EM CORE

EM核心

• 批准号: 10686981
• 负责人: Jeff W Lichtman
• 金额: $ 45.63万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-25 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10686981
• 关键词: Animals; Axon; Behavior; Behavioral; Biological Models; Brain; Cardiovascular system; Cells; Characteristics; Code; Collaborations; Consumption; Coupled; Data; Data Set; Dimensions; Education and Outreach; Electron Microscopy; Environment; Extracellular Space; Fishes; Fluorescence Microscopy; Generations; Goals; Grant; Heart; Human; Illusions; Image; Infrastructure; Label; Learning; Machine Learning; Manuals; Maps; Methods; Mind; Mission; Modeling; Modernization; Modification; Molecular; Morphologic artifacts; Motor; Motor output; Nervous System; Neural Interconnection; Neurons; Organ; Output; Oxygen; Pathway interactions; Phase; Physics; Process; Regulation; Research; Research Personnel; Resolution; Role; Scanning Electron Microscopy; Sensory; Series; Signal Transduction; Speed; Stains; Structure; Synapses; System; Techniques; Technology; Testing; Time; Tissues; Vertebrates; Zebrafish; analytical tool; automated segmentation; connectome; excitatory neuron; experience; image processing; improved; interest; light microscopy; microscopic imaging; millimeter; mind body interaction; nanoscale; natural language; neural; neural circuit; neuronal patterning; postsynaptic; presynaptic; psychobiology; query tools; sample fixation; sensory input; tool; two photon microscopy

Electron Microscopy Core The synaptic circuits that underlie vertebrate behavior are of interest to all of the investigators on this grant (and neuroscientists more generally). Generating neural circuits in zebrafish is the principal goal of this core. Getting this connectivity entails revealing four essential features of neural circuitry for each neuron: first, rendering its local input at sufficient resolution to detect all the impinging synaptic connections, second, identifying the sign of these connections, third, identifying the cells giving rise to this presynaptic input and fourth, identifying the postsynaptic cells innervated by this neuron's axon. Ideally, these requirements need to be met for all the neurons in one vertebrate brain because without this information, neuroscientists cannot accurately trace signals, synapse by synapse, from sensory input to motor output -- a profound impediment to deciphering of how a brain's structure relates to its behavioral repertoire. Owing to the rapid pace of technological and computational improvements, it is now feasible for the first time to create a whole vertebrate animal connectional map. The methods we will use automate a serial section electron microscopy pipeline so that high resolution (nanoscale) images can be acquired over large (millimeter scale) volumes. This core will use a modern serial-section multibeam scanning electron microscopy approach that we have been developing for about 10 years 1 2 3 4 5 6 7 8 9 10 11 12 13 14 and combine this acquisition workflow with sophisticated image processing, leveraging machine learning expertise and infrastructure at Google, the Advanced Physics Lab at Johns Hopkins, ground truth and proofreading taking place in the Outreach and Training Core and the proofreading and manual tracing described in Project 3. One major purpose of this Core is to assure that we do this integration successfully. One important aspect of this integration that will make the connectional map more useful analytically, will be to oversee the technical aspects of the overlay of essential fluorescence microscopy data derived from the same fish. This light microscopy-based data reveals whether neurons are excitatory or inhibitory and the activity patterns of neurons during eight behaviors. We believe this Correlated Light and Electron Microscopy (CLEM) will add powerful new dimensions to the analysis of the wiring diagram of a behaving vertebrate.

电子显微镜核心 脊椎动物行为背后的突触回路是该基金会所有研究人员感兴趣的（以及 神经科学家更普遍）。在斑马鱼中生成神经回路是这个核心的主要目标。得到这个 连通性需要揭示每个神经元的神经回路的四个基本特征：首先，将其局部输入呈现在 足够的分辨率来检测所有的撞击突触连接，第二，识别这些连接的符号， 第三，识别引起这种突触前输入的细胞，第四，识别受突触后神经元支配的突触后细胞。 这个神经元的轴突理想情况下，一个脊椎动物大脑中的所有神经元都需要满足这些要求， 神经科学家无法精确地追踪从感觉输入到运动输出的信号， --这是破解大脑结构与其行为库之间关系的一个严重障碍。由于快速 随着技术和计算的进步，现在第一次可以创造一个完整的脊椎动物 动物连接图我们将使用的方法自动化连续切片电子显微镜流水线， 可以在大（毫米尺度）体积上获取分辨率（纳米级）图像。该核心将使用现代 我们已经开发了大约10年的连续切片多束扫描电子显微镜方法1 2 3 4 5 6 7 8 9 10 11 12 13 14和联合收割机将此采集工作流程与复杂的图像处理相结合，利用机器学习 谷歌的专业知识和基础设施，约翰霍普金斯大学的高级物理实验室，地面实况和校对 在外联和培训核心以及项目3中所述的校对和人工追踪中，一个主要 本核心的目的是确保我们成功地进行这种整合。这种整合的一个重要方面， 使连接图更有用的分析，将是监督技术方面的覆盖必不可少的 来自同一条鱼的荧光显微镜数据。这种基于光学显微镜的数据揭示了神经元是否 兴奋性或抑制性以及八种行为中神经元的活动模式。我们相信这种相关的光， 电子显微镜（CLEM）将增加强大的新的层面分析的布线图的行为 脊椎动物。