Scalable computational tools for reverse engineering neural circuits from histolo

描述（由申请人提供）：我们建议为未来大规模的皮层电路逆向工程开发必要的计算基础设施。神经科学研究人员正在使用共聚焦和电子显微摄影（EM）技术以高分辨率扫描神经组织。他们的目标是获取生物神经系统中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这么大的数据集。我们将开发一个支持远程可视化和任意大小卷分析的开源系统。我们的系统将被命名为“Open SSECRETT”，并使合作努力能够开发神经元和突触连接的自动分割。提出的系统将围绕远程数据访问构建，以便地理位置不同的研究小组可以合作完成从数据库中分割神经元的巨大任务。自定义客户端将实现各种分割算法，结果将放在中央数据库中。这将允许算法及其结果被共享和比较。我们还将开发标准客户端，使人们能够普遍访问和探索巨大的数据。

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.