Lyman is a high-level ecosystem for analyzing neuroimaging data using open-source software. It aims to support an analysis workflow that is powerful, flexible, and reproducible, while automating as much of the processing as possible.

Online documentation can be found here

Python 2.7 or 3.6

• FSL

• Freesurfer

• ANTS (optional)

• Core scientific Python environment (ipython, numpy, scipy, matplotlib)

• pandas

• scikit-learn

• scikit-image

• nipype

• nibabel

• seaborn

• moss

To install the released version, just do

pip install lyman

You may instead want to use the development version from Github, by running

pip install git+https://github.com/mwaskom/lyman.git

All stages of processing assume that your anatomical data have been processed in Freesurfer (recon-all)

• run_warp.py: estimate anatomical normalization

• anatomy_snapshots.py: generate static images summarizing the Freesurfer reconstruction.

• run_fmri.py: perform subject-level functional preprocessing and analyses

• make_masks.py: generate ROI masks in native EPI space from a variety of sources

• run_group.py: perform basic whole-brain mixed-effects analyses

• surface_snapshots.py: plot the results of the subject- and group-level models on a surface mesh

The processing scripts generate a variety of static images that can be used for quality control and understanding the analysis. The best way to browse these is with the ziegler app, which runs in the browser and makes it easy to visualize the data.

https://github.com/mwaskom/lyman

Please submit any bugs you encounter to the Github issue tracker.

You can exercise the unit-test suite by running nosetests in the source directory.

Released under a BSD (3-clause) license