Software for Automated Reconstruction of Structure & Dynamics of Neural Circuits

自动结构重建软件

• 批准号: 9030044
• 负责人: ARMEN STEPANYANTS
• 金额: $ 34.06万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9030044
• 关键词: Address; Algorithms; Biological Neural Networks; Brain; Brain Mapping; Cells; Communities; Computer software; Computer-Assisted Image Analysis; Computers; Data; Data Set; Dendritic Spines; Development; Electron Microscopy; Goals; Image; Imagery; In Vitro; Individual; Knowledge; Label; Learning; Machine Learning; Manuals; Maps; Methods; Microscopy; Monitor; Morphologic artifacts; Morphology; Mus; Nature; Nervous system structure; Neurites; Neurons; Neurosciences; Optics; Population; Positioning Attribute; Process; Recording of previous events; Research; Resolution; Shapes; Stimulus; Structure; Synapses; Techniques; Technology; Time; Tissues; Tracer; Uncertainty; base; brain tissue; experience; graphical user interface; high throughput technology; image processing; improved; in vivo; in vivo imaging; light microscopy; nervous system disorder; neural circuit; neuronal cell body; open source; presynaptic; public health relevance; reconstruction; relating to nervous system; response; scale up; statistics; tool; user-friendly

 DESCRIPTION (provided by applicant): Our understanding of brain functions is hindered by the lack of detailed knowledge of synaptic connectivity in the underlying neural network. While synaptic connectivity of small neural circuits can be determined with electron microscopy, studies of connectivity on a larger scale, e.g. whole mouse brain, must be based on light microscopy imaging. It is now possible to fluorescently label subsets of neurons in vivo and image their axonal and dendritic arbors in 3D from multiple brain tissue sections. The overwhelming remaining challenge is neurite tracing, which must be done automatically due to the high-throughput nature of the problem. Currently, there are no automated tools that have the capacity to perform tracing tasks on the scale of mammalian neural circuits. Needless to say, the existence of such a tool is critical both for basic mapping of synaptic connectivity in normal brains, as well as for describing the changes in the nervous system which underlie neurological disorders. With this proposal we plan to continue the development of Neural Circuit Tracer - software for accurate, automated reconstruction of the structure and dynamics of neurites from 3D light microscopy stacks of images. Our goal is to revolutionize the existing functionalities of the software, making it possible to: (i) automatically reconstruct axonal and dendritic arbors of sparsely labeled populations of neurons from multiple stacks of images and (ii) automatically track and quantify changes in the structures of presynaptic boutons and dendritic spines imaged over time. We propose to utilize the latest machine learning and image processing techniques to develop multi-stack tracing, feature detection, and computer-guided trace editing capabilities of the software. All tools and datasets created as part of this proposal will be made available to the research community.

 描述(由申请人提供)：我们对大脑功能的理解因缺乏关于基本神经网络中突触连接的详细知识而受到阻碍。虽然小神经回路的突触连通性可以用电子显微镜来确定，但更大范围的连通性研究，例如整个小鼠脑，必须基于光学显微镜成像。现在可以对活体神经元的亚群进行荧光标记，并从多个脑组织切片中以3D方式显示它们的轴突和树突。压倒性的剩余挑战是轴突跟踪，由于问题的高通量性质，这必须自动完成。目前，还没有能够在哺乳动物神经回路规模上执行追踪任务的自动化工具。不用说，这种工具的存在对于绘制正常大脑中突触连接的基本图谱，以及描述神经疾病背后的神经系统变化都是至关重要的。有了这项建议，我们计划继续开发神经电路追踪器-软件，用于从3D光学显微镜图像堆叠准确、自动化地重建神经突起的结构和动力学。我们的目标是使该软件的现有功能发生革命性的变化，使其能够：(I)从多组图像中自动重建稀疏标记的神经元群体的轴突和树突树枝，以及(Ii)自动跟踪和量化随时间成像的突触前突触和树突结构的变化。我们建议利用最新的机器学习和图像处理技术来开发该软件的多堆栈跟踪、特征检测和计算机引导的轨迹编辑功能。作为此建议书的一部分创建的所有工具和数据集将提供给 研究社区。