Population based models of cortical drug response: insights from anaesthesia

A great explanatory gap lies between the molecular pharmacology of psychoactive agents and the neurophysiological changes they induce, as recorded by neuroimaging modalities. Causally relating the cellular actions of psychoactive compounds to their influence on population activity is experimentally challenging. Recent developments in the dynamical modelling of neural tissue have attempted to span this explanatory gap between microscopic targets and their macroscopic neurophysiological effects via a range of biologically plausible dynamical models of cortical tissue. Such theoretical models allow exploration of neural dynamics, in particular their modification by drug action. The ability to theoretically bridge scales is due to a biologically plausible averaging of cortical tissue properties. In the resulting macroscopic neural field, individual neurons need not be explicitly represented (as in neural networks). The following paper aims to provide a non-technical introduction to the mean field population modelling of drug action and its recent successes in modelling anaesthesia

Tracking electroencephalographic changes using distributions of linear models : application to propofol-vased depth of anesthesia monitoring

Objective: Tracking brain states with electrophysiological measurements often relies on short-term averages of extracted features and this may not adequately capture the variability of brain dynamics. The objective is to assess the hypotheses that this can be overcome by tracking distributions of linear models using anesthesia data, and that anesthetic brain state tracking performance of linear models is comparable to that of a high performing depth of anesthesia monitoring feature. Methods: Individuals' brain states are classified by comparing the distribution of linear (auto-regressive moving average-ARMA) model parameters estimated from electroencephalographic (EEG) data obtained with a sliding window to distributions of linear model parameters for each brain state. The method is applied to frontal EEG data from 15 subjects undergoing propofol anesthesia and classified by the observers assessment of alertness/sedation (OAA/S) scale. Classification of the OAA/S score was performed using distributions of either ARMA parameters or the benchmark feature, Higuchi fractal dimension. Results: The highest average testing sensitivity of 59% (chance sensitivity: 17%) was found for ARMA (2, 1) models and Higuchi fractal dimension achieved 52%, however, no statistical difference was observed. For the same ARMA case, there was no statistical difference if medians are used instead of distributions (sensitivity: 56%). Conclusion: The model-based distribution approach is not necessarily more effective than a median/short-term average approach, however, it performs well compared with a distribution approach based on a high performing anesthesia monitoring measure. Significance: These techniques hold potential for anesthesia monitoring and may be generally applicable for tracking brain states

Human factors in information technology

BACKGROUND: Current electroencephalogram (EEG)-derived measures provide information on cortical activity and hypnosis but are less accurate regarding subcortical activity, which is expected to vary with the degree of antinociception. Recently, the neurophysiologically based EEG measures of cortical input (CI) and cortical state (CS) have been shown to be prospective indicators of analgesia/antinociception and hypnosis, respectively. In this study, we compared CI and an alternate measure of CS, the composite cortical state (CCS), with the Bispectral Index (BIS) and another recently developed measure of antinociception, the composite variability index (CVI). CVI is an EEG-derived measure based on a weighted combination of BIS and estimated electromyographic activity. By assessing the relationship between these indices for equivalent levels of hypnosis (as quantified using the BIS) and the nociceptive-antinociceptive balance (as determined by the predicted effect-site concentration of remifentanil), we sought to evaluate whether combining hypnotic and analgesic measures could better predict movement in response to a noxious stimulus than when used alone. METHODS: Time series of BIS and CVI indices and the raw EEG from a previously published study were reanalyzed. In our current study, the data from 80 patients, each randomly allocated to a target hypnotic level (BIS 50 or BIS 70) and a target remifentanil level (Remi-0, -2, -4 or -6 ng/mL), were included in the analysis. CCS, CI, BIS, and CVI were calculated or quantified at baseline and at a number of intervals after the application of the Observer's Assessment of Alertness/Sedation scale and a subsequent tetanic stimulus. The dependency of the putative measures of antinociception CI and CVI on effect-site concentration of remifentanil was then quantified, together with their relationship to the hypnotic measures CCS and BIS. Finally, statistical clustering methods were used to evaluate the extent to which simple combinations of antinociceptive and hypnotic measures could better detect and predict response to stimulation. RESULTS: Before stimulation, both CI and CVI differentiated patients who received remifentanil from those who were randomly allocated to the Remi-0 group (CI: Cohen's d = 0.65, 95% confidence interval, 0.48-0.83; CVI: Cohen's d = 0.72, 95% confidence interval, 0.56-0.88). Strong correlations between BIS and CCS were found (at different periods: 0.55 < R-2 < 0.68, P < 0.001). Application of the Observer's Assessment of Alertness/Sedation stimulus was associated with changes in CI and CCS, whereas, subsequent to the application of both stimuli, changes in all measures were seen. Pairwise combinations of CI and CCS showed higher sensitivity in detecting response to stimulation than CVI and BIS combined (sensitivity [99% confidence interval], 75.8% [52.7%-98.8%] vs 42% [15.4%-68.5%], P = 0.006), with specificity for CI and CCS approaching significance (52% [34.7%-69.3%] vs 24% [9.1%-38.9%], P = 0.0159). CONCLUSIONS: Combining electroencephalographically derived hypnotic and analgesic quantifiers may enable better prediction of patients who are likely to respond to tetanic stimulation

Neural mass model-based tracking of anesthetic brain states

Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain states using the unscented Kalman filter applied to estimate parameters of the Jansen-Rit cortical population model is evaluated through the application of propofol-based anesthetic state monitoring. In particular, 15 subjects underwent propofol anesthesia induction from awake to anesthetised while behavioral responsiveness was monitored and frontal electroencephalographic signals were recorded. The unscented Kalman filter Jansen-Rit model approach applied to frontal electroencephalography achieved reasonable testing performance for classification of the anesthetic brain state (sensitivity: 0.51; chance sensitivity: 0.17; nearest neighbor sensitivity 0.75) when compared to approaches based on linear (autoregressive moving average) modeling (sensitivity 0.58; nearest neighbor sensitivity: 0.91) and a high performing standard depth of anesthesia monitoring measure, Higuchi Fractal Dimension (sensitivity: 0.50; nearest neighbor sensitivity: 0.88). Moreover, it was found that the unscented Kalman filter based parameter estimates of the inhibitory postsynaptic potential amplitude varied in the physiologically expected direction with increases in propofol concentration, while the estimates of the inhibitory postsynaptic potential rate constant did not. These results combined with analysis of monotonicity of parameter estimates, error analysis of parameter estimates, and observability analysis of the Jansen-Ritmodel, along with considerations of extensions of the Jansen-Ritmodel, suggests that the Jansen-Ritmodel combined with unscented Kalman filtering provides a valuable reference point for future real-time brain state tracking studies. This is especially true for studies of more complex, but still computationally efficient, neural models of anesthesia that can more accurately track the anesthetic brain state, while simultaneously inferring underlying physiological changes that can potentially provide useful clinical information. Crown Copyright (C) 2016 Published by Elsevier Inc. All rights reserved