Consciousness and Complexity during Unresponsiveness Induced by Propofol, Xenon, and Ketamine

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1 Current Biology Supplemental Information Consciousness and Complexity during Unresponsiveness Induced by Propofol, Xenon, and Ketamine Simone Sarasso, Melanie Boly, Martino Napolitani, Olivia Gosseries, Vanessa Charland- Verville, Silvia Casarotto, Mario Rosanova, Adenauer Girardi Casali, Jean-Francois Brichant, Pierre Boveroux, Steffen Rex, Giulio Tononi, Steven Laureys, and Marcello Massimini

2 Figure S1 Figure S1. For all the investigated parameters wakefulness conditions were comparable across experiments (Related to Figure 1 and Figure 3). A, global cortical reactivity as measured by the Global Mean Field Power (GMFP) in the three experiments for the wakefulness condition. Each trace (color coded) represents the grand average (thick line) ± SEM (thin lines) GMFP normalized for each participant on the mean baseline value (100 ms pre-stimulus). For each experiment, a large overlap of GMFP time-course is visible, confirmed by separate pairwise T-tests performed on individual GMFP time-series values (same as in Figure 1B). No time-point returned significant differences (p>0.05) for any of the contrasts. B, average (± SEM) binarized significant post-tms currents across cortical sources and time-points (cumulated between 8 and 400 ms post-tms) during wakefulness for the propofol, xenon, and ketamine experiments color-coded as in A. A One-way ANOVA showed no main experiment effect F (2, 15) =.27, p=.77. C, average (± SEM) PCI values for the propofol, xenon, and ketamine experiments color-coded as in A and B. A One-way ANOVA showed no main experiment effect F (2, 15) =.27, p=.77.

3 Figure S2 Figure S2. Effects of propofol, xenon and ketamine on spontaneous EEG (Related to Figure 1 and Figure 2). A, representative 20-second epochs derived from electrode Fz for each experiment and condition (wakefulness, grey traces; propofol, blue traces; xenon, black traces; ketamine, red traces). B,

4 representative spectrograms (Short-time Fourier transform applied on 1-s windows with 0.1 s overlap, binsize: 0.01Hz) calculated over 1 min of continuous EEG signal derived from electrode Fz for each experiment and condition (wakefulness, top row; propofol, bottom left; xenon, bottom middle; ketamine, bottom right). C, spectral values calculated by averaging each participant s power spectral density in the specified frequency range bins (see Supplemental Experimental Procedures) across the 60 electrodes. Bars represent the average (± SEM) spectral density difference (in db) from wakefulness (W) for the different frequency ranges across participants. Color-coded asterisks indicate significant difference from wakefulness (p<0.05). Supplemental Experimental Procedures Participants Eighteen healthy participants (8 males, age years) were enrolled in the study. All subjects gave written informed consent. The experimental protocol was approved by the local ethical committee of the University of Liège (Liège, Belgium). Before the experiment, medical history and physical examinations were performed to exclude medical conditions that were incompatible with the anesthesia and/or the TMS procedure. TMS was performed in accordance with current safety guidelines [S1,S2]. After inclusion in the experimental protocol, participants were randomly assigned to one of the three experiments (N=6 for propofol, xenon and ketamine, respectively). Procedures All experimental procedures were performed at the Centre Hospitalier Universitaire (CHU) in Liege, Belgium. Only one type of anesthetic was administered to a given participant. For each experiment half of the subjects were stimulated over Broadmann Area (BA) 6 and half over BA 7. For each experiment, a first 6 to 8-min TMS-EEG session (up to 250 stimuli, with a ms randomly jittered period) was collected before drug administration while the participants were fully responsive (Ramsay Scale score 2). Following drug administration, repeated assessments of responsiveness were performed at 30-s intervals and, upon

5 reaching deep unresponsiveness (Ramsay Scale score 6, corresponding to no response external stimuli) in three consecutive assessments, EEG recordings started and a TMS-EEG session was replicated adopting the same stimulation parameters (e.g. cortical target, intensity). During the responsive wakefulness condition, a 10-minute spontaneous EEG recording was performed before TMS-EEG was acquired, while during the drug-induced unresponsiveness condition spontaneous EEG was continuously acquired starting ~3 min before to ~3 min after TMS-EEG recordings. At the end of EEG recordings, anesthesia was discontinued, participants were allowed to recover, and assessments of responsiveness were continued every 30 s. Before the beginning of all experimental procedures, participants were given metoclopramide (2mg) to minimize possible complications in the event of nausea and vomiting caused by the anesthetic drug. In addition, participant s electrocardiogram, non-invasive blood pressure, SaO2, exhaled CO2, and axillary skin temperature were continuously monitored. In order to assess the presence/absence of conscious experience during anesthesia-induced behavioral unresponsiveness, retrospective reports were collected in all participants after awakening. For this purpose, after participants recovered responsiveness (as assessed by three consecutive Ramsey Scale score 2 evaluations), they were asked to report their previous conscious experience ( what was going on through your mind before awakening? ). Participants were asked to confirm their retrospective reports one hour after recovering responsiveness. Experience was defined as any kind of mental activity, which included thoughts, dreams, images, emotions, etc. Responses were recorded and lumped into two categories: 1) no conscious experience/no recall; 2) conscious experiences, when the participant could describe the content of the experiences. Below we report a representative excerpt from a ketamine dream after emergence collected in one subject participating in the ketamine experiment: First I had the feeling of falling backwards into space. [ ] I then found myself in a futuristic spatial vessellike environment, discussing with two people I know about some abstract theoretical problem I am interested in, and we concluded that there was no solution, and that we disagreed on some fundamental points. At the end of this brief discussion I came up with the strong conclusion that everything I thought before was wrong, that nothing I believed was true anymore [ ]. At this stage, the space of my dream

6 became slippery, I fell again with a feeling of shrinking space where everything became oblique, and I could see through the window a very large, white sphere in the intersideral space. [ ] I continued to fall and arrived in another room, still thinking that I had to find a solution to our theoretical problem using some kind of technology or way of calculation that did not exist yet; I had at that point a vision of a futuristic environment with a lot of machines, and some intense discussions with some unknown people about this mathematical problem. Then, reality started to fade and shrink further, the space of my dream flattened and virtually disappeared, and instead there was the apparition of something like a white and bright shape invading progressively the whole space. I would describe this scene as a beautiful end-of-the-world vision, with a real sensation of beauty, very impressive and strange, but no anxiety associated with it. TMS-EEG apparatus For TMS, we employed a focal figure-of-eight coil (mean/outer winding diameter 50/70 mm, biphasic pulse shape, pulse length 280 ms, focal area of the stimulation 0.68 cm2) driven by a Mobile Stimulator Unit (eximia TMS Stimulator, Nexstim Plc., Finland). The targeted cortical areas were identified on each subject s T1 anatomical MRI, acquired with a 3-T GE scanner. We controlled TMS parameters by means of a navigated brain stimulation (NBS) system (Nexstim Plc., Finland) employing a 3D infrared tracking position sensor unit to locate the relative positions of the coil and subject s head within the reference space of individual MRI scans. NBS system also estimated, online, the distribution and the intensity (expressed in V/m) of the intracranial electric field induced by TMS. Across experiments, stimulation intensity was set at an intensity of about 110 V/m, above the threshold (50 V/m) for a significant EEG response [S3]. EEG measurements were performed using a TMS-compatible 60-channel EEG amplifier (Nexstim Plc., Finland). This device prevents amplifier saturation and reduces, or abolishes, the magnetic artifacts induced by the coil s discharge via a sample-and-hold circuit activated about 50 ms before the TMS pulse and released 2.5 ms after [S4]. To further optimize TMS compatibility, the impedance at all electrodes was kept below 5 kω. EEG signals were referenced to an additional electrode on the forehead, filtered ( Hz) and sampled at 1450 Hz. Two extra sensors were used to record the electrooculogram. Moreover,

7 participants wore inserted earplugs through which a noise masking, reproducing the time-varying frequency components of the TMS-associated click, was played throughout each TMS-EEG session in order to prevent click -induced EEG auditory evoked potentials [S5] as well as eye blinks or eye muscle reactions [S6]. Additionally, the bone conduction produced by the mechanical vibration of the TMS was minimized by placing a thin foam layer between the coil and the EEG cap [S7]. Anesthetic procedures Given that the depth of anesthesia is hard to compare when using compounds acting on different molecular targets, the anesthetic procedures for the three experiments were aimed at reaching a common behavioral state, i.e. unresponsiveness, systematically assessed by means of repeated Ramsey Scale administrations. Thus, we attained Ramsay Scale score 6 for all the subjects in the three experiments by employing anesthetic procedures based on previous works. Propofol. Propofol anesthesia was induced by a certified senior anesthesiologist through an intravenous catheter placed into a vein of the right hand or forearm. Throughout the study, the subjects breathed spontaneously, and additional oxygen (5 liters/min) was given through a loosely fitting plastic face mask. Anesthesia was obtained with a target-controlled infusion (Alaris TIVA; CareFusion). The pump was controlled by a software algorithm based on the Marsh adult pharmacokinetic model for propofol [S8] set at 3µg/ml. When the appropriate effect site concentration was reached, a 5-minute equilibration period was allowed to ensure equilibration of propofol repartition between compartments. Xenon. Xenon anesthesia was induced by a certified senior anesthesiologist. Before xenon administration, we performed a denitrogenation with 100% oxygen applied via a facial mask. Once completed, a 2-3 mg/kg bolus of propofol was administered through a right arm or forearm intravenous catheter in order to allow the insertion of a laryngeal mask securing the airway. Xenon was then introduced progressively and propofol was withdrawn. We then waited for spontaneous reduction of plasmatic propofol concentration (propofol washout) until calculated propofol plasma concentration were below 1µg/ml, [S9], within a subanesthetic range [S10] after which anesthesia was maintained with xenon (62.5±2.5 % in oxygen) using

8 a Dräger PhysioFlex closed circuit ventilator (Dräger; Luebeck, Germany). Participants were undergoing assisted spontaneous ventilation with pressure control maintaining normocapnia and received a total amount of xenon ranging from 24 to 32 liters. Ketamine. Ketamine anesthesia was induced by a certified senior anesthesiologist using a 2 mg/kg intravenous infusion (diluted in 10mL of 0.9% normal saline) of racemic ketamine (Ketalar, Pfizer Ltd, Istanbul, Turkey) over 2 minutes and maintained by continuous ketamine infusion at 0.05 mg/kg/min (Baxter infusion pump AS40A; Baxter Healthcare Corp., Deerfield, IL) over the entire experimental procedures. Spontaneous EEG Data Analysis Spontaneous EEG data were off-line filtered ( Hz) with a 3rd order IIR Butterworth digital filter with an attenuation of -3dB at 0.5 and 40 (using the filtfilt function in the Matlab signal processing toolbox). Bad channels were rejected based on visual inspection [ 10% of channels per recording; mixed-model ANOVA Experiment (Propofol, Xenon, Keamine) X Condition (Wakefulness, Unresponsiveness) F (2,15) =.88 p=.43]. Rejected channels were then interpolated using spherical splines. Continuous data were then split into contiguous 2-second segments (excluding those segments during which TMS-EEG recordings were performed). Segments containing movements were excluded from the analysis based on visual inspection (max/min retained EEG segments across experiments and conditions: 300/86). After reducing the number of independent component to the number of good, non-interpolated channels by performing Singular Value Decomposition, independent component analysis [ICA; [S11]] was used to remove ocular, muscle, and electrocardiograph artifacts using EEGLAB routines. Only ICA components with specific activity patterns and component maps characteristic of artifactual activity [S12] were removed [see [S13] for examples]. The signal for each channel was re-referenced to the average of all channels. For each EEG derivation, power spectral density estimates were computed by fast Fourier transform (FFT) in 2-s Hamming window (applying the pwelch function in the Matlab signal processing toolbox). Spectral power estimates were thus performed with a 0.5 Hz bin resolution. Average power density across segments was computed for Slow

9 Wave Activity (SWA; Hz), theta (5-8 Hz), alpha (8-12 Hz), sigma (12-16 Hz), beta (16-30 Hz) and gamma (30-40 Hz) frequency bands. TMS-EEG Data Analysis Data analysis was performed using Matlab R2006a (The MathWorks). All trials that contained spontaneous blinks, eye movement, or muscle artifacts were rejected, channels with bad signal quality or large residual artifacts were excluded, EEG-data were filtered ( Hz) with a 3rd order IIR Butterworth digital filter with an attenuation of -3dB at 0.5 and 45 (using the filtfilt function in the Matlab signal processing toolbox), sampled at 725Hz and referenced to the common average reference. Each TMS-evoked response was obtained by averaging artifact-free trials [mixed-model ANOVA Experiment (Propofol, Xenon, Ketamine) X Condition (Wakefulness, Unresponsiveness) F (2,15) =.13 p=.87]. In order to quantify cortical reactivity, we measured the overall amount of electrical activity induced by TMS by calculating the Global Mean Field Power [GMFP] from the 60 channels averaged signals. Finally, the primary electromagnetic sources of scalp EEG activity were calculated by performing source modeling, and the significant responses were estimated by applying a nonparametric bootstrap-based statistical procedure to TMS-evoked cortical currents as in [S14]. The ensuing spatiotemporal matrices were then binarized and processed in order to derive the Perturbational Complexity Index (PCI), an empirical measure of brain complexity, which gauges the amount of information contained in the integrated response of the thalamocortical system to a direct perturbation calculated as the normalized Lempel-Ziv complexity of the spatiotemporal pattern of cortical activation triggered by the TMS perturbation. Statistical Analysis Comparisons between conditions (wakefulness, unresponsiveness) within the same experiment were performed by means of the non-parametric Wilcoxon signed-rank test (p<0.05). When testing differences across experiments, mixed-model analyses of variance (ANOVA) were performed. To test contrasts, post hoc two-tailed t-tests were used (p<0.05, Bonferroni corrected).

10 Supplemental References S1. Wassermann EM. Risk and safety of repetitive transcranial magnetic stimulation: report and suggested guidelines from the International Workshop on the Safety of Repetitive Transcranial Magnetic Stimulation, June 5-7, Electroencephalogr Clin Neurophysiol 1998;108:1 16. S2. Rossi S, Hallett M, Rossini PM, Pascual-Leone A, Safety of TMS Consensus Group. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clin Neurophysiol 2009;120: doi: /j.clinph S3. Casali AG, Casarotto S, Rosanova M, Mariotti M, Massimini M. General indices to characterize the electrical response of the cerebral cortex to TMS. Neuroimage 2010;49: doi: /j.neuroimage S4. Virtanen DJ, Ruohonen J, Näätänen R, Ilmoniemi RJ. Instrumentation for the measurement of electric brain responses to transcranial magnetic stimulation. Med Biol Eng Comput 1999;37: doi: /bf S5. Nikouline V, Ruohonen J, Ilmoniemi RJ. The role of the coil click in TMS assessed with simultaneous EEG. Clinical Neurophysiology 1999;110: doi: /s (99)00070-x. S6. Ilmoniemi RJ, Kičić D. Methodology for combined TMS and EEG. Brain Topography 2010;22: doi: /s S7. Braack EM Ter, de Vos CC, van Putten MJAM. Masking the Auditory Evoked Potential in TMS-EEG: A Comparison of Various Methods. Brain Topogr 2015;28: doi: /s z. S8. Marsh B, White M, Morton N, Kenny GN. Pharmacokinetic model driven infusion of propofol in children. Br J Anaesth 1991;67:41 8. S9. Rex S, Schaefer W, Meyer PH, Rossaint R, Boy C, Setani K, et al. Positron emission tomography study of regional cerebral metabolism during general anesthesia with xenon in humans. Anesthesiology 2006;105: S10. Fiset P, Paus T, Daloze T, Plourde G, Meuret P, Bonhomme V, et al. Brain mechanisms of propofolinduced loss of consciousness in humans: a positron emission tomographic study. J Neurosci 1999;19: S11. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 2004;134:9 21. doi: /j.jneumeth S12. Jung TP, Makeig S, Humphries C, Lee TW, McKeown MJ, Iragui V, et al. Removing electroencephalographic artifacts by blind source separation. Psychophysiology 2000;37: S13. Hulse BK, Landsness EC, Sarasso S, Ferrarelli F, Guokas JJ, Wanger T, et al. A postsleep decline in auditory evoked potential amplitude reflects sleep homeostasis. Clin Neurophysiol 2011;122: doi: /j.clinph

11 S14. McCubbin J, Yee T, Vrba J, Robinson SE, Murphy P, Eswaran H, et al. Bootstrap significance of low SNR evoked response. Journal of Neuroscience Methods 2008;168: doi: /j.jneumeth

Supplementary Information on TMS/hd-EEG recordings: acquisition and preprocessing

Supplementary Information on TMS/hd-EEG recordings: acquisition and preprocessing Stability of the coil position was assured by using a software aiming device allowing the stimulation only when the deviation