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There was no impact of amyloid load and no interaction between amyloid load and neurodegeneration for PLV and wPLI in alpha and theta bands. Men had lower PLV alpha and lower PLV theta (FDR-corrected P<0.0001) (Supplementary Fig. 10). There was no impact of age, educational level, ApoE4 status and hippocampal volume on PLV and wPLI in alpha and theta bands. 224 electrodes topographical analysis for wPLI and PLV We evaluated topographical differences across FC measures between the control group (A-N-) and the three other groups, We did multiple linear regression of average wPLI and PLV on all scalp electrodes to assess the impact of amyloid load and brain metabolism on these EEG metrics, adjusting on the following potential confounding variables: age, gender, education level, ApoE4 status and hippocampal volume ,
, All p-values were adjusted on ApoE4 status, gender, education level, age and hippocampal volume. Pvalues were corrected for multiplicity on 224 electrodes by cluster permutation test
, FDR-corrected P=0.1323, respectively) and a significant interaction between neurodegeneration status and electrodes for PLV alpha and PLV theta (FDR-corrected P<0.0001) and for wPLI theta (FDR-corrected P=0.0315). A-N+ subjects presented a decrease of PLV alpha and PLV theta in fronto-central and parieto-occipital regions compared to A-N-subjects (Supplementary Fig. 11). PLV alpha and PLV theta decreased in fronto-central and parieto-occipital regions in N+ subjects, There was a main effect of neurodegeneration status for PLV alpha and PLV theta (P=0.0065
, There was no correlation between wSMI alpha and PLV alpha (R2=0.01, p=0.143) and between wSMI theta and wPLI theta (R2=0.00, p=0.436). wSMI theta and PLV theta were anti-correlated (R2=0.17, p<0.001). Anti-correlation between wSMI theta and PLV theta explains why while wSMI theta increased in fronto-central regions in N+ subjects, Correlation between wSMI, PLV and wPLI wSMI alpha was correlated with wPLI alpha (R2=0.22, p<0.001) (Supplementary Fig. 13)
In contrast to PLV, instead of measuring basic oscillatory correlations, wSMI assesses the non-linear coupling of information sharing among distant networks. wSMI presents several other advantages, including a fast and robust estimation of the signals' entropies and the absence of spurious correlations between EEG signals arising from common sources, These results can be linked to the distinct information provided by each of the FC markers. It has been shown that wSMI has a higher sensitivity towards nonlinear interactions between signals while wPLI has optimal sensitivity for both linear and nonlinear interactions, 2011. ,
There was no significant difference between FC matrices after FDR correction on 91 inter-ROI connections, neither for wSMI, PLV or wPLI. These results can be explained by a lack of power due to the analysis of a small random sample of subjects from each group (n=25) and multiple comparison correction on 91 inter-ROI connections ,
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