QuNex, Qualitative Neuroimaging Environment and Toolbox, is a comprehensive neuroimaging preprocessing and analysis environment that can be deployed in source or containerized form in a variety of environments from personal computers to high-performance clusters and on the web. It is designed to support reproducible, state-of-the-art, high-throughput research. QuNex is developed jointly by MBLab, and Anticevic and Murray labs at Yale University.

QuNex is available as open-source software in both source code and container form, as is documentation at qunex.yale.edu. Users are encouraged to post questions, advice, bug reports, and feature requests at forum.qunex.yale.edu. Here you will find blog posts explaining some of the design decisions and use cases, describing analytical features, and revealing other details about QuNex.

For a general overview, see the BioRxiv preprint. The posts and related materials are also available in a GitHub repository.



Slice timing correction in QuNex

Why and how of slice timing correction When acquiring a BOLD time series, each frame in a time series usually consists of a brain volume acquired slice by slice within a time span of a single TR. The TR can vary significantly depending on a number of factors, including in-plane


Tutorial: Finding and Assigning Peaks in Task-Related fMRI Analysis using Qunex

Brain activity data inferred from functional MR images (fMRI) are stored in a huge voxel space when acquired, resulting in heavy computational operations and cumbersome data analysis, if no other data reduction approaches are applied. The first common approach to data reduction is to project cortical brain activity onto a


Nuisance signal regression

QuNex enables flexible estimation and removal of nuisance signals in fMRI data. This allows the user to: