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.

项目摘要 基于模型的学习使个人有能力考虑行动或事件的具体结果。这 促进灵活的决策。尽管我们知道有助于基于模型的学习的大脑区域，但 促进这些区域中这些灵活表示的更广泛的途径和电路较少 探索。鉴于物质使用障碍的特征是在基于模型的决策中定义，所以 在了解有助于基于模型的学习的神经元电路中，我们无法进行临床 治疗这些缺陷的进展。因此，该提案的总体目标是公开中立 介导基于模型的决策的电路。 我们团队和其他人的最新证据已经实施了通风区多巴胺神经元（VTADA） 对于基于驾驶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 仅限于将一般价值分配给提示，这阻止了该信号对更灵活的贡献 相关关系表征基于模型的学习。这项工作是我们研究如何的催化剂 该多巴胺信号用于基于模型的学习所需的电路。我们对 通往Basolatelar杏仁核（VTADABLA）和下丘脑（VTADALH）的多巴胺途径。我们 已经表明，BLA和LH对于开发基于模型的关联很重要。但是， BLA和LH都有助于基于模型的学习提示代理奖励，这些区域的功能 当涉及到更多的盘式预测指标时，会发出分歧。具体而言，BLA对于使用圆盘预测变量仍然很重要 为了预测奖励，而LH反对学习识别预测指标。 VTADA如何投射到 BLA或LH最喜欢的强化学习通常是基于模型的学习。那我们假设 中脑多巴胺对BLA的预测和LH调解了基于详细模型的关联的编码 允许优先考虑与奖励最相关的信息的记忆。 利用两个实验室的重叠和完整的专业知识和观点，我们将发现 这两个非典型多巴胺电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异性操纵和记录技术的多方面方法 社会行为任务的背景，以揭示功能VTADA对BLA和LH的投影 学习。我们将使用细胞类型和投影特异性的光遗传学抑制，刺激和记录 VTADABLA和VTADA指示可以揭示这些途径的作用。我们将使用下一代 多巴胺传感器可提供BLA和LH中多巴胺释放的新测量。最后，我们 化学上抑制VTADA对BLA或LH的抑制，而光学成像BLA或LH神经元活性 阐明多巴胺输入对学习和决策有关的活动的贡献。