Contribution of non-canonical dopamine pathways to model-based learning

非典型多巴胺通路对基于模型的学习的贡献

• 批准号: 10607923
• 负责人: Melissa Sharpe
• 金额: $ 58.23万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2028-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10607923
• 关键词: Adaptive Behaviors; Address; Algorithms; Amygdaloid structure; Automobile Driving; Basic Science; Behavior; Behavior assessment; Behavioral; Brain region; Clinical; Coupled; Cues; Data; Decision Making; Desire for food; Development; Diagnosis; Dissection; Distal; Dopamine; Emotional; Environment; Event; Exposure to; Fright; Functional disorder; Future; Genetic; Goals; Hypothalamic structure; Individual; Knowledge; Lateral; Learning; Link; Measurement; Mediating; Memory; Mental disorders; Methods; Midbrain structure; Modeling; Modernization; Neural Pathways; Neurobehavioral Manifestations; Neurons; Neurosciences; Optics; Outcome; Pathologic; Pathway interactions; Phase; Policies; Process; Psyche structure; Psychological reinforcement; Research; Rewards; Role; Sensory; Signal Transduction; Stimulus; Structure; Substance Use Disorder; System; Techniques; Testing; Ventral Tegmental Area; Work; catalyst; cell type; directional cell; dopaminergic neuron; feeding; flexibility; innovation; interest; model development; neural; neural circuit; next generation; novel; optical imaging; optogenetics; prevent; prospective; response; sensor; theories; tool

PROJECT SUMMARY Model-based learning affords individuals the ability to contemplate the specific outcomes of actions or events. This facilitates flexible decision making. While we know of brain regions that contribute to model-based learning, the wider pathways and circuits that facilitate development of these flexible representations in these regions are less explored. Given that substance use disorders are characterized by deficits in model-based decision making, a gap in the knowledge of the neural circuits contributing to model-based learning prevents us from making clinical advances in the treatment of these deficits. The overarching goal of this proposal is, thus, to expose the neural circuits that mediate model-based decision making. Recent evidence from our team and others has implicated ventral tegmental area dopamine neurons (VTADA) as critical to driving model-based learning. This was surprising because phasic VTADA activity was typically restricted to assigning general value to cues, which prevents this signal from contributing to more flexible associative relationships characterizing model-based learning. This work acts as our catalyst to investigate how this dopamine signal is used in the circuits necessary for model-based learning. We are particularly interested in the dopamine pathways to the basolateral amygdala (VTADABLA) and lateral hypothalamus (VTADALH). We have shown that BLA and LH are important for the development of model-based associations. However, while the BLA and LH both contribute to model-based learning about cues proximal to rewards, the function of these regions diverge when it comes to more distal predictors. Specifically, the BLA remains important for using distal predictors to predict rewards, while the LH opposes learning about distal predictors. It is unknown how VTADA projections to BLA or LH facilitate reinforcement learning generally, or model-based learning specifically. Thus, we hypothesize that midbrain dopamine projections to the BLA and LH mediate the encoding of detailed model-based associative memories that allow prioritization of information most relevant to rewards. Capitalizing on the overlapping and complementary expertise and perspectives from two labs, we will uncover the function of these two non-canonical dopamine circuits in model-based learning. We will use a symmetrical and multifaceted approach using modern cell-type and projection-specific manipulation and recording techniques in the context of sophistical behavioral tasks to reveal the function VTADA projections to BLA and LH in proximal and distal learning. We will use cell-type and projection-specific optogenetic inhibition, stimulation, and recording of the VTADABLA and VTADALH pathways to expose the role of these pathways. We will use next-generation dopamine sensors to provide novel measurements of dopamine release in BLA and LH. Finally, we chemogenetically inhibit VTADA projections to BLA or LH while optically imaging BLA or LH neuronal activity to elucidate the contribution of dopamine input to learning- and decision-related activity.

项目总结 基于模型的学习使个人能够思考行动或事件的具体结果。这 促进灵活的决策制定。虽然我们知道大脑区域对基于模型的学习有贡献，但 在这些区域中促进这些灵活表示的发展的更宽的路径和电路较少 探索过了。鉴于物质使用障碍的特点是在基于模型的决策制定方面存在缺陷， 在神经回路的知识中，有助于基于模型的学习阻止了我们进行临床 这些缺陷的治疗进展。因此，这一提议的首要目标是暴露神经 调解基于模型的决策的电路。 来自我们团队和其他人的最新证据表明，腹侧被盖区多巴胺神经元(VTADA) 作为推动基于模型的学习的关键。这是令人惊讶的，因为相性VTADA活动通常是 仅限于为提示赋值，这阻止了此信号对更灵活的 联想关系是基于模型的学习的特征。这项工作充当了我们的催化剂，研究如何 这种多巴胺信号被用于基于模型的学习所需的电路中。我们特别感兴趣的是 杏仁基底外侧核(VTADABla)和下丘脑外侧核(VTADALh)的多巴胺通路。我们 已经表明血乳酸和黄体生成素对于基于模型的协会的发展是重要的。然而，虽然 血乳酸和黄体生成素都有助于基于模型的学习，学习奖赏附近的线索，这些区域的功能 当涉及到更远端的预测指标时，就会出现分歧。具体地说，血乳酸对于使用远端预测指标仍然很重要 来预测奖励，而黄体生成素反对学习远端预测因子。目前尚不清楚VTADA如何预测 BLA或LH一般促进强化学习，或具体地促进基于模型的学习。因此，我们假设 中脑多巴胺投射到血乳酸和黄体生成素参与了详细的基于模型的联想的编码 允许对与奖励最相关的信息进行优先排序的记忆。 利用来自两个实验室的重叠和互补的专业知识和观点，我们将揭示 这两个非规范的多巴胺回路在基于模型的学习中的作用。我们将使用对称的AND 使用现代细胞类型和投影特定的操作和记录技术的多方面方法 智力行为任务的背景以揭示VTADA投射到BLA和LH近端和远端的功能 学习。我们将使用细胞类型和投射特异性的光遗传抑制、刺激和记录 VTADABla和VTADALh通路揭示这些通路的作用。我们将使用下一代 多巴胺传感器，提供血乳酸和黄体生成素中多巴胺释放的新测量。最后，我们 化学遗传学抑制VTADA向BLA或LH的投射，同时光学成像BLA或LH神经元的活动 阐明多巴胺输入对学习和决策相关活动的贡献。