medusa.connectivity package

Submodules

medusa.connectivity.amplitude_connectivity module

In this module you will find useful functions to apply optimized amplitude-based connectivity metrics. Enjoy!

@authors: Víctor Rodríguez-González and Diego Marcos-Martínez

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.phase_connectivity module

In this module you will find useful functions to apply optimized phase-based connectivity metrics. Enjoy!

@authors: Víctor Rodríguez-González and Diego Marcos-Martínez

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].