Junifer

junifer (JUelich NeuroImaging FEature extractoR) is a data handling and feature extraction library targeted towards neuroimaging data specifically functional MRI data. It is a tool conceived to extract features from neuroimaging data in an easy-to-use manner, with minimal coding and minimal user expertise in the internal aspects.

Unlike other tools like FSL, SPM, AFNI, etc., junifer is not a toolbox to pre-process data, but a toolbox to extract features from previously pre-processed data.

The main idea is that you have a set of images (e.g. a set of functional MRI, structural MRI, diffusion MRI, etc.) and you want to extract features to later use in statistical analyses or machine learning (for example, using julearn).

Junifer is a no-code tool that allows parametrizing each step of a feature extraction pipeline in a text file, using the simple YAML syntax specification. By specifying the dataset (or its structure) and a list of markers to compute, the user can easily compute all the features required for their ML models. And with a just a few more lines of text, all the processing can be done in computational clusters. Among Junifer’s most prominent features are a vast list of built-in datasets, parcellations, masks and markers, as well as processing in native and standard spaces (various MNI). Feature extraction is transparent and reproducible, as the full pipeline configuration is stored within each output file.

More info: https://juaml.github.io/junifer