Bootstrap Analysis for Stable Clustering in Python


Keywords
parcellation bagging functional neuroimaging nipype nipy nibabel nilearn scikit-learn
License
MIT
Install
pip install PyBASC==1.2.0

Documentation

PyBASC: Bootstrap Analysis of Stable Clusters in Python

Build Status Code Coverage

PyBASC is an open source , Nipype-based, parcellation package for preprocessed functional MRI data. Designed for use by both novice and expert users, PyBASC allows users to create individual and group level functional parcellations using a wide variety of clustering methods.

Core Dependencies

Python 3.6

Package Tested version
Scikit-learn 0.18.2
NumPy 1.13.1
NiBabel 2.1.0
SciPy 0.19.1
NiLearn 0.2.6
NiPype 0.13.1

Installation & Quick Start


  • Install from command line using pip
pip install PyBASC
  • Setup PyBASC from command line
cd /path/to/PyBASC
python setup.py install

Support

Please use GitHub issues for questions, bug reports or feature requests.

References

This package is based on the following work:

  • Garcia-Garcia, M., Nikolaidis, A., Bellec, P., Craddock, R. C., Cheung, B., Castellanos, F. X., & Milham, M. P. (2017). Detecting stable individual differences in the functional organization of the human basal ganglia. NeuroImage.
  • Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neuroimage, 51(3), 1126-1139.
  • Bellec, P., Marrelec, G., & Benali, H. (2008). A bootstrap test to investigate changes in brain connectivity for functional MRI. Statistica Sinica, 1253-1268.