Multivariate Multiscale Entropy (mMSE) as a tool for understanding the resting-state EEG signal dynamics: the spatial distribution and sex/gender- related differences

Abstract

Abstract Background The study aimed to determine the resting-state EEG (rsEEG) dynamics quantified using the multivariate Multiscale Entropy (mMSE), and the sex/gender (s/g) differences in the mMSE features. The rsEEG was acquired from 95 healthy adults. For each channel set the AUC, that represents the total complexity, the MaxSlope and AvgEnt referring to the entropy at the fine- and coarse-grained scales, respectively, were extracted. The difference in the entropy between the #9 and #4 timescale (DiffEnt) was also calculated. Results We found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlope and DiffEnt but lower AvgEnt in all channel sets and there were no s/g differences in the AUC. Conclusions Novel results of the present study are: 1) an identification of the mMSE features that capture entropy at the fine and the coarse timescales in the channel sets corresponding to the main resting-state networks; 2) an indication of the sex/gender differences in these features.