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

基本信息

批准号：
10580719
负责人：
Horng-An Edward Nieh
金额：
$ 15.36万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10580719
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

项目摘要

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了解人口代码可以帮助我们更新更好地了解物质使用障碍 (SUD) 背后的复杂机制。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 刺激可以引起从喂养到社交互动的各种行动，这提出了使用最新非线性的项目。降维技术提取代表人口活动的低维流形然后可以使用这些流形以几何方式组织异质单个神经元活动的模式。实现该项目的主要目标——区分自然奖励寻求的 LH 神经群体编码此外，新的计算建模方法也将得到应用。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。