Analysis of REM sleep without atonia in 22q11.2 deletion syndrome determined by domiciliary polysomnography: a cross sectional study.

STUDY OBJECTIVES: Our aim is to evaluate the presence of REM sleep without atonia (RWA), the objective hallmark of REM sleep Behaviour Disorder (RBD), as prodromal marker of Parkinson's disease (PD), in an adult cohort of 22q11.2 deletion syndrome (22qDS). METHODS: Sleep quality was assessed by means of Pittsburgh quality scale index (PSQI), and RBD symptoms by means of RBD questionnaire-Hong-Kong (RBDQ-HK). Attended domiciliary video-Polysomnography (v-PSG) were performed in 26 adults (18-51 years, 14 females) 22qDS patients. Electromyogram during REM sleep was analyzed by means of SINBAR procedure at 3-second time resolution (miniepochs). RESULTS: An overall poor sleep quality was observed in the cohort and high RBDQ-HK score in 7 of the 26 patients, two additional patients with positive dream enactment reported by close relatives had low score of RBDQ-HK. Nevertheless, SINBAR RWA scores were lower than cut-off threshold for RWA (mean 5.5%, range 0-12.2%). TST and the percentage of light sleep (N1) were increased, with preserved proportions of N2 and N3. Participants reported poor quality of sleep (mean PSQI > 5), with prolonged sleep latency in the v-PSG. No subjects exhibit evident dream enactment episodes during recording sessions. CONCLUSIONS: RWA was absent in the studied cohort of 22qDS adult volunteers according to validated polysomnographic criteria. High RBDQ-HK scores do not correlate with v-PSG results among 22qDS individuals.

REM sleep-dependent short-term and long-term hourglass processes in the ultradian organization and recovery of REM sleep in the rat.

STUDY OBJECTIVES: To evaluate the contribution of long-term and short-term REM sleep homeostatic processes to REM sleep recovery and the ultradian organization of the sleep wake cycle. METHODS: Fifteen rats were sleep recorded under a 12:12 LD cycle. Animals were subjected during the rest phase to two protocols (2T2I or 2R2I) performed separately in non-consecutive experimental days. 2T2I consisted of 2 h of total sleep deprivation (TSD) followed immediately by 2 h of intermittent REM sleep deprivation (IRD). 2R2I consisted of 2 h of selective REM sleep deprivation (RSD) followed by 2 h of IRD. IRD was composed of four cycles of 20-min RSD intervals alternating with 10 min of sleep permission windows. RESULTS: REM sleep debt that accumulated during deprivation (9.0 and 10.8 min for RSD and TSD, respectively) was fully compensated regardless of cumulated NREM sleep or wakefulness during deprivation. Protocol 2T2I exhibited a delayed REM sleep rebound with respect to 2R2I due to a reduction of REM sleep transitions related to enhanced NREM sleep delta-EEG activity, without affecting REM sleep consolidation. Within IRD permission windows there was a transient and duration-dependent diminution of REM sleep transitions. CONCLUSIONS: REM sleep recovery in the rat seems to depend on a long-term hourglass process activated by REM sleep absence. Both REM sleep transition probability and REM sleep episode consolidation depend on the long-term REM sleep hourglass. REM sleep activates a short-term REM sleep refractory period that modulates the ultradian organization of sleep states.

Envelope analysis links oscillatory and arrhythmic EEG activities to two types of neuronal synchronization.

Traditionally, EEG is understood as originating from the synchronous activation of neuronal populations that generate rhythmic oscillations in specific frequency bands. Recently, new neuronal dynamics regimes have been identified (e.g. neuronal avalanches) characterized by irregular or arrhythmic activity. In addition, it is starting to be acknowledged that broadband properties of EEG spectrum (following a 1/f law) are tightly linked to brain function. Nevertheless, there is still no theoretical framework accommodating the coexistence of these two EEG phenomenologies: rhythmic/narrowband and arrhythmic/broadband. To address this problem, we present a new framework for EEG analysis based on the relation between the Gaussianity and the envelope of a given signal. EEG Gaussianity is a relevant assessment because if EEG emerges from the superposition of uncorrelated sources, it should exhibit properties of a Gaussian process, otherwise, as in the case of neural synchronization, deviations from Gaussianity should be observed. We use analytical results demonstrating that the coefficient of variation of the envelope (CVE) of Gaussian noise (or any of its filtered sub-bands) is the constant 4π-1≈0.523, thus enabling CVE to be a useful metric to assess EEG Gaussianity. Furthermore, a new and highly informative analysis space (envelope characterization space) is generated by combining the CVE and the envelope average amplitude. We use this space to analyze rat EEG recordings during sleep-wake cycles. Our results show that delta, theta and sigma bands approach Gaussianity at the lowest EEG amplitudes while exhibiting significant deviations at high EEG amplitudes. Deviations to low-CVE appeared prominently during REM sleep, associated with theta rhythm, a regime consistent with the dynamics shown by the synchronization of weakly coupled oscillators. On the other hand, deviations to high-CVE, appearing mostly during NREM sleep associated with EEG phasic activity and high-amplitude Gaussian waves, can be interpreted as the arrhythmic superposition of transient neural synchronization events. These two different manifestations of neural synchrony (low-CVE/high-CVE) explain the well-known spectral differences between REM and NREM sleep, while also illuminating the origin of the EEG 1/f spectrum.

The Sleep-Wake Cycle in the Nicotinic Alpha-9 Acetylcholine Receptor Subunit Knock-Out Mice.

There is a neural matrix controlling the sleep-wake cycle (SWC) embedded within high ranking integrative mechanisms in the central nervous system. Nicotinic alpha-9 acetylcholine receptor subunit (alpha-9 nAChR) participate in physiological processes occurring in sensory, endocrine and immune systems. There is a relationship between the SWC architecture, body homeostasis and sensory afferents so that disruption of afferent signaling is expected to affect the temporal organization of sleep and wake states. The analysis of the SWC of 9 nAChR knock-out animals may help to reveal the contribution of alpha-9 nAChR to sleep chronobiological determinants. Here we explore the polysomnogram in chronically implanted alpha-9 nAChR knock-out (KO) and wild-type (WT) individuals of the hybrid CBA/Sv129 mouse strain. Records were obtained in isolation chambers under a stable 12:12 light:dark cycle (LD). To unmask the 24-h modulation of the SWC a skeleton photoperiod (SP) protocol was performed. Under LD the daily quota (in %) of wakefulness (W), NREM sleep and REM sleep obtained in KO and WT animals were 45, 48 and 7, and 46, 46 and 8 respectively. Both groups exhibit nocturnal phase preference of W as well as diurnal and unimodal phase preference of NREM and REM sleep. The acrophase mean angles of KO vs. WT genotypes were not different (Zeitgeber Time: 6.5 vs. 14.9 for W, 4.3 vs. 2.8 for NREM sleep and 5.3 vs. 3.4 for REM sleep, respectively). Transference to SP do not affect daily state quotas, phase preferences and acrophases among genotypes. Unmasking phenomena of the SWC such as wake increment during the rest phase under SP was evident only among WT mice suggesting the involvement of retinal structures containing alpha-9 nAChR in masking processes. Furthermore, KO animals exhibit longer NREM and REM sleep episodes that is independent of illumination conditions. Consolidated diurnal NREM sleep contributed to obtain higher values of NREM sleep delta-EEG activity among KO mice during rest phase. In conclusion, circadian and sleep homeostatic aspects of the SWC are operative among alpha-9 nAChR KO animals. We propose that alpha-9 nAChR participate in retinal signaling processes responsible of the positive masking of sleep by light.

Envelope analysis of the airflow signal to improve polysomnographic assessment of sleep disordered breathing.

STUDY OBJECTIVES: Given the detailed respiratory waveform signal provided by the nasal cannula in polysomnographic (PSG) studies, to quantify sleep breathing disturbances by extracting a continuous variable based on the coefficient of variation of the envelope of that signal. DESIGN: Application of an algorithm for envelope analysis to standard nasal cannula signal from actual polysomnographic studies. SETTING: PSG recordings from a sleep disorders center were analyzed by an algorithm developed on the Igor scientific data analysis software. PATIENTS OR PARTICIPANTS: Recordings representative of different degrees of sleep disordered breathing (SDB) severity or illustrative of the covariation between breathing and particularly relevant factors and variables. INTERVENTIONS: The method calculated the coefficient of variation of the envelope for each 30-second epoch. The normalized version of that coefficient was defined as the respiratory disturbance variable (RDV). The method outcome was the all-night set of RDV values represented as a time series. MEASUREMENTS AND RESULTS: RDV quantitatively reflected departure from normal sinusoidal breathing at each epoch, providing an intensity scale for disordered breathing. RDV dynamics configured itself in recognizable patterns for the airflow limitation (e.g., in UARS) and the apnea/hypopnea regimes. RDV reliably highlighted clinically meaningful associations with staging, body position, oximetry, or CPAP titration. CONCLUSIONS: Respiratory disturbance variable can assess sleep breathing disturbances as a gradual phenomenon while providing a comprehensible and detailed representation of its dynamics. It may thus improve clinical diagnosis and provide a revealing descriptive tool for mechanistic sleep disordered breathing modeling. Respiratory disturbance variable may contribute to attaining simplified screening methodologies, novel diagnostic criteria, and insightful research tools.

Visualization and clustering of sleep states in a frequency domain feature space.

Sleep studies assess the recurrent manifestation of stereotype configurations of relevant biosignals. These configurations are known as states (Wake, REM sleep and NonREM sleep) and stages (N1-N3 within NREM sleep). These two fundamental descriptive domains, time course and variable configuration, can be readily rendered available through improved visualization techniques. Time course is summarized by EEG spectrograms, instantaneous frequency analysis of cardio-respiratory signals and other sleep dependent variables. State and stage configurations can be further evidenced as clusters in 2D or 3D spaces whose axis are sleep-relevant extracted variables. The latter techniques also allows for visualization of transition process as pathways from one cluster to another.

The time course of the probability of transition into and out of REM sleep.

STUDY OBJECTIVES: A model of rapid eye movement (REM) sleep expression is proposed that assumes underlying regulatory mechanisms operating as inhomogenous Poisson processes, the overt results of which are the transitions into and out of REM sleep. DESIGN: Based on spontaneously occurring REM sleep episodes ("Episode") and intervals without REM sleep ("Interval"), 3 variables are defined and evaluated over discrete 15-second epochs using a nonlinear logistic regression method: "Propensity" is the instantaneous rate of into-REM transition occurrence throughout an Interval, "Volatility" is the instantaneous rate of out-of-REM transition occurrence throughout an Episode, and "Opportunity" is the probability of being in non-REM (NREM) sleep at a given time throughout an Interval, a requisite for transition. SETTING: 12:12 light:dark cycle, isolated boxes. PARTICIPANTS: Sixteen male Sprague-Dawley rats. INTERVENTIONS: None. Spontaneous sleep cycles. MEASUREMENTS AND RESULTS: The highest levels of volatility and propensity occur, respectively, at the very beginning of Episodes and Intervals. The new condition stabilizes rapidly, and variables reach nadirs at minute 1.25 and 2.50, respectively. Afterward, volatility increases markedly, reaching values close to the initial level. Propensity increases moderately, the increment being stronger through NREM sleep bouts occurring at the end of long Intervals. Short-term homeostasis is evidenced by longer REM sleep episodes lowering propensity in the following Interval. CONCLUSIONS: The stabilization after transitions into Episodes or Intervals and the destabilization after remaining for some time in either condition may be described as resulting from continuous processes building up during Episodes and intervals. These processes underlie the overt occurrence of transitions.

On-line analysis of biosignals for the automation of total and specific sleep deprivation in the rat

A computer-based system that automates sleep studies, including sleep deprivation paradigms, is described. The system allows for total or REM-specific sleep deprivation and is based on a reliable, fast-responding, on-line state detection algorithm linked to a dependable intervention device. Behavioral state detection is achieved by dimension reduction of short-term EEG power spectrum. Interventions are made by serial outputs to servomotors that move a cage with different patterns and variable intensity. The system can adapt itself to individual characteristics and to changes in recording conditions. Customized protocols can be designed by defining the states or stages to be deprived, including scheduling temporal patterns. A detailed analysis of the relevant signals during and after deprivation is readily available. Data is presented from two experimental designs in rats. One consisted of specific REM-sleep short-term deprivation and the other of 10-hour total sleep deprivation. An outline of conceptual and practical considerations involved in the automation of laboratory set-ups oriented to biosignal analysis is provided. Careful monitoring of sleep EEG variables during sleep deprivation suggests peculiarities of brain functioning in that condition. A corollary is that sleep deprivation should not be considered to be merely a forced prolonged wakefulness.

On-line analysis of biosignals for the automation of total and specific sleep deprivation in the rat.

A computer-based system that automates sleep studies, including sleep deprivation paradigms, is described. The system allows for total or REM-specific sleep deprivation and is based on a reliable, fast-responding, on-line state detection algorithm linked to a dependable intervention device. Behavioral state detection is achieved by dimension reduction of short-term EEG power spectrum. Interventions are made by serial outputs to servomotors that move a cage with different patterns and variable intensity. The system can adapt itself to individual characteristics and to changes in recording conditions. Customized protocols can be designed by defining the states or stages to be deprived, including scheduling temporal patterns. A detailed analysis of the relevant signals during and after deprivation is readily available. Data is presented from two experimental designs in rats. One consisted of specific REM-sleep short-term deprivation and the other of 10-hour total sleep deprivation. An outline of conceptual and practical considerations involved in the automation of laboratory set-ups oriented to biosignal analysis is provided. Careful monitoring of sleep EEG variables during sleep deprivation suggests peculiarities of brain functioning in that condition. A corollary is that sleep deprivation should not be considered to be merely a forced prolonged wakefulness.

Frequency domain analysis of sleep EEG for visualization and automated state detection.

Conventional analysis of EEG signals for sleep scoring is based on the time domain assessment of wave patterns. Human experts carry out this task relying on the direct visualization of EEG epochs. Techniques that enhance an intuitive visualization may encourage a wider use of more abstract descriptors, such as frequency domain features. This paper presents a feature extraction method for EEG signals based on FFT and principal component analysis. The result of the method is a characterization of EEG epochs with only two variables. Density plots of this 2D projection show compact clusters that correspond to sleep behavioral states. The distance to the centroid of a cluster is a reliable scoring criterion which is both easy to visualize and easy to automate. The techniques presented here have been shown to work reliably for both human and rat sleep studies.