Novel Multi-Depth Two-Photon Microscope for Measuring Neuronal Network Plasticity

用于测量神经元网络可塑性的新型多深度双光子显微镜

• 批准号: 10058191
• 负责人: Matthew Shtrahman
• 金额: $ 59.13万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10058191
• 关键词: Age; Alzheimer' s Disease; Auditory; Behavior; Behavioral; Brain; Brain region; Calcium; Cells; Cerebral cortex; Cognition; Detection; Disease; Environment; Episodic memory; Equilibrium; Esthesia; Fire - disasters; Fluorescence; Frequencies; Head; Hippocampus (Brain); Image; Impairment; Label; Lasers; Learning; Learning Disorders; Left; Maps; Measures; Memory; Memory impairment; Microscope; Mus; Neurons; Neurosciences; Optics; Pathology; Pattern; Performance; Play; Population; Process; Pyramidal Cells; Resolution; Rewards; Running; Sampling; Scanning; Sensory; Source; Spottings; Stream; Techniques; Time; Training; Visual; adaptive optics; calcium indicator; computer studies; data streams; dentate gyrus; design; deviant; entorhinal cortex; experience; experimental study; granule cell; imaging system; improved; in vivo; insight; memory encoding; memory recall; novel; place fields; recruit; spatiotemporal; treadmill; two-photon

PROJECT SUMMARY Faced with a continuous stream of data, the hippocampus must strike a balance between forming new, discrete memories (pattern separation) and generalizing information across similar experiences (pattern completion). Computational and experimental studies over the past 50 years have begun to delineate the hippocampal circuits involved in these processes, yet a fundamental question is still unanswered: How do the different subregions within the hippocampus integrate time-varying information during experience to form episodic memories? Whether exploring a novel environment, scanning a visual scene, or recognizing a familiar tune, the sensations used to construct and recall memories are not experienced simultaneously but rather as a dynamic stream of information or temporal pattern. Our proposal seeks to understand how the hippocampus, and in particular the dentate gyrus (DG) and CA1 subfields, integrate time-varying information during learning to perform temporal pattern separation and completion. Understanding these mechanisms will provide crucial insights into why our ability to learn and remember declines with age and how hippocampal pathology leads to significant memory impairment in disorders such as Alzheimer’s disease. In order to understand how different subregions process temporal information and to fully capture learning in real time, one must be able to measure activity within two or more networks simultaneously with single cell resolution. In principle, this can be accomplished using two-photon calcium imaging of distinct neuronal populations labeled with green and red fluorescence indicators respectively. However, many circuits in the mammalian brain, including the hippocampus, are laminar where networks are separated by hundreds of microns or more in depth. Microscope objectives are optimized to image only a single focal plane, and optical aberrations severely degrade the laser excitation spot when large axial displacements of ~100 µm or greater from the objective’s ideal focal plane are introduced. To overcome this challenge, we propose to construct a novel two photon imaging system that utilizes both remote focusing and adaptive optics to focus two laser sources with distinct excitation wavelengths at different arbitrary depths. This novel two-photon microscope will allow us to record neuronal activity within both the CA1 and deeper DG subfields simultaneously for the first time, investigating how these subregions process temporal information and modify network activity during learning

项目总结 面对源源不断的数据流，海马体必须在形成新的、 离散记忆(模式分离)和跨相似体验的概括信息(模式 完成)。在过去的50年里，计算和实验研究开始描绘出 然而，一个基本的问题仍然没有得到回答：海马区是如何 海马体内的不同亚区在体验过程中整合时变信息以形成 情节记忆？无论是探索新环境，扫描视觉场景，还是识别熟悉的 曲调，用来构建和回忆记忆的感觉不是同时体验的，而是作为一种 动态信息流或时间模式。我们的提案试图了解海马体是如何， 特别是齿状回(DG)和CA1亚区，在学习过程中整合时变信息 执行时间模式分离和完成。了解这些机制将提供至关重要的 洞察为什么我们的学习和记忆能力随着年龄的增长而下降，以及海马区的病理如何导致 阿尔茨海默病等疾病的严重记忆障碍。 为了了解不同的子区域如何处理时间信息并完全捕获学习 实时地，人们必须能够用单个单元同时测量两个或更多网络内的活动 决议。原则上，这可以通过对不同神经元的双光子钙成像来完成。 分别用绿色和红色荧光指示器标记的种群。然而，世界上的许多赛道 哺乳动物的大脑，包括海马体，是层状的，其中的网络被数百微米隔开 或者更深入一些。显微镜物镜经过优化，只成像一个焦平面，光学像差 当激光的轴向位移为~100微米或更大时，激光激发光斑严重退化 介绍了物镜的理想焦平面。为了克服这一挑战，我们建议构建一个新的两个 光子成像系统，利用远程聚焦和自适应光学来聚焦两个具有 在不同的任意深度有不同的激发波长。这种新型的双光子显微镜将使我们能够 首次同时记录CA1和更深的DG亚区内的神经元活动， 研究这些子区域在学习过程中如何处理时间信息和修改网络活动