The Administration of Ketamine Is Associated with Dose-Dependent Stabilization of Cortical Dynamics in Humans

Abstract

During wakefulness, external stimuli elicit conscious experiences. In contrast, dreams and drug-induced dissociated states are characterized by vivid internally generated conscious experiences and reduced ability to perceive external stimuli. Understanding the physiological distinctions between normal wakefulness and dissociated states may therefore disambiguate signatures of responsiveness to external stimuli from those that underlie conscious experience. The hypothesis that conscious experiences are associated with brain criticality has received considerable theoretical and experimental support. Consistent with this hypothesis, statistical signatures of criticality are similar in normal wakefulness and dissociative states but are abolished in dreamless sleep and under anesthesia. Thus, while statistical measures of criticality are associated with the ability to have conscious experience, they do not readily distinguish between perception of the external world from internally generated percepts. Here, we investigate distinct, dynamical, signatures of criticality during escalating ketamine doses in high-density EEG in human male volunteers. We show that during normal wakefulness, EEG is found at a critical point between damped and exploding oscillations. With increasing doses of ketamine, as dissociative symptoms intensify, activity is progressively stabilized-most prominently at higher frequencies. We also show that stabilization is a more reliable marker of the effects of ketamine than conventional measures such as power spectra. These findings suggest that stabilization of cortical dynamics correlates with decreased ability to respond to and perceive external stimuli rather than the ability to have conscious experiences per se. Altogether, these results suggest that combining statistical and dynamical criticality measures may distinguish wakefulness, dissociation, and unconsciousness.

Keywords: EEG; consciousness; dissociated state; dynamics; ketamine.

Figure 1.

Ketamine delivery protocol. Traces detailing the predicted plasma concentration (orange trace, right y-axis) in tandem with the ketamine infusion rate (teal trace, left y-axis) in a representative experiment. “Infusion On” and “Infusion Off” labels, respectively, indicate when the drug infusion started and ended. Blue bars labeled Recording Period indicate times when predicted drug concentration was stable and EEG recording was used for resting state analysis. Infusion rate is continuously adjusted to ensure a stable predicted plasma concentration of ketamine.

Figure 2.

Illustration of EEG signal decomposition into eigenmodes. Five-hundred millisecond window of four channels of hypothetical EEG signal is shown (Simulated EEG). These data were generated by an autoregressive model. The power spectrum below traces shows the average across channels. For the sake of illustration, the EEG contains two frequencies (alpha, 10 Hz, and gamma, 33 Hz). The overall EEG signal can be approximately decomposed into two components (modes). Note that the first mode oscillates at the alpha band, while the second mode oscillates at gamma (power spectra shown below traces). The amplitude of the signal in each channel of the mode reflects the loadings of the corresponding eigenvector. Note that channel 1 is dominated by gamma oscillations, but channel 3 has predominantly alpha oscillations. This difference in power arises because the first eigenvector has a large component at channel 3 while the second eigenvector has a large component in channel 1. While the average amplitude of the oscillation differs from channel to channel, the dynamics of the decay of the mode are the same across all channels. The modulus of the eigenvalue for the first eigenmode is ∼1 and, consequently, the signal does not decay over time. We refer to such modes as critical. The second eigenmode, in contrast, is damped (the modulus of the eigenvalue is <1). This timescale of the decay is governed by the modulus of the eigenvalue.

Figure 3.

Ketamine-induced changes to the EEG power spectrum. A, 5 s of EEG from an occipital channel in one subject at baseline and all ketamine concentrations. Note the attenuation of alpha and the increase in higher frequency oscillations that accompany ketamine administration. B, Spectral power expressed as z-scores relative to baseline (scale shown by color bar), calculated within each frequency band, and averaged across all subjects, plotted with respect to location on the scalp surface. While we observe a global power reduction, an increase in gamma power is also observed, most notably at the lower ketamine concentration.

Figure 4.

Spectral changes by frequency and region. Bootstrapped estimates of the mean change in power at each frequency at each region (error bars indicate 95% confidence intervals). Bootstrapping procedure is described in methods subsection “Spectral Bootstrap Analysis.” As seen in Figure 4, the most pronounced change is the decrease in alpha oscillations.

Figure 5.

Stabilization of dynamics during the administration of increasing concentrations of ketamine. A, Distribution of criticality indices (|l|) is shown for each 0.5 s window during baseline (0 μg/ml) and three ketamine concentrations. The distributions are averaged across subjects (x-axis shows the time during 1 min recording in each condition; y-axis denotes |l|, color encodes fraction of modes represented). As many criticality indices are close to zero and do not contribute to the dynamics, the y-axis starts at 0.7 to emphasize the modes that contribute to the overall dynamics. As ketamine dose increases, the fraction of modes near criticality decreases. B, Distribution of criticality indices computed for shuffle surrogate datasets (Materials and Methods) are plotted in the same fashion as A. Note significant disruption of overall pattern of criticality in time-shuffled data.

Figure 6.

Ketamine-induced stabilization is observed across most frequencies in a dose-dependent fashion. Each subplot shows data from a single subject. The contours were generated to include 90% of the eigenvalues in each subject at each ketamine concentration. Note stabilization occurring across all frequencies in a dose-dependent fashion. The most marked stabilization can be observed at higher frequencies (20 Hz and above).

Figure 7.

Criticality-based classifier predicts ketamine concentration more reliably than Spectrum-based classifier. A, Out-of-bag error rates for multi-class Random Forest classifiers trained on Criticality versus Power Spectral Density (PSD). The error rate is the pooled estimate across all ketamine concentrations. B, Variable Importance (quantified as mean decrease in Gini impurity) for Criticality Classifier versus PSD classifier. C, D, Confusion Matrices for Criticality and PSD classifier, respectively. Note that the criticality classifier makes fewer errors at each ketamine concentration. E, Variance explained plotted as a function of number of principal components (PCs) for changes in the distribution of criticality indices and of the spectra (teal and orange respectively) for all subjects. F, Median (across time in each subject) projection onto first principal component of the criticality dataset by dose. Each colored line represents a subject; overlain boxplots show interquartile interval (whiskers show 1.5 times the interquartile range). G, Same as F but for the spectra.