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 上瘾的药物篡夺了用于学习和记忆的正常神经机制，以产生病理认知 这可能导致强迫性毒品使用。一个突出的例子是重新曝光与药物相关 环境环境，可以强烈地引起人类和动物模型的药物缓解。 海马形成对于空间和上下文学习至关重要 这种类型的药物秘密关联的编码。尽管有数十年的海马研究 分子和细胞适应以及寻求毒品的行为，我们仍然缺乏清楚的了解 海马电路参与获取和维持不良的药物秘密关联以及如何 海马中的神经动力学被转化为支持寻求药物的行为。而且，没有 专门针对药物相关记忆以治疗药物使用障碍的干预措施。在这里，与 针对神经动力学的计算建模培训和高级遗传学的发展 和成像工具，我旨在通过阐明神经回路机制来填补这些知识差距 海马用于药物秘密关联，并探测我们是否可以使用A逆转这种关联 基于内存的干预。初步数据表明阿片类药物奖励与戒断介导的关联 学习对CA1神经元中的不同空间变量具有明显的影响，氯胺酮能够 重置不良的上下文表示以破坏与药物相关记忆的检索。目的 1，我将研究与药物相关信息如何改变海马中的神经编码 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Miniscope成像 有条件的地方偏好/厌恶，我将学会构建线性 - 非线性泊松（LNP）模型，以揭示如何 在正面和负强化下的药物膜下文关联会影响CA1的神经编码，以供位置， 头方向，跑步速度及其连接。对于AIM 2，我将检验氯胺酮的假设 干扰通过恢复功能细胞类型的不良表现来学习的药物秘密关联 （例如，将细胞）达到其正常状态。我将获得有关阿片类药物提取的专业知识，并调查氯胺酮 通过靶向记忆重新固定并揭示相应的戒断对撤回秘密关联的影响 CA1神经动力学的变化。对于AIM 3，我将阐明神经电路组件和用于编码的动态 与药物相关的上下文信息，这是海马CA1的主要下游靶标。 这项研究将利用我在目标1和2中的培训来促进我们对处理原理的理解 大脑中与药物相关的信息。拟议的培训和研究在一起，不仅会帮助我 建立独立的研究计划，但也提供了对海马的机械理解 神经元编码并表示与药物相关的上下文信息，并阐明了开发新颖的信息 药物使用障碍的治疗疗法。