Toolbox for estimation and interpretation of whole-brain linear effective connectivity
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README.md

These script in Python 3.6 are a small package to analyze fMRI signals. It is a collaborative work with Andrea Insabato, Vicente Pallarés, Gorka Zamora-López and Nikos Kouvaris. If you use this toolbox, please cite the relevant papers below.

Whole-brain linear effective connectivity (WBLEC) estimation

The script ParameterEstimation.py calculates the spatiotemporal functional connectivity for each session (or run) and subject from the BOLD time series. Then, it calls the model optimization (function in WBLECmodel.py) and stores the model estimates (effective connectivity matrix embedded in the Jacobian J and input variances Sigma) in an array. The data are:

  • BOLD time series in ts_emp.npy
  • structural connectivity in SC_anat.npy
  • ROI labels in ROI_labels.npy

Classification

The script Classification.py compares the performances of two classifiers (multinomial linear regressor and 1-nearest-neighbor) in identifying subjects from EC taken as a biomarker.

References

Data are from: Gilson M, Deco G, Friston K, Hagmann P, Mantini D, Betti V, Romani GL, Corbetta M. Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions. Neuroimage 2017; doi.org/10.1016/j.neuroimage.2017.09.061

Model optimization is described in: Gilson M, Moreno-Bote R, Ponce-Alvarez A, Ritter P, Deco G. Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome. PLoS Comput Biol 2016, 12: e1004762; dx.doi.org/10.1371/journal.pcbi.1004762

Classification procedure is described in: Pallares V, Insabato A, Sanjuan A, Kuehn S, Mantini D, Deco G, Gilson M. Subject- and behavior-specific signatures extracted from fMRI data using whole-brain effective connectivity. Biorxiv, doi.org/10.1101/201624

The classification script uses the scikit.learn library (http://scikit-learn.org)