Characterization of the sleep EEG in acutely depressed men using detrended fluctuation analysis.

OBJECTIVE: The aim of the present paper is to study the fluctuations of the sleep EEG over various time scales during a specific pathological condition: major depressive episode. Focus is made on scaling behaviour, which is the signature of the absence of characteristic time scale, and the presence of long-range correlations associated to physiological constancy preservation, variability reduction and mostly adaptability. METHODS: Whole night sleep electroencephalogram signals were recorded in 24 men: 10 untreated patients with a major depressive episode (41.70+/-8.11 years) and 14 healthy subjects (42.43+/-5.67 years). Scaling in these time series was investigated with detrended fluctuation analysis (time range: 0.16-2.00s). Scaling exponents (alpha) were determined in stage 2, slow wave sleep (stages 3 and 4) and during REM sleep. Forty-five epochs of 20s were chosen randomly in each of these stages. RESULTS: The median values of alpha were lower in patients during stage 2 and SWS. CONCLUSIONS: Major depressive episodes are characterized by a modification in the correlation structure of the sleep EEG time series. The finding which shows decreasing rate of the temporal correlations being different within the two groups in stage 2 and SWS provides an electrophysiologic argument that the underlying neuronal dynamics are modified during acute depression. SIGNIFICANCE: The observed modifications in scaling behaviour in acutely depressed patients could be an explanation of the sleep fragmentation and instability found during major depressive episode.     

Spectral analysis of the sleep onset period in primary insomnia

ObjectiveTo compare the EEG power spectra characteristics of the sleep onset period (SOP) in patients with sleep onset insomnia (SOI), sleep maintenance insomnia (SMI) and good sleepers (GS).MethodsThe time course of EEG power density (1–40 Hz) during the SOP was examined in thirty subjects (SOI patients: N = 10, SMI patients: N = 10, GS: N = 10).ResultsThe EEG power of the beta2 frequency band (18–29.75 Hz) was significantly lower in SOI than in SMI in the period preceding sleep onset. The alpha power was significantly higher for the SMI group compared to GS before sleep onset. Despite the lack of statistical significance, several differences in EEG dynamics were observed in SOI compared to two other groups: delta power increased slower after sleep onset; beta2 and 3 (18–29.75 and 30–39.75 Hz) power decrease less abruptly before sleep onset; beta1 (15–17.75 Hz) power increase through the whole SOP.ConclusionsThe lower level of beta2 frequency band in SOI and the differences in dynamics in delta and beta bands may suggest that a mechanism other than hyperarousal participates in etiology of SOI.SignificanceSOI and SMI patients have different spectral characteristics in SOP, thus future studies should avoid the inclusion of mixed insomnia samples.     

Sleep deprivation in narcoleptic subjects: effect on sleep stages and EEG power density.

Sleep of 8 narcoleptic and 8 control subjects was recorded under baseline (i.e., prior wakefulness 16 h) and after 24 h without sleep. During both baseline and recovery total sleep time and stage 2 non-REM sleep were significantly decreased in narcoleptic subjects. Slow wave activity (i.e., EEG power density in the range of 0.75-4.5 Hz) decayed exponentially during baseline and after sleep deprivation in both narcoleptic and control subjects. During both baseline and recovery EEG power density in delta and sigma frequencies in non-REM sleep was enhanced in narcoleptic subjects relative to controls. In REM sleep differences in the same direction were present in delta and beta frequencies. After sleep deprivation EEG power density in non-REM sleep was elevated in delta and some higher frequencies in both patients and controls, but the response to sleep deprivation was stronger in narcoleptic subjects. These data show that in narcoleptic subjects regulatory processes underlying non-REM sleep homeostasis are operative and indicate that the response to sleep deprivation is stronger than in control subjects.     

Excessive daytime sleepiness in man: multiple sleep latency measurement in narcoleptic and control subjects.

Excessive daytime sleepiness is a complaint characterizing many disorders of the wakefulness--sleep cycle. This paper addresses the complaint of sleepiness objectively by an attempt to differentiate a group of control subjects from a group of patients with unambiguous narcolepsy. Fourteen control and 27 narcoleptic subjects were evaluated by one of three protocols involving nocturnal recordings, detailed interviews, and 5 or more 20-min opportunities to sleep offered at 2-h intervals beginning at 10.00 o'clock, +/- 30 min. Each 20-min opportunity to sleep was given to subjects lying in a darkened quiet room and asked to try to fall asleep. Polysomnographic variables were monitored and sleep was scored in 30-sec epochs by standard criteria. The interval from the start of each test to the first epoch of NREM (including stage 1 sleep) or REM sleep was called sleep latency. In two of the protocols, the subjects were awakened immediately after sleep onset. In the third protocol, the subjects were awakened after 10 min of sleep. Narcoleptics consistently fell asleep much more readily than did control subjects. We conclude that the Multiple Sleep latency test, in addition to providing opportunities to clinically document sleep onset REM sleep periods, can demonstrate pathological sleepiness. Based on these data, we suggest that an average sleep latency less than 5 min be set as the minimum cutoff point for pathological sleepiness.     

Application of automated REM and slow wave sleep analysis: I. Normal and depressed subjects

Computerized analysis of rapid eye movement (REM) and delta electroencephalographic (EEG) sleep patterns in normal and depressed subjects offers opportunities to examine sleep more precisely than previously possible. In the present study, automated REM analyses demonstrated good reliability with traditional manual procedures in both normal and depressed subjects. However, automated delta analyses correlated well with traditional scoring in normal subjects, but not in depressed patients. These findings suggest the use of automated delta techniques similar to those employed in this report or spectral analytic techniques in the following types of studies: specificity of delta sleep in various psychiatric syndromes, changes in delta sleep produced by the administration of psychotropic agents, relationships between delta sleep and sleep-related neuroendocrine patterns, and, finally, relationships between delta sleep patterns and other biological rhythms such as activity and temperature.     

Preadult onset vs. adult onset of major depressive disorder: a replication study

OBJECTIVE: In the first 1500 participants with major depressive disorder (MDD) that entered the sequenced treatment alternatives to relieve depression (STAR*D) study, those with preadult onset MDD were more likely to be women and to have a more chronic, severe and disabling form of depression than those with adult onset MDD. This study seeks to replicate these findings. METHOD: The second wave of STAR*D enrollees included 2541 out-patients with MDD, divided into preadult (before age 18) and adult (age 18 or later) onset groups. RESULTS: Participants with a preadult onset of MDD (38%) were younger, ill for longer and more likely to be women than those with adult onset MDD (62%). After adjusting for age, duration of illness and gender, participants with preadult onset MDD also had higher rates of family history of depression, more past suicide attempts, and lower rates of obsessive compulsive and panic disorder. CONCLUSION: Preadult onset MDD may be associated with a more familial form of depression with more suicidality than adult onset MDD.     

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

ObjectiveManifestations 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.MethodsEEG 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.ResultsThirteen 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.ConclusionsEEG 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.     

Cortical connectivity modulation during sleep onset: A study via graph theory on EEG data

Sleep onset is characterized by a specific and orchestrated pattern of frequency and topographical EEG changes. Conventional power analyses of electroencephalographic (EEG) and computational assessments of network dynamics have described an earlier synchronization of the centrofrontal areas rhythms and a spread of synchronizing signals from associative prefrontal to posterior areas. Here, we assess how “small world” characteristics of the brain networks, as reflected in the EEG rhythms, are modified in the wakefulness–sleep transition comparing the pre‐ and post‐sleep onset epochs. The results show that sleep onset is characterized by a less ordered brain network (as reflected by the higher value of small world) in the sigma band for the frontal lobes indicating stronger connectivity, and a more ordered brain network in the low frequency delta and theta bands indicating disconnection on the remaining brain areas. Our results depict the timing and topography of the specific mechanisms for the maintenance of functional connectivity of frontal brain regions at the sleep onset, also providing a possible explanation for the prevalence of the frontal‐to‐posterior information flow directionality previously observed after sleep onset. Hum Brain Mapp 38:5456–5464, 2017. © 2017 Wiley Periodicals, Inc.     

Digital period analysis of sleep EEG in depression.

The period-analyzed sleep electroencephalogram (EEG) was compared in a group of 9 depressed outpatients and 9 age-matched normal controls. Both groups showed rhythms in beta, delta, and theta activity with an approximately 90-min period. The phase and coherence between fast and slow frequency EEG measures, however, differed significantly in the two groups. Beta and delta rhythms were less coherent in the depressed outpatient sample. The control group showed higher coherence and a strong coupling of beta and delta activity. These preliminary data suggest that depression may be associated with some degree of ultradian rhythm disturbances though periodicity is unaffected.     

Sleep onset and first cycle of sleep in human subjects: change with time of day.

The first cycle of sleep was studied in different situations: normal night sleep, naps, diurnal sleep after night shifts (3 x 8 shift workers). Results show two types of first cycle: some started with SWS (normal cycles), others with REM (sleep onset REM periods: SOREMPs). (1) Normal cycles: the length of SWS in the first cycle was positively correlated with prior wakefulness; conversely, the latency of SWS decreased as prior wakefulness increased; the decrease was due to the decrease in the length of the previous stage II or of the sleep onset latency (SOL). Length of sleep onset (SO) showed only few variations. The structure of the first cycle of shift workers' sleep probably reflects an important sleep loss. (2) SOREMPs occcurred during diurnal sleep. Some hypotheses about these cycles are discussed including REM 'pressures' (circadian, sleep loss) and inter-individual variations.     

Detection of sleep onset by analysis of slow eye movements: a preliminary study of MSLT recordings

BackgroundExcessive daytime sleepiness (EDS) adversely impacts daily activity and quality of life. Evaluation of EDS should be as easy and effective as possible. Multiple sleep latency test (MSLT) represents the standard in EDS evaluation. It is, however, a long and expensive test. Slow eye movements (SEMs) occurring at the wake–sleep transition could be an easy and reliable marker of sleepiness. We have developed an automatic method for the detection of SEMs. In the present preliminary work we compare standard measurement of EDS with visual and automatic detection of SEMs, both performed on MSLT recordings.MethodsWe compared sleep latency, obtained upon standard analysis of MSLT with visually and automatically detected SEM latency, in MSLT tests performed in a population of 20 subjects, (10 Obstructive Sleep Apnea Syndrome (OSAS) patients and 10 patients with normal MSLT).ResultsThere were no significant differences between SEMs latency and standard determined sleep latency both in OSAS and normal MSLT patients. Automatic and visual analysis of SEMs gave comparable results. Both SEMs latency and sleep latency were significantly shorter in OSAS patients than normal MSLT patients.ConclusionSEMs can be easily detected automatically and represent an effective marker of sleepiness in those conditions usually characterised by sleep onset with NREM sleep. Their performance equals that of standard measurements of sleep onset in MSLT recordings at least for OSAS and normal MSLT patients. Our study is, however, still preliminary and needs confirmation on a larger number of patients and in other clinical conditions characterised by EDS.     

Seizures of sleep onset: clinical and therapeutical aspects.

We studied the clinical characteristics and therapeutic response of 10 patients who presented with seizures shortly after falling asleep as the only or main manifestation of their epilepsy. The clinical evaluation was designed to investigate the appearance of seizures during diurnal and nocturnal sleep. The pharmacological response to coffee or amphetamine was investigated after normalization of previous anticonvulsant treatment. Seven of 10 patients (70%) presented with seizures during the siesta, and in 3 of 10, seizures occurred if they fell asleep at any time of the day. Seizures were detected in later hours of the night in five patients as sleep reinitiation seizures or seizures without previous awakening. The response to anticonvulsants alone was disappointing. The addition of coffee or amphetamine suppressed seizures of sleep beginning at night or during the siesta. Neither of them had an influence on sleep diurnal seizures outside of the siesta or on seizures of nocturnal sleep reinitiation. Suppression of coffee or amphetamine was followed by reappearance of the seizures.     

Academic and professional paths of narcoleptic patients: the Narcowork study

Objective/BackgroundTo study educational and professional pathways of narcoleptic patients and examine demographic, disease-related and environmental factors associated with a better academic and professional prognosis.Patients/MethodsIn sum, 69 narcoleptic patients (51 narcolepsy type 1 and 18 narcolepsy type 2, age 42.5 ± 18.2 years) were enrolled in this pilot monocentric cross-sectional study with a comparison group (80 age- and sex-matched controls) between October 2017 and July 2018 in Lyon Center for Sleep Medicine. They completed questionnaires about their academic and professional trajectories and specific scales of quality of life (EuroQol quality of life scale EQ-5D-3L), depression (beck depression inventory, BDI), sleepiness (Epworth Sleepiness Scale, ESS) and narcoleptic symptoms severity (narcolepsy severity scale, NSS).ResultsNo difference in grade repetition or final obtained diploma was observed between patients and controls, but patients evaluated their academic curricula as more difficult (45.5% vs 16.9%, p = 0.0007), complained for more attentional deficits (75% vs 22.1%, p < 0.0001), and had needed more educational reorientation (28.6% vs 9.9%, p = 0.01). Even if no difference was observed in occupational category and professional status, patients expressed significantly less satisfaction about their work. Patients had more signs of depression [OR severe depression = 4.4 (1.6–12.6), p = 0.02] and their quality of life was significantly decreased (67.3 ± 18.4 vs 80.6 ± 13.2, p = 0.0007) as compared to controls. Multivariate analysis showed that a more favorable professional career was associated with a better quality of life.ConclusionsEducational and professional pathways do not seem to be significantly impaired in narcoleptic patients, but their experience and quality of life are affected. These findings may allow to reassure patients and should lead to a more comprehensive management of the disease.Clinical trial registrationNarcowork, https://clinicaltrials.gov/ct2/show/NCT03173378, N° NCT03173378.     

Quantitative analysis of sleep EEG microstructure in the time-frequency domain

A number of phasic events influence sleep quality and sleep macrostructure. The detection of arousals and the analysis of cyclic alternating patterns (CAP) support the evaluation of sleep fragmentation and instability. Sixteen polygraphic overnight recordings were visually inspected for conventional Rechtscaffen and Kales scoring, while arousals were detected following the criteria of the American Sleep Disorders Association (ASDA). Three electroencephalograph (EEG) segments were associated to each event, corresponding to background activity, pre-arousal period and arousal. The study was supplemented by the analysis of time-frequency distribution of EEG within each subtype of phase A in the CAP. The arousals were characterized by the increase of alpha and beta power with regard to background. Within NREM sleep most of the arousals were preceded by a transient increase of delta power. The time-frequency evolution of the phase A of the CAP sequence showed a strong prevalence of delta activity during the whole A1, but high amplitude delta waves were found also in the first 2/3 s of A2 and A3, followed by desynchronization. Our results underline the strict relationship between the ASDA arousals, and the subtype A2 and A3 within the CAP: in both the association between a short sequence of transient slow waves and the successive increase of frequency and decrease of amplitude characterizes the arousal response.     

A study of the interhemispheric correlation during sleep in elderly subjects

The interhemispheric relationship during sleep in elderly subjects was studied throughout the night by a minute-by-minute computation of two linear correlation coefficients between right and left EEG activities. One of these coefficients (X delta) related to the 1-4-Hz band activity, and the other (X sigma) to the 12.5-14.5-Hz band activity. For five of the six subjects examined, it was found that the rapid-eye-movement (REM) mean values of both coefficients were significantly different from the nonrapid-eye-movement (NREM) values. A comparison between this elderly group and a control group of young subjects, examined previously, did not reveal any significant shift, either for the REM or for the NREM mean values of the coefficients.     

The vanishing point of the mismatch negativity at sleep onset.

OBJECTIVE: To determine when the mismatch negativity (MMN) disappears at sleep onset, event-related potentials (ERPs) were recorded continuously from wakefulness to sleep. METHODS: Ten healthy young students were told to fall asleep ignoring the tones presented through a loudspeaker above their head. Standard (1000 Hz, P=0.90), high deviant (1200 Hz, P=0.05), and low deviant (1050 Hz, P=0.05) tones were presented in a quasirandom order with a fixed stimulus onset asynchrony of 500 ms. ERP waveforms were obtained separately for 5 successive stages characterized by typical electroencephalographic (EEG) patterns of the sleep onset period. The EEG staging was made manually with very short (5 s) scoring epochs. RESULTS: The MMN appeared in wakefulness and in the early phase of stage 1 sleep (EEG stage II) but disappeared when low-voltage theta waves emerged after alpha flattening (EEG stage III). Instead, P240 and N360 developed particularly for high deviant tones. CONCLUSIONS: Concurrently with the disappearance of alpha waves, the automatic change detection system in wakefulness seems to stop operating and a different sleep-specific system becomes dominant.