Orbitofrontal modulation of dopamine during value-based decision-making

基于价值的决策过程中多巴胺的眶额调节

• 批准号: 10607543
• 负责人: Andrew Mah
• 金额: $ 3.65万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10607543
• 关键词: Action Potentials; Address; Animals; Axon; Behavior; Behavioral; Behavioral Model; Brain; Calcium; Cells; Cognitive; Computer Models; Corpus striatum structure; Data; Decision Making; Development; Dissociation; Dopamine; Elements; Environment; Fiber; Future; Genetic; Goals; Interneurons; Knowledge; Leadership; Learning; Link; Machine Learning; Maintenance; Measures; Mediating; Mentors; Midbrain structure; Modeling; Neuromodulator; Neurosciences; New York; Nucleus Accumbens; Outcome; Pathology; Photometry; Play; Positioning Attribute; Postdoctoral Fellow; Prefrontal Cortex; Presynaptic Terminals; Psyche structure; Psychology; Rattus; Research; Rewards; Role; Signal Transduction; Structure; System; Techniques; Training; Universities; Viral; Work; Writing; base; calcium indicator; cholinergic; collaborative environment; design; designer receptors exclusively activated by designer drugs; dopaminergic neuron; expectation; experience; experimental study; improved; insight; interest; neural circuit; neuropsychiatric disorder; novel; relating to nervous system; reward processing; sensor; skills; symposium; theories; tool

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.

项目概要 多巴胺是一种重要的神经调节剂，可以介导从以前的结果中学习（“回顾性” 学习）通过编码奖励预测错误——经验奖励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皮质来编码更抽象的信息。 预测错误，例如有关任务或环境的隐藏状态的错误。精确的电路机制 这些抽象隐藏状态预测错误的背后仍不清楚。该提案有两个主要目标。 首先，我将描述与执行部分任务的大鼠的隐藏状态推理相关的多巴胺活动。 可观察的状态。其次，我将确定产生多巴胺状态预测错误的电路机制 在这个任务中。我将使用计算模型和最先进的遗传和病毒工具，包括纤维 光度测定法测量多巴胺活性和前额皮质投射特异性化学遗传学沉默， 解决这些目标。我将在细胞体钙动态水平以及 在轴突释放水平，可以解离。该提案将描述多区域神经网络 获取和维护环境抽象表示的电路。结果 将深入了解神经精神疾病的病理学和治疗，其特点是 奖励处理中断。 我在纽约大学 (NYU) 的共同发起人 Christine Constantinople 博士和 Paul Glimcher 博士 大鼠行为和系统神经科学实验以及计算方面的免费经验 分别对决策进行建模。我将接受的培训将使我能够追求真正的综合性 涉及实验和理论之间密切相互作用的研究。强者、协作者 纽约大学的环境使其成为我追求这些研究目标的理想场所。我的训练计划提供了 从具有广泛、经过验证的专业知识的共同导师团队中获取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、专业人士的出席 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 完成拟议的研究，并过渡到我感兴趣的领域的博士后职位。