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中文摘要
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描述(由申请人提供):这项工作的总体目标是开发和验证一套新的技术,这些技术可以快速和常规地生成神经系统组织(有时称为连接体)的电流图。由于神经元过程和连接它们的突触体积很小,因此需要以纳米级的分辨率进行重建;神经元连接的分布式特性需要重建大体积,延伸超过一毫米或更多。我们的方法通过结合新的切片、电子显微镜成像和重建技术来解决这两个看似不相容的挑战。总之,这些进步将使我们获得数据和绘制电路的速度至少比以前快1000倍。作为第一个测试,我们将完整地重建老鼠的视网膜回路。对视网膜结构和功能的了解足以使其成为验证该方法的合适组织。同时,这一背景将使我们能够提出并解决关于神经回路的重要问题,这些问题将直接适用于大脑。然后,我们将使用该方法比较年轻成人和老年视网膜中的神经回路,从而深入了解与年龄相关的神经衰退的结构基础。最后,我们将测试这种连接组方法在人体组织中的应用。这里介绍的新方法将在几个方面改变神经科学。首先,它将有助于阐明大脑功能的结构基础。它还将提供关于神经回路在生命早期是如何完善的以及在老年时是如何改变的见解。其次,应用于越来越多的人类行为障碍的动物模型,它将帮助研究人员深入研究认知、行为和情感的病理,其中一些可能是由神经回路的错误连接引起的。最后,该方法可以应用于任何生物组织,其中多个大体积标本的三维重建将提供信息。
英文摘要
DESCRIPTION (provided by applicant): The overall goal of this work is to develop and validate a new suite of technologies that can rapidly and routinely generate circuit diagrams of nervous system tissue, sometimes called connectomes. The small size of neuronal processes and the synapses that connect them require that reconstruction be done at nanometer resolution; the distributed nature of neuronal connectivity requires reconstruction of large volumes, extending over a millimeter or more. Our method meets these two seemingly incompatible challenges by combining novel sectioning, electron microscopic imaging and reconstruction technologies. Together, these advances will allow us to acquire data and map circuits at least 1000-fold faster than has previously been possible. As a first test, we will reconstruct the retinal circuit of a mouse in its entirety. Enough is known about retinal structure and function to make this an appropriate tissue to validate the method. At the same time, this background will allow us to pose and solve important problems about neural circuits that will be directly applicable to the brain. We will then use the method to compare neural circuits in young adult and aged retina, providing insight into the structural basis of age-related neural decline. Finally, we will test the application of this connectomic method to human tissue. The new methods introduced here will transform neuroscience in several ways. First, it will allow elucidation of the structural underpinnings of brain function. It will also provide insight into how neural circuits are refined in early life and altered in old age. Second, applied to the ever increasing number of animal models of human behavioral disorders, it will help researchers delve into pathologies of cognition, behavior, and affect, some of which likely arise from miswiring of neural circuits. Finally, the method can be applied to any biological tissue where three-dimensional reconstruction of multiple large-volume specimens would be informative.
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BRAIN CONNECTS: A Center for High-throughput Integrative Mouse Connectomics
  • 批准号:
    10665380
  • 项目类别:
  • 资助金额:
    $1449.72万
  • 财政年份:
    2023
  • 负责人:
    Jeff W Lichtman
  • 依托单位:
BRAIN CONNECTS: Rapid and Cost‐effective Connectomics with Intelligent Image Acquisition, Reconstruction, and Querying
  • 批准号:
    10663654
  • 项目类别:
  • 资助金额:
    $209.59万
  • 财政年份:
    2023
  • 负责人:
    Jeff W Lichtman
  • 依托单位:
A Tool for Synapse-level Circuit Analysis of Human Cerebral Cortex Specimens.
  • 批准号:
    10670926
  • 项目类别:
  • 资助金额:
    $59.45万
  • 财政年份:
    2021
  • 负责人:
    Jeff W Lichtman
  • 依托单位:
A Tool for Synapse-level Circuit Analysis of Human Cerebral Cortex Specimens.
  • 批准号:
    10271724
  • 项目类别:
  • 资助金额:
    $116.9万
  • 财政年份:
    2021
  • 负责人:
    Jeff W Lichtman
  • 依托单位:
海外基金