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