Generalized Information Equilibrium Approaches to EEG Sleep Stage Discrimination

Recent advances in neuroscience have raised the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG) signals is via power-law distributed neuronal avalanches, while EEG signals are nonstationary. Therefore, spectral analysis of EEG may miss many properties inherent in such signals. A complete understanding of such dynamical systems requires knowledge of the underlying nonequilibrium thermodynamics. In recent work by Fielitz and Borchardt (2011, 2014), the concept of information equilibrium (IE) in information transfer processes has successfully characterized many different systems far from thermodynamic equilibrium. We utilized a publicly available database of polysomnogram EEG data from fourteen subjects with eight different one-minute tracings of sleep stage 2 and waking and an overlapping set of eleven subjects with eight different one-minute tracings of sleep stage 3. We applied principles of IE to model EEG as a system that transfers (equilibrates) information from the time domain to scalp-recorded voltages. We find that waking consciousness is readily distinguished from sleep stages 2 and 3 by several differences in mean information transfer constants. Principles of IE applied to EEG may therefore prove to be useful in the study of changes in brain function more generally

Pilot Safety Evaluation of Varenicline for the Treatment of Methamphetamine Dependence.

Despite the worldwide extent of methamphetamine dependence, no medication has been shown to effectively treat afflicted individuals. One relatively unexplored approach is modulation of cholinergic system function. Animal research suggests that enhancement of central cholinergic activity, possibly at nicotinic acetylcholine receptors (nAChRs), can reduce methamphetamine-related behaviors. Further, preliminary findings indicate that rivastigmine, a cholinesterase inhibitor, may reduce craving for methamphetamine after administration of the drug in human subjects. We therefore performed a double-blind, placebo-controlled, crossover pilot study of the safety and tolerability of varenicline in eight methamphetamine-dependent research subjects. Varenicline is used clinically to aid smoking cessation, and acts as a partial agonist at α4β2 nAChRs with full agonist properties at α7 nAChRs. Oral varenicline dose was titrated over 1 week to reach 1 mg bid, and then was co-administered with 30 mg methamphetamine, delivered in ten intravenous infusions of 3 mg each. Varenicline was found to be safe in combination with IV methamphetamine, producing no cardiac rhythm disturbances or alterations in vital sign parameters. No adverse neuropsychiatric sequelae were detected either during varenicline titration or following administration of methamphetamine. The results suggest that varenicline warrants further investigation as a potential treatment for methamphetamine dependence

Multifractal detrended fluctuation analysis of human EEG: preliminary investigation and comparison with the wavelet transform modulus maxima technique.

Recently, many lines of investigation in neuroscience and statistical physics have converged to raise the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG) signals is nonlinear, with self-affine dynamics, while scalp-recorded EEG signals themselves are nonstationary. Therefore, traditional methods of EEG analysis may miss many properties inherent in such signals. Similarly, fractal analysis of EEG signals has shown scaling behaviors that may not be consistent with pure monofractal processes. In this study, we hypothesized that scalp-recorded human EEG signals may be better modeled as an underlying multifractal process. We utilized the Physionet online database, a publicly available database of human EEG signals as a standardized reference database for this study. Herein, we report the use of multifractal detrended fluctuation analysis on human EEG signals derived from waking and different sleep stages, and show evidence that supports the use of multifractal methods. Next, we compare multifractal detrended fluctuation analysis to a previously published multifractal technique, wavelet transform modulus maxima, using EEG signals from waking and sleep, and demonstrate that multifractal detrended fluctuation analysis has lower indices of variability. Finally, we report a preliminary investigation into the use of multifractal detrended fluctuation analysis as a pattern classification technique on human EEG signals from waking and different sleep stages, and demonstrate its potential utility for automatic classification of different states of consciousness. Therefore, multifractal detrended fluctuation analysis may be a useful pattern classification technique to distinguish among different states of brain function

Comparison between MF-DFA spectra of from waking and sleep stage 2.

<p>For 14 subjects with 8 m of EEG from both waking and sleep stage 2 per subject, EEG was divided into 16 segments of 30 s each, and MF-DFA spectra were calculated for each segment (224 segments for each state of consciousness). Average MF-DFA spectra for each consciousness state shown here were calculated by averaging across individual spectrum values for each subject. **: p<0.001 for effect of state of consciousness by general linear modeling based on mean_h.</p