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Functional_connectivity_analysis

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Elham Barzegaran edited this page Jan 22, 2021 · 3 revisions

Here, we use a time- and frequency-resolved measure of functional connectivity. We use an optimized Kalman filter to model dependencies between LFPs (Pascucci et al., 2020) and calculate the information Partial Directed Coherence (iPDC) (Takahashi et al., 2010). iPDC provides a multivariate measure of directed functional connectivity (Granger causality) with high temporal and frequency resolution.

To run the functional connectivity analysis you can use "PDC_analysis.py"

You will need to download pydynet toolbox and add it to the project path: https://github.com/joanrue/pydynet

References

Pascucci D, Rubega M, Plomp G (2020) Modeling time-varying brain networks with a self-tuning optimized Kalman filter. PLOS Comput Biol 16:e1007566.

Takahashi DY, Baccalá LA, Sameshima K (2010) Information theoretic interpretation of frequency domain connectivity measures. Biol Cybern 103:463–469.

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