Orbitofrontal modulation of dopamine during value-based decision-making

项目摘要 多巴胺是一种重要的神经调质，介导从先前结果中学习（“回顾性”） 学习）通过编码奖励预测错误-经验和预期奖励之间的差异。 然而，最近的研究表明，多巴胺可能使用前额叶皮层编码更抽象的信息， 预测错误，例如关于任务或环境的隐藏状态的错误。确切的电路机制 这些抽象的隐藏状态预测错误的基础仍然不清楚。这项建议有两个主要目标。 首先，我将描述与隐藏状态推理相关的多巴胺活动，在大鼠执行一项任务时， 可观察的国家其次，我将确定产生多巴胺状态预测错误的电路机制 在这项任务中。我将使用计算机建模和最先进的遗传和病毒工具，包括纤维 光度法来测量多巴胺活性和前额皮质的投射特异性化学基因沉默， 实现这些目标。我将在细胞体钙动力学水平上测量多巴胺活性， 在轴突释放的水平上，它可以被分离。该建议将描述多区域神经 这些回路是获取和维护环境抽象表征的基础。结果 将提供深入了解神经精神疾病的病理学和治疗，其特征在于 扰乱了奖励流程 我在纽约大学（NYU）的共同赞助人，克莉丝汀·君士坦丁堡博士和保罗·格里姆彻博士， 在大鼠行为和系统神经科学实验中的免费经验，以及计算 决策模型，分别。我将接受的培训将使我能够追求真正的融合 实验与理论密切相关的研究。强大的，协作的 纽约大学的环境使它成为我追求这些研究目标的理想场所。我的培训计划提供了一个 从具有广泛、成熟专业知识的共同导师团队获得必要技能的详细策略 在相关技术中。技术培训，以及频繁的数据演示，专业人员的出席 课程，研讨会和会议，以及我的写作和领导技能的发展将使我能够 完成研究计划，并在我感兴趣的领域过渡到博士后职位。

Project Summary Dopamine is an important neuromodulator that mediates learning from previous outcomes (“retrospective” learning) by encoding reward prediction errors — the difference between experienced and expected rewards. However, recent work has suggested that dopamine might use the prefrontal cortex to encode more abstract prediction errors, such as errors about the hidden state of a task or environment. The exact circuit mechanisms underlying these abstract hidden-state prediction errors remains unclear. This proposal has two major goals. First, I will characterize dopamine activity related to hidden-state inference in rats performing a task with partially observable states. Second, I will identify the circuit mechanisms that generate dopamine state prediction errors in this task. I will use computational modeling and state-of-the-art genetic and viral tools, including fiber photometry to measure dopamine activity and projection-specific chemogenetic silencing of prefrontal cortex, to address these goals. I will measure dopamine activity both at the level of cell-body calcium dynamics, as well as at the level of axonal release, which can be dissociated. This proposal will describe the multi-regional neural circuits that underlie the acquisition and maintenance of abstract representations of the environment. The results will provide insight into the pathology and treatment of neuropsychiatric disorders, which are characterized by disrupted reward processing. My co-sponsors at New York University (NYU), Dr. Christine Constantinople and Dr. Paul Glimcher, have complimentary experience in behavioral and systems neuroscience experiments in rats, and computational modeling of decision-making, respectively. The training I will receive will allow me to pursue truly integrative research that involves the close interplay between experiments and theory. The strong, collaborative environment at NYU makes it an ideal place for me to pursue these research goals. My training plan provides a detailed strategy for acquiring the necessary skills from a team of co-mentors with extensive, proven expertise in the relevant techniques. Technical training, as well as frequent data presentations, attendance of professional courses, seminars, and conferences, and development of my writing and leadership skills will equip me to complete the proposed research, and transition to a post-doctoral position in my field of interest.