General Anesthesia and Altered States of Arousal: A Systems Neuroscience Analysis

Placing a patient in a state of general anesthesia is crucial for safely and humanely performing most surgical and many nonsurgical procedures. How anesthetic drugs create the state of general anesthesia is considered a major mystery of modern medicine. Unconsciousness, induced by altered arousal and/or cognition, is perhaps the most fascinating behavioral state of general anesthesia. We perform a systems neuroscience analysis of the altered arousal states induced by five classes of intravenous anesthetics by relating their behavioral and physiological features to the molecular targets and neural circuits at which these drugs are purported to act. The altered states of arousal are sedation-unconsciousness, sedation-analgesia, dissociative anesthesia, pharmacologic non-REM sleep, and neuroleptic anesthesia. Each altered arousal state results from the anesthetic drugs acting at multiple targets in the central nervous system. Our analysis shows that general anesthesia is less mysterious than currently believed.Massachusetts General Hospital. Dept. of Anesthesia and Critical CareNational Institutes of Health (U.S.) (Director's Pioneer Award DP10D003646)National Institutes of Health (U.S.) (New Innovator Award DP2OD006454)National Institutes of Health (U.S.) (Grant K25-NS057580)National Institutes of Health (U.S.) (Training Program in Sleep, Circadian and Respiratory Neurobiology HL07901

Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression

Objective. Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit.National Institutes of Health (U.S.) (Award DP1-OD003646)National Institutes of Health (U.S.) (Award DP2-OD006454)National Institutes of Health (U.S.) (Award K08-GM094394)Burroughs Wellcome Fund (Award 1010625

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

Robust time-varying multivariate coherence estimation: Application to electroencephalogram recordings during general anesthesia

Coherence analysis characterizes frequency-dependent covariance between signals, and is useful for multivariate oscillatory data often encountered in neuroscience. The global coherence provides a summary of coherent behavior in high-dimensional multivariate data by quantifying the concentration of variance in the first mode of an eigenvalue decomposition of the cross-spectral matrix. Practical application of this useful method is sensitive to noise, and can confound coherent activity in disparate neural populations or spatial locations that have a similar frequency structure. In this paper we describe two methodological enhancements to the global coherence procedure that increase robustness of the technique to noise, and that allow characterization of how power within specific coherent modes change through time.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

Signal processing methods for functional magnetic resonance imaging

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references.by Patrick L. Purdon.M.S

Multimodal neuroimaging with simultaneous EEG and high-field fMRI

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2005.Includes bibliographical references.Simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (tMRI) is an important emerging tool in functional neuroimaging with the potential to reveal new mechanisms for brain function by combining the high spatial resolution of fMRI with the high temporal resolution of EEG. Applications for this technique include studies of sleep, epilepsy, and anesthesia, as well as basic sensory, perceptual, and cognitive processes. Unlike methods that combine these modalities from separate recordings, simultaneous recordings can reveal temporal correlations between EEG and fMRI. Simultaneous recordings also eliminate environmental confounds inherent with separate recordings. MRI systems produce electromagnetic interference that can corrupt sensitive electrophysiological recordings, making simultaneous recordings challenging. Gradient switching and RF pulses can saturate EEG amplifiers, and cardiac pulsation within the static magnetic field produces large artifact signals ("ballistocardiogram") that confound EEG analysis. In this Ph.D. thesis, we develop an EEG acquisition system compatible with fMRI at 3 and 7 Tesla, a method for eliminating the ballistocardiogram artifact using adaptive filtering, and use these methods to study the 40-Hz auditory steady-state response (ASSR). The adaptive filtering method outperforms existing standard methods by up to 600%. The ASSR is a sub-microvolt level auditory evoked potential related to sleep, consciousness, and anesthesia.(cont.) Simultaneous recordings of ASSR and fMRI reveal that spontaneous fluctuations in the amplitude of the ASSR are represented throughout the auditory system, from cortex to brainstem, suggesting that brainstem structures play an important role in generating the 40-Hz ASSR and that integration of sensory information across multiple hierarchical scales, including the earliest portions of the central nervous system, may constitute an important component of awareness or arousal.by Patrick L. Purdon.Ph.D

Using EEG markers to make inferences about anaesthetic-induced altered states of arousal

Editor—Gaskell and colleagues conducted a secondary analysis of a heterogeneous, multicentre database to study the relationship between the presence of frontal alpha-delta EEG patterns and volitional responses assessed after anaesthesia induction using an isolated forearm technique. The authors conclude that that neither the presence of the frontal alpha-delta EEG patterns, nor any other EEG measure that they evaluated, reliably correlated with the volitional responses. Based on the data the authors present, this statement is not correct.National Institutes of Health (U.S.) (Award R01 GM104948)National Institutes of Health (U.S.) (Award P01 GM118629)National Institutes of Health (U.S.) (Award R01 AG053582)National Institutes of Health (U.S.) (Award R01AG056015

A differential autoregressive modeling approach within a point process framework for non-stationary heartbeat intervals analysis

Modeling heartbeat variability remains a challenging signal-processing goal in the presence of highly non-stationary cardiovascular control dynamics. We propose a novel differential autoregressive modeling approach within a point process probability framework for analyzing R-R interval and blood pressure variations. We apply the proposed model to both synthetic and experimental heartbeat intervals observed in time-varying conditions. The model is found to be extremely effective in tracking non-stationary heartbeat dynamics, as evidenced by the excellent goodness-of-fit performance. Results further demonstrate the ability of the method to appropriately quantify the non-stationary evolution of baroreflex sensitivity in changing physiological and pharmacological conditions.National Institutes of Health (U.S.) (Grant R01-HL084502)National Institutes of Health (U.S.) (Grant K25-NS05758)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 CRC UL1 RR025758