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: 7804320
• 负责人: CHRISTOPHER CHARLES LAW
• 金额: $ 24.88万
• 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/7804320
• 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; Methods; 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 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; man; 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逆向皮层电路的计算基础架构。神经科学研究人员正在使用共聚焦和电子显微照片（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中所有神经元和突触的详细图。通过自动化，现在可以获取之前的量量。但是，目前无法分析如此大的数据集。我们将开发一个开源系统，该系统支持远程可视化和分析任意尺寸的体积。我们的系统将被命名为“ Open Ssecrett”，并能够为开发神经元和突触连接的自动分割而进行协作。提出的系统将围绕远程数据访问进行架构，以便地理上不同的研究小组可以协作从数据库中的卷中分割神经元的巨大任务。自定义客户端将实施各种细分算法，结果将放回中央数据库中。这将允许算法及其结果共享和比较。我们还将开发标准客户，以允许通用访问来查看和探索巨大的数据。 公共卫生相关性：自从发现高尔基体染色以来，追踪细胞已经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，早期技术只能通过成像一些精选的神经元来揭示复杂的神经过程。由电子显微照片产生的高分辨率体积使整个组织中的所有细胞都可以追溯到。但是，由于轴突在大距离上建立连接，因此有必要对大型组织块进行图像以获得完整的电路。自动化分区和成像现在能够生成此类卷，但是目前没有任何软件可以分析所得数据。在纳米EM尺度上扫描立方厘米的组织（图1）将产生数百粒数据！甚至要查看如此大的数据，更不用说从中分段神经元电路了。我们建议开发一个可扩展的软件数据库，该数据库管理Exabyte尺寸的量。它将支持一个研究算法的研究人员社区，以自动细分神经元并分析产生的电路。