neurodatascience / dFC

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

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.

• 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.