Editorial: Investigation of brain functional connectivity from electroencephalogram data

Functional connectivity is usually inferred from the correlation in the activity of the nodes based on the 13 BOLD or synchrony in the EEG or MEG signals. The present Research Topic focuses on the investigation 14 of FC using electroencephalography (EEG) that, thanks to its high temporal resolution, enables recording 15 brain activity patterns that could appear in short timescales and allows to conduct spectral analysis, which 16 is not possible with BOLD signals. On the other hand, EEG signal is often noisy and highly affected by 17 volume conduction and common source issues. For this reason, appropriate methodologies for processing 18 the EEG data are of utmost importance, to assure reliable evaluation of the FC measurements (Shi et al.(2019)).The articles in the current Research Topic investigate various applications, using different approaches.The following is a brief introduction to the contributions. 1. Kaminsky and Blinowska, who had previously proposed the widely used Direct Transfer 23 Function (DTF) metric, provide a guide toward future research in FC analysis in EEG 24 (Kaminski and Blinowska (2022)). In particular, their paper analyzes different FC approaches (e.g.,linear vs. nonlinear, bivariate vs. multivariate methods, etc.) with respect to the effect that these choi-26 ces have on addressing issues due to noise, volume conduction, common drive, etc. Characterization 27 of networks is also treated with critical analysis of the definition of "smallworldness". 2022)). Since GFS is known to be influenced by noise, they propo-45 sed an anti-noise algorithm that reduces its effect showing that patients with a good outcome (GO)46 have a higher EEG power in all the four analyzed frequency bands (i.e., δ, θ, α and β). Using the severity-of-disease classification system APACHE II, they also found that α band is the most useful 48 in predicting GO in SCA survivors.

Abstract licence: CC BY

5 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.