Characterizing the underlying population code to understand the functional organization of the hippocampus and the lateral hypothalamus

表征潜在的群体代码以了解海马和下丘脑外侧的功能组织

• 批准号: 10828039
• 负责人: Horng-An Edward Nieh
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10828039
• 关键词: Affect; Algorithms; Anatomy; Animal Behavior; Animals; Appetitive Behavior; Area; Behavior; Behavioral; Brain; Calcium; Cells; Characteristics; Chronic; Cocaine; Code; Complex; Computer Models; Computing Methodologies; Data; Decision Making; Detection; Dimensions; Dorsal; Drug Addiction; Drug Modelings; Exhibits; Female; Food; Goals; Head; Heterogeneity; Hippocampus; Hypothalamic structure; Image; Imaging technology; Implant; Individual; Indwelling Catheter; Injections; Intervention; Lateral; Learning; Left; Light; Location; Measures; Methods; Modeling; Mus; Nature; Neurons; Noise; Optics; Pattern; Pharmaceutical Preparations; Phase; Population; Research; Resolution; Rewards; Rodent; Role; Self Administration; Sex Behavior; Sex Differences; Social Interaction; Substance Use Disorder; Technical Expertise; Techniques; Technology; Testing; Therapeutic Intervention; Training; cell type; cocaine seeking; drinking; drug seeking behavior; experimental study; feeding; high dimensionality; instrumentation; interest; lens; male; microscopic imaging; neural; neuromechanism; novel; optogenetics; place fields; programs; reward processing; sex; side effect; skills; targeted treatment; tool; two-photon; virtual reality

Project Summary/Abstract Recent advancements in neural recording/imaging technologies and computational methods have generated a renewed interest in studying coordinated population activity. Understanding the population code can help us better understand the complex mechanisms behind substance use disorders (SUD). One leading idea is that high-dimensional neural activity, such as simultaneous recordings from hundreds to thousands of cells, can lie on low-dimensional manifolds, such that a handful of latent variables can accurately describe the activity of all recorded neurons. The lateral hypothalamus (LH) is a brain area well-known for its functional diversity – individual cells respond with great heterogeneity to a wide range of appetitive behaviors, and LH stimulation can evoke a variety of actions ranging from feeding to social interaction. This project proposes to use the latest nonlinear dimensionality reduction techniques to extract the low-dimensional manifolds representing population activity patterns that geometrically organize the heterogeneous single neuron activity. These manifolds can then be used to achieve this project’s main goal – differentiating LH neural population encoding of natural reward-seeking behaviors and maladaptive drug-seeking behaviors. In addition, novel computational modeling methods will be used to perform unsupervised detection of internal neural states that guide animals switching between these two reward-seeking behaviors. Finally, state-of-the-art cellular-resolution simultaneous stimulation and imaging microscopy will be used to casually perturb animal behavior and/or neural activity patterns by activating sequences of neurons along trajectories on the low-dimensional manifolds. Importantly, Aims 1 and 2 support these goals by offering training in the use of the necessary computational and instrumentation techniques. Ultimately, results obtained from this project will advance our understanding of the neural mechanisms separating harmful drug-seeking behaviors and useful natural reward-seeking behaviors, such that SUD treatments with more precise targets can be developed that minimize unwanted side effects.

项目概要/摘要 神经记录/成像技术和计算方法的最新进展已经产生了 对研究协调的人口活动重新产生了兴趣。了解人口代码可以帮助我们 更好地了解物质使用障碍 (SUD) 背后的复杂机制。一个主导思想是 高维神经活动，例如数百到数千个细胞的同时记录，可能会谎言 在低维流形上，这样少数潜在变量就可以准确地描述所有的活动 记录的神经元。下丘脑外侧 (LH) 是一个以其功能多样性而闻名的大脑区域——个体 细胞对各种食欲行为的反应具有很大的异质性，LH 刺激可以引起 从喂食到社交互动等各种行为。本项目拟采用最新的非线性 降维技术提取代表人口活动的低维流形 以几何方式组织异质单个神经元活动的模式。然后可以使用这些歧管 实现该项目的主要目标——区分自然奖励寻求的 LH 神经群体编码 行为和适应不良的药物寻求行为。此外，新颖的计算建模方法将 用于对内部神经状态进行无监督检测，引导动物在这两种状态之间切换 寻求奖励的行为。最后，最先进的细胞分辨率同步刺激和成像 显微镜将用于通过激活来随意扰乱动物行为和/或神经活动模式 沿着低维流形上的轨迹的神经元序列。重要的是，目标 1 和 2 支持 通过提供使用必要的计算和仪器技术的培训来实现这些目标。 最终，该项目获得的结果将增进我们对神经机制的理解 将有害的药物寻求行为和有用的自然奖励寻求行为分开，例如 SUD 可以开发具有更精确目标的治疗方法，以最大限度地减少不必要的副作用。