skbold - utilities and tools for machine learning on BOLD-fMRI data
The Python package
skbold offers a set of tools and utilities for
machine learning analyses of functional MRI (BOLD-fMRI) data.
Instead of (largely) reinventing the wheel, this package builds upon an
existing machine learning framework in Python: scikit-learn.
The modules of skbold are applicable in several 'stages' of
typical pattern analyses (see image below), including pattern estimation,
data representation, pattern preprocessing, feature selection/extraction,
and model evaluation/feature visualization.
Please see skbold's ReadTheDocs page for more info on how to use skbold!
Installation & dependencies
Although the package is very much in development, it can be installed using pip:
$ pip install skbold
However, the pip-version is likely behind compared to the code on Github, so to get the most up to date version, use git:
$ pip install git+https://github.com/lukassnoek/skbold.git@master
Skbold is largely Python-only (both Python2.7 and Python3) and is built around the "PyData" stack, including:
And it uses the awesome nibabel package
for reading/writing nifti-files. Also, skbold uses FSL
applywarp functions) to transform files from functional
(native) to standard (here: MNI152 2mm) space. These FSL-calls are embedded in the
convert2mni functions, so avoid this functionality if
you don't have a working FSL installation.
Authors & credits
License and citing skbold
The code is BSD (3-clause) licensed. If you use 'skbold' in your research, I would appreciate if you'd reference the following:
Snoek, L. (2017). Skbold: utilities and tools for machine learning on BOLD-fMRI data (version 0.4.0). https://doi.org/10.5281/zenodo.1064090.
Suggestions, issues, and contributing
If you have suggestions or issues, please submit an issue to the issue-tracker. Also, I would love contributions to skbold from others! In case you want to contribute, fork this repository and submit a pull-request with your contribution!