Computational neuroeconomic models of addiction-quantifying progression and treatment in opioid use disorder

成瘾量化进展和阿片类药物使用障碍治疗的计算神经经济模型

• 批准号: 9751824
• 负责人: PAUL W GLIMCHER
• 金额: $ 42.62万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9751824
• 关键词: Addictive Behavior; Address; Adherence; Affect; Area; Attitude; Automobile Driving; Base of the Brain; Behavior; Behavior Therapy; Behavioral; Biological; Biology; Brain; Choice Behavior; Chronic Disease; Clinical; Code; Cognitive; Communities; Data; Decision Making; Development; Diagnostic; Dropout; Economics; Effectiveness; Etiology; Event; Failure; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Healthcare Systems; Heroin; Impulsivity; Individual; Interdisciplinary Study; Intervention; Investigation; Knowledge; Measurement; Measures; Medical; Modeling; Monitor; Morbidity - disease rate; National Institute of Drug Abuse; Neurobiology; Occupational; Opiate Addiction; Opioid; Outcome; Pathologic; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacology; Phenotype; Politics; Population; Positioning Attribute; Predictive Value; Prefrontal Cortex; Probability; Process; Provider; Psychiatry; Recovery; Refractory; Relapse; Resistance; Rewards; Risk; Sampling; Strategic Planning; Symptoms; System; Testing; Time; Treatment Effectiveness; Treatment Efficacy; Ventral Striatum; Work; addiction; base; behavior change; behavioral economics; brain behavior; cingulate cortex; clinical predictors; clinically relevant; cognitive function; comparative; computational neuroscience; cost; drug of abuse; global health; individual patient; information processing; interest; medication-assisted treatment; mortality; neural circuit; neurobiological mechanism; neuroeconomics; novel; opioid abuse; opioid use disorder; opioid user; outcome forecast; patient subsets; phenotypic data; predict clinical outcome; prescription opioid; prognostic; relating to nervous system; response; smartphone Application; social; success; theories; tool

Project Summary/Abstract Opioid use disorder (OUD) is a debilitating chronic disease producing a growing burden on patients, providers, and the healthcare system. From 2002 to 2013, OUD rates have more than doubled and the number of individuals seeking treatment for the first time has more than quadrupled, driving unprecedented medical, scientific, and political interest in the etiology, pathophysiology, and treatment of OUD. Excellent treatments for opioid addiction exist, but their effectiveness is limited by lack of adherence to medication, treatment dropout, and relapse. There is scant knowledge about the neural and cognitive factors associated with and perhaps underlying treatment success or failure. A key goal of the present proposal is to develop reliable objective predictors of which individuals may need additional intervention and when best to intervene, i.e., when there is a risk for imminent relapse or treatment dropout. To do so, we propose to develop and test a computational neuroeconomic approach to quantifying the behavioral and neural features of addiction during OUD treatment. This computational approach to psychiatry seeks to understand circuit-level information processing in neural systems and how these mechanisms relate to normal and pathophysiological behavior. We hypothesize that quantifying individual subject choice behavior - via a longitudinally-sampled array of neuroeconomic decision tasks and models - provides information to: (1) distinguish relevant clinical populations (patients vs. controls and patient subgroups); (2) assist clinical prognosis (future treatment efficacy); (3) dynamically track ongoing clinical status (e.g. likelihood of relapse); and (4) examine the neural basis of behavioral changes in the recovery process. Specifically, we hypothesize that clinical status during treatment is characterized by the position and trajectory of individual subjects in a multidimensional space of decision parameters (quantifying impulsivity, risk tolerance, and ambiguity attitude). To test this hypothesis, we propose to longitudinally track the behavior and neural activity of patients seeking treatment for OUD. In Aim 1, we test the hypothesis that single-timepoint multidimensional decision data provides diagnostic and prognostic information, categorizing different clinical subpopulations (OUD patients vs. controls, treatment responsive vs. treatment refractory patients). In Aim 2, we test the hypothesis that dynamic multidimensional decision data tracks and predicts time-varying changes in clinical status, including the probability of future relapse. In Aim 3, we test the hypothesis that static and dynamic features of multidimensional decision data reflect corresponding features and changes in integrated value coding in specific neural circuits. Understanding how decision-related computations reflect clinical status is critical to closing the explanatory gap between biology and behavior in addiction. If successful, this approach offers both basic scientific and translational benefits: a clearer understanding of how and why behavior changes in addiction treatment, and easily-implementable tools to monitor treatment effectiveness and clinical course in individual patients.

项目概要/摘要 阿片类药物使用障碍 (OUD) 是一种使人衰弱的慢性疾病，给患者、提供者、 和医疗保健系统。从 2002 年到 2013 年，OUD 费率增加了一倍多，并且 首次寻求治疗的人数增加了四倍多，推动了前所未有的医疗、 对 OUD 的病因学、病理生理学和治疗的科学和政治兴趣。优秀的治疗方法 阿片类药物成瘾确实存在，但其有效性因缺乏服药依从性、治疗中途退出、 并复发。对于与之相关的神经和认知因素知之甚少，也许 潜在的治疗成功或失败。本提案的一个关键目标是制定可靠的目标 哪些人可能需要额外干预以及何时最好进行干预的预测因素，即何时 即将复发或停止治疗的风险。为此，我们建议开发并测试一个计算 量化 OUD 治疗期间成瘾行为和神经特征的神经经济学方法。 这种精神病学的计算方法旨在理解神经系统中的电路级信息处理 系统以及这些机制如何与正常和病理生理行为相关。我们假设 量化个体受试者的选择行为——通过一系列纵向采样的神经经济决策 任务和模型 - 提供信息以：(1) 区分相关临床人群（患者与对照） 和患者亚组）； （2）辅助临床预后（未来治疗效果）； (3)动态跟踪正在进行的 临床状态（例如复发的可能性）； (4) 检查行为变化的神经基础 恢复过程。具体来说，我们假设治疗期间的临床状态的特点是 决策参数多维空间中个体主体的位置和轨迹（量化 冲动、风险承受能力和模糊态度）。为了检验这个假设，我们建议纵向跟踪 寻求 OUD 治疗的患者的行为和神经活动。在目标 1 中，我们检验以下假设： 单时间点多维决策数据提供诊断和预后信息、分类 不同的临床亚群（OUD 患者与对照、治疗反应性与治疗难治性 患者）。在目标 2 中，我们测试动态多维决策数据跟踪和预测的假设 临床状态随时间变化的变化，包括未来复发的可能性。在目标 3 中，我们测试 假设多维决策数据的静态和动态特征反映了相应的特征 以及特定神经回路中综合值编码的变化。了解决策如何相关 反映临床状态的计算对于缩小生物学和行为之间的解释差距至关重要 瘾。如果成功，这种方法将提供基本的科学和转化效益： 了解成瘾治疗中行为变化的方式和原因，以及易于实施的工具 监测个体患者的治疗效果和临床过程。