Frontostriatal Rhythms Underlying Reinforcement Learning.

项目总结 许多神经精神障碍，包括强迫症、情绪障碍和成瘾， 涉及折衷的评估和决策过程，以及不适应的学习联想。一个 事实证明，在协调这些不同的临床症状方面有用的计算框架是 强化学习(RL)，它规定了如何以最佳方式与环境交互以最大限度地 潜在的好处和减轻负面后果。过去几十年的研究表明， 额纹状体脑回路是RL的关键机械部件。然而，它的确切性质是 额叶皮质和纹状体之间的相互作用，以及这种交流是如何实现的，仍然存在 不清楚。 目前的建议集中在眶前皮质(OFC)和尾状核(CN)。OFC赋值 来刺激我们的环境，这使我们能够做出最优的决定。然而，它包含的内容很少 有关潜在运动反应的信息。我们的假设是，OFC将价值信息传递给CN， 其中，它可用于选择将导致最高价值结果的选择响应。我们 假设这种通信是通过在 选择的时间。 为了验证这一假设，我们将同时记录OFC的单个神经元和局部场电位 和CN，在清醒状态下，行为正常的动物被训练来执行RL任务。我们将特别关注 区域间LFP的时空动力学，以及它们与局部神经元计算的关系。我们 将通过应用特定频率来测试OFC theta在额纹状体沟通中的因果作用 微刺激OFC的同时记录CN的神经活动。最后，我们将确定 我们是否可以使用“闭环”控制来操纵RL过程，在这种控制中，我们使用神经测量 OFC theta控制微刺激在CN中的应用。综上所述，这项提议的结果将 为OFC和CN在RL中的作用提供趋同的相关和因果证据，以及确定 这两个地区进行交流的机制。此外，它还将为未来的BMI奠定基础 方法侧重于额叶边缘干预，以操纵适应不良的联系。

Project summary Many neuropsychiatric disorders, including obsessive-compulsive disorder, mood disorders and addiction, involve compromised evaluative and decision-making processes, and maladaptive learned associations. A computational framework that has proven useful in reconciling these different clinical symptoms is reinforcement learning (RL), which dictates how to optimally interact with the environment to maximize potential benefits and mitigate negative consequences. Work over the past several decades has revealed that frontostriatal brain circuits are key mechanistic components of RL. However, the precise nature of the interaction between the frontal cortex and the striatum, and how this communication is achieved, remains unclear. The current proposal focuses on orbitofrontal cortex (OFC) and the caudate nucleus (CN). OFC assigns values to stimuli in our environment, which enables us to make optimal decisions. However, it contains little information about potential motor responses. Our hypothesis is that OFC transfers value information to CN, where it in can be used to select the choice response that will lead to the highest value outcome. We hypothesize that this communication occurs via a phase reset of the local field potential in the theta band at the time of the choice. To test this hypothesis, we will simultaneously record both single neurons and local field potentials from OFC and CN in awake, behaving animals trained to perform an RL task. We will particularly focus on the spatiotemporal dynamics in LFPs between regions, and their relationship to local neuronal computations. We will test the causal role of OFC theta in enabling frontostriatal communication by applying frequency specific microstimulation to OFC while simultaneously recording neural activity in CN. Finally, we will determine whether we can manipulate RL processes using `closed-loop' control, in which we use neural measurements of OFC theta to control the application of microstimulation to CN. Taken together, the results of this proposal will provide convergent correlative and causal evidence for the role of OFC and CN in RL, as well as determine the mechanism by which the two areas communicate. In addition, it will lay the groundwork for future BMI approaches focused on frontolimbic interventions to manipulate maladaptive associations.