A new mutual information measure to estimate functional connectivity: preliminary study

EL SAYED HUSSEIN JOMAA, MOHAMAD; VAN BOGAERT, PATRICK; JRAD, NISRINE; COLOMINAS, MARCELO ALEJANDRO; HUMEAU-HEURTIER, ANNE

Congreso; 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2019

IEEE

Functional Connectivity (FC) is a powerful tool to investigate brain networks both in rest and while performing tasks. Functional magnetic resonance imaging (fMRI) gave good spatial estimation of FC but lacked the temporal resolution. Electroencephalography (EEG) allows estimating FC with good temporal resolution. In this study we introduce a new method based on Mutual Information and Multivariate Improved Weighted Multi-scale Permutation Entropy to estimate FC of brain using EEG. We applied this method on resting-state EEG signals from healthy children. Using network measures of nodes and Wilcoxon signed-rank test, we identified the most important nodes in the estimated networks. Comparing the localization of those outstanding nodes with the regions involved in resting-state networks (RSNs) estimated from fMRI showed that our proposal is efficient in the identification of nodes belonging to RSNs and could be used as a general estimator for FC without having to band-pass the signals into frequency bands.