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The idea to estimate the statistical interdependence among (interacting)brain regions has motivated numerous researchers to investigate how theresulting connectivity patterns and networks may organize themselves under anyconceivable scenario. Even though this idea is not at initial stages, itspractical application is still far to be widespread. One concurrent cause maybe related to the proliferation of different approaches that aim to catch theunderlying correlation among the (interacting) units. This issue has probablycontributed to hinder the comparison among different studies. Not only allthese approaches go under the same name (functional connectivity), but theyhave been often tested and validated using different methods, therefore, makingit difficult to understand to what extent they are similar or not. In thisstudy, we aim to compare a set of different approaches commonly used toestimate the functional connectivity on a public EEG dataset representing apossible realistic scenario. As expected, our results show that source-levelEEG connectivity estimates and the derived network measures, even thoughpointing to the same direction, may display substantial dependency on the(often arbitrary) choice of the selected connectivity metric and thresholdingapproach. In our opinion, the observed variability reflects ambiguity andconcern that should always be discussed when reporting findings based on anyconnectivity metric.

On the variability of functional connectivity and network measures in source-reconstructed EEG time-series