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

Johns Hopkins University, Baltimore MD

Investigators

Abstract

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.