Enhanced synchronization of gamma activity between frontal lobes during REM sleep as a function of REM sleep deprivation in man

Graph-informed convolutional autoencoder to classify brain responses during sleep

Automated machine-learning algorithms that analyze biomedical signals have been used to identify sleep patterns and health issues. However, their performance is often suboptimal, especially when dealing with imbalanced datasets. In this paper, we present a robust sleep state (SlS) classification algorithm utilizing electroencephalogram (EEG) signals. To this aim, we pre-processed EEG recordings from 33 healthy subjects. Then, functional connectivity features and recurrence quantification analysis were extracted from sub-bands. The graphical representation was calculated from phase locking value, coherence, and phase-amplitude coupling. Statistical analysis was used to select features with p-values of less than 0.05. These features were compared between four states: wakefulness, non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep during presenting auditory stimuli, and REM sleep without stimuli. Eighteen types of different stimuli including instrumental and natural sounds were presented to participants during REM. The selected significant features were used to train a novel deep-learning classifiers. We designed a graph-informed convolutional autoencoder called GICA to extract high-level features from the functional connectivity features. Furthermore, an attention layer based on recurrence rate features extracted from EEGs was incorporated into the GICA classifier to enhance the dynamic ability of the model. The proposed model was assessed by comparing it to baseline systems in the literature. The accuracy of the SlS-GICA classifier is 99.92% on the significant feature set. This achievement could be considered in real-time and automatic applications to develop new therapeutic strategies for sleep-related disorders.

Patterns of Intrahemispheric EEG Asymmetry in Insomnia Sufferers: An Exploratory Study

Individuals with insomnia present unique patterns of electroencephalographic (EEG) asymmetry between homologous regions of each brain hemisphere, yet few studies have assessed asymmetry within the same hemisphere. Increase in intrahemispheric asymmetry during rapid eye movement (REM) sleep in good sleepers (GS) and disruption of REM sleep in insomnia sufferers (INS) both point out that this activity may be involved in the pathology of insomnia. The objective of the present exploratory study was to evaluate and quantify patterns of fronto-central, fronto-parietal, fronto-occipital, centro-parietal, centro-occipital and parieto-occipital intrahemispheric asymmetry in GS and INS, and to assess their association with sleep-wake misperception, daytime anxiety and depressive symptoms, as well as insomnia severity. This paper provides secondary analysis of standard EEG recorded in 43 INS and 19 GS for three nights in a sleep laboratory. Asymmetry measures were based on EEG power spectral analysis within 0.3–60 Hz computed between pairs of regions at frontal, central, parietal and occipital derivations. Repeated-measures ANOVAs were performed to assess group differences. Exploratory correlations were then performed on asymmetry and sleep-wake misperception, as well as self-reported daytime anxiety and depressive symptoms, and insomnia severity. INS presented increased delta and theta F3/P3 asymmetry during REM sleep compared with GS, positively associated with depressive and insomnia complaints. INS also exhibited decreased centro-occipital (C3/O1, C4/O2) and parieto-occipital (P3–O1, P4/O2) theta asymmetry during REM. These findings suggest that INS present specific patterns of intrahemispheric asymmetry, partially related to their clinical symptoms. Future studies may investigate the extent to which asymmetry is related to sleep-wake misperception or memory impairments.

The Color of Noise and Weak Stationarity at the NREM to REM Sleep Transition in Mild Cognitive Impaired Subjects

In Older Adults (OAs), Electroencephalogram (EEG) slowing in frontal lobes and a diminished muscle atonia during Rapid Eye Movement sleep (REM) have each been effective tracers of Mild Cognitive Impairment (MCI), but this relationship remains to be explored by non-linear analysis. Likewise, data provided by EEG, EMG (Electromyogram) and EOG (Electrooculogram)—the three required sleep indicators—during the transition from REM to Non-REM (NREM) sleep have not been related jointly to MCI. Therefore, the main aim of the study was to explore, with results for Detrended Fluctuation Analysis (DFA) and multichannel DFA (mDFA), the Color of Noise (CN) at the NREM to REM transition in OAs with MCI vs. subjects with good performances. The comparisons for the transition from NREM to REM were made for each group at each cerebral area, taking bilateral derivations to evaluate interhemispheric coupling and anteroposterior and posterior networks. In addition, stationarity analysis was carried out to explore if the three markers distinguished between the groups. Neuropsi and the Mini-Mental State Examination (MMSE) were administered, as well as other geriatric tests. One night polysomnography was applied to 6 OAs with MCI (68.1 ± 3) and to 7 subjects without it (CTRL) (64.5 ± 9), and pre-REM and REM epochs were analyzed for each subject. Lower scores for attention, memory and executive funcions and a greater index of arousals during sleep were found for the MCI group. Results confirmed that EOGs constituted significant markers of MCI, increasing the CN for the MCI group in REM sleep. The CN of the EEG from the pre-REM to REM was higher for the MCI group vs. the opposite for the CTRL group at frontotemporal areas. Frontopolar interhemispheric scaling values also followed this trend as well as right anteroposterior networks. EMG Hurst values for both groups were lower than those for EEG and EOG. Stationarity analyses showed differences between stages in frontal areas and right and left EOGs for both groups. These results may demonstrate the breakdown of fractality of areas especially involved in executive functioning and the way weak stationarity analyses may help to distinguish between sleep stages in OAs.

Effect of two GABA-ergic drugs on the cognitive functions of rapid eye movement in sleep-deprived and recovered rats

Rapid eye movement (REM) sleep is closely associated with nervous functions. The present study aimed to evaluate the effects of gabazine and tiagabine on the cognitive functions (CF) of REM sleep-deprived and sleep recovered rats. Rats were divided into REM sleep deprivation, blank control (CC) and environmental groups. The REM sleep deprivation group was further divided into non-operation (nonOP), sham-operated (Sham), gabazine (SR) and tiagabine groups. Each group was evaluated over five time points: Sleep deprived for 1 day (SD 1 day), SD 3 day, SD 5 day, sleep recovery 6 h (RS 6 h) and RS 12 h. A rat model of REM sleep deprivation was established by a modified multi-platform water method, with CF assessed by Morris water maze. Hypothalamic γ-aminobutyric acid (GABA) and glutamic acid contents were measured via high performance liquid chromatography. The number and morphology of hypocretin (Hcrt) neurons and Fos in the hypothalamus, and GABAARα1-induced integral optical density were detected by immunofluorescence. Compared to the CC group, the nonOP and Sham group rats CF were significantly diminished, Fos-positive and Fos-Hcrt double positive cells were significantly increased, and GABA content and GABAARα1 expression levels were significantly elevated (P<0.05). The tiagabine and CC groups exhibited similar results at three time points. The CF of rats in the SR group were diminished and the number of Fos-positive and Fos-Hcrt double positive cells were significantly increased (P<0.05) at RS 6 h and RS l2 h. GABA content and GABAARα1 expression levels were significantly increased in the SR group at all time points (P<0.05), whereas only GABAARα1 expression levels were significantly increased in the tiagabine group at SD 5 day (P<0.05). The results of the present study indicated that REM sleep deprivation diminished CF, increased the number of Hcrt neurons, GABA content and GABAARα1 expression. Furthermore, all alterations were positively correlated with deprivation time and corrected by sleep recovery, as demonstrated by single-factor multi-level variance analysis at the various time points in each group. Therefore, the Hcrt nervous system may be an eligible therapeutic target for the treatment of insomnia.

Coherent neocortical gamma oscillations decrease during REM sleep in the rat

Higher cognitive functions require the integration and coordination of large populations of neurons in cortical and subcortical regions. Oscillations in the high frequency band (30-100 Hz) of the electroencephalogram (EEG), that have been postulated to be a product of this interaction, are involved in the binding of spatially separated but temporally correlated neural events, which results in a unified perceptual experience. The extent of this functional connectivity can be examined by means of the mathematical algorithm called "coherence", which is correlated with the "strength" of functional interactions between cortical areas. As a continuation of previous studies in the cat [6,7], the present study was conducted to analyze EEG coherence in the gamma band of the rat during wakefulness (W), non-REM (NREM) sleep and REM sleep. Rats were implanted with electrodes in different cortical areas to record EEG activity, and the magnitude squared coherence values within the gamma frequency band of EEG (30-48 and 52-100 Hz) were determined. Coherence between all cortical regions in the low and high gamma frequency bands was greater during W compared with sleep. Remarkably, EEG coherence in the low and high gamma bands was smallest during REM sleep. We conclude that high frequency interactions between cortical areas are radically different during sleep and wakefulness in the rat. Since this feature is conserved in other mammals, including humans, we suggest that the uncoupling of gamma frequency activity during REM sleep is a defining trait of REM sleep in mammals.