Estimation of Time-Varying Spectral Peaks and Decomposition of EEG Spectrograms

Chronic Caffeine Consumption, Alone or Combined with Agomelatine or Quetiapine, Reduces the Maximum EEG Peak, As Linked to Cortical Neurodegeneration, Ovarian Estrogen Receptor Alpha, and Melatonin Receptor 2

RATIONALE

Evidence of the effects of chronic caffeine (CAFF)-containing beverages, alone or in combination with agomelatine (AGO) or quetiapine (QUET), on electroencephalography (EEG), which is relevant to cognition, epileptogenesis, and ovarian function, remains lacking. Estrogenic, adenosinergic, and melatonergic signaling is possibly linked to the dynamics of these substances.

OBJECTIVES

The brain and ovarian effects of CAFF were compared with those of AGO + CAFF and QUET + CAFF. The implications of estrogenic, adenosinergic, and melatonergic signaling and the brain-ovarian crosstalk were investigated.

METHODS

Adult female rats were administered AGO (10 mg/kg), QUET (10 mg/kg), CAFF, AGO + CAFF, or QUET + CAFF, once daily for 8 weeks. EEG, estrous cycle progression, and microstructure of the brain and ovaries were examined. Brain and ovarian 17β-estradiol (E2), antimullerian hormone (AMH), estrogen receptor alpha (E2Rα), adenosine receptor 2A (A2AR), and melatonin receptor 2 (MT2R) were assessed.

RESULTS

CAFF, alone or combined with AGO or QUET, reduced the maximum EEG peak, which was positively linked to ovarian E2Rα, negatively correlated to cortical neurodegeneration and ovarian MT2R, and associated with cystic ovaries. A large corpus luteum emerged with AGO + CAFF and QUET + CAFF, antagonizing the CAFF-mediated increased ovarian A2AR and reduced cortical E2Rα. AGO + CAFF provoked TTP delay and increased ovarian AMH, while QUET + CAFF slowed source EEG frequency to δ range and increased brain E2.

CONCLUSIONS

CAFF treatment triggered brain and ovarian derangements partially antagonized with concurrent AGO or QUET administration but with no overt affection of estrus cycle progression. Estrogenic, adenosinergic, and melatonergic signaling and brain-ovarian crosstalk may explain these effects.

Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification

Transient oscillatory events in the sleep electroencephalogram represent short-term coordinated network activity. Of particular importance, sleep spindles are transient oscillatory events associated with memory consolidation, which are altered in aging and in several psychiatric and neurodegenerative disorders. Spindle identification, however, currently contains implicit assumptions derived from what waveforms were historically easiest to discern by eye, and has recently been shown to select only a high-amplitude subset of transient events. Moreover, spindle activity is typically averaged across a sleep stage, collapsing continuous dynamics into discrete states. What information can be gained by expanding our view of transient oscillatory events and their dynamics? In this paper, we develop a novel approach to electroencephalographic phenotyping, characterizing a generalized class of transient time-frequency events across a wide frequency range using continuous dynamics. We demonstrate that the complex temporal evolution of transient events during sleep is highly stereotyped when viewed as a function of slow oscillation power (an objective, continuous metric of depth-of-sleep) and phase (a correlate of cortical up/down states). This two-fold power-phase representation has large intersubject variability-even within healthy controls-yet strong night-to-night stability for individuals, suggesting a robust basis for phenotyping. As a clinical application, we then analyze patients with schizophrenia, confirming established spindle (12-15 Hz) deficits as well as identifying novel differences in transient non-rapid eye movement events in low-alpha (7-10 Hz) and theta (4-6 Hz) ranges. Overall, these results offer an expanded view of transient activity, describing a broad class of events with properties varying continuously across spatial, temporal, and phase-coupling dimensions.

A Novel EEG Derived Measure of Disrupted Delta Wave Activity during Sleep Predicts All-Cause Mortality Risk

RATIONALE

Conventional markers of sleep disturbance, based on manual electroencephalography scoring, may not adequately capture important features of more fundamental electroencephalography-related sleep disturbance.

OBJECTIVES

This study aimed to determine if more comprehensive power-spectral measures of delta wave activity during sleep are stronger independent predictors of mortality than conventional sleep quality and disturbance metrics.

METHODS

Power spectral analysis of the delta frequency band and spectral entropy-based markers to quantify disruption of electroencephalography delta power during sleep were performed to examine potential associations with mortality risk in the Sleep Heart Health Study cohort (N = 5804). Adjusted Cox proportional hazard models were used to determine the association between disrupted delta wave activity at baseline and all-cause mortality over an ~11y follow-up period.

RESULTS

Disrupted delta electroencephalography power during sleep was associated with a 32% increased risk of all-cause mortality compared with no fragmentation (hazard ratios 1.32 [95% confidence interval 1.14, 1.50], after adjusting for total sleep time and other clinical and life-style related covariates including sleep apnea. The association was of similar magnitude to a reduction in total sleep time from 6.5h to 4.25h. Conventional measures of sleep quality, including wake after sleep onset and arousal index were not predictive of all-cause mortality.

CONCLUSIONS

Delta wave activity disruption during sleep is strongly associated with all-cause mortality risk, independent of traditional potential confounders. Future investigation into the potential role of delta sleep disruption on other specific adverse health consequences such as cardiometabolic, mental health and safety outcomes has considerable potential to provide unique neurophysiological insight.