EM Core

电磁核心

基本信息

批准号：
10241481
负责人：
Jeff W Lichtman
金额：
$ 57.38万
依托单位：
HARVARD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-25 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10241481
关键词：
3-Dimensional Atlases Axon Behavior Brain Cell model Cells Communities Comprehension Computer Assisted Computer software Consumption Custom Data Data Set Decision Making Development Electron Microscope Electron Microscopy Electron Microscopy Facility Feedback Fishes Fluorescence Functional Imaging Generations Goals Grant High Performance Computing Human Human Resources Illusions Image Infrastructure Lateral Learning Manuals Methods Microtomy Modeling Nervous System Physiology Nervous system structure Neurons Neurosciences Physiological Procedures Process Production Research Research Personnel Resolution Roentgen Rays Sampling Speed Standardization Structure Synapses Technology Testing Thick Time Training X ray microscopy Zebrafish automated segmentation base cell type cluster computing design image registration innovation insight interest live cell imaging machine vision microCT model building nanometer nanometer resolution nanoscale neural circuit programs relating to nervous system theories tool user-friendly

项目摘要

EM Core - Abstract While the neural circuits that underlie behavior are of interest to all of the investigators on this grant (and a substantial part of the entire neuroscience community), there have been very few technical approaches that actually provide this kind of information across all levels at which circuits function, including the level of synaptic connections. This electron microscopy core is explicitly designed to provide the “wiring diagrams” of neural circuits in an efficient way. Much of our effort over the past 5 years has been to transform serial electron microscopy of large volumes (such as the fish nervous system) from a heroic to a more mundane enterprise. This transformation required innovations in hardware and software to abbreviate all the time-consuming steps in the connectomic pipeline. In particular we: 1) automated ultra-thin sectioning (using a tape-based approach), 2) automated image acquisition (using a custom multibeam serial electron microscope), 3) automated stitching and registration of the image data on high performance computing clusters, 4) automated segmentation of neurons and synapses on a GPU cluster, and 5) semi-automated proofreading and rendering of the neural circuits with custom software. Because of these developments, we can routinely collect tens of thousands of sections losslessly at 30 nm thickness and acquire images of them at lateral resolutions of 4 x 4 nanometers. This voxel size (480 nm3 ) provides enough detail for human or machine vision methods to trace out the finest aspects of neural connectivity. Obtaining this information about neural circuits is relevant inasmuch as it provides insight into circuit function. Hence the tremendous benefit of doing electron microscopy on functionally imaged samples - a main goal of this proposal. Acquiring these circuits is also relevant if neuronal connectivity can be associated with cells of particular types, hence the significant benefit of doing analysis of cell types that have been defined in the fish atlas associated with this proposal. Finally, these circuit diagrams provide ground truth for testing and refining computational theories of brain function, another important prong of this proposal. Because of the speed of the EM Core approaches, we have the ability to acquire datasets of many different fish that each have been used in particular experimental or live-cell imaging contexts. The overarching goal being to provide synaptic level structural information for all research questions where such detailed data can enhance our comprehension of the way fish behavior is instantiated in its nervous system.

EM 核心 - 摘要虽然这项资助的所有研究人员都对行为背后的神经回路感兴趣（并且很大一部分整个神经科学界），实际上很少有技术方法能够提供这种电路功能的各个级别的信息，包括突触连接的级别。显微镜核心的明确设计是为了以有效的方式提供神经回路的“接线图”。过去 5 年的努力一直致力于改造大体积的串行电子显微镜（例如鱼神经系统）从一个英雄企业到一个更平凡的企业，这种转变需要硬件和软件的创新。为了缩短连接组管道中所有耗时的步骤，我们特别是：1）自动化超薄。切片（使用基于磁带的方法），2）自动图像采集（使用定制的多束串行电子显微镜），3）在高性能计算集群上自动拼接和配准图像数据，4） GPU 集群上神经元和突触的自动分割，以及 5) 半自动校对和渲染由于这些发展，我们可以定期收集数以万计的神经回路。无损切片厚度为 30 nm，并以 4 x 4 纳米的横向分辨率获取它们的图像。尺寸（480 nm3）为人类或机器视觉方法提供了足够的细节，以追踪神经网络的最精细方面获取有关神经回路的信息是相关的，因为它可以提供对神经回路的深入了解。因此，对功能成像样品进行电子显微镜检查具有巨大的好处——这是其主要目标。提议。如果神经连接可以与特定类型的细胞相关联，那么获得这些电路也是相关的，因此对与本提案相关的鱼类图谱中定义的细胞类型进行分析具有显着的好处。最后，这些电路图为测试和完善大脑功能的计算理论提供了基础事实，该提案的另一个重要方面是由于 EM Core 方法的速度，我们有能力获取。许多不同鱼类的数据集，每种鱼类都已在特定的实验或活细胞成像环境中使用。总体目标是为所有研究问题提供突触级结构信息，其中此类详细数据可以增强我们对鱼类行为在其神经系统中实例化方式的理解。