Neural circuit mechanisms of drug-context associations in the hippocampus

海马区药物关联的神经回路机制

• 批准号: 10723049
• 负责人: Yanjun Sun
• 金额: $ 15.13万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723049
• 关键词: Advanced Development; Affect; Animal Behavior; Animal Model; Association Learning; Behavioral; Brain; Brain region; Cells; Clinical; Code; Cognition; Cognitive; Color; Communication; Computer Models; Data; Development; Drug Targeting; Drug usage; Exposure to; Genetic; Goals; Head; Hippocampal Formation; Hippocampus; Image; Imaging Device; Individual; Intervention; Ketamine; Knowledge; Knowledge acquisition; Label; Learning; Maps; Mediating; Memory; Methods; Modeling; Molecular; Morphine; Mus; Nature; Negative Reinforcements; Neurons; Opioid; Output; Pathologic; Perception; Pharmaceutical Preparations; Positioning Attribute; Property; Public Health; Research; Retrieval; Rewards; Running; Speed; Substance Use Disorder; Testing; Therapeutic; Training; Withdrawal; cell type; conditioned place preference; conditioning; drug craving; drug relapse; drug reward; drug seeking behavior; drug withdrawal; high dimensionality; human model; in vivo; in vivo imaging; insight; machine learning method; neural; neural circuit; neural model; neuroimaging; novel; novel therapeutics; opioid withdrawal; programs; prolonged abstinence; psychostimulant; response; skill acquisition; substance use treatment; tool; virus genetics

PROJECT ABSTRACT Addictive drugs usurp the normal neural machinery for learning and memory to generate pathological cognition that can lead to compulsive drug usage. One prominent example is re-exposure to a drug-associated environmental context, which robustly induces drug relapse in both humans and animal models. The hippocampal formation, which is critical for spatial and contextual learning, is well positioned to support the encoding of this type of drug-context association. Despite decades of hippocampal studies on drug-evoked molecular and cellular adaptations and drug-seeking behaviors, we still lack a clear understanding of which hippocampal circuits are involved in acquiring and maintaining maladapted drug-context associations and how neural dynamics in the hippocampus are transformed to support drug-seeking behavior. Moreover, there are no interventions that specifically target the drug-associated memories to treat substance use disorders. Here, with the proposed training in computational modeling for neural dynamics and the development of advanced genetic and imaging tools, I aim to fill these knowledge gaps by elucidating the neural circuit mechanisms in the hippocampus for drug-context associations and probing whether we can reverse this association using a memory-based intervention. Preliminary data suggest opioid reward vs. withdrawal-mediated associative learning have distinct effects on representing different spatial variables in CA1 neurons and ketamine was able to reset the maladapted contextual representation to disrupt the retrieval of drug-associated memories. For Aim 1, I will investigate how drug-associated information alters the neural coding in the hippocampus for multiple spatial variables that are critical for the perception of a given context. Using miniscope imaging in morphine conditioned place preference/aversion, I will learn to build linear-nonlinear Poisson (LNP) models to reveal how drug-context associations under positive vs. negative reinforcement affect the neural coding of CA1 for position, head orientation, running speed and their conjunctions. For Aim 2, I will test the hypothesis that Ketamine disrupts learned drug-context associations by restoring the maladapted representations of functional cell types (e.g., place cells) to their normal state. I will acquire expertise on opioid withdrawal and investigate ketamine’s effect on withdrawal-context associations by targeting memory reconsolidation and reveal the corresponding change in neural dynamics of CA1. For Aim 3, I will elucidate neural circuit assembly and dynamics for coding drug-associated contextual information in the subiculum, a major downstream target of the hippocampal CA1. This study will leverage my training in Aim 1 and 2 to advance our understanding of the principles for processing drug-associated information in the brain. Together, the proposed training and studies will not only help me to establish an independent research program but also provide a mechanistic understanding of how hippocampal neurons encode and represent drug-associated contextual information and shed light on developing novel therapeutic treatments for substance use disorders.

项目摘要 成瘾药物侵占学习和记忆的正常神经机制，产生病态认知 这可能会导致强迫性药物使用。一个突出的例子是再次接触与药物相关的药物。 环境背景，在人类和动物模型中都会强烈诱导药物复发。这 海马体的形成对于空间和情境学习至关重要，它能够很好地支持 这种类型的药物-背景关联的编码。尽管对药物诱发的海马体研究已有数十年 分子和细胞适应以及药物寻求行为，我们仍然缺乏清楚的了解 海马回路参与获取和维持不适应的药物环境关联以及如何 海马体中的神经动力学发生转变以支持寻求药物的行为。此外，没有 专门针对与药物相关的记忆来治疗药物滥用障碍的干预措施。在这里，与 拟议的神经动力学计算模型培训和高级遗传的发展 和成像工具，我的目标是通过阐明神经回路机制来填补这些知识空白 海马体进行药物与环境的关联，并探讨我们是否可以使用 基于记忆的干预。初步数据表明阿片类药物奖励与戒断介导的联想 学习对代表 CA1 神经元的不同空间变量有明显的影响，氯胺酮能够 重置不适应的情境表征，从而破坏与药物相关的记忆的检索。为了目标 1，我将研究药物相关信息如何改变海马体中的多种神经编码 对于给定上下文的感知至关重要的空间变量。在吗啡中使用微型显微镜成像 条件性地点偏好/厌恶，我将学习构建线性非线性泊松（LNP）模型来揭示如何 正强化和负强化下的药物-情境关联会影响 CA1 位置的神经编码， 头部方向、跑步速度及其结合。对于目标 2，我将检验以下假设：氯胺酮 通过恢复功能细胞类型的不适应表征来破坏习得的药物与背景的关联 （例如，将细胞）恢复到正常状态。我将获得有关阿片类药物戒断的专业知识并研究氯胺酮的 通过针对记忆重新巩固对戒断-情境关联的影响，并揭示相应的 CA1 神经动力学的变化。对于目标 3，我将阐明神经回路组装和编码动力学 下托中与药物相关的背景信息，下托是海马 CA1 的主要下游目标。 这项研究将利用我在目标 1 和 2 中的培训来加深我们对处理原理的理解 大脑中与药物相关的信息。总之，拟议的培训和学习不仅会帮助我 建立一个独立的研究计划，同时也提供对海马如何机制的理解 神经元编码并表示与药物相关的上下文信息，并为开发新的药物提供了线索 物质使用障碍的治疗方法。