EEG power spectra response to a 4-h phase advance and gaboxadol treatment in 822 men and women.

STUDY OBJECTIVE: To explore the effect of gaboxadol on NREM EEG in transient insomnia using power spectral analysis and evaluate the response between men and women. METHODS: This was a randomized, double-blind, 3-way, parallel-group transient insomnia study in 22 sleep laboratories. After a baseline night (N1), subjects underwent a 4-h phase-advance of their habitual sleep time the following night (N2). Healthy subjects aged 18-64 y were given single-blind placebo on N1 followed by double-blind treatment on N2 (gaboxadol 10 mg [n = 271], 15 mg [n = 274], or placebo [n = 277]) RESULTS: At baseline, women showed significantly greater values in low frequency activity (< 10 Hz) and in high spindle/low beta frequency activity (14-18 Hz) compared to men. During the phase advance (placebo N2-baseline N1), there was a significant increase in power within the high spindle/low beta frequency range (15-17 Hz) and a significant reduction in beta activity (20-32 Hz), which was greater in women than men. Gaboxadol induced a significant (dose-related) increase in low frequencies (< 8 Hz) and a significant (dose-related) decrease within the alpha/spindle range (11-12 Hz). The effect was dependent upon sex, with a greater magnitude of effect observed in women than men. CONCLUSION: Gaboxadol shows a characteristic NREM EEG spectral profile in a model of transient insomnia. Men and women show clear differences in NREM EEG activity at baseline, to gaboxadol treatment and to phase-shifts in habitual sleep/wake times. The exact mechanisms underlying the sex differences remain unclear, but sex is an important variable in studies evaluating sleep and gaboxadol. TRIAL REGISTRY INFORMATION: TRIAL REGISTRY: www.clinicaltrials.gov, study identifier: NCT00102167.

Alterations in cyclic alternating pattern associated with phase advanced sleep are differentially modulated by gaboxadol and zolpidem.

OBJECTIVE: to evaluate cyclic alternating pattern (CAP) in a phase advance model of transient insomnia and the effects of gaboxadol and zolpidem. DESIGN: a randomized, double-blind, cross-over study in which habitual sleep time was advanced by 4 h. SETTING: 6 sleep research laboratories in US PARTICIPANTS: 55 healthy subjects (18-57 y) INTERVENTIONS: Gaboxadol 15 mg (GBX), zolpidem 10 mg (ZOL), and placebo (PBO). MEASUREMENTS: routine polysomnographic (PSG) measures, CAP, spectral power density, and self-reported sleep measures RESULTS: The phase advance model of transient insomnia produced significant changes in CAP parameters. Both GBX and ZOL significantly and differentially modified CAP parameters in the direction of more stable sleep. GBX brought the CAP rate in stage 1 sleep and slow wave sleep (SWS) closer to baseline levels but did not significantly change the CAP rate in stage 2. ZOL reduced the CAP rate in stage 2 to near baseline levels, whereas the CAP rate in stage 1 and SWS was reduced substantially below baseline levels. The CAP parameter A1 index (associated with SWS and sleep continuity) showed the highest correlation with self-reported sleep quality, higher than any traditional PSG, spectral, or other self-reported measures. CONCLUSION: disruptions in CAP produced by phase advanced sleep were significantly and differentially modulated by gaboxadol and zolpidem. The relative independence of CAP parameters from other electrophysiological measures of sleep, their high sensitivity to sleep disruption, and their strong association with subjective sleep quality suggest that CAP variables may serve as valuable endpoints in future insomnia research.

A Bayesian transformation model for wavelet shrinkage.

Wavelet shrinkage estimators, in general, make the additive normal noise assumption and disregard the nonlinear nature of contamination. We develop Bayesian wavelet shrinkage estimators (based on the power transformations in the linear model) to accommodate a broad class of noise models in image processing applications. We intend to admit, under one roof, the widespread additive model, the product models common in imaging (such as in synthetic aperture radar (SAR) imagery), as well as noise that may exist amid these two extremes. Tactful prior elicitation in this model, such as the simultaneous assignment of mixture priors for wavelet coefficients and the transformation, imparts flexibility and ample insight into the underlying structure. The model permits estimation with unknown noise structure for reasonably unimodal and well-behaved (on the tails) distributions, wherein it can outperform common shrinkage estimators. Extensions with multiple transformations and Markov random field priors are also considered for adaptation to local variations in contamination. Modern Markov chain Monte Carlo (MCMC) Bayesian computation has been used for simulations and several examples are reported in the paper.