FIGURE 5.

Stationary versus dynamic connectivity (PDD). Results are, apart from (b), shown for the beta band. Panel (a) shows a short segment from a single subject of whole brain connectivity fluctuation for genuine data (called dynamic connectivity depicted in blue) and surrogate data with preserved stationary connectivity (depicted in red). There were brief periods when dynamic connectivity exceeds stationary connectivity (depicted in black). Panel (b) shows the goodness‐of‐fit (R ²) of the eigenmode approach for the stationary connectivity case (red) and for the case whenever dynamic connectivity exceeded stationary connectivity (blue). For all frequency bands predictions are better for dynamic connectivity. Panel (c) shows the estimated parameters for the same conditions as in (b). An asterisk refers to a significant difference (p < 0.001). Dynamic network for time‐points when dynamic connectivity exceeded stationary connectivity are shown in (e), which shows a sensorimotor network, a right temporal network, a visual and frontal network. Eigenmode predicted brain maps and mapping coefficients are depicted in (f) and (d), respectively. $⟨{\mathrm{p}}_{\mathrm{i}}⟩$, average eigenmode coefficients; dFC, dynamic functional connectivity; comp., component, PDD, phase difference derivative, R ², explained variance