.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.10161176.svg :target: https://zenodo.org/doi/10.5281/zenodo.10161176 .. image:: https://img.shields.io/pypi/v/pydfc.svg :target: https://pypi.org/project/pydfc/ :alt: Pypi Package

pydfc

An implementation of several well-known dynamic Functional Connectivity (dFC) assessment methods.

Simply install pydfc using the following steps:

• conda create --name pydfc_env python=3.11
• conda activate pydfc_env
• pip install pydfc

The dFC_methods_demo.ipynb illustrates how to load data and apply each of the dFC methods implemented in the pydfc toolbox individually. The multi_analysis_demo.ipynb illustrates how to use the pydfc toolbox to apply multiple dFC methods at the same time on a dataset and compare their results.

For more details about the implemented methods and the comparison analysis see our paper <https://doi.org/10.1093/gigascience/giae009>_.

• Mohammad Torabi, Georgios D Mitsis, Jean-Baptiste Poline, On the variability of dynamic functional connectivity assessment methods, GigaScience, Volume 13, 2024, giae009, https://doi.org/10.1093/gigascience/giae009.