Neural control of Reinforcement Learning in SUD

项目摘要/摘要 由于两者的长期变化，物质使用障碍(Sud)的有效治疗已被证明具有挑战性。 在药物离开系统后持续很长时间的行为和神经功能。药物引起的神经细胞改变 可塑性尤其重要，因为它们可以扰乱与药物相关的行为以及适应 基础状态下的行为，因此有必要更全面地了解神经元是如何 作为这种可塑性基础的种群控制着基本行为，因为它们与奖励加工和 动力。基于价值的决策和激励的核心是伏隔核，它是 主要由D1和D2中等棘突投射神经元(MSN)组成，这些神经元被认为具有相反的 D1MSN促进奖励和D2MSN促进厌恶对行为的影响。一大堆工作 概述了在这些种群中发生的转录、生理和体内编码变化 并将这些变化与寻求和吸毒联系在一起。然而，这些 细胞群体也完整地参与学习、选择和执行目标导向的行为，以及 作为药物和非药物刺激的线索-奖赏联系的关键神经底物。目前， 在这些种群中编码的准确信息，以及它们如何指导不同群体的适应行为 具体情况仍有待明确阐明。通过组合复杂的增援任务，可以分离 基于刺激和结果值的行为行为与记录和操作D1的光学方法 和D2 MSN在清醒、行为正常的小鼠中，我们将定义在这些细胞中编码的特定信息 人口。在目标1中，我将利用纤维光度法钙成像来定义d1和d1的时间特征 D2在复杂行为任务中的中棘神经元(MSN)。我们假设--基于稳健 初步数据--d1和d2 MSN不是简单的“奖励”和“厌恶”，而是编码特定的 学习和执行行为的组成部分。在目标2中，我将利用光遗传学来操纵d1的活动。 和D2 MSN分别在离散时间点，以证明这些功能的重要性 神经元群体对强化学习的影响。虽然这项提议包含使用一些 创新技术，是通过将这些技术与技术相结合而获得的技术和理论培训 复杂的行为任务，将提供解决以下问题所需的基础专业知识和概念性思维 更大的问题是，大脑中的可塑性如何因药物暴露而改变，从而支持发育 苏德的。总而言之，这项建议将提供一个特殊的培训机会，同时提供 基因定义的神经元群体内发生的特定变化的基本证据 适应状态和非适应状态。此外，这些发现最终可以帮助我们理解如何 对一种劫持正常适应系统的疾病进行治疗性治疗。

Project Summary/Abstract Substance use disorder (SUD) has proven challenging to effectively treat due to the long-term changes in both behavior and neuronal function that persist long after drug has left the system. Drug-induced changes in neuronal plasticity are particularly important as they can disrupt both drug-associated behaviors as well as adaptive behaviors in basal states, thus necessitating a more comprehensive understanding of how the neuronal populations that underlie this plasticity control basal behaviors as they relate to reward processing and motivation. At the core of value-based decision making and motivation is the nucleus accumbens, which is primarily composed of D1 and D2 medium spiny projection neurons (MSNs) that are thought to have opposing actions on behavior with D1 MSNs promoting reward and D2 MSNs promoting aversion. A large body of work has outlined the transcriptional, physiological, and in vivo encoding alterations in these populations that occur as a result of drug exposure and has linked these alterations to drug seeking and consumption. However, these cellular populations are also integrally involved in learning, selecting, and executing goal-oriented behaviors, and function as a key neural substrate of cue-reward associations for drug and non-drug stimuli. Currently, the precise information that is encoded within these populations, and how they guide adaptive behaviors in different contexts, remains to be definitively elucidated. By combining complex reinforcement tasks that can dissociate behavioral action from stimulus and outcome value with optical approaches for recording and manipulating D1 and D2 MSNs in awake, behaving mice we will define the specific information that is encoded within these cellular populations. In Aim 1, I will utilize fiber photometry calcium imaging to define the temporal signature of D1 and D2 medium spiny neurons (MSNs) during complex behavioral tasks. We hypothesize - based on robust preliminary data - that D1 and D2 MSNs are not simply “rewarding” and “aversive”, but instead encode specific components of learned and executed behavior. In Aim 2, I will utilize optogenetics to manipulate activity in D1 and D2 MSNs respectively during discrete time points to demonstrate the functional importance of these neuronal populations to reinforcement learning. While this proposal encompasses the use of a number of innovative techniques, it is the technical and theoretical training, gained in combining these techniques with complex behavioral tasks, that will provide the foundational expertise and conceptual thinking needed to address larger questions regarding how plastic changes in the brain in response to drug exposure support development of SUD. Together, this proposal will provide an exceptional training opportunity while simultaneously providing foundational evidence for the specific changes that occur within genetically defined neuronal populations in adaptive – and maladaptive – states. Furthermore, these findings can ultimately inform our understanding of how to therapeutically approach treatment of a disorder that hijacks normal adaptive systems.