pydfc 1.0.4

This package aims to provide a comprehensive framework for assessing dynamic functional connectivity (dFC) using multiple methods and comparing results across methods.

pydfc

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

Simply do these steps in the main repository directory to learn how to use the dFC functions:

• conda create --name pydfc_env python=3.11

• conda activate pydfc_env

• pip install -e '.'

• run the code cells in demo jupyter notebooks

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.

• Torabi M, Mitsis GD, Poline JB. On the variability of dynamic functional connectivity assessment methods. bioRxiv. 2023:2023-07.