Avoidance learning and pain circuit dysfunction in alcohol use disorders

酒精使用障碍中的回避学习和疼痛回路功能障碍

• 批准号: 10349780
• 负责人: Thang Manh Le
• 金额: $ 13.1万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-10 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10349780
• 关键词: Address; Adult; Affective; Aftercare; Alcohol abuse; Alcohol consumption; Alcohol dependence; Alcohols; Analgesics; Anti-Anxiety Agents; Arousal; Avoidance Learning; Bayesian Modeling; Behavioral; Biological; Brain; Cessation of life; Characteristics; Chronic; Classification; Clinical; Clinical assessments; Cognitive; Complex; Consumption; Coping Behavior; Cues; Data; Development; Development Plans; Diagnosis; Diagnostic; Emotional; Environment; Esthesia; Etiology; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Heavy Drinking; Image; Individual; Inpatients; Intervention; Investigation; Learning; Link; Machine Learning; Maintenance; Medial; Mentors; Modeling; Motivation; Negative Reinforcements; Neurobiology; Neurosciences; Neurosciences Research; Outcome; Pain; Pain management; Participant; Pathway interactions; Patients; Performance; Phase; Physiological; Play; Prefrontal Cortex; Process; Property; Psychophysiology; Publishing; Punishment; Relapse; Reporting; Research; Research Personnel; Resources; Rewards; Risk Factors; Role; Severities; Shock; Statistical Algorithm; Structure; Supervision; Techniques; Time; Training; Treatment outcome; United States; Universities; addiction; alcohol abuse therapy; alcohol consequences; alcohol relapse; alcohol seeking behavior; alcohol use disorder; alcoholism prevention; behavioral impairment; brain dysfunction; career; career development; chronic alcohol ingestion; cognitive control; cognitive neuroscience; connectome based predictive modeling; coping; diagnostic accuracy; drinking; follow-up; improved; individualized medicine; innovation; maladaptive behavior; meetings; midbrain central gray substance; neuroimaging; novel; problem drinker; psychologic; recruit; relapse patients; relapse prediction; relating to nervous system; research and development; response; social; social deficits; substance abuse treatment; treatment program

PROJECT SUMMARY / ABSTRACT Alcohol use disorder (AUD) is a chronic debilitating condition that accounts for over half of all substance abuse treatment cases in the United States. Most AUD patients relapse despite treatment. It is increasingly recognized that deficits in avoidance learning are critically involved in motivating habitual and heavy alcohol use. Specifically, due to alcohol’s anxiolytic and analgesic properties, many engage in drinking to avoid painful physical and affective states. Paradoxically, chronic alcohol use is associated with increased pain reactivity and decreased cognitive control. These changes reinforce compulsive drinking as a maladaptive avoidance strategy and further compromise learning of its harmful consequences. Yet, the neural, physiological, and psychological processes inter-relating avoidance learning dysfunction with the maintenance and relapse of AUD remain poorly understood. This K99/R00 proposal addresses this critical gap in research. To this end, we propose to collect functional magnetic resonance imaging (fMRI) data in treatment- seeking AUD patients during a probabilistic learning task which features pain and reward. Our first aim is to characterize dysfunctions of the brain circuits supporting pain reactivity and cognitive control during avoidance learning in AUD patients, as compared to social drinkers. In the second aim, we will evaluate how the neural markers, along with clinical, physiological and behavioral metrics, may be used to (1) diagnostically distinguish clinical characteristics; and (2) model the key pathophysiological pathways that sustain habitual alcohol use. In the third aim, we will identify the risk factors that best predict relapse during the 12-month follow-up, thus offering clinical implications for improving treatment outcomes. The long-term goal of the candidate is to start an independent career in neuroscience research of alcohol addiction. This proposed study will support this goal by serving as a launchpad for the candidate to transition to an independent investigator. The candidate has trained extensively in cognitive neuroscience and devoted himself to the field of addiction neuroscience. By conducting this study, the candidate will broaden his training in the clinical and neurobiological investigation of AUD as well as gain expertise in machine learning and Bayesian modeling. The candidate has identified his training needs and assembled a team of expert mentors for this K99/R00 proposal. The training plan includes structured mentoring, supervised research, formal coursework, presentations at scientific meetings, and professional development. The exceptional research environment and intellectual resources at Yale University will allow the candidate to receive ample guidance, learn novel techniques, and gain independence, while pursuing the research he is passionate about.

项目摘要/摘要