Scalable computational tools for reverse engineering neural circuits from histolo

histolo 用于逆向工程神经电路的可扩展计算工具

• 批准号: 7997180
• 负责人: CHRISTOPHER CHARLES LAW
• 金额: $ 24.96万
• 依托单位: KITWARE, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-12-07 至 2011-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7997180
• 关键词: Algorithms; Architecture; Automation; Axon; Back; Biomedical Engineering; Cells; Client; Collaborations; Communities; Complex; Computer software; Conflict (Psychology); Custom; Data; Data Set; Databases; Doctor of Philosophy; Drops; Electrons; Engineering; Feedback; Funding; Future; Goals; Golgi Apparatus; Growth; Histology; Human Resources; Image; Imagery; Individual; Java; Label; Laws; Lead; Libraries; Manuals; Maps; Metadata; Microscope; Microtomy; Modeling; Morphology; Names; Nervous system structure; Neurons; Neuropil; Neurosciences; Organism; Phase; Process; Property; Protocols documentation; Relative (related person); Request for Applications; Research; Research Design; Research Infrastructure; Research Methodology; Research Personnel; Resolution; Running; Scanning; Seeds; Skeleton; Slice; Small Business Innovation Research Grant; Staging; Staining method; Stains; Support System; Synapses; System; Techniques; Testing; Tissue Sample; Tissues; Trees; Universities; Visual; Work; cluster computing; computer infrastructure; computerized tools; design; file format; flexibility; graphical user interface; image processing; interest; nanometer; neural circuit; open source; parallel processing; programs; prototype; public health relevance; relating to nervous system; repository; research and development; skeletal; software development; tool

DESCRIPTION (provided by applicant): We propose to develop the computational infrastructure necessary for future large-scale reverse engineering of cortical circuits. Neuroscience researchers are using confocal and electron micrograph (EM) techniques to scan neural tissue at high resolution. Their goal is to capture a detailed map of all neurons and synapses within the nervous system of an organism. Through automation, it is now possible to acquire petabyte size volumes. However, there is no way to currently analyze such large datasets. We will develop an open-source system that supports remote visualization and analysis of arbitrary sized volumes. Our system will be named "Open SSECRETT" and enable a collaborative effort to develop automatic segmentation of neurons and synaptic connections. The proposed system will be architected around remote data access so that geographically diverse research groups can collaborate on the enormous task of segmenting neurons from volumes in the database. Custom clients will implement various segmentation algorithms and the results will be put back in a central database. This will allow the algorithms and their results to be shared and compared. We will also develop standard clients that will allow universal access to view and explore the immense data. PUBLIC HEALTH RELEVANCE: Since the discovery of Golgi staining, tracing cells has revealed how individual neurons form connections in neural tissue[1]. Unfortunately, early techniques could only reveal complex neural processes by imaging a few select neurons. High-resolution volumes, generated by electron micrographs, allow all cells in a block of tissue to be traced. However, since axons make connections across large distances, it is necessary to image large tissue blocks in order to get a complete circuit. Automated sectioning and imaging are now capable of generating such volumes, but no software currently available can analyze the resulting data. Scanning a cubic centimeter of tissue at nanometer EM scale (figure 1) would produce hundreds of petabytes of data! It is a challenge to even view such large data, let alone segment circuits of neurons from it. We propose developing a scalable software database that manages exabyte sized volumes. It will support a community of researchers who are working on algorithms to automatically segment neurons and analyze resulting circuits.

描述（由申请人提供）：我们建议开发未来大规模皮层电路逆向工程所需的计算基础设施。神经科学研究人员正在使用共焦和电子显微 (EM) 技术以高分辨率扫描神经组织。他们的目标是捕获生物体神经系统内所有神经元和突触的详细图谱。通过自动化，现在可以获得 PB 大小的卷。然而，目前还没有办法分析如此大的数据集。我们将开发一个开源系统，支持任意大小体积的远程可视化和分析。我们的系统将被命名为“Open SSECRETT”，并支持合作开发神经元和突触连接的自动分割。所提出的系统将围绕远程数据访问进行构建，以便地理上不同的研究小组可以协作完成从数据库中分割神经元的艰巨任务。定制客户端将实施各种分段算法，结果将放回到中央数据库中。这将允许共享和比较算法及其结果。我们还将开发标准客户端，允许普遍访问和探索海量数据。 公共卫生相关性：自从发现高尔基染色以来，追踪细胞揭示了单个神经元如何在神经组织中形成连接[1]。不幸的是，早期技术只能通过对少数选定的神经元进行成像来揭示复杂的神经过程。由电子显微照片生成的高分辨率体积可以追踪组织块中的所有细胞。然而，由于轴突在很长的距离上进行连接，因此有必要对大的组织块进行成像以获得完整的电路。自动切片和成像现在能够生成这样的体积，但目前没有可用的软件可以分析所得数据。以纳米 EM 尺度扫描一立方厘米的组织（图 1）将产生数百 PB 的数据！即使查看如此大的数据也是一个挑战，更不用说从中分割神经元电路了。我们建议开发一个可扩展的软件数据库来管理 EB 大小的卷。它将支持研究人员社区，他们正在研究自动分割神经元并分析结果电路的算法。