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.

项目概要 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 有利于灵活决策。虽然我们知道有助于基于模型的学习的大脑区域， 促进这些地区灵活代表发展的更广泛途径和回路较少 探索过。鉴于物质使用障碍的特点是基于模型的决策存在缺陷，因此存在差距 对基于模型的学习的神经回路的了解阻止了我们进行临床研究 治疗这些缺陷的进展。因此，该提案的总体目标是揭示神经网络 调解基于模型的决策的电路。 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） 对于推动基于模型的学习至关重要。这是令人惊讶的，因为阶段性 VTADA 活动通常是 仅限于为提示分配一般值，这阻止了该信号有助于更灵活 表征基于模型的学习的关联关系。这项工作是我们研究如何 该多巴胺信号用于基于模型的学习所需的电路中。我们特别感兴趣的是 多巴胺通路通往基底外侧杏仁核（VTADABLA）和外侧下丘脑（VTADALH）。我们 已经表明 BLA 和 LH 对于基于模型的关联的发展非常重要。然而，虽然 BLA 和 LH 都有助于基于模型的学习，了解接近奖励的线索以及这些区域的功能 当涉及到更远端的预测变量时，会出现分歧。具体来说，BLA 对于使用远端预测因子仍然很重要 预测奖励，而 LH 反对学习远端预测因子。目前尚不清楚 VTADA 如何预测 BLA 或 LH 通常有助于强化学习，特别是基于模型的学习。因此，我们假设 中脑多巴胺投射到 BLA 和 LH 介导基于模型的详细联想的编码 允许优先考虑与奖励最相关的信息的记忆。 利用两个实验室重叠和互补的专业知识和观点，我们将发现 这两个非规范多巴胺回路在基于模型的学习中的功能。我们将使用对称和 使用现代细胞类型和投影特定操作和记录技术的多方面方法 复杂的行为任务背景，揭示近端和远端 BLA 和 LH 的 VTADA 功能预测 学习。我们将使用细胞类型和投射特异性的光遗传学抑制、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 BLA 和 LH 中多巴胺释放的新测量。最后，我们 化学遗传学抑制 VTADA 对 BLA 或 LH 的投射，同时对 BLA 或 LH 神经元活动进行光学成像 阐明多巴胺输入对学习和决策相关活动的贡献。