The Effects of Daytime Psilocybin Administration on Sleep: Implications for Antidepressant Action.

Serotonergic agonist psilocybin is a psychedelic with antidepressant potential. Sleep may interact with psilocybin's antidepressant properties like other antidepressant drugs via induction of neuroplasticity. The main aim of the study was to evaluate the effect of psilocybin on sleep architecture on the night after psilocybin administration. Regarding the potential antidepressant properties, we hypothesized that psilocybin, similar to other classical antidepressants, would reduce rapid eye movement (REM) sleep and prolong REM sleep latency. Moreover, we also hypothesized that psilocybin would promote slow-wave activity (SWA) expression in the first sleep cycle, a marker of sleep-related neuroplasticity. Twenty healthy volunteers (10 women, age 28-53) underwent two drug administration sessions, psilocybin or placebo, in a randomized, double-blinded design. Changes in sleep macrostructure, SWA during the first sleep cycle, whole night EEG spectral power across frequencies in non-rapid eye movement (NREM) and REM sleep, and changes in subjective sleep measures were analyzed. The results revealed prolonged REM sleep latency after psilocybin administration and a trend toward a decrease in overall REM sleep duration. No changes in NREM sleep were observed. Psilocybin did not affect EEG power spectra in NREM or REM sleep when examined across the whole night. However, psilocybin suppressed SWA in the first sleep cycle. No evidence was found for sleep-related neuroplasticity, however, a different dosage, timing, effect on homeostatic regulation of sleep, or other mechanisms related to antidepressant effects may play a role. Overall, this study suggests that potential antidepressant properties of psilocybin might be related to changes in sleep.

Objective and Subjective Characteristics of Vigilance under Different Narrow-Bandwidth Light Conditions: Do Shorter Wavelengths Have an Alertness-Enhancing Effect?

The aim of this study was to explore the effects of 20 min of narrow-bandwidth light exposure of different wavelengths (455, 508, and 629 nm, with irradiance of 14 µW/cm2) on various neuropsychological and neurophysiological parameters of vigilance in healthy volunteers and to provide further evidence of the behavioral (subjective sleepiness, reaction time) and electrophysiological (P300 and spectral characteristics) responses to light. The results show that the short-wavelength light condition (455 nm) was found to be most effective in terms of its alerting effect for the following variables: subjective sleepiness, latency of P300 response, and absolute EEG power in higher beta (24-34 Hz) and gamma (35-50 Hz) range at each of the 19 recording electrodes. However, no differences in current power density were observed at the level of cortical EEG sources estimated by exact low-resolution electromagnetic tomography. Our results are in line with other research that shows significant alerting effects of blue (short-wavelength) light in comparison to lights of longer wavelengths. Our results confirm earlier findings that exposure to short-wavelength light during the day may enhance cognitive performance in task-specific scenarios.

An Unsupervised Multichannel Artifact Detection Method for Sleep EEG Based on Riemannian Geometry.

In biomedical signal processing, we often face the problem of artifacts that distort the original signals. This concerns also sleep recordings, such as EEG. Artifacts may severely affect or even make impossible visual inspection, as well as automatic processing. Many proposed methods concentrate on certain artifact types. Therefore, artifact-free data are often obtained after sequential application of different methods. Moreover, single-channel approaches must be applied to all channels alternately. The aim of this study is to develop a multichannel artifact detection method for multichannel sleep EEG capable of rejecting different artifact types at once. The inspiration for the study is gained from recent advances in the field of Riemannian geometry. The method we propose is tested on real datasets. The performance of the proposed method is measured by comparing detection results with the expert labeling as a reference and evaluated against a simpler method based on Riemannian geometry that has previously been proposed, as well as against the state-of-the-art method FASTER. The obtained results prove the effectiveness of the proposed method.

Structural and construct validity of the Czech version of the Pittsburgh Sleep Quality Index in chronic insomnia.

OBJECTIVES: The Pittsburgh Sleep Quality Index (PSQI) is one of the widely used and recommended measures of assessing sleep quality in chronic insomnia; however certain psychometric properties of the questionnaire are still unknown in this group of patients. The present study aimed to examine the internal consistency, and structural and convergent validity of the Czech version of the PSQI in chronic insomnia patients. The usefulness of the standard and alternative scoring systems was also investigated in relation to symptoms of sleepiness, insomnia, depression, and anxiety. METHODS: In our study, 105 participants filled out a series of questionnaires including PSQI, Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), and Beck Depression and Anxiety Inventories (BDI, BAI). RESULTS: The internal consistency of the questionnaire using Cronbach's alpha was 0.608. A series of confirmatory factor analyses revealed adequate fit for three structures. A three-factor model descriptively stood out among the rest but subsequent correlational analyses did not provide sufficient support for accepting an alternative scoring model. CONCLUSIONS: The results highlight the issue of structural variance of the PSQI and in chronic insomnia point to the important role of the PSQI components of daytime dysfunction and sleep disturbances in showing comorbid symptoms with daytime sleepiness and psychopathology.