Association of neurocognitive functioning with sleep stage dissociation and REM sleep instability in medicated patients with schizophrenia.

Many patients with schizophrenia present with impaired cognitive functioning and sleep disturbances. Dissociated stages of sleep represent instability within distinct sleep regulatory cerebral networks. Previous studies found increased rates of rapid eye movement (REM) sleep abnormalities in patients with schizophrenia and a positive association with psychopathology. In this study, we examined if sleep stage dissociation and REM sleep instability was associated with neurocognitive functioning in a sample of medicated patients with schizophrenia. The analyses were performed on 31 baseline polysomnographic recordings as well as baseline data on neurocognitive performance. Regression models were built with the cognitive composite score as primary dependent variable and measures of sleep stage dissociation, including REM sleep without atonia (RSWA), REM sleep without eye movements, non-REM sleep with eye movements, REM sleep percentage in REM periods and REM sleep stability as independent variables. Analyses were adjusted for age, gender, total antipsychotic dose, total benzodiazepine dose, and symptom severity. After correction for multiple testing, we found that the neurocognitive composite score was inversely associated with the degree of RSWA. Exploratory analyses with the cognitive sub scores as dependent variables showed that RSWA was associated with cognitive performance across several sub domains. Dissociated sleep stages, specifically the RSWA feature, might represent a new treatment target for improving cognitive impairment in patients with schizophrenia.

Data-Driven Analysis of EEG Reveals Concomitant Superficial Sleep During Deep Sleep in Insomnia Disorder.

Study Objectives: The subjective suffering of people with Insomnia Disorder (ID) is insufficiently accounted for by traditional sleep classification, which presumes a strict sequential occurrence of global brain states. Recent studies challenged this presumption by showing concurrent sleep- and wake-type neuronal activity. We hypothesized enhanced co-occurrence of diverging EEG vigilance signatures during sleep in ID. Methods: Electroencephalography (EEG) in 55 cases with ID and 64 controls without sleep complaints was subjected to a Latent Dirichlet Allocation topic model describing each 30 s epoch as a mixture of six vigilance states called Topics (T), ranked from N3-related T1 and T2 to wakefulness-related T6. For each stable epoch we determined topic dominance (the probability of the most likely topic), topic co-occurrence (the probability of the remaining topics), and epoch-to-epoch transition probabilities. Results: In stable epochs where the N1-related T4 was dominant, T4 was more dominant in ID than in controls, and patients showed an almost doubled co-occurrence of T4 during epochs where the N3-related T1 was dominant. Furthermore, patients had a higher probability of switching from T1- to T4-dominated epochs, at the cost of switching to N3-related T2-dominated epochs, and a higher probability of switching from N2-related T3- to wakefulness-related T6-dominated epochs. Conclusion: Even during their deepest sleep, the EEG of people with ID express more N1-related vigilance signatures than good sleepers do. People with ID are moreover more likely to switch from deep to light sleep and from N2 sleep to wakefulness. The findings suggest that hyperarousal never rests in ID.

A comparative study of methods for automatic detection of rapid eye movement abnormal muscular activity in narcolepsy.

OBJECTIVE: To evaluate rapid eye movement (REM) muscular activity in narcolepsy by applying five algorithms to electromyogram (EMG) recordings, and to investigate its value for narcolepsy diagnosis. PATIENTS/METHODS: A modified version of phasic EMG metric (mPEM), muscle activity index (MAI), REM atonia index (RAI), supra-threshold REM EMG activity metric (STREAM), and Frandsen method (FR) were calculated from polysomnography recordings of 20 healthy controls, 18 clinic controls (subjects suspected with narcolepsy but finally diagnosed without any sleep abnormality), 16 narcolepsy type one without REM sleep behavior disorder (RBD), nine narcolepsy type one with RBD, and 18 narcolepsy type two. Diagnostic value of metrics in differentiating between groups was quantified by area under the receiver operating characteristic curve (AUC). Correlations among the metrics and cerebrospinal fluid hypocretin-1 (CSF-hcrt-1) values were calculated using linear models. RESULTS: All metrics excluding STREAM found significantly higher muscular activity in narcolepsy one cases versus controls (p < 0.05). Moreover, RAI showed high sensitivity in the detection of RBD. The mPEM achieved the highest AUC in differentiating healthy controls from narcoleptic subjects. The RAI best differentiated between narcolepsy 1 and 2. Lower CSF-hcrt-1 values correlated with high muscular activity quantified by mPEM, sMAI, lMAI, PEM and FR (p < 0.05). CONCLUSIONS: This automatic analysis showed higher number of muscle activations in narcolepsy 1 compared to controls. This finding might play a supportive role in diagnosing narcolepsy and in discriminating narcolepsy subtypes. Moreover, the negative correlation between CSF-hcrt-1 level and REM muscular activity supported a role for hypocretin in the control of motor tone during REM sleep.

Sleep spindle density in narcolepsy.

BACKGROUND: Patients with narcolepsy type 1 (NT1) show alterations in sleep stage transitions, rapid-eye-movement (REM) and non-REM sleep due to the loss of hypocretinergic signaling. However, the sleep microstructure has not yet been evaluated in these patients. We aimed to evaluate whether the sleep spindle (SS) density is altered in patients with NT1 compared to controls and patients with narcolepsy type 2 (NT2). METHODS: All-night polysomnographic recordings from 28 NT1 patients, 19 NT2 patients, 20 controls (C) with narcolepsy-like symptoms, but with normal cerebrospinal fluid hypocretin levels and multiple sleep latency tests, and 18 healthy controls (HC) were included. Unspecified, slow, and fast SS were automatically detected, and SS densities were defined as number per minute and were computed across sleep stages and sleep cycles. The between-cycle trends of SS densities in N2 and NREM sleep were evaluated within and between groups. RESULTS: Between-group comparisons in sleep stages revealed no significant differences in any type of SS. Within-group analyses of the SS trends revealed significant decreasing trends for NT1, HC, and C between first and last sleep cycle. Between-group analyses of SS trends between first and last sleep cycle revealed that NT2 differ from NT1 patients in the unspecified SS density in NREM sleep, and from HC in the slow SS density in N2 sleep. CONCLUSIONS: SS activity is preserved in NT1, suggesting that the ascending neurons to thalamic activation of SS are not significantly affected by the hypocretinergic system. NT2 patients show an abnormal pattern of SS distribution.

Sleep stability and transitions in patients with idiopathic REM sleep behavior disorder and patients with Parkinson's disease.

OBJECTIVE: Patients with idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) are at high risk of developing Parkinson's disease (PD). As wake/sleep-regulation is thought to involve neurons located in the brainstem and hypothalamic areas, we hypothesize that the neurodegeneration in iRBD/PD is likely to affect wake/sleep and REM/non-REM (NREM) sleep transitions. METHODS: We determined the frequency of wake/sleep and REM/NREM sleep transitions and the stability of wake (W), REM and NREM sleep as measured by polysomnography (PSG) in 27 patients with PD, 23 patients with iRBD, 25 patients with periodic leg movement disorder (PLMD) and 23 controls. Measures were computed based on manual scorings and data-driven labeled sleep staging. RESULTS: Patients with PD showed significantly lower REM stability than controls and patients with PLMD. Patients with iRBD had significantly lower REM stability compared with controls. Patients with PD and RBD showed significantly lower NREM stability and significantly more REM/NREM transitions than controls. CONCLUSIONS: We conclude that W, NREM and REM stability and transitions are progressively affected in iRBD and PD, probably reflecting the successive involvement of brain stem areas from early on in the disease. SIGNIFICANCE: Sleep stability and transitions determined by a data-driven approach could support the evaluation of iRBD and PD patients.

The diagnostic value of power spectra analysis of the sleep electroencephalography in narcoleptic patients.

OBJECTIVE: Manifestations of narcolepsy with cataplexy (NC) include disturbed nocturnal sleep - hereunder sleep-wake instability, decreased latency to rapid eye movement (REM) sleep, and dissociated REM sleep events. In this study, we characterized the electroencephalography (EEG) of various sleep stages in NC versus controls. METHODS: EEG power spectral density (PSD) was computed in 136 NC patients and 510 sex- and age-matched controls. Features reflecting differences in PSD curves were computed. A Lasso-regularized regression model was used to find an optimal feature subset, which was validated on 19 NC patients and 708 non-NC patients from a sleep clinic. Reproducible features were analyzed using receiver operating characteristic (ROC) curves. RESULTS: Thirteen features were selected based on the training dataset. Three were applicable in the validation dataset, indicating that NC patients show (1) increased alpha power in REM sleep, (2) decreased sigma power in wakefulness, and (3) decreased delta power in stage N1 versus wakefulness. Sensitivity of these features ranged from 4% to 10% with specificity around 98%, and it did not vary substantially with and without treatment. CONCLUSIONS: EEG spectral analysis of REM sleep, wake, and differences between N1 and wakefulness contain diagnostic features of NC. These traits may represent sleepiness and dissociated REM sleep in patients with NC. However, the features are not sufficient for differentiating NC from controls, and further analysis is needed to completely evaluate the diagnostic potential of these features.

Sleep-stage transitions during polysomnographic recordings as diagnostic features of type 1 narcolepsy.

OBJECTIVE: Type 1 narcolepsy/hypocretin deficiency is characterized by excessive daytime sleepiness, sleep fragmentation, and cataplexy. Short rapid eye movement (REM) latency (≤15 min) during nocturnal polysomnography (PSG) or during naps of the multiple sleep latency test (MSLT) defines a sleep-onset REM sleep period (SOREMP), a diagnostic hallmark. We hypothesized that abnormal sleep transitions other than SOREMPs can be identified in type 1 narcolepsy. METHODS: Sleep-stage transitions (one to 10 epochs to one to five epochs of any other stage) and bout length features (one to 10 epochs) were extracted from PSGs. The first 15 min of sleep were excluded when a nocturnal SOREMP was recorded. F(0.1) measures and receiver operating characteristic curves were used to identify specific (≥98%) features. A data set of 136 patients and 510 sex- and age-matched controls was used for the training. A data set of 19 cases and 708 sleep-clinic patients was used for the validation. RESULTS: (1) ≥5 transitions from ≥5 epochs of stage N1 or W to ≥2 epochs of REM sleep, (2) ≥22 transitions from ≥3 epochs of stage N2 or N3 to ≥2 epochs of N1 or W, and (3) ≥16 bouts of ≥6 epochs of N1 or W were found to be highly specific (≥98%). Sensitivity ranged from 16% to 30%, and it did not vary substantially with and without medication or a nocturnal SOREMP. In patients taking antidepressants, nocturnal SOREMPs occurred much less frequently (16% vs. 36%, p < 0.001). CONCLUSIONS: Increased sleep-stage transitions notably from ≥2.5 min of W/N1 into REM are specifically diagnostic for narcolepsy independent of a nocturnal SOREMP.