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能够思考行动或事件的具体结果。这 便于灵活决策。虽然我们知道大脑区域有助于基于模型的学习， 在这些地区，促进这些灵活表征发展的更广泛的途径和电路较少 探讨了鉴于物质使用障碍的特点是缺乏基于模型的决策， 在神经回路的知识有助于基于模型的学习阻止我们作出临床 这些缺陷的治疗进展。因此，这项提议的首要目标是， 调节基于模型的决策的电路。 我们团队和其他人最近的证据表明腹侧被盖区多巴胺神经元（VTADA） 对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活性通常是 局限于赋予一般价值的线索，这阻止了这个信号有助于更灵活的 关联关系表征基于模型的学习。这项工作作为我们的催化剂来研究如何 该多巴胺信号用于基于模型的学习所必需的电路中。我们特别感兴趣的是 基底外侧杏仁核（VTADA BLA）和外侧下丘脑（VTADA LH）的多巴胺通路。我们 已经表明，BLA和LH是重要的发展模型为基础的协会。然而，虽然 BLA和LH都有助于基于模型的学习接近奖励的线索，这些区域的功能 当涉及到更远的预测因素时会产生分歧。具体而言，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。目前尚不清楚VTADA预测如何 BLA或LH通常促进强化学习，或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介导了详细的基于模型的联想编码， 这些记忆允许优先考虑与奖励最相关的信息。 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 这两个非典型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， 多方面的方法，使用现代细胞类型和投影特定的操纵和记录技术， 背景下的sophistically行为任务，以揭示功能VTADA的投射到BLA和LH的近端和远端 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 β-BLA和VTADA β-LH通路，以揭示这些通路的作用。我们将使用下一代 多巴胺传感器，以提供新的测量多巴胺释放的BLA和LH。最后我们 化学发生抑制VTADA投射到BLA或LH，同时光学成像BLA或LH神经元活性， 阐明多巴胺输入对学习和决策相关活动的贡献。