Fig. 1: Changes in global properties of phase dynamics induced by loss of consciousness.
From: Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics
a BOLD band-pass signals (0.04–0.07 Hz) for two sample ROIs. The instantaneous phases, ϕj(t) and ϕk(t), of each signal were computed using the Hilbert transform. b At each time frame, the interaction between ROIs was given by the instantaneous phase difference, Δϕjk(t) = ∣ϕj(t) − ϕk(t)∣, which can be represented as vectors in the unit circle of the complex plane. c Phase-interaction matrices Pjk(t) were calculated as the cosine of the phase difference, \(\cos ({{\Delta }}{\phi }_{jk}(t))\), at time t. All global measures used afterwards were based on the phase-interaction matrices. d–e The structure of phase interactions was described in terms of the integration and the segregation of the time-averaged phase interaction matrix (see “Methods”). f We quantified the temporal fluctuations of the mean phase synchrony (i.e., the average over ROIs of matrix P(t)) through its temporal standard deviation. g To detect the existence of recurrent synchronization patterns, we computed the FCD comparing phase-interaction matrices at different times (see “Methods”). Briefly, the FCD represents the (cosine) similarities between phase-interaction matrices at times t and t0 for all possible pairs (t, t0). The panel shows the average similarity for each experimental condition. In panels (d–g), each dot represents a participant and the boxes represent the measure’s distribution. Boxplots represent the mean of the measures' values with a 95% confidence interval (dark) and 1 SD (light). Differences between groups were assessed using one-way ANOVA followed by FDR p-value correction. *: p < 0.05; **: p < 0.01; ***: p < 0.001 (see Table 1 for details).
