Neurobehavioral Mechanisms of Choices to Smoke Cannabis in Cannabis Use Disorder

项目摘要/摘要 物质使用障碍(SODS)的一个显著症状是持续选择药物，代价是 其他更适应的奖励，但对指导这一关键行为的神经行为因素知之甚少 病理学。在我们初步研究的基础上，这项申请寻求弥合决定之间的差距 科学和成瘾神经科学，总体目标是确定如何选择药物和其他奖励 吸毒成瘾的大脑与吸毒者制造的大脑不同，吸毒者更容易控制自己的使用。 鉴于大麻使用的日益普遍，我们将重点关注吸食大麻的人(4次/月)， 招募大样本的大麻使用障碍(CUD)患者(n=60)和非大麻患者(n=60)。 参与者将接受为期3天的住院方案，包括功能磁共振成像和行为选择任务，以及 实验室大麻自行管理。我们将研究决策过程中的两个关键过程 大麻和替代增强剂的选择(玩机会游戏)：1)神经编码 选择过程中的主观价值(SV)信号；以及2)强化学习过程指导选择。在……里面 健康的大脑，评估回路(例如，腹内侧前额叶皮质)已被可靠地证明 定量表示可用期权的奖励值，以“共同货币”计算。 选择。还没有研究在关于药物和其他奖励的决定中消除SV编码的歧义 人类吸毒者，尽管毒品被高估，替代品被认为是有贡献的 对肥胖症的发展和表现是至关重要的。根据初步数据，我们预计 患有CUD的人将显示用于大麻的增强型SV信号和用于替代方案的钝化SV信号 与非CUD参与者相比，奖励。我们对强化学习的研究将评估相关的 基于模型和无模型的学习系统的贡献，被认为是更深思熟虑和更多的基础 习惯性选择行为分别对大麻和替代报酬做出决定。基于模型的 学习需要使用选择和结果之间关联的认知模型，并促进 慎重的选择。无模型学习只使用奖励历史来指导选择，这是 习惯性的决定。我们预计，药物强化的决定将招募更多的无模型学习。 在CUD参与者中，导致对药物的持续适应不良选择超过其他奖励。要评估 这些决策过程的临床相关性，我们还将调查SV信号之间的关联， 强化学习，以及用自我报告和同伴报告测量的功能损害。因此，通过描绘 决策过程区分更受控制的药物使用与更根深蒂固的、不适应的模式 使用，这个项目将解决成瘾神经科学的一个核心挑战：理解 指导肥胖症主要症状表达的神经行为机制。

Project Summary/Abstract A hallmark symptom of Substance Use Disorders (SUDs) is persistent choice for drugs at the expense of other, more adaptive rewards, yet little is known about the neurobehavioral factors guiding this key behavioral pathology. Building on our preliminary studies, this application seeks to bridge the gap between decision science and addiction neuroscience with the overall aim of identifying how choices for drugs and other rewards in the `addicted' brain differ from those made by drug users who are more readily able to control their use. Given the ever increasing prevalence of cannabis use, we will focus on cannabis smokers (4x/month), recruiting large samples of individuals with (n=60) and without (n=60) Cannabis Use Disorder (CUD). Participants will undergo a 3-day inpatient protocol comprising fMRI and behavioral choice tasks and laboratory cannabis self-administration. We will investigate two key processes of decision-making during choices for cannabis and an alternative reinforcer (opportunities to play a game of chance): 1) neural encoding of subjective value (SV) signals during choices; and 2) reinforcement learning processes guiding choices. In the healthy brain, valuation circuitry (e.g. ventromedial Prefrontal Cortex) has reliably been shown to quantitatively represent the reward values of available options, calculated in a `common currency' during choice. No research has disambiguated SV encoding during decisions about drugs and other rewards in human drug users, despite the overvaluation of drugs and undervaluation of alternatives thought to contribute critically to the development and manifestation of SUDs. Based on preliminary data, we anticipate that individuals with CUD will show enhanced SV signaling for cannabis and blunted SV signals for the alternative reward compared to non-CUD participants. Our investigation of reinforcement learning will assess the relative contribution of model-based and model-free learning systems, thought to underlie more deliberative and more habitual choice behavior respectively, to decisions about cannabis and the alternative reward. Model-based learning entails use of a cognitive model of associations between choices and outcomes, and facilitates deliberative choice. Model-free learning uses only the reward history to guide choices, and is a feature of habitual decisions. We expect that decisions made for drug reinforcement will recruit more model-free learning in CUD participants, contributing to persistent maladaptive choices for drugs over other rewards. To assess the clinical relevance of these decision processes, we will also investigate associations between SV signals, reinforcement learning, and functional impairment measured by self- and peer-report. Thus, by delineating the decision processes differentiating more controlled drug use from more entrenched, maladaptive patterns of use, this project will address a central challenge for addiction neuroscience: understanding the neurobehavioral mechanisms guiding the expression of a cardinal symptom of SUDs.