Phase-amplitude coupling (PAC) between slow and alpha oscillations.

<p>Coupling between ongoing slow phase and alpha power over occipital channels delineated in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004669#pcbi.1004669.g005" target="_blank">Fig 5A</a> (top left) shifted from a trough-max to a peak-max (C) distribution in the drowsy group during moderate sedation, resulting in a significant interaction between group and sedation in PAC values (A). Crucially, these subject-wise PAC values significantly correlated with drug concentrations measured in blood across both groups during moderate sedation (B).</p

Experimental manipulation and measurement of behaviour and propofol concentration in blood plasma.

<p>(A) Resting state EEG data were collected for four ~7 minute periods from each participant: at baseline before administration of propofol, at mild sedation, moderate sedation, and finally at recovery. Each resting state data collection was followed by a two-choice speeded response task to assess behavioural responsiveness. Blood samples were collected and analysed offline to measure and correlate actual levels of propofol in plasma with EEG measures. (B) Two sub-groups of participants, <i>responsive</i> and <i>drowsy</i>, were identified based on binomial modelling of the change in their behavioural responsiveness due to sedation. (C) Reaction times in the responsive group were slower during moderate sedation. (D) Measured drug concentrations in blood plasma overlapped between the two groups.</p

Alpha band power changes as a function of sedation.

<p>(A) Alpha power topography in the drowsy group progressively switched from an occipital to a frontal pattern during moderate sedation, while the responsive group remained stable throughout. (B) The resulting interaction between group and level of sedation on their alpha power contributions from frontal vs. occipital channels was statistically significant (F(3) = 10.1, p = 0.0008). (C) Even amongst the responsive group, reduction in relative occipital alpha power during moderate sedation was correlated with relatively slower reaction time, relative to baseline.</p

Baseline alpha band networks predict loss of responsiveness during moderate sedation.

<p>Participants in the drowsy group had relatively lower small-worldness (A) already at baseline. Median split of participants based on baseline small-worldness predicted eventual loss of responsiveness (B) despite similar blood levels of drug concentration during moderate sedation (C).</p

Summary of EEG data analysis pipeline.

<p>Cross-spectral density between channel pairs was estimated using dwPLI. Symmetric connectivity matrices generated were thresholded before the estimation of graph-theoretic metrics. In the connectivity matrix shown (bottom left), the threshold has been set to depict only top 30% of strongest connections. In the network topograph (bottom middle), intra-modular links in modules identified by the Louvain algorithm are indicated by colour.</p

Summary of alpha connectivity changes.

<p>There were significant differences in the levels of local clustering (A) and global path lengths (C) between responsive and drowsy groups during mild and moderate sedation. Alpha networks in the drowsy group were hence significantly less locally and globally efficient. Crucially, these differences between the groups were apparent even at baseline, when the groups were behaviourally indistinguishable. Within the responsive group, decreasing levels of local (B) and global (D) efficiency were associated with slowing of reaction times during moderate sedation, relative to baseline. There were also significant differences in meso-scale modularity (E) and the presence of hub-like nodes with high participation coefficients (F) between responsive and drowsy groups during moderate sedation. Alpha networks in the drowsy group were more modular, with weaker hubs, even at baseline. Error bars depict standard error of the mean.</p

Global Graph Theory Analysis.

<p>Columns indicate each resting-state condition, and rows indicate each graph metric. The Y-axis shows raw metric scores, and the X-axis shows the range of thresholds, from 50 to 800, in steps of 50 (excluding extreme values where networks disaggregate). <i>Boxes indicate significant and trend differences between JM and the control sample. Blue shadows represent controls</i>' <i>standard deviation area.</i></p

Empathy for pain task (EPT).

<p><u>Neutral condition results:</u> (A) categorization accuracy in percent; (B) reaction time in seconds of Harmful behavior and (C) average pain rating scores for each question after scenes of the neutral condition. <u>Intentional condition results:</u> (D) average empathy-related judgments scores for each question after scenes of this condition. <i>* expressed significant differences between the DD patient and control sample.</i></p

Resting-state networks.

<p>Most-often reported networks in previous research that contain groups of brain regions highly correlated with each other.</p

Heartbeat Detection Task (HBD).

<p>The Accuracy Index can vary between 0 and 1, with higher scores indicating better interoceptive sensitivity. <i>* indicates significant differences between JM and the control sample.</i></p