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

三个相得益彰的变化正在彻底改变神经科学家研究大脑的方式。首先，实验进展使神经生物学家能够“清理”大脑，使它们变得透明，但一组神经元除外，这些神经元可以根据它们的位置、反应特性和基因构成进行选择。其次，技术进步导致显微镜可以同时成像整个大脑的“薄片”，从而能够快速获取整个大脑的体积。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。总而言之，这将导致采用这种试验性模式的人数大幅上升。然而，获取数据是科学螺旋式上升的一步，这将产生变革性的科学结果。接下来的步骤是计算性的。该项目将开发网络基础设施资源和软件，以实现大清晰度脑成像数据集的存储和访问、对齐和注册以参考解剖图谱和数据集的可视化。将提供细胞体的自动识别和定位以及统计分析的附加能力。PIS将为大学生举办年度黑客马拉松，并为本科生赞助暑期实习计划，以扩大神经科学软件开发方面的教育努力。最后，将创建符合移动标准的数字教育内容，以补充现有的在线课程，针对STEM学生，并教育全球公民。该项目将建立一个原型管道，在原始Clarity大脑上运行，并输出艾伦参考地图集中每个地区细胞位置的统计数据，以及根据不同背景对跨地区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微分同形度量映射，用于分割的深度学习和随机森林，以及用于分析结果概念的统计图论)，以及现代计算工具，包括促进完全重现性的Docker Containers，以及用于实现可伸缩分析的半外部存储算法和云计算。为了接触更广泛的社区并教育他们如何使用这些工具，该项目将提供部署在云中的教程。总而言之，这将促进庞大的用户社区轻松地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界。

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.