Software for Automated Reconstruction of Structure & Dynamics of Neural Circuits

 描述（由申请人提供）：我们对大脑功能的理解受到缺乏对底层神经网络中突触连接的详细知识的阻碍。虽然小神经回路的突触连接可以用电子显微镜来确定，但更大规模的连接研究，例如整个小鼠大脑，必须基于光学显微镜成像。现在可以在体内荧光标记神经元亚群，并从多个脑组织切片中以3D方式对其轴突和树突进行成像。压倒性的剩余挑战是神经突追踪，由于问题的高通量性质，必须自动完成。目前，还没有自动化工具能够在哺乳动物神经回路的规模上执行跟踪任务。不用说，这种工具的存在对于正常大脑中突触连接的基本映射以及描述神经系统疾病背后的神经系统变化都至关重要。有了这个提议，我们计划继续开发神经回路示踪剂-软件，用于从3D光学显微镜图像堆栈中准确，自动重建神经突的结构和动力学。我们的目标是彻底改变该软件的现有功能，使其能够：（i）从多堆图像中自动重建稀疏标记的神经元群体的轴突和树突，以及（ii）自动跟踪和量化突触前结构的变化随着时间的推移成像的终扣和树枝状棘。我们建议利用最新的机器学习和图像处理技术来开发软件的多堆栈跟踪，特征检测和计算机引导的跟踪编辑功能。作为本提案一部分创建的所有工具和数据集将提供给 研究社区。

 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.