P.128 Quantitative EEG Detects REM Sleep Microstructure

Background: Rapid eye movement sleep (REM) is divided into phasic and tonic microstates. Phasic REM is defined by presence of REMs with reportedly greater antiepileptic effect. We assessed whether quantitative EEG (QEEG) software can detect REM microstates. Methods: We applied artifact reduction and detection trends from QEEG software (Persyst 14) on 18 patients undergoing 30 day-night high density EEG recordings in the epilepsy monitoring unit. We identified phasic REM as 10-second epochs of previously human-scored REM that demonstrated presence of either vertical or horizontal eye movements on the QEEG artifact detection panel. Remaining epochs were identified as tonic REM. Results: Out of 91.2 average minutes of REM (range 24.5-167.5) per recording, a mean of 2.5% (range 0-18.9%) demonstrated eye movements intensive enough for QEEG artifact detection to be identified as phasic REM. On average, only 40% (range 0-500%) of eye movements per recording was flagged as vertical. Conclusions: These findings provide proof-of-concept that QEEG can automatically assess REM microstructure by readily detecting phasic and tonic REM. These findings also confirm that most REMs are horizontal. Having the ability to easily and automatically detect phasic versus tonic REM can help further future studies examining the antiepileptic effect of REM sleep.

Obstructive Sleep Apnea Syndrome and Features of the Neurophysiological Sleep Pattern

The high prevalence of obstructive sleep apnea syndrome (OSA) causes a steady interest in this pathology. In recent years, one of the urgent problems in modern somnology is the assessment of the main mechanisms of neuronal dysfunction during the day and at night in OSA, the ideas about which, to a large extent, remain contradictory and not fully understood. One of the modern methods for assessing neuronal dysfunction during sleep is the study of the sleep microstructure, and for its assessment, the method of analysis of cyclic alternating pattern (CAP), an EEG marker of unstable sleep, is used. The cyclic alternating pattern is found both in the sleep of adults and children with various sleep disorders and, in particular, with OSAS, therefore, it is a sensitive tool for studying sleep disorders throughout life. With the elimination of night hypoxia against the background of CPAP therapy, the sleep microstructure is restored, the spectral characteristics of the EEG change, and a decrease in the number of EEG arousals after treatment leads to the restoration of daytime functioning. Understanding the role of short-term EEG activations of the brain during sleep can provide significant data on sleep functions in health and disease. Despite the improving diagnosis of sleep disorders using machine algorithms, assessing the relationship of structures and functions of the brain during sleep, neurophysiological data are not entirely clear, which requires further research. In this review, we tried to analyze the results of the main studies of the neurophysiological sleep pattern in OSA against the background of respiratory support during sleep.

Microsleep disturbances are associated with noradrenergic dysfunction in Parkinson’s disease

Study Objectives Parkinson’s disease (PD) commonly involves degeneration of sleep-wake regulating brainstem nuclei; likewise, sleep-wake disturbances are highly prevalent in PD patients. As polysomnography macroparameters typically show only minor changes in PD, we investigated sleep microstructure, particularly cyclic alternating pattern (CAP), and its relation to alterations of the noradrenergic system in these patients. Methods We analysed 27 PD patients and 13 healthy control (HC) subjects who underwent over-night polysomnography and 11C-MeNER positron emission tomography for evaluation of noradrenaline transporter density. Sleep macroparameters as well as CAP metrics were evaluated according to the consensus statement from 2001. Statistical analysis comprised group comparisons and correlation analysis of CAP metrics with clinical characteristics of PD patients as well as noradrenaline transporter density. Results PD patients and HC subjects were comparable in demographic characteristics (age, sex, body mass index) and polysomnography macroparameters. CAP rate as well as A index differed significantly between groups, with PD patients having a lower CAP rate (46.7 ± 6.6% versus 38.0 ± 11.6%, p = 0.015) and lower A index (49.0 ± 8.7/hour versus 40.1 ± 15.4/hour, p = 0.042). In PD patients, both CAP metrics correlated significantly with diminished noradrenaline transporter density in arousal prompting brainstem nuclei (locus coeruleus, raphe nuclei) as well as arousal propagating brain structures like thalamus and bitemporal cortex. Conclusions Sleep microstructure is more severely altered than sleep macrostructure in PD patients and is associated with widespread dysfunction of the noradrenergic arousal system.

‘SleepCycles’ package for R - A free software tool for the detection of sleep cycles from sleep staging

The detection of sleep cycles in human sleep data (i.e. polysomnographically assessed sleep stages) enables fine-grained analyses of ultradian variations in sleep microstructure (e.g. sleep spindles, and arousals), or other amplitude- and frequency-specific electroencephalographic features during sleep. While many laboratories have software that is used internally, reproducibility requires the availability of open source software. Therefore, we here introduce the ‘SleepCycles’ package for R, an open-source software package that identifies sleep cycles and their respective (non-) rapid eye movement ([N]REM) periods from sleep staging data. Additionally, each (N)REM period is subdivided into parts of equal duration, which may be useful for further fine-grained analyses. The detection criteria are, with some adaptations, largely based on criteria originally proposed by Feinberg and Floyd (1979). The latest version of the package can be downloaded from the Comprehensive R Archives Network (CRAN).•The package ‘SleepCycles’ for R allows to identify sleep cycles and their respective NREM and REM from sleep staging results.•Besides the cycle detection, NREM and REM are also split into parts of equal duration (percentiles) thereby allowing for a better temporal resolution across the night and temporal alignment of sleep cycles with different durations among different night recordings.

Is Sleep Disruption a Cause or Consequence of Alzheimer’s Disease? Reviewing Its Possible Role as a Biomarker

In recent years, the idea that sleep is critical for cognitive processing has gained strength. Alzheimer’s disease (AD) is the most common form of dementia worldwide and presents a high prevalence of sleep disturbances. However, it is difficult to establish causal relations, since a vicious circle emerges between different aspects of the disease. Nowadays, we know that sleep is crucial to consolidate memory and to remove the excess of beta-amyloid and hyperphosphorilated tau accumulated in AD patients’ brains. In this review, we discuss how sleep disturbances often precede in years some pathological traits, as well as cognitive decline, in AD. We describe the relevance of sleep to memory consolidation, focusing on changes in sleep patterns in AD in contrast to normal aging. We also analyze whether sleep alterations could be useful biomarkers to predict the risk of developing AD and we compile some sleep-related proposed biomarkers. The relevance of the analysis of the sleep microstructure is highlighted to detect specific oscillatory patterns that could be useful as AD biomarkers.

Advances in Understanding the Relationship between Sleep and Attention Deficit-Hyperactivity Disorder (ADHD)

Starting from the consolidated relationship between sleep and cognition, we reviewed the available literature on the association between Attention Deficit-Hyperactivity Disorder (ADHD) and sleep. This review analyzes the macrostructural and microstructural sleep features, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria (PRISMA). We included the polysomnographic studies published in the last 15 years. The results of macrostructural parameters are mixed. Almost half of the 18 selected investigations did not find differences between sleep architecture of children with ADHD and controls. Five studies observed that children with ADHD show a longer Rapid Eye Movement (REM) sleep duration than controls. Eight studies included microstructural measures. Remarkable alterations in sleep microstructure of ADHD are related to slow wave activity (SWA) and theta oscillations, respectively, during Non-REM (NREM) and REM sleep. Specifically, some studies found higher SWA in the ADHD group than controls. Similarly, higher theta activity appears to be detrimental for memory performance and inhibitory control in ADHD. These patterns could be interpreted as a maturational delay in ADHD. Also, the increased amount of these activities would be consistent with the hypothesis that the poor sleep could imply a chronic sleep deprivation in children with ADHD, which in turn could affect their cognitive functioning.