NeuroNex Innovation Award: Towards Automatic Analysis of Multi-Terabyte Cleared Brains

NeuroNex 创新奖：实现多 TB 级清晰大脑的自动分析

• 批准号: 1707298
• 负责人: Joshua Vogelstein
• 金额: $ 80万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707298&HistoricalAwards=false
• 关键词: NeuroNex; Innovation; Award; Towards; Automatic

Three complimentary changes are revolutionizing the way neuroscientists study the brain. First, experimental advances allow neurobiologists to "clear" brains so that they become transparent, with the exception of a set of neurons that can be selected on the basis of their location, response properties, and genetic make-up. Second, technological advances have resulted in microscopes that can simultaneously image an entire "sheet" of this brain, thereby enabling rapid acquisition of whole brain volumes. Third, researchers are taking steps to educate neuroscientists to acquire these data. Together, this will result in a massive upswing in adoption of this experimental modality. However, acquiring the data is one step in the upward spiral of science that will yield transformative scientific results. The subsequent steps are computational. This project will develop cyberinfrastructure resources and software that enable storage and access of large CLARITY brain imaging datasets, alignment and registration to reference anatomical atlas and visualization of the datasets. Additional capabilities for automatic identification and localization of cell bodies and statistical analysis will be provided. The PIs will run annual hackathons for college students and sponsor a summer internship program for undergraduates to broaden the educational efforts in software development for neuroscience. Finally, mobile compliant digital education content will be created to complement existing online courses to target STEM students, and educate global citizens.This project will build a prototype pipeline that operates on raw CLARITY brains and outputs the statistics of locations of cells in each region in the Allen Reference Atlas, as well as estimates of connectivity and similarity across regions and conditioned on different contexts. To do so, the PIs will leverage modern mathematical statistics (such as Large Deformation Diffeomorphic Metric Mapping for registration, Deep Learning and Random Forests for segmentation, and Statistical Graph Theory for analysis of the resulting conenctomes), as well as modern computational tools, including Docker containers to facilitate full reproducability, and semi-external memory algorithms and cloud computing to enable scalable analytics. To reach out to the broader community and educate them in the use of these tools, this project will provide tutorials deployed in the cloud. Together, this will facilitate the large community of users to both collect and analyze their data with ease. Many of the tools developed as part of this project will be easily extensible to other experimental modalities and neuroscience communities.

三个互补的变化正在彻底改变神经科学家研究大脑的方式。首先，实验的进步使神经生物学家能够“清理”大脑，使它们变得透明，除了一组可以根据其位置，反应特性和遗传组成进行选择的神经元。其次，技术进步导致显微镜可以同时对整个大脑的“薄片”进行成像，从而能够快速获取整个大脑体积。第三，研究人员正在采取措施教育神经科学家获取这些数据。总之，这将导致这种实验模式的采用率大幅上升。然而，获取数据是科学螺旋上升的一步，将产生变革性的科学成果。后续步骤是计算性的。该项目将开发网络基础设施资源和软件，以便存储和访问大型脑成像数据集，对齐和配准参考解剖图谱以及数据集的可视化。将提供细胞体的自动识别和定位以及统计分析的额外能力。PI将为大学生举办年度黑客松，并为本科生赞助暑期实习计划，以扩大神经科学软件开发的教育工作。最后，将创建符合移动的数字教育内容，以补充现有的针对STEM学生的在线课程，并教育全球公民。该项目将建立一个原型管道，该管道对原始的大脑进行操作，并输出艾伦参考图谱中每个区域中细胞位置的统计数据，以及跨区域和不同背景条件下的连通性和相似性的估计。为此，PI将利用现代数理统计（例如用于配准的大变形同构度量映射，用于分割的深度学习和随机森林，以及用于分析所得连接组的统计图论），以及现代计算工具，包括Docker容器以促进完全再现性，以及半外部内存算法和云计算以实现可扩展的分析。为了接触更广泛的社区并教育他们使用这些工具，该项目将提供部署在云中的教程。总之，这将有助于大型用户社区轻松收集和分析他们的数据。作为该项目的一部分开发的许多工具将很容易扩展到其他实验模式和神经科学社区。