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

项目总结/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率增加了一倍多， 第一次寻求治疗的人增加了四倍多，推动了前所未有的医疗， 在病因学，病理生理学和治疗OUD的科学和政治利益。卓越的治疗， 阿片类药物成瘾是存在的，但它们的有效性受到缺乏药物依从性，治疗退出， 和复发关于神经和认知因素的知识很少， 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， 预测哪些个体可能需要额外干预以及何时最好进行干预，即，当有 即将复发或退出治疗的风险。为此，我们建议开发和测试一个计算 神经经济学方法来量化OUD治疗期间成瘾的行为和神经特征。 这种精神病学的计算方法旨在了解神经元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 量化个体主体选择行为-通过神经经济学决策的连续采样阵列 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（未来治疗疗效）;（3）动态跟踪正在进行的 临床状态（例如复发的可能性）;以及（4）检查患者行为变化的神经基础。 恢复过程。具体而言，我们假设治疗期间的临床状态的特征是 在决策参数的多维空间中的个体对象的位置和轨迹（量化 冲动性、风险容忍度和模糊态度）。为了验证这一假设，我们建议纵向跟踪 寻求治疗OUD的患者的行为和神经活动。在目标1中，我们检验了以下假设： 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，治疗应答与治疗难治 患者）。在目标2中，我们测试了动态多维决策数据跟踪和预测的假设 临床状态的时变变化，包括未来复发的可能性。在目标3中，我们测试 假设多维决策数据静态和动态特征反映了相应的特征 以及特定神经回路中的积分值编码的变化。了解与决策相关的 计算反映临床状态是至关重要的，以缩小生物学和行为之间的解释差距， 成瘾如果成功的话，这种方法提供了基本的科学和转化的好处： 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， 监测个别患者的治疗效果和临床过程。

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.