Abstract In response to the BRAIN Initiative “Integration and Analysis of BRAIN Initiative Data” FOA, we propose further developing nTracer2, a cloud-based platform for brain image visualization and neuron tracing in the web browser. To evaluate nTracer2’s real-world performance, we deployed nTracer2 at the NIH Brain Image Library (BIL) at the Pittsburgh Supercomputer Center to handle whole mouse brain fMOST fluorescence image datasets. In a public data access test, up to 50 users across the globe smoothly explored this dataset simultaneously. In this project, our team will work on four Aims. In Aim 1, we will establish our novel SISF data framework as a scalable image data standard, which includes creating better data format conversion tools, compression filters, and documentation for SISF. In Aim 2, we will continue to develop the nTracer2 (content delivery network) CDN server, which includes upgrading the data management interface and optimizing the performance of the reconstruction database. In Aim 3, we will continue to develop the nTracer2 application server, which includes developing user management tools, optimizing high-performance computing performance for existing brain registration and machine vision functions, integrating imaging datasets taken by multimodal microscopy modalities, and expanding its neuroinformatics functions. In Aim 4, we will deploy full-stack nTracer2 at BIL and integrate CCF-registered multimodal datasets stored at multiple NIH repositories.

Narrative Neuronal cell type, morphology, and connectivity are important attributes for characterizing neural developmental and degenerative diseases. Funded by the NIH BRAIN Initiative, many technology development and neuroscience research groups, including our own, created a vast amount of image data through novel high-content and high-throughput assays. As it is not economically and technically possible for each lab to install the computational resources required to manage image data of this scale, NIH has funded multiple data repositories at supercomputer centers across the US. However, current methods and available tools do not allow easy access to those data by depositing researchers, let alone the general public. Sharing and reusing these existing data have become a pressing need in the research community. Our project builds on software developed in our labs and will continue to develop and deploy them to allow data to be unsiloed from NIH repositories and reused natively without downloading entire multi-terabyte datasets to the user's computer.