal of this proposal is to characterize novel computational biases in learning and decision-making in PTSD. The dominant understanding of PTSD emphasizes heightened fear learning and threat detection, and weakened fear extinction / inhibition. While this model explains many aspects of PTSD, there is a growing gap between our models of PTSD and the emerging literature defining the normative computational mechanisms of learning and decision-making. This proposal aims to bridge this gap by defining computational biases in two clinically-relevant domains of learning and decision-making in PTSD. First, the mechanisms by which individuals with PTSD prefer avoiding threat at the expense of losing potential reward is not understood. This bias in approach-avoidance conflict resolution is an essential feature of the clinical presentation of PTSD, and though threat processing and reward processing have separately been characterized in PTSD, how threat and reward processing interact to result in biases towards avoidance has never been investigated. Second, dysregulation of context-depending (i.e., latent state) learning has clear clinical implications in PTSD: generalization of threat learning outside the trauma context is related to the development of PTSD; generalization of extinction learning outside of the clinical context is related to the treatment of PTSD. However, computational models of context-modulated learning have not been used to understand these processes in PTSD. The current project proposes to use computational modeling of learning and decision- making in novel tasks that probe the behavioral and brain mechanisms of approach-avoidance biases (Specific Aim 1) and context-modulated (i.e., latent state) learning (Specific Aim 2). A case-controlled design would be used, in which healthy adults, trauma-exposed adults without PTSD, trauma-exposed adults with PTSD, and adults with non-PTSD anxiety disorders would undergo novel learning and decision-making tasks during fMRI with concurrent psychophysiological assessment. By defining novel computational biases in learning and decision-making in PTSD, the project 1) would bridge the gap between our understanding of PTSD and our the growing science of computational mechanisms of learning, 2) has the potential to explain clinically-relevant features of dysfunction in PTSD, and 3) would provide targets for tracking trajectories of PTSD development and treatment, and stimulate novel methods for treating PTSD that go beyond the traditional fear conditioning and extinction models.

项目总结