In this review, we focused on data-driven methods to estimate functional connectivity based on phase coupling by MEG. All of these methods are here formulated for static connectivity analysis, which requires averages over a theoretically infinite number of data segments. In practice, data length is always finite, and the reliability of these methods is impacted by the overall number of available data time points. This issue has been discussed in simulated data for linear coupling methods (e.g., Sommariva et al., 2017), while for cross-frequency coupling methods this is still to be explicitly investigated. Moreover, to date several evidences have been provided concerning the changes of functional connectivity patterns across different time-scales (Breakspears et al., 2004). The extension of the above described methods to the study of dynamical phase coupling is straightforwardly achievable through the use of the Time-Frequency resolved versions of these metrics, at least for task-induced connectivity.
