COINSTAC (Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) promotes collaborative research by removing large barriers to traditional data-centric approaches. It allows groups of users to run common analyses on their own machines over their own datasets with ease. The results of these analyses are synchronized to the cloud and undergo aggregate analysis processes using all contributor data. Federated (decentralized) pipelines enable distributed, iterative, and feature-rich analyses, opening up new possibilities for collaborative computation. It also offers data anonymity through differentially private algorithms, so members do not need to fear protected health information (PHI) traceback.

The goal of this discussion is to briefly introduce COINSTAC and its new features/algorithms to perform statistical analysis on various datasets. New features include: Doing statistical analysis using Singularity containers Command line pre-processing tools New algorithms including but not limited to Decentralized Source Based Morphometry New COINSTAC federated analysis architecture powered by NVIDIA FLARE

We would like to hear feedback about our software such as how to improve the experience for researchers. We welcome anyone who wants to contribute to this open source and open data project with their datasets, algorithms, and code. We would also like to work with other organizations to pursue grants together, including small business grants. Collaborating with other organizations is the best way for us to answer interesting neuroscience-related questions that would not have been possible without COINSTAC and COINSTAC Vaults.

OSR 2024 | Emergent Session 5: Enabling federated analysis using NVIDIA FLARE powered COINSTAC architecture and showcase new algorithms