Statistical modeling of behavioral dynamics during propofol-induced loss of consciousness.

Background: Accurate quantitative analysis of the changes in responses to external stimuli is crucial for characterizing the timing of loss and recovery of consciousness induced by anesthetic drugs. We studied induction and emergence from unconsciousness achieved by administering a computer-controlled infusion of propofol to ten human volunteers. We evaluated loss and recovery of consciousness by having subjects execute every 4s two interleaved computer delivered behavioral tasks: responding to verbal stimuli (neutral words or the subject's name), or less salient stimuli of auditory clicks.

A transition in brain state during propofol-induced unconsciousness.

Rhythmic oscillations shape cortical dynamics during active behavior, sleep, and general anesthesia. Cross-frequency phase-amplitude coupling is a prominent feature of cortical oscillations, but its role in organizing conscious and unconscious brain states is poorly understood. Using high-density EEG and intracranial electrocorticography during gradual induction of propofol general anesthesia in humans, we discovered a rapid drug-induced transition between distinct states with opposite phase-amplitude coupling and different cortical source distributions.

Electroencephalogram signatures of loss and recovery of consciousness from propofol.

Unconsciousness is a fundamental component of general anesthesia (GA), but anesthesiologists have no reliable ways to be certain that a patient is unconscious. To develop EEG signatures that track loss and recovery of consciousness under GA, we recorded high-density EEGs in humans during gradual induction of and emergence from unconsciousness with propofol. The subjects executed an auditory task at 4-s intervals consisting of interleaved verbal and click stimuli to identify loss and recovery of consciousness.

Assessing the effects of pharmacological agents on respiratory dynamics using time-series modeling.

Developing quantitative descriptions of how stimulant and depressant drugs affect the respiratory system is an important focus in medical research. Respiratory variables-respiratory rate, tidal volume, and end tidal carbon dioxide-have prominent temporal dynamics that make it inappropriate to use standard hypothesis-testing methods that assume independent observations to assess the effects of these pharmacological agents. We present a polynomial signal plus autoregressive noise model for analysis of continuously recorded respiratory variables.

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.

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.

Correcting for serial dependence in studies of respiratory dynamics.

Understanding the physiological impact of drug treatments on patients is important in assessing their performance and determining possible side effects. While this effect might be best determined in individual subjects, conventional methods assess treatment performance by averaging a physiological measure of interest before and after drug administration for n subjects. Summarizing large numbers of time-series observations in two means for each subject in this way results in significant information loss.

Methylphenidate actively induces emergence from general anesthesia.

Background: Although accumulating evidence suggests that arousal pathways in the brain play important roles in emergence from general anesthesia, the roles of monoaminergic arousal circuits are unclear. In this study, the authors tested the hypothesis that methylphenidate (an inhibitor of dopamine and norepinephrine transporters) induces emergence from isoflurane general anesthesia.

Methods: Using adult rats, the authors tested the effect of intravenous methylphenidate on time to emergence from isoflurane general anesthesia.

Decomposition of neurological multivariate time series by state space modelling.

Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms.

Asymmetric control mechanisms of bimanual coordination: an application of directed connectivity analysis to kinematic and functional MRI data.

Mirror-symmetrical bimanual movement is more stable than parallel bimanual movement. This is well established at the kinematic level. We used functional MRI (fMRI) to evaluate the neural substrates of the stability of mirror-symmetrical bimanual movement.

Akaike causality in state space. Instantaneous causality between visual cortex in fMRI time series.

We present a new approach of explaining instantaneous causality in multivariate fMRI time series by a state space model. A given single time series can be divided into two noise-driven processes, a common process shared among multivariate time series and a specific process refining the common process. By assuming that noises are independent, a causality map is drawn using Akaike noise contribution ratio theory.

Modelling non-stationary variance in EEG time series by state space GARCH model.

We present a new approach to modelling non-stationarity in EEG time series by a generalized state space approach. A given time series can be decomposed into a set of noise-driven processes, each corresponding to a different frequency band. Non-stationarity is modelled by allowing the variances of the driving noises to change with time, depending on the state prediction error within the state space model.