  The idea to estimate the statistical interdependence among (interacting)
brain regions has motivated numerous researchers to investigate how the
resulting connectivity patterns and networks may organize themselves under any
conceivable scenario. Even though this idea is not at initial stages, its
practical application is still far to be widespread. One concurrent cause may
be related to the proliferation of different approaches that aim to catch the
underlying correlation among the (interacting) units. This issue has probably
contributed to hinder the comparison among different studies. Not only all
these approaches go under the same name (functional connectivity), but they
have been often tested and validated using different methods, therefore, making
it difficult to understand to what extent they are similar or not. In this
study, we aim to compare a set of different approaches commonly used to
estimate the functional connectivity on a public EEG dataset representing a
possible realistic scenario. As expected, our results show that source-level
EEG connectivity estimates and the derived network measures, even though
pointing to the same direction, may display substantial dependency on the
(often arbitrary) choice of the selected connectivity metric and thresholding
approach. In our opinion, the observed variability reflects ambiguity and
concern that should always be discussed when reporting findings based on any
connectivity metric.

Neurons and Cognition;Signal Processing