2.4. Functional connectivity analysis

Functional connectivity matrices were computed using the ‘EEG source connectivity’ method (Hassan et al., 2014, Hassan et al., 2015b). It includes two main steps: i) solving the EEG inverse problem to reconstruct the temporal dynamics of the cortical regions and ii) measuring the functional connectivity between these reconstructed regional time series (Fig. 1). The weighted Minimum Norm Estimate (wMNE) was used to reconstruct the dynamics of the cortical sources. The functional connectivity was then computed between the reconstructed sources using the phase synchronization (PS) method. To measure the PS, the phase locking value (PLV) method was used as described in (Lachaux et al., 1999). This measure (range between 0 and 1) reflects true interactions between two oscillatory signals through quantification of the phase relationships. The PLVs were estimated at six frequency bands [delta (0.5–4 Hz); theta (4–8 Hz); alpha1 (8–10 Hz); alpha2 (10–13 Hz); beta (13–30 Hz); gamma (30–45 Hz)]. The choice of wMNE/PLV was supported by two comparative analyses performed in (Hassan et al., 2014, Hassan et al., 2016) that reported the superiority of wMNE/PLV over other combinations of five inverse algorithms and five connectivity measures. Briefly, in (Hassan et al., 2016), the network identified by each of the inverse/connectivity combination used to identify cortical brain networks from scalp EEG was compared to a reference network. The combination that showed the highest similarity between scalp-EEG-based network and reference network (using a network similarity algorithm) was considered as the optimal combination. This was the case for the wMNE/PLV.

Structure of the investigation. Patients were categorized by their cognitive performance 1) cognitively intact subjects, 2) patients with mild cognitive impairment and 3) patients with severe cognitive impairment. The demographic and clinical features of the three groups are summarized in Table 1. The performance and the neuropsychological test of the three groups are also described in Table 2 (see (Dujardin et al., 2015) for more description about the database). Data: Dense-EEGs were recorded using 128 electrodes during resting state (eye closed). The MRIs of the subjects were also available. The cortical sources were reconstructed by solving the inverse problem using the weighted Minimum Norm Estimate (wMNE) method. An anatomical parcellation was applied on the MRI template producing 68 regions of interest (Desikan-Killany atlas) computed using Freesurfer (Fischl, 2012) and then imported for further processing into brainstorm (Tadel et al., 2011). The functional connectivity was computed between the 68 regional time series using the Phase Locking Value (PLV) method at six frequency bands: [delta (0.5–4 Hz); theta (4–8 Hz); alpha 1 (8–10 Hz); alpha 2 (10–13 Hz); beta (13–30 Hz); gamma (30–45 Hz)]. The connectivity matrices were compared between the groups using two level of network analysis i) High-level topology where we computed four network metrics: the clustering coefficient, the strength, the characteristic path length and the global efficiency and ii) edge-wise analysis where we computed the between-group statistical analysis at the level of each connections in the network using the Network Based Statistics (NBS) approach (Zalesky et al., 2010a).

The inverse solutions were computed using Brainstorm (Tadel et al., 2011). The network measures and network visualization were performed using BCT (Rubinov and Sporns, 2010) and EEGNET (Hassan et al., 2015b) respectively. See Fig. S1 in the Supplementary materials for more details about the dense-EEG source connectivity method.