PMC

Mean PLI (upper panel) and mean Phase Coherence (lower panel) for the alpha band, displayed as a colour-coded map (unthresholded) on a schematic of the parcellated template brain.

Mean PLI (left column, thresholded at p = 0.05) and mean relative power (right column) for alpha, beta and gamma bands (top to bottom), displayed as a colour-coded map on a schematic of the parcellated template brain (see for unthresholded results). See for a list of the areas with significant mean PLI. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Mean PLI (left column, thresholded at p = 0.05) and mean relative power (right column) for alpha, beta and gamma bands (top to bottom), displayed as a colour-coded map on a schematic of the parcellated template brain (see Supplementary Fig. 4 for unthresholded results). See for a list of the areas with significant mean PLI.

Mean PLI versus mean relative power for the different frequency bands. Note that there is a significant positive linear relationship between PLI and relative power, for all frequency bands, except the gamma band. Also note that, for each frequency band separately, the mean PLI varies over only a limited range, and that the variance in PLI that can be explained by source power is relatively small (R² = 51%, 12%, 75%, 72% and 2% for the delta, theta, alpha, beta and gamma bands respectively).

Functional connectivity and relationship with the beamformer weights for the alpha band. a) Mean PLI adjacency matrix. The separation between anatomical groupings (from left to right: occipital, parietal/central, temporal, frontal) is denoted by a solid line, the separation between left and right hemisphere within each anatomical grouping is denoted by a dotted line (see for details); b) mean Phase Coherence adjacency matrix; c) mean (squared) correlation between beamformer weights for each ROI (with the diagonal set to zero). Each element in this matrix was computed as follows: for each subject, the square of the correlation between the beamformer weights for a ROI and another ROI was computed. The mean over subjects of this value was then computed; d) Scatter plot of the (squared) correlation between beamformer weights and the PLI and (e) Phase Coherence.

Flow chart of analysis steps. The anatomical MRI is co-registered with the MEG and subsequently spatially normalised to a template MRI. Voxels in the template MRI are labelled using the Talairach Daemon Database. Voxels with the same label are defined as a ROI and transformed to the individual's co-registered MRI. The volume conductor model, based on the co-registered MRI, together with the data covariance created from selected time-frequency windows in the MEG data, is used to compute beamformer weights for the target locations in these ROIs. The MEG data are then projected through the beamformer weights in order to create time-series (virtual electrodes) for these voxels. For each frequency band separately, a single time-series is constructed for each ROI (see ) and the functional connectivity between the different ROIs is estimated by computing the Phase Lag Index (PLI) or Phase Coherence. Graph theory can subsequently be applied to the resulting adjacency matrix in order to characterise the functional network formed by the interacting ROIs (see ).
Flow chart of analysis steps. The anatomical MRI is co-registered with the MEG and subsequently spatially normalised to a template MRI. Voxels in the template MRI are labelled using the Talairach Daemon Database. Voxels with the same label are defined as a ROI and transformed to the individual's co-registered MRI. The volume conductor model, based on the co-registered MRI, together with the data covariance created from selected time-frequency windows in the MEG data, is used to compute beamformer weights for the target locations in these ROIs. The MEG data are then projected through the beamformer weights in order to create time-series (virtual electrodes) for these voxels. For each frequency band separately, a single time-series is constructed for each ROI (see ) and the functional connectivity between the different ROIs is estimated by computing the Phase Lag Index (PLI) or Phase Coherence. Graph theory can subsequently be applied to the resulting adjacency matrix in order to characterise the functional network formed by the interacting ROIs (see Supplementary material).