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

TL;DR

This study compares various methods for estimating functional connectivity from source-reconstructed EEG data, revealing significant variability depending on the metrics and thresholds used, which impacts the interpretation of brain network analyses.

Contribution

It systematically evaluates the variability of connectivity and network measures across different approaches on a realistic EEG dataset, highlighting the importance of methodological choices.

Findings

Connectivity estimates vary substantially with different metrics.

Network measures depend on thresholding approaches.

Results emphasize the need for careful interpretation of connectivity analyses.

Abstract

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.