Description:
pydfc 1.0.4
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.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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