Phase-based measures of cross-frequency coupling in brain electrical dynamics under general anesthesia

The state of general anesthesia (GA) is associated with an increase in spectral power in scalp electroencephalogram (EEG) at frequencies below 40 Hz, including spectral peaks in the slow oscillation (SO, 0.1-1 Hz) and α (8-14 Hz) bands. Because conventional power spectral analyses are insensitive to possible cross-frequency coupling, the relationships among the oscillations at different frequencies remain largely unexplored. Quantifying such coupling is essential for improving clinical monitoring of anesthesia and understanding the neuroscience of this brain state. We tested the usefulness of two measures of cross-frequency coupling: the bispectrum-derived SynchFastSlow, which is sensitive to phase-phase coupling in different frequency bands, and modulogram analysis of coupling between SO phase and α rhythm amplitude. SynchFastSlow, a metric that is used in clinical depth-of-anesthesia monitors, showed a robust correlation with the loss of consciousness at the induction of propofol GA, but this could be largely explained by power spectral changes without considering cross-frequency coupling. Modulogram analysis revealed two distinct modes of cross-frequency coupling under GA. The waking and two distinct states under GA could be discriminated by projecting in a two-dimensional phase space defined by the SynchFastSlow and the preferred SO phase of α activity. Our results show that a stereotyped pattern of phase-amplitude coupling accompanies multiple stages of anesthetic-induced unconsciousness. These findings suggest that modulogram analysis can improve EEG based monitoring of brain state under GA.National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant K25-NS057580)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385)National Institutes of Health (U.S.) (Grant R01-MH071847

Bayesian analysis of trinomial data in behavioral experiments and its application to human studies of general anesthesia

Accurate quantification of loss of response to external stimuli is essential for understanding the mechanisms of loss of consciousness under general anesthesia. We present a new approach for quantifying three possible outcomes that are encountered in behavioral experiments during general anesthesia: correct responses, incorrect responses and no response. We use a state-space model with two state variables representing a probability of response and a conditional probability of correct response. We show applications of this approach to an example of responses to auditory stimuli at varying levels of propofol anesthesia ranging from light sedation to deep anesthesia in human subjects. The posterior probability densities of model parameters and the response probability are computed within a Bayesian framework using Markov Chain Monte Carlo methods.National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant K25-NS057580)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385)National Institutes of Health (U.S.) (Grant R01-MH071847

Beamforming approach to phase-amplitude modulation analysis of multi-channel EEG

Phase-amplitude modulation is a form of cross frequency coupling where the phase of one frequency influences the amplitude of another higher frequency. It has been observed in neurophysiological recordings during sensory, motor, and cognitive tasks, as well as during general anesthesia. In this paper, we describe a novel beamforming procedure to improve estimation of phase-amplitude modulation. We apply this method to 64-channel EEG data recorded during propofol general anesthesia. The method improves the sensitivity of phase-amplitude analyses, and can be applied to a variety of multi-channel neuroscience data where phase-amplitude modulation is present.National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant K25-NS057580)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385

Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics

<div><p>The sleep onset process (SOP) is a dynamic process correlated with a multitude of behavioral and physiological markers. A principled analysis of the SOP can serve as a foundation for answering questions of fundamental importance in basic neuroscience and sleep medicine. Unfortunately, current methods for analyzing the SOP fail to account for the overwhelming evidence that the wake/sleep transition is governed by continuous, dynamic physiological processes. Instead, current practices coarsely discretize sleep both in terms of state, where it is viewed as a binary (wake or sleep) process, and in time, where it is viewed as a single time point derived from subjectively scored stages in 30-second epochs, effectively eliminating SOP dynamics from the analysis. These methods also fail to integrate information from both behavioral and physiological data. It is thus imperative to resolve the mismatch between the physiological evidence and analysis methodologies. In this paper, we develop a statistically and physiologically principled dynamic framework and empirical SOP model, combining simultaneously-recorded physiological measurements with behavioral data from a novel breathing task requiring no arousing external sensory stimuli. We fit the model using data from healthy subjects, and estimate the instantaneous probability that a subject is awake during the SOP. The model successfully tracked physiological and behavioral dynamics for individual nights, and significantly outperformed the instantaneous transition models implicit in clinical definitions of sleep onset. Our framework also provides a principled means for cross-subject data alignment as a function of wake probability, allowing us to characterize and compare SOP dynamics across different populations. This analysis enabled us to quantitatively compare the EEG of subjects showing reduced alpha power with the remaining subjects at identical response probabilities. Thus, by incorporating both physiological and behavioral dynamics into our model framework, the dynamics of our analyses can finally match those observed during the SOP.</p></div

A Comparison of Propofol- and Dexmedetomidine-induced Electroencephalogram Dynamics Using Spectral and Coherence Analysis

Background:: Electroencephalogram patterns observed during sedation with dexmedetomidine appear similar to those observed during general anesthesia with propofol. This is evident with the occurrence of slow (0.1 to 1 Hz), delta (1 to 4 Hz), propofol-induced alpha (8 to 12 Hz), and dexmedetomidine-induced spindle (12 to 16 Hz) oscillations. However, these drugs have different molecular mechanisms and behavioral properties and are likely accompanied by distinguishing neural circuit dynamics. Methods:: The authors measured 64-channel electroencephalogram under dexmedetomidine (n = 9) and propofol (n = 8) in healthy volunteers, 18 to 36 yr of age. The authors administered dexmedetomidine with a 1-µg/kg loading bolus over 10 min, followed by a 0.7 µg kg−1 h−1 infusion. For propofol, the authors used a computer-controlled infusion to target the effect-site concentration gradually from 0 to 5 μg/ml. Volunteers listened to auditory stimuli and responded by button press to determine unconsciousness. The authors analyzed the electroencephalogram using multitaper spectral and coherence analysis. Results:: Dexmedetomidine was characterized by spindles with maximum power and coherence at approximately 13 Hz (mean ± SD; power, −10.8 ± 3.6 dB; coherence, 0.8 ± 0.08), whereas propofol was characterized with frontal alpha oscillations with peak frequency at approximately 11 Hz (power, 1.1 ± 4.5 dB; coherence, 0.9 ± 0.05). Notably, slow oscillation power during a general anesthetic state under propofol (power, 13.2 ± 2.4 dB) was much larger than during sedative states under both propofol (power, −2.5 ± 3.5 dB) and dexmedetomidine (power, −0.4 ± 3.1 dB). Conclusion:: The results indicate that dexmedetomidine and propofol place patients into different brain states and suggest that propofol enables a deeper state of unconsciousness by inducing large-amplitude slow oscillations that produce prolonged states of neuronal silence.National Institutes of Health (U.S.) (Grant DP2-OD006454)National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant TR01-GM104948

Heterogeneity in healthy subjects: An SOP phenotype with alpha power dropout before the cessation of behavioral activity.

<p>The (A) spectrogram, (B) behavioral responses, (C) the wake probability curve, (D) and the clinical hypnogram are shown for a subject with this SOP. The wake probability curve captures persistence of behavioral responses after alpha power declines, a feature that is not evident in hypnogram-based binary models of sleep onset (E). The Bayesian likelihood analysis (F) shows that wake probability significantly outperforms (99.99% Bayesian credible interval of the difference distribution falls above zero) all of the instantaneous transition models in the ability to correctly predict the behavioral responses for this subject.</p

A data-driven model of sleep onset process dynamics.

<p>(A) As the SOP progresses, Pr(Wake), the probability that the subject is awake, decreases. As Pr(Wake) decreases, the probability of a correct behavioral response, the EMG squeeze amplitude, and the alpha power will also decrease, while the delta and theta power will increase. (B) We can then use experimental data to estimate Pr(Wake) over time. We define state processes representing the activity of the systems underlying the motor, alpha power, and delta-theta power observations. The combined information from all states represents the level of all activity related to waking, and is used along with the behavioral task responses to estimate the wake probability curve, which tracks Pr(Wake) over time.</p

Fitting the model to experimental data during the course of sleep initiation.

<p>(A) The simultaneously observed EMG (black dots), and EEG observations (black curves), and behavioral responses (green  =  correct, red  =  incorrect) from the experiment are used to estimate the wake probability curve (B), which shows Pr(Wake), the probability of a correct response given the EEG and EMG data, over time. The wake probability curve acts as a statistically principled means of tracking the dynamics sleep initiation, and agrees well with features of the EEG spectrogram (C), the clinical hypnogram (D). In (A) the model estimates of the EMG and EEG mean (curves) and 95% confidence intervals (shaded regions) are shown (EMG: red, alpha: cyan, theta: magenta, delta: green). For all state estimates, we compute the distribution medians (colored curves), and 95% confidence intervals (shaded regions) of the model estimates.</p