- QuNex is an integrated neuroimaging environment explicitly architected for scale and high throughput of ‘big data’, stress-tested via parallel processing of >10,000 multi-modal imaging sessions across scanner vendors.
- Turnkey engine enables frictionless deployment of entire pipelines within high-performance compute environments (e.g. SLURM) through a flexible scheduler system via a single command line call.
- QuNex container features leading community tools, including dcm2niix, FSL, Connectome Workbench, HCP Pipelines, PALM, and FreeSurfer with Octave and R Statistical Environment pre-installed. For a full list of featured software see QuNex documentation.
- QuNex is optimized for cloud-based deployment and enabled for seamless XNAT interoperability via the XNAT docker container service.
QuNex Development
The QuNex Suite is co-developed and maintained by the Anticevic Lab at Yale University, the Mind and Brain Lab at University of Ljubljana, and the Murray Lab at Yale University.
QuNex is explicitly architected to allow flexible and rapid addition of functions developed around its core tools. If you wish to access the source and / or contribute to the development of QuNex please email zailyn.tamayo@yale.edu.
Featured Collaborative Initiatives
- QuNex support of HCP Pipelines is developed in collaboration with David Van Essen, Matt Glasser and Michael Harms at Washington University in St. Louis.
- Diffusion processing workflows featured in QuNex are developed in collaboration with Stam Sotiropoulos at University of Nottingham, CoNI Lab.
- QuNex support of XNAT interoperability is a collaboration with the Neuroinformatics Research Group (NRG) at Washington University in St. Louis.