medusa.connectivity package

Submodules

medusa.connectivity.amplitude_connectivity module

medusa.connectivity.amplitude_connectivity.aec(data, ort=True)[source]

This method implements the amplitude envelope correlation (using GPU if available). Based on the “ort” param, the signals could be orthogonalized before the computation of the amplitude envelope correlation.

References

Liu, Z., Fukunaga, M., de Zwart, J. A., & Duyn, J. H. (2010). Large-scale spontaneous fluctuations and correlations in brain electrical activity observed with magnetoencephalography. Neuroimage, 51(1), 102-111.

Hipp, J. F., Hawellek, D. J., Corbetta, M., Siegel, M., & Engel, A. K. (2012). Large-scale cortical correlation structure of spontaneous oscillatory activity. Nature neuroscience, 15(6), 884-890.

O’Neill, G. C., Barratt, E. L., Hunt, B. A., Tewarie, P. K., & Brookes, M. J. (2015). Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods. Physics in Medicine & Biology, 60(21), R271.

Parameters:

  • data (numpy.ndarray) – MEEG Signal. Allowed dimensions: [n_epochs, n_samples, n_channels] and [n_samples, n_channels].

  • ort (bool) – If True, the signals on “data” will be orthogonalized before the computation of the amplitude envelope correlation.

Returns:

aec – aec-based connectivity matrix. [n_epochs, n_channels, n_channels].

Return type:

numpy.ndarray

medusa.connectivity.amplitude_connectivity.iac(data, ort=True)[source]

This method implements the instantaneous amplitude correlation (using GPU if available). Based on the “ort” param, the signals could be orthogonalized before the computation of the amplitude envelope correlation.

References

Tewarie, P., Liuzzi, L., O’Neill, G. C., Quinn, A. J., Griffa, A., Woolrich, M. W., … & Brookes, M. J. (2019). Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity. Neuroimage, 200, 38-50.

O’Neill, G. C., Barratt, E. L., Hunt, B. A., Tewarie, P. K., & Brookes, M. J. (2015). Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods. Physics in Medicine & Biology, 60(21), R271.

Parameters:

  • data (numpy.ndarray) – MEEG Signal. Allowed dimensions: [n_epochs, n_samples, n_channels] and [n_samples, n_channels].

  • ort (bool) – If True, the signals on “data” will be orthogonalized before the computation of the instantaneous amplitude correlation.

Returns:

iac – iac-based connectivity matrix. [n_epochs, n_channels, n_channels, n_samples].

Return type:

numpy 2D square matrix

medusa.connectivity.frequency_connectivity module

medusa.connectivity.phase_connectivity module

medusa.connectivity.phase_connectivity.phase_connectivity(data, measure=None)[source]

This method implements three phase-based connectivity parameters: PLV, PLI, and wPLI.

References

PLV: Mormann, F., Lehnertz, K., David, P., & Elger, C. E. (2000). Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Physica D: Nonlinear Phenomena, 144(3-4), 358-369.

PLI: Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., & Hallett, M. (2004). Identifying true brain interaction from EEG data using the imaginary part of coherency. Clinical neurophysiology, 115(10), 2292-2307.

wPLI: Vinck, M., Oostenveld, R., Van Wingerden, M., Battaglia, F., & Pennartz, C. M. (2011). An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage, 55(4), 1548-1565.

Note

PLV is sensitive to volume conduction effects

Parameters:

  • data (numpy matrix) – MEEG Signal. Allowed dimensions: [n_epochs, n_samples, n_channels], [n_samples, n_channels].

  • measure (str or None) – Key of the phase connectivity measure to calculate: “plv”, “pli” or “wpli”. If None, the three phase connectivity measures are calculated.

Returns:

  • plv (numpy 3D square matrix) – plv-based connectivity matrix. [n_epochs, n_channels, n_channels].

  • pli (numpy 3D square matrix) – pli-based connectivity matrix. [n_epochs, n_channels, n_channels].

  • wpli (numpy 3D square matrix) – wpli-based connectivity matrix. [n_epochs, n_channels, n_channels].