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Neural wearables can enable life-saving drowsiness and health monitoring for pilots and drivers. While existing in-cabin sensors may provide alerts, wearables can enable monitoring across more environments. Current neural wearables are promising but most require wet-electrodes and bulky electronics. This work showcases in-ear, dry-electrode earpieces used to monitor drowsiness with compact hardware. The employed system integrates additive-manufacturing for dry, user-generic earpieces, existing wireless electronics, and offline classification algorithms. Thirty-five hours of electrophysiological data were recorded across nine subjects performing drowsiness-inducing tasks. Three classifier models were trained with user-specific, leave-one-trial-out, and leave-one-user-out splits. The support-vector-machine classifier achieved an accuracy of 93.2% while evaluating users it has seen before and 93.3% when evaluating a never-before-seen user. These results demonstrate wireless, dry, user-generic earpieces used to classify drowsiness with comparable accuracies to existing state-of-the-art, wet electrode in-ear and scalp systems. Further, this work illustrates the feasibility of population-trained classification in future electrophysiological applications.

STUDY OBJECTIVES: Recently, criteria have been drawn up for large muscle group movements during sleep (LMM), defined as movements lasting for 3-45 seconds in adults, which are often accompanied by changes in sleep stage, arousals, and increases in heart rate. The aim of this study was to characterize LMM in restless legs syndrome (RLS) in order to better evaluate their impact on the neurophysiology of the disorder and, therefore, the possible clinical implications. METHODS: Consecutive, drug-free patients diagnosed with RLS and controls, aged 18 years or more, were retrospectively enrolled. Leg movement activity-short-interval (SILMS), periodic (PLMS), and isolated (ISOLMS) leg movements during sleep-and LMM were detected and scored. RESULTS: In total, 100 patients and 67 controls were recruited. All movement measures were significantly higher in RLS. A significant positive correlation was found between LMM and ISOLMS index but not PLMS index in both groups. LMM index showed a significant negative correlation with total sleep time, sleep efficiency, and percentage of sleep stages N3 and R, as well as a significant positive correlation with the number of awakenings, and percentage of sleep stages N1 and N2 only in patients with RLS. No significant correlation was found between either LMM or PLMS index and RLS severity. CONCLUSIONS: Different types of movements, including SILMS, ISOLMS, and LMM, play somewhat distinct roles in sleep neurophysiology in RLS. Notably, LMM, a newly recognized category of movements, demonstrates associations with sleep architecture instability and fragmentation, arousals, and awakenings, suggesting potential clinical implications.

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

BACKGROUND: Sleep dysfunction is disabling in people with Parkinsons disease and is linked to worse motor and non-motor outcomes. Sleep-specific adaptive Deep Brain Stimulation has the potential to target pathophysiologies of sleep. OBJECTIVE: Develop an adaptive Deep Brain Stimulation algorithm that modulates stimulation parameters in response to intracranially classified sleep stages. METHODS: We performed at-home, multi-night intracranial electrocorticography and polysomnogram recordings to train personalized linear classifiers for discriminating the N3 NREM sleep stage. Classifiers were embedded into investigational Deep Brain Stimulators for N3 specific adaptive DBS. RESULTS: We report high specificity of embedded, autonomous, intracranial electrocorticography N3 sleep stage classification across two participants and provide proof-of-principle of successful sleep stage specific adaptive Deep Brain Stimulation. CONCLUSION: Multi-night cortico-basal recordings and sleep specific adaptive Deep Brain Stimulation provide an experimental framework to investigate sleep pathophysiology and mechanistic interactions with stimulation, towards the development of therapeutic neurostimulation paradigms directly targeting sleep dysfunction.

Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, and amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300 ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.

In humans, sleep spindles are 10- to 16-Hz oscillations lasting approximately 0.5-2 s. Spindles, along with cortical slow oscillations, may facilitate memory consolidation by enabling synaptic plasticity. Early recordings of spindles at the scalp found anterior channels had overall slower frequency than central-posterior channels. This robust, topographical finding led to dichotomizing spindles as "slow" versus "fast," modeled as two distinct spindle generators in frontal versus posterior cortex. Using a large dataset of intracranial stereoelectroencephalographic (sEEG) recordings from 20 patients (13 female, 7 male) and 365 bipolar recordings, we show that the difference in spindle frequency between frontal and parietal channels is comparable to the variability in spindle frequency within the course of individual spindles, across different spindles recorded by a given site, and across sites within a given region. Thus, fast and slow spindles only capture average differences that obscure a much larger underlying overlap in frequency. Furthermore, differences in mean frequency are only one of several ways that spindles differ. For example, compared with parietal, frontal spindles are smaller, tend to occur after parietal when both are engaged, and show a larger decrease in frequency within-spindles. However, frontal and parietal spindles are similar in being longer, less variable, and more widespread than occipital, temporal, and Rolandic spindles. These characteristics are accentuated in spindles which are highly phase-locked to posterior hippocampal spindles. We propose that rather than a strict parietal-fast/frontal-slow dichotomy, spindles differ continuously and quasi-independently in multiple dimensions, with variability due about equally to within-spindle, within-region, and between-region factors.SIGNIFICANCE STATEMENT Sleep spindles are 10- to 16-Hz neural oscillations generated by cortico-thalamic circuits that promote memory consolidation. Spindles are often dichotomized into slow-anterior and fast-posterior categories for cognitive and clinical studies. Here, we show that the anterior-posterior difference in spindle frequency is comparable to that observed between different cycles of individual spindles, between spindles from a given site, or from different sites within a region. Further, we show that spindles vary on other dimensions such as duration, amplitude, spread, primacy and consistency, and that these multiple dimensions vary continuously and largely independently across cortical regions. These findings suggest that multiple continuous variables rather than a strict frequency dichotomy may be more useful biomarkers for memory consolidation or psychiatric disorders.

We provide evidence that human sleep is a competitive arena in which cognitive domains vie for limited resources. Using pharmacology and effective connectivity analysis, we demonstrate that long-term memory and working memory are served by distinct offline neural mechanisms that are mutually antagonistic. Specifically, we administered zolpidem to increase central sigma activity and demonstrated targeted suppression of autonomic vagal activity. With effective connectivity, we determined the central activity has greater causal influence over autonomic activity, and the magnitude of this influence during sleep produced a behavioral trade-off between offline long-term and working memory processing. These findings suggest a sleep switch mechanism that toggles between central sigma-dependent long-term memory and autonomic vagal-dependent working memory processing.

In healthy, young individuals, a reduction in cardiovascular output and a shift from sympathetic to parasympathetic (vagal) dominance is observed from wake into stages of nocturnal and daytime sleep. This cardiac autonomic profile, measured by heart rate variability (HRV), has been associated with significant benefits for cardiovascular health. Aging is associated with decreased nighttime sleep quality and lower parasympathetic activity during both sleep and resting. However, it is not known whether age-related dampening of HRV extends to daytime sleep, diminishing the cardiovascular benefits of naps in the elderly. Here, we investigated this question by comparing the autonomic activity profile between young and older healthy adults during a daytime nap and a similar period of wakefulness (quiet wake; QW). For each condition, from the electrocardiogram (ECG), we obtained beat-to-beat HRV intervals (RR), root mean square of successive differences between adjacent heart-beat-intervals (RMSSD), high-frequency (HF), low-frequency (LF) power, and total power (TP), HF normalized units (HFnu ), and the LF/HF ratio. As previously reported, young subjects showed a parasympathetic dominance during NREM, compared with REM, prenap rest, and WASO. Moreover, older, compared to younger, adults showed significantly lower vagally mediated HRV (measured by RMSSD, HF, HFnu ) during NREM. Interestingly, however, no age-related differences were detected during prenap rest or QW. Altogether, our findings suggest a sleep-specific reduction in parasympathetic modulation that is unique to NREM sleep in older adults.

Sleep spindles are intermittent bursts of 11-15 Hz EEG waves that occur during non-rapid eye movement sleep. Spindles are believed to help maintain sleep and to play a role in sleep-dependent memory consolidation. Here we applied an automated sleep spindle detection program to our large longitudinal sleep EEG dataset (98 human subjects, 6-18 years old, >2000 uninterrupted nights) to evaluate maturational trends in spindle wave frequency, density, amplitude, and duration. This large dataset enabled us to apply nonlinear as well as linear age models, thereby extending the findings of prior cross-sectional studies that used linear models. We found that spindle wave frequency increased with remarkable linearity across the age range. Central spindle density increased nonlinearly to a peak at age 15.1 years. Central spindle wave amplitude declined in a sigmoidal pattern with the age of fastest decline at 13.5 years. Spindle duration decreased linearly with age. Of the four measures, only spindle amplitude showed a sex difference in dynamics such that the age of most rapid decline in females preceded that in males by 1.4 years. This amplitude pattern, including the sex difference in timing, paralleled the maturational pattern for δ (1-4 Hz) wave power. We interpret these age-related changes in spindle characteristics as indicators of maturation of thalamocortical circuits and changes in sleep depth. These robust age-effects could facilitate the search for cognitive-behavioral correlates of spindle waveforms and might also help guide basic research on EEG mechanisms and postnatal brain maturation.SIGNIFICANCE STATEMENT The brain reorganization of adolescence produces massive changes in sleep EEG. These changes include the morphology and abundance of sleep spindles, an EEG marker of non-rapid eye movement sleep believed to reflect offline memory processes and/or protection of the sleep state. We analyzed >2000 nights of longitudinal sleep EEG from 98 subjects (age 6-18 years old) to investigate maturational changes in spindle amplitude, frequency, density, and duration. The large dataset enabled us to detect nonlinear as well as linear age changes. All measures showed robust age effects that we hypothesize reflect the maturation of thalamocortical circuits and decreasing sleep depth. These findings could guide further research into the cognitive-behavioral correlates of sleep spindles and their underlying brain mechanisms.

ObjectiveTo determine the best of commonly used methods for computing the rate of decline in non-rapid eye movement (NREM) sleep EEG delta power overnight (Delta Decline) in terms of vulnerability to missing data and to evaluate whether this rate is slower in insomnia patients than healthy controls (HC).MethodsFifty-one insomnia patients and 53 HC underwent 6 nights of polysomnography. Four methods for estimating Delta Decline were compared (exponential and linear best-fit functions using NREM (1) episode mean, (2) peak, and (3) total delta power and (4) delta power for all available NREM epochs). The best method was applied to compare groups on linear and exponential rates of Delta Decline.ResultsBest-fit models using all available NREM epochs were significantly less vulnerable to deviation due to missing data than other methods. Insomnia patients displayed significantly slower linear and exponential Delta Decline than HC.ConclusionsComputing Delta Decline using all available NREM epochs was the best of the methods studied for minimizing the effects of missing data. Insomnia patients display slower Delta Decline, which is not explained by differences in total sleep time or wake after sleep onset.SignificanceThis study supports using all available NREM epochs in Delta Decline computation and suggests a slower rate in insomnia.

Rapid eye movement (REM) sleep behavior disorder (RBD) is characterized by disrupting motor enactments during REM sleep, but also cognitive impairments across several domains. In addition to REM sleep abnormalities, we hypothesized that RBD patients may also display EEG abnormalities during NREM sleep. We collected all-night recordings with 256-channel high-density EEG in nine RBD patients, predominantly early-onset medicated individuals, nine sex- and age- matched healthy controls, and nine additional controls with matched medications and comorbidities. Power spectra in delta to gamma frequency bands were compared during both REM and NREM sleep, between phasic and tonic REM sleep, and between the first versus last cycle of NREM sleep. Controls, but not RBD patients, displayed a decrease in beta power during phasic compared to tonic REM sleep. Compared to controls, RBD patients displayed a reduced decline in SWA from early to late NREM sleep. Overnight changes in the distribution of the amplitude of slow waves were also reduced in RBD patients. Without suppression of beta rhythms during phasic REM sleep, RBD patients might demonstrate heightened cortical arousal, favoring the emergence of behavioral episodes. A blunted difference between REM sleep sub-stages may constitute a sensitive biomarker for RBD. Moreover, reduced overnight decline in SWA suggests a reduced capacity for synaptic plasticity in RBD patients, which may favor progression towards neurodegenerative diseases.

The presence of dreams in human sleep, especially in REM sleep, and the detection of physiologically similar states in mammals has led many to ponder whether animals experience similar sleep mentation. Recent advances in our understanding of the anatomical and physiological correlates of sleep stages, and thus dreaming, allow a better understanding of the possibility of dream mentation in nonhuman mammals. Here, we explore the potential for dream mentation, in both non-REM and REM sleep across mammals. If we take a hard-stance, that dream mentation only occurs during REM sleep, we conclude that it is unlikely that monotremes, cetaceans, and otariid seals while at sea, have the potential to experience dream mentation. Atypical REM sleep in other species, such as African elephants and Arabian oryx, may alter their potential to experience REM dream mentation. Alternatively, evidence that dream mentation occurs during both non-REM and REM sleep, indicates that all mammals have the potential to experience dream mentation. This non-REM dream mentation may be different in the species where non-REM is atypical, such as during unihemispheric sleep in aquatic mammals (cetaceans, sirens, and Otariid seals). In both scenarios, the cetaceans are the least likely mammalian group to experience vivid dream mentation due to the morphophysiological independence of their cerebral hemispheres. The application of techniques revealing dream mentation in humans to other mammals, specifically those that exhibit unusual sleep states, may lead to advances in our understanding of the neural underpinnings of dreams and conscious experiences.

BACKGROUND: Seizures are an under-reported feature of the SATB2-associated syndrome phenotype. We describe the electroencephalographic findings and seizure semiology and treatment in a population of individuals with SATB2-associated syndrome. METHODS: We performed a retrospective review of 101 individuals with SATB2-associated syndrome who were reported to have had a previous electroencephalographic study to identify those who had at least one reported abnormal result. For completeness, a supplemental survey was distributed to the caregivers and input from the treating neurologist was obtained whenever possible. RESULTS: Forty-one subjects were identified as having at least one prior abnormal electroencephalography. Thirty-eight individuals (93%) had epileptiform discharges, 28 (74%) with central localization. Sleep stages were included as part of the electroencephalographies performed in 31 individuals (76%), and epileptiform activity was recorded during sleep in all instances (100%). Definite clinical seizures were diagnosed in 17 individuals (42%) with a mean age of onset of 3.2 years (four months to six years), and focal seizures were the most common type of seizure observed (42%). Six subjects with definite clinical seizures needed polytherapy (35%). Delayed myelination and/or abnormal white matter hyperintensities were seen on neuroimaging in 19 individuals (61%). CONCLUSIONS: Epileptiform abnormalities are commonly seen in individuals with SATB2-associated syndrome. A baseline electroencephalography that preferably includes sleep stages is recommended during the initial evaluation of all individuals with SATB2-associated syndrome, regardless of clinical suspicion of epilepsy.

Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence of synchronized oscillations in the electroencephalogram (EEG). Although rapid eye movement (REM) sleep is also associated with diminished arousal levels, it is characterized by a desynchronized, 'wake-like' EEG. This observation implies that reduced arousal states are not necessarily only defined by synchronous oscillatory activity. Using intracranial and surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of the electrophysiological power spectrum, which reflects the non-oscillatory, scale-free component of neural activity, delineates wakefulness from propofol anesthesia, NREM and REM sleep. Critically, the spectral slope discriminates wakefulness from REM sleep solely based on the neurophysiological brain state. Taken together, our findings describe a common electrophysiological marker that tracks states of reduced arousal, including different sleep stages as well as anesthesia in humans.

Human anterior and posterior hippocampus (aHC, pHC) differ in connectivity and behavioral correlates. Here we report physiological differences in humans of both sexes. During NREM sleep, the human hippocampus generates sharpwave ripples (SWRs) similar to those which in rodents mark memory replay. We show that while pHC generates SWRs, it also generates approximately as many spindle ripples (SSR: ripples phase-locked to local spindles). In contrast, SSRs are rare in aHC. Like SWRs, SSRs often co-occur with neocortical theta bursts (TBs), downstates (DSs), sleep spindles (SSs), and upstates (USs), which coordinate cortico-hippocampal interactions and facilitate consolidation in rodents. SWRs co-occur with these waves in widespread cortical areas, especially frontocentral. These waves typically occur in the sequence TB-DS-SS-US, with SWRs usually occurring before SS-US. In contrast, SSRs occur ∼350 ms later, with a strong preference for co-occurrence with posterior-parietal SSs. pHC-SSs were strongly phase-locked with parietal-SSs, and pHC-SSRs were phase-coupled with pHC-SSs and parietal-SSs. Human SWRs (and associated replay events, if any) are separated by ∼5 s on average, whereas ripples on successive SSR peaks are separated by only ∼80 ms. These distinctive physiological properties of pHC-SSR enable an alternative mechanism for hippocampal engagement with neocortex.SIGNIFICANCE STATEMENT Rodent hippocampal neurons replay waking events during sharpwave ripples (SWRs) in NREM sleep, facilitating memory transfer to a permanent cortical store. We show that human anterior hippocampus also produces SWRs, but spindle ripples predominate in posterior. Whereas SWRs typically occur as cortex emerges from inactivity, spindle ripples typically occur at peak cortical activity. Furthermore, posterior hippocampal spindle ripples are tightly coupled to posterior parietal locations activated by conscious recollection. Finally, multiple spindle ripples can recur within a second, whereas SWRs are separated by ∼5 s. The human posterior hippocampus is considered homologous to rodent dorsal hippocampus, which is thought to be specialized for consolidation of specific memory details. We speculate that these distinct physiological characteristics of posterior hippocampal spindle ripples may support a related function in humans.

Recent proposals suggest that sleep may be a factor associated with accumulation of two core pathological features of Alzheimer's disease (AD): tau and β-amyloid (Aβ). Here we combined PET measures of Aβ and tau, electroencephalogram sleep recordings, and retrospective sleep evaluations to investigate the potential utility of sleep measures in predicting in vivo AD pathology in male and female older adults. Regression analyses revealed that the severity of impaired slow oscillation-sleep spindle coupling predicted greater medial temporal lobe tau burden. Aβ burden was not associated with coupling impairment but instead predicted the diminished amplitude of

Wakefulness, rapid eye movement (REM) sleep, and non-rapid eye movement (NREM) sleep are characterized by distinct electroencephalogram (EEG), electromyogram (EMG), and autonomic profiles. The circuit mechanism coordinating these changes during sleep-wake transitions remains poorly understood. The past few years have witnessed rapid progress in the identification of REM and NREM sleep neurons, which constitute highly distributed networks spanning the forebrain, midbrain, and hindbrain. Here we propose an arousal-action circuit for sleep-wake control in which wakefulness is supported by separate arousal and action neurons, while REM and NREM sleep neurons are part of the central somatic and autonomic motor circuits. This model is well supported by the currently known sleep and wake neurons. It can also account for the EEG, EMG, and autonomic profiles of wake, REM, and NREM states and several key features of their transitions. The intimate association between the sleep and autonomic/somatic motor control circuits suggests that a primary function of sleep is to suppress motor activity.

Study objectivesTo determine whether there is a consistent epiglottic pressure value that predicts respiratory arousal from sleep.MethodsThirty-one patients with obstructive sleep apnea underwent overnight polysomnography while instrumented with an epiglottic catheter to measure airway pressures. Nadir epiglottic pressures during respiration events (obstructive apneas/hypopneas) terminated with or without arousals were compared. The events were selected by two methods, (1) 20 events with/without arousals were randomly selected, and (2) Events were sampled in pairs (one terminated with arousal and one without arousal) to minimize the effect of sleep duration/stage on the measurement.ResultsA total of 1,317 respiratory events were analyzed. There was substantial variability in nadir epiglottic pressure within an individual and among different individuals. The average pressure of 20 randomly selected events with arousals was (-21.2 ± 11.2, ranged -6.68 to -63.34 cm H2O). The nadir epiglottic pressure during respiratory events in NREM stage 2 sleep terminated with arousals was more negative compared with those terminated without arousals using both sampling methods (-23.5 vs. -18.5 cm H2O, p = 0.007 and -20.3 vs. -16.3 cm H2O, p < 0.001).ConclusionsThere were very different levels of epiglottic pressures that preceded arousals within and among individuals. However, cortical arousals are associated with a level of more negative epiglottic pressure compared to events terminated without arousal, findings which support the concept of a respiratory arousal threshold.Clinical trial registrationThe study used existing data to study methodology (from clinical trial "The Impact of Arousal Threshold in Obstructive Sleep Apnea" https://clinicaltrials.gov/show/NCT02264353) and it is not a clinical trial.

BackgroundAlthough they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain.MethodWe present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors.ResultsThe simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted.Comparison with existing methodsPrevious models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales.ConclusionsThis multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.

Despite sleep's recognized biological importance, it has been remarkably difficult to demonstrate changes in brain physiology with reduced sleep durations. In a study of adolescents, we varied sleep durations by restricting time in bed for four nights of either 10, 8.5 or 7 h. Shorter sleep durations significantly decreased waking electroencephalogram (EEG) power in a wide range of frequencies with both eyes closed and eyes open in central and occipital leads. These findings suggest new research directions and raise the possibility that waking EEG power density could provide a non-invasive test for biologically sufficient sleep.

While anatomical pathways between forebrain cognitive and brainstem autonomic nervous centers are well-defined, autonomic-central interactions during sleep and their contribution to waking performance are not understood. Here, we analyzed simultaneous central activity via electroencephalography (EEG) and autonomic heart beat-to-beat intervals (RR intervals) from electrocardiography (ECG) during wake and daytime sleep. We identified bursts of ECG activity that lasted 4-5 s and predominated in non-rapid-eye-movement sleep (NREM). Using event-based analysis of NREM sleep, we found an increase in delta (0.5-4 Hz) and sigma (12-15 Hz) power and an elevated density of slow oscillations (0.5-1 Hz) about 5 s prior to peak of the heart rate burst, as well as a surge in vagal activity, assessed by high-frequency (HF) component of RR intervals. Using regression framework, we show that these Autonomic/Central Events (ACE) positively predicted post-nap improvement in a declarative memory task after controlling for the effects of spindles and slow oscillations from sleep periods without ACE. No such relation was found between memory performance and a control nap. Additionally, NREM ACE negatively correlated with REM sleep and learning in a non-declarative memory task. These results provide the first evidence that coordinated autonomic and central events play a significant role in declarative memory consolidation.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.

There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formation and dissolution of active cortical sources and brain networks. However, unsupervised approaches to identify and model these changes in brain dynamics as continuous transitions between quasi-stable brain states using unlabeled, noninvasive recordings of brain activity have been limited. This study explores the use of adaptive mixture independent component analysis (AMICA) to model multichannel electroencephalographic (EEG) data with a set of ICA models, each of which decomposes an adaptively learned portion of the data into statistically independent sources. We first show that AMICA can segment simulated quasi-stationary EEG data and accurately identify ground-truth sources and source model transitions. Next, we demonstrate that AMICA decomposition, applied to 6-13 channel scalp recordings from the CAP Sleep Database, can characterize sleep stage dynamics, allowing 75% accuracy in identifying transitions between six sleep stages without use of EEG power spectra. Finally, applied to 30-channel data from subjects in a driving simulator, AMICA identifies models that account for EEG during faster and slower response to driving challenges, respectively. We show changes in relative probabilities of these models allow effective prediction of subject response speed and moment-by-moment characterization of state changes within single trials. AMICA thus provides a generic unsupervised approach to identifying and modeling changes in EEG dynamics. Applied to continuous, unlabeled multichannel data, AMICA may likely be used to detect and study any changes in cognitive states.

Background:Angelman syndrome (AS) is a neurodevelopmental disorder characterized by intellectual disability, speech and motor impairments, epilepsy, abnormal sleep, and phenotypic overlap with autism. Individuals with AS display characteristic EEG patterns including high-amplitude rhythmic delta waves. Here, we sought to quantitatively explore EEG architecture in AS beyond known spectral power phenotypes. We were motivated by studies of functional connectivity and sleep spindles in autism to study these EEG readouts in children with AS. Methods:We analyzed retrospective wake and sleep EEGs from children with AS (age 4-11) and age-matched neurotypical controls. We assessed long-range and short-range functional connectivity by measuring coherence across multiple frequencies during wake and sleep. We quantified sleep spindles using automated and manual approaches. Results:During wakefulness, children with AS showed enhanced long-range EEG coherence across a wide range of frequencies. During sleep, children with AS showed increased long-range EEG coherence specifically in the gamma band. EEGs from children with AS contained fewer sleep spindles, and these spindles were shorter in duration than their neurotypical counterparts. Conclusions:We demonstrate two quantitative readouts of dysregulated sleep composition in children with AS-gamma coherence and spindles-and describe how functional connectivity patterns may be disrupted during wakefulness. Quantitative EEG phenotypes have potential as biomarkers and readouts of target engagement for future clinical trials and provide clues into how neural circuits are dysregulated in children with AS.

Study objectivesWe present an automated sleep electroencephalogram (EEG) spectral analysis pipeline that includes an automated artifact detection step, and we test the hypothesis that spectral power density estimates computed with this pipeline are comparable to those computed with a commercial method preceded by visual artifact detection by a sleep expert (standard approach).MethodsEEG data were analyzed from the C3-A2 lead in a sample of polysomnograms from 161 older women participants in a community-based cohort study. We calculated the sensitivity, specificity, accuracy, and Cohen's kappa measures from epoch-by-epoch comparisons of automated to visual-based artifact detection results; then we computed the average EEG spectral power densities in six commonly used EEG frequency bands and compared results from the two methods using correlation analysis and Bland-Altman plots.ResultsAssessment of automated artifact detection showed high specificity [96.8%-99.4% in non-rapid eye movement (NREM), 96.9%-99.1% in rapid eye movement (REM) sleep] but low sensitivity (26.7%-38.1% in NREM, 9.1-27.4% in REM sleep). However, large artifacts (total power > 99th percentile) were removed with sensitivity up to 87.7% in NREM and 90.9% in REM, with specificities of 96.9% and 96.6%, respectively. Mean power densities computed with the two approaches for all EEG frequency bands showed very high correlation (≥0.99). The automated pipeline allowed for a 100-fold reduction in analysis time with regard to the standard approach.ConclusionDespite low sensitivity for artifact rejection, the automated pipeline generated results comparable to those obtained with a standard method that included manual artifact detection. Automated pipelines can enable practical analyses of recordings from thousands of individuals, allowing for use in genetics and epidemiological research requiring large samples.

ObjectiveAlthough the importance of sleep is increasingly recognized, the lack of robust and efficient algorithms hinders scalable sleep assessment in healthy persons and those with sleep disorders. Polysomnography (PSG) and visual/manual scoring remain the gold standard in sleep evaluation, but more efficient/automated systems are needed. Most previous works have demonstrated algorithms in high agreement with the gold standard in healthy/normal (HN) individuals-not those with sleep disorders.MethodsThis paper presents a statistical framework that automatically estimates whole-night sleep architecture in patients with obstructive sleep apnea (OSA)-the most common sleep disorder. Single-channel frontal electroencephalography was extracted from 65 HN/OSA sleep studies, and decomposed into 11 spectral features in 60 903 30 s sleep epochs. The algorithm leveraged kernel density estimation to generate stage-specific likelihoods, and a 5-state hidden Markov model to estimate per-night sleep architecture.ResultsComparisons to full PSG expert scoring revealed the algorithm was in fair agreement with the gold standard (median Cohen's kappa = 0.53). Further, analysis revealed modest decreases in median scoring agreement as OSA severity increased from HN (kappa = 0.63) to severe (kappa = 0.47). A separate implementation on HN data from the Physionet Sleep-EDF Database resulted in a median kappa = 0.65, further indicating the algorithm's broad applicability.ConclusionResults of this work indicate the proposed single-channel framework can emulate expert-level scoring of sleep architecture in OSA.SignificanceAlgorithms constructed to more accurately model physiological variability during sleep may help advance automated sleep assessment, for practical and general use in sleep medicine.

Sleep staging is a fundamental but time consuming process in any sleep laboratory. To greatly speed up sleep staging without compromising accuracy, we developed a novel framework for performing real-time automatic sleep stage classification. The client-server architecture adopted here provides an end-to-end solution for anonymizing and efficiently transporting polysomnography data from the client to the server and for receiving sleep stages in an interoperable fashion. The framework intelligently partitions the sleep staging task between the client and server in a way that multiple low-end clients can work with one server, and can be deployed both locally as well as over the cloud. The framework was tested on four datasets comprising ≈1700 polysomnography records (≈12000 hr of recordings) collected from adolescents, young, and old adults, involving healthy persons as well as those with medical conditions. We used two independent validation datasets: one comprising patients from a sleep disorders clinic and the other incorporating patients with Parkinson's disease. Using this system, an entire night's sleep was staged with an accuracy on par with expert human scorers but much faster (≈5 s compared with 30-60 min). To illustrate the utility of such real-time sleep staging, we used it to facilitate the automatic delivery of acoustic stimuli at targeted phase of slow-sleep oscillations to enhance slow-wave sleep.

BACKGROUND: Sleep disturbances are prominent correlates of acute mood episodes and inadequate recovery in bipolar disorder (BD), yet the mechanistic relationship between sleep physiology and mood remains poorly understood. Using a series of pre-sleep mood inductions and overnight sleep recording, this study examined the relationship between overnight mood regulation and a marker of sleep intensity (non-rapid eye movement sleep slow wave activity; NREM SWA) during the interepisode phase of BD. METHODS: Adults with interepisode BD type 1 (BD; n = 20) and healthy adult controls (CTL; n = 23) slept in the laboratory for a screening night, a neutral mood induction night (baseline), a happy mood induction night, and a sad mood induction night. NREM SWA (0.75-4.75 Hz) was derived from overnight sleep EEG recordings. Overnight mood regulation was evaluated using an affect grid pleasantness rating post-mood induction (pre-sleep) and the next morning. RESULTS: Overnight mood regulation did not differ between groups following the sad or happy inductions. SWA did not significantly change for either group on the sad induction night compared with baseline. In BD only, SWA on the sad night was related to impaired overnight negative mood regulation. On the happy induction night, SWA increased relative to baseline in both groups, though SWA was not related to overnight mood regulation for either group. CONCLUSIONS: These findings indicate that SWA disruption may play a role in sustaining negative mood state from the previous night in interepisode BD. However, positive mood state could enhance SWA in bipolar patients and healthy adults.

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep.

Sleep spindles promote the consolidation of motor skill memory in young adults. Older adults, however, exhibit impoverished sleep-dependent motor memory consolidation. The underlying pathophysiological mechanism(s) explaining why motor memory consolidation in older adults fails to benefit from sleep remains unclear. Here, we demonstrate that male and female older adults show impoverished overnight motor skill memory consolidation relative to young adults, with the extent of impairment being associated with the degree of reduced frontal fast sleep spindle density. The magnitude of the loss of frontal fast sleep spindles in older adults was predicted by the degree of reduced white matter integrity throughout multiple white matter tracts known to connect subcortical and cortical brain regions. We further demonstrate that the structural integrity of selective white matter fiber tracts, specifically within right posterior corona radiata, right tapetum, and bilateral corpus callosum, statistically moderates whether sleep spindles promoted overnight consolidation of motor skill memory. Therefore, white matter integrity within tracts known to connect cortical sensorimotor control regions dictates the functional influence of sleep spindles on motor skill memory consolidation in the elderly. The deterioration of white matter fiber tracts associated with human brain aging thus appears to be one pathophysiological mechanism influencing subcortical-cortical propagation of sleep spindles and their related memory benefits.SIGNIFICANCE STATEMENT Numerous studies have shown that sleep spindle expression is reduced and sleep-dependent motor memory is impaired in older adults. However, the mechanisms underlying these alterations have remained unknown. The present study reveals that age-related degeneration of white matter within select fiber tracts is associated with reduced sleep spindles in older adults. We further demonstrate that, within these same fiber tracts, the degree of degeneration determines whether sleep spindles can promote motor memory consolidation. Therefore, white matter integrity in the human brain, more than age per se, determines the magnitude of decline in sleep spindles in later life and, with it, the success (or lack thereof) of sleep-dependent motor memory consolidation in older adults.

OBJECTIVE:Differentiating pathologic and physiologic high-frequency oscillations (HFOs) is challenging. In patients with focal epilepsy, HFOs occur during the transitional periods between the up and down state of slow waves. The preferred phase angles of this form of phase-event amplitude coupling are bimodally distributed, and the ripples (80-150 Hz) that occur during the up-down transition more often occur in the seizure-onset zone (SOZ). We investigated if bimodal ripple coupling was also evident for faster sleep oscillations, and could identify the SOZ. METHODS:Using an automated ripple detector, we identified ripple events in 40-60 min intracranial electroencephalography (iEEG) recordings from 23 patients with medically refractory mesial temporal lobe or neocortical epilepsy. The detector quantified epochs of sleep oscillations and computed instantaneous phase. We utilized a ripple phasor transform, ripple-triggered averaging, and circular statistics to investigate phase event-amplitude coupling. RESULTS:We found that at some individual recording sites, ripple event amplitude was coupled with the sleep oscillatory phase and the preferred phase angles exhibited two distinct clusters (p

Every night, the human brain produces thousands of downstates and spindles during non-REM sleep. Previous studies indicate that spindles originate thalamically and downstates cortically, loosely grouping spindle occurrence. However, the mechanisms whereby the thalamus and cortex interact in generating these sleep phenomena remain poorly understood. Using bipolar depth recordings, we report here a sequence wherein: (1) convergent cortical downstates lead thalamic downstates; (2) thalamic downstates hyperpolarize thalamic cells, thus triggering spindles; and (3) thalamic spindles are focally projected back to cortex, arriving during the down-to-upstate transition when the cortex replays memories. Thalamic intrinsic currents, therefore, may not be continuously available during non-REM sleep, permitting the cortex to control thalamic spindling by inducing downstates. This archetypical cortico-thalamo-cortical sequence could provide the global physiological context for memory consolidation during non-REM sleep.

Upper airway muscle activity is reportedly elevated during slow-wave sleep (SWS) when compared with lighter sleep stages. To uncover the possible mechanisms underlying this elevation, we explored the correlation between different indices of central and reflex inspiratory drive, such as the changes in airway pressure and end-expiratory CO2 and the changes in the genioglossus (GG) and tensor palatini (TP) muscle activity accompanying transitions from the lighter N2 to the deeper N3 stage of non-rapid eye movement (NREM) sleep in healthy young adult men. Forty-six GG and 38 TP continuous electromyographic recordings were obtained from 16 men [age: 20 ± 2.5 (SD) yr; body mass index: 22.5 ± 1.8 kg/m2] during 32 transitions from NREM stages N2 to N3. GG but not TP activity increased following transition into N3 sleep, and the increase was positively correlated with more negative airway pressure, increased end-tidal CO2, increased peak inspiratory flow, and increased minute ventilation. None of these correlations was statistically significant for TP. Complementary GG and TP single motor unit analysis revealed a mild recruitment of GG units and derecruitment of TP units during the N2 to N3 transitions. These findings suggest that, in healthy individuals, the increased GG activity during SWS is driven primarily by reflex stimulation of airway mechanoreceptors and central chemoreceptors.NEW & NOTEWORTHY The characteristic increase in the activity of the upper airway dilator muscle genioglossus during slow-wave sleep (SWS) in young healthy individuals was found to be related to increased stimulation of airway mechanoreceptors and central chemoreceptors. No evidence was found for the presence of a central SWS-specific drive stimulating genioglossus activity in young healthy individuals. However, it remains to be determined whether a central drive exists in obstructive sleep apnea patients.

Study objectivesDaytime sleepiness increases across adolescence. This increase is commonly attributed to insufficient sleep durations resulting from increasingly limited time in bed. We tested the effects of 3 sleep schedules on daytime sleepiness and whether these effects changed with age in early adolescence.MethodsIn 77 children ranging in age from 9.9 to 14 years, objective (multiple sleep latency test [MSLT]) and subjective (Karolinska sleepiness scale [KSS]) sleepiness was measured following 4 consecutive nights of either 7, 8.5, or 10 hours in bed. All participants completed all 3 sleep schedules. The order in which they completed the schedules was not randomized but was accounted for in all statistical analyses.ResultsTime in bed restriction decreased sleep duration and increased objective and subjective daytime sleepiness. Although the sleep durations did not change with age, the likelihood of falling asleep during the MSLT increased with age. Nevertheless, sleep restriction produced a greater increase in MSLT-measured sleepiness in younger participants. Subjective sleepiness measured with the KSS increased with shorter sleep duration, but this effect did not change with age.ConclusionsIncreasing objective daytime sleepiness in early adolescence cannot simply be attributed to reduced sleep due to restricted sleep schedules. We propose that some of the increased daytime sleepiness of adolescents is a consequence of adolescent brain reorganization driven by synaptic pruning which decreases the intensity of waking brain activity.

Sleep can be characterized along multiple dimensions. We investigated whether an aggregate measure of sleep health was associated with prevalent and incident clinically significant depression symptoms in a cohort of older women. Participants were older women (mean age 80.1 years) who completed baseline (n = 6485) and follow-up (n = 3806) visits, approximately 6 years apart, in the Study of Osteoporotic Fractures (SOF). Self-reported sleep over the past 12 months was categorized as "good" or "poor" across 5 dimensions: satisfaction with sleep duration, daytime sleepiness, mid-sleep time, sleep onset latency, and sleep duration. An aggregate measure of sleep health was calculated by summing the number of "poor" dimensions. Clinically significant depression symptoms were defined as a score ≥6 on the Geriatric Depression Scale. Relationships between sleep health and depression symptoms were evaluated with multivariate logistic regression, adjusting for health measures and medications. Individual sleep health dimensions of sleep satisfaction, daytime sleepiness, mid-sleep time, and sleep onset latency were significantly associated with prevalent depression symptoms (odds ratios [OR] = 1.26-2.69). Sleep satisfaction, daytime sleepiness, and sleep onset latency were significantly associated with incident depression symptoms (OR = 1.32-1.79). The number of "poor" sleep health dimensions was associated in a gradient fashion with greater odds of prevalent (OR = 1.62-5.41) and incident (OR = 1.47-3.15) depression symptoms. An aggregate, multidimensional measure of sleep health was associated with both prevalent and incident clinically-significant depression symptoms in a gradient fashion. Future studies are warranted to extend these findings in different populations and with different health outcomes.

Since their discovery almost one century ago, sleep spindles, 0.5-2s long bursts of oscillatory activity at 9-16Hz during NREM sleep, have been thought to be global and relatively uniform throughout the cortex. Recent work, however, has brought this concept into question but it remains unclear to what degree spindles are global or local and if their properties are uniform or location-dependent. We addressed this question by recording sleep in eight patients undergoing evaluation for epilepsy with intracranial electrocorticography, which combines high spatial resolution with extensive cortical coverage. We find that spindle characteristics are not uniform but are strongly influenced by the underlying cortical regions, particularly for spindle density and fundamental frequency. We observe both highly isolated and spatially distributed spindles, but in highly skewed proportions: while most spindles are restricted to one or very few recording channels at any given time, there are spindles that occur over widespread areas, often involving lateral prefrontal cortices and superior temporal gyri. Their co-occurrence is affected by a subtle but significant propagation of spindles from the superior prefrontal regions and the temporal cortices towards the orbitofrontal cortex. This work provides a brain-wide characterization of sleep spindles as mostly local graphoelements with heterogeneous characteristics that depend on the underlying cortical area. We propose that the combination of local characteristics and global organization reflects the dual properties of the thalamo-cortical generators and provides a flexible framework to support the many functions ascribed to sleep in general and spindles specifically.

ObjectiveThis study examined the reciprocal associations between sleep macrostructure and levels of cellular inflammation in rheumatoid arthritis (RA) patients and controls.MethodsRA patients (n = 24) and matched controls (n = 48) underwent all-night polysomnography, along with assessment of spontaneous- and Toll-like receptor-4-stimulated monocytic production of tumor necrosis factor α (TNF) and interleukin (IL)-6 at 11:00 PM and 8:00 AM.ResultsAs compared with controls, RA patients showed lower levels of sleep efficiency (mean [standard deviation], 88.1 [6.1] versus 83.8 [7.0]), a higher percentage stage 3 sleep (9.3 [6.4] versus 13.1 [6.9]), and higher levels of percentage of monocytes either spontaneously expressing TNF at 11:00 PM (log transformed, 1.07 [0.28] versus 1.22 [0.17]), and higher Toll-like receptor-4-stimulated production of IL6 at 8:00 AM (log transformed, 3.45 [0.80] versus 3.83 [0.39]). Higher levels of stimulated production of TNF at 11:00 PM were associated with higher sleep efficiency (0.74). In turn, sleep efficiency had a countervailing relationship on TNF production at 8:00 AM (-0.64). Higher levels of spontaneous and stimulated production of IL6 at 11:00 PM were associated with more stage 3 (0.39), stage 4 (0.43), and slow-wave sleep (0.49), with evidence that stage 4 had a countervailing relationship on IL6 production at 8:00 AM (-0.60).ConclusionsRA patients show evidence of sleep fragmentation, greater sleep depth, and higher levels of cellular inflammation. Sleep maintenance and sleep depth show countervailing relationships with evening and morning levels of monocytic production of TNF and IL-6, respectively, which support the hypothesis of a feedback loop between sleep maintenance, slow-wave sleep, and cellular inflammation that is cytokine specific.

The link between the combined action of neuromodulators in the brain and global brain states remains a mystery. In this study, using biophysically realistic models of the thalamocortical network, we identified the critical intrinsic and synaptic mechanisms, associated with the putative action of acetylcholine (ACh), GABA and monoamines, which lead to transitions between primary brain vigilance states (waking, non-rapid eye movement sleep [NREM] and REM sleep) within an ultradian cycle. Using ECoG recordings from humans and LFP recordings from cats and mice, we found that during NREM sleep the power of spindle and delta oscillations is negatively correlated in humans and positively correlated in animal recordings. We explained this discrepancy by the differences in the relative level of ACh. Overall, our study revealed the critical intrinsic and synaptic mechanisms through which different neuromodulators acting in combination result in characteristic brain EEG rhythms and transitions between sleep stages.

IntroductionExcessive daytime sleepiness (EDS) has been shown to be associated with depression; however, this relationship has not been confirmed among Hispanic Americans.MethodThis study examined the link between EDS and depression among Hispanic Americans (N = 411) and explored the potential moderating roles of age, gender, income, education, health status, and acculturation. The Epworth Sleepiness Scale and Patient Health Questionnaire-9 measured EDS and depression, respectively.ResultsHierarchical linear regression demonstrated that EDS was significantly related to depression. Receiver operating characteristic curve analysis suggested that the Epworth Sleepiness Scale discriminated with adequate sensitivity and specificity between participants with moderately severe depression and those with less severe symptoms. No sociodemographic variables moderated the EDS-depression relationship.ConclusionThese findings suggest that depression should be considered when Hispanic Americans present with EDS.

Study objectivesSchool night total sleep time decreases across adolescence (9-18 years) by 10 min/year. This decline is comprised entirely of a selective decrease in NREM sleep; REM sleep actually increases slightly. Decreasing sleep duration across adolescence is often attributed to insufficient time in bed. Here we tested whether sleep restriction in early adolescence produces the same sleep stage changes observed on school nights across adolescence.MethodsAll-night sleep EEG was recorded in 76 children ranging in age from 9.9 to 14.0 years. Each participant kept 3 different sleep schedules that consisted of 3 nights of 8.5 h in bed followed by 4 nights of either 7, 8.5, or 10 h in bed. Sleep stage durations and NREM delta EEG activity were compared across the 3 time in bed conditions.ResultsShortening time in bed from 10 to 7 hours reduced sleep duration by approximately 2 hours, roughly equal to the decrease in sleep duration we recorded longitudinally across adolescence. However, sleep restriction significantly reduced both NREM (by 83 min) and REM (by 47 min) sleep. Sleep restriction did not affect NREM delta EEG activity.ConclusionsOur findings suggest that the selective NREM reduction and the small increase in REM we observed longitudinally across 9-18 years are not produced by sleep restriction. We hypothesize that the selective NREM decline reflects adolescent brain maturation (synaptic elimination) that reduces the need for the restorative processes of NREM sleep.

BackgroundSleep disruption in critically ill adults can result in acute decrements in cognitive function, including delirium, but it is underdiagnosed in the setting of the intensive care unit (ICU). Although sleep stages can be assessed by polysomnography (PSG), acquisition and interpretation of PSG is costly, is labor intensive, is difficult to do over an extended period of time with critically ill patients (multiple days of continuous recording), and may interfere with patient care. In this pilot study, we investigated the feasibility and utility of monitoring sleep in the ICU setting using a portable electroencephalography (EEG) monitor, the SedLine brain monitor.MethodsWe first performed a baseline comparison study of the SedLine brain monitor by comparing its recordings to PSG recorded in a sleep laboratory (n = 3). In a separate patient cohort, we enrolled patients in the ICU who were monitored continuously with the SedLine monitor for sleep disruption (n = 23). In all enrolled patients, we continuously monitored their EEG. The raw EEG was retrieved and sleep stages and arousals were analyzed by a board-certified technologist. Delirium was measured by a trained research nurse using the Confusion Assessment Method developed for the ICU.ResultsFor all enrolled patients, we continuously monitored their EEGs and were able to retrieve the raw EEGs for analysis of sleep stages. Overall, the SedLine brain monitor was able to differentiate sleep stages, as well as capture arousals and transitions between sleep stages compared with the PSG performed in the sleep laboratory. The percentage agreement was 67% for the wake stage, 77% for the non-rapid eye movement (REM) stage (N1 = 29%, N2 = 88%, and N3 = 6%), and 89% for the REM stage. The overall agreement was measured with the use of weighted kappa, which was 0.61, 95% confidence interval, 0.58 to 0.64. In the ICU study, the mean recording time for the 23 enrolled patients was 19.10 hours. There were several signs indicative of poor-quality sleep, where sleep was distributed throughout the day, with reduced time spent in REM (1.38% ± 2.74% of total sleep time), and stage N3 (2.17% ± 5.53% of total sleep time) coupled with a high arousal index (34.63 ± 19.04 arousals per hour). The occurrence of ICU delirium was not significantly different between patients with and without sleep disruption.ConclusionsOur results suggest the utility of a portable EEG monitor to measure different sleep stages, transitions, and arousals; however, the accuracy in measuring different sleep stages by the SedLine monitor varies compared with PSG. Our results also support previous findings that sleep is fragmented in critically ill patients. Further research is necessary to develop portable EEG monitors that have higher agreement with PSG.

ObjectiveChanges in slow waves during non-rapid eye movement (NREM) sleep in response to acute total sleep deprivation are well-established measures of sleep homeostasis. This investigation utilized high-density electroencephalography (hdEEG) to examine topographic changes in slow waves during repeated partial sleep deprivation.MethodsTwenty-four participants underwent a 6-day sleep restriction protocol. Spectral and period-amplitude analyses of sleep hdEEG data were used to examine changes in slow wave energy, count, amplitude, and slope relative to baseline.ResultsChanges in slow wave energy were dependent on the quantity of NREM sleep utilized for analysis, with widespread increases during sleep restriction and recovery when comparing data from the first portion of the sleep period, but restricted to recovery sleep if the entire sleep episode was considered. Period-amplitude analysis was less dependent on the quantity of NREM sleep utilized, and demonstrated topographic changes in the count, amplitude, and distribution of slow waves, with frontal increases in slow wave amplitude, numbers of high-amplitude waves, and amplitude/slopes of low amplitude waves resulting from partial sleep deprivation.ConclusionsTopographic changes in slow waves occur across the course of partial sleep restriction and recovery.SignificanceThese results demonstrate a homeostatic response to partial sleep loss in humans.

Benzodiazepines are commonly used medications that alter sleep spindles during non-rapid eye movement (NREM) sleep, however the topographic changes to these functionally significant waveforms have yet to be fully elucidated. This study utilized high-density electroencephalography (hdEEG) to investigate topographic changes in sleep spindles and spindle-range activity caused by temazepam during NREM sleep in 18 healthy adults. After an accommodation night, sleep for all participants was recorded on two separate nights after taking either placebo or oral temazepam 15 mg. Sleep was monitored using 256-channel hdEEG. Spectral analysis and spindle waveform detection of sleep EEG data were performed for each participant night. Global and topographic data were subsequently compared between temazepam and placebo conditions. Temazepam was associated with significant increases in spectral power from 10.33 to 13.83 Hz. Within this frequency band, temazepam broadly increased sleep spindle duration, and topographically increased spindle amplitude and density in frontal and central-posterior regions, respectively. Higher frequency sleep spindles demonstrated increased spindle amplitude and a paradoxical decrease in spindle density in frontal and centroparietal regions. Further analysis demonstrated temazepam both slowed the average frequency of spindle waveforms and increased the relative proportion of spindles at peak frequencies in frontal and centroparietal regions. These findings suggest that benzodiazepines have diverse effects on sleep spindles that vary by frequency and cortical topography. Further research that explores the relationships between topographic and frequency-dependent changes in pharmacologically-induced sleep spindles and the functional effects of these waveforms is indicated.

OBJECTIVES: To investigate the association of mood and anxiety symptoms with sleep architecture (the distribution of sleep stages) in community-dwelling older men. METHOD: We used in-home unattended polysomnography to measure sleep architecture in older men. Men were categorized into 4 mental health categories: (a) significant depressive symptoms only (DEP+ only, Geriatric Depression Scale ≥ 6), (b) significant anxiety symptoms only (ANX+ only, Goldberg Anxiety Scale ≥ 5), (c) significant depressive and anxiety symptoms (DEP+/ANX+), or (d) no significant depressive or anxiety symptoms (DEP-/ANX-). RESULTS: Compared with men without clinically significant symptomology, men with depressive symptoms spent a higher percentage of time in Stage 2 sleep (65.42% DEP+ only vs 62.47% DEP-/ANX-, p = .003) and a lower percentage of time in rapid eye movement sleep (17.05% DEP+ only vs 19.44% DEP-/ANX-, p = .0005). These differences persisted after adjustment for demographic/lifestyle characteristics, medical conditions, medications, and sleep disturbances, and after excluding participants using psychotropic medications. The sleep architecture of ANX+ or DEP+/ANX+ men did not differ from asymptomatic men. DISCUSSION: Depressed mood in older adults may be associated with accelerated age-related changes in sleep architecture. Longitudinal community-based studies using diagnostic measures are needed to further clarify relationships among common mental disorders, aging, and sleep.

Independent evidence associates β-amyloid pathology with both non-rapid eye movement (NREM) sleep disruption and memory impairment in older adults. However, whether the influence of β-amyloid pathology on hippocampus-dependent memory is, in part, driven by impairments of NREM slow wave activity (SWA) and associated overnight memory consolidation is unknown. Here we show that β-amyloid burden in medial prefrontal cortex (mPFC) correlates significantly with the severity of impairment in NREM SWA generation. Moreover, reduced NREM SWA generation was further associated with impaired overnight memory consolidation and impoverished hippocampal-neocortical memory transformation. Furthermore, structural equation models revealed that the association between mPFC β-amyloid pathology and impaired hippocampus-dependent memory consolidation was not direct, but instead statistically depended on the intermediary factor of diminished NREM SWA. By linking β-amyloid pathology with impaired NREM SWA, these data implicate sleep disruption as a mechanistic pathway through which β-amyloid pathology may contribute to hippocampus-dependent cognitive decline in the elderly.

An automated wireless system (WS) for sleep monitoring was recently developed and validated for assessing nighttime sleep. Here, we aimed to evaluate the validity of the WS to correctly monitor daytime sleep during naps compared to polysomnography (PSG). We found that the WS underestimated wake, sleep onset latency, and wake after sleep onset. Meanwhile, it overestimated total sleep time, sleep efficiency, and duration of REM sleep. Sensitivity was moderate for wake (58.51%) and light sleep (66.92%) and strong for deep sleep (83.46%) and REM sleep (82.12%). These results demonstrated that the WS had a low ability to detect wake and systematically overscored REM sleep, implicating the WS as an inadequate substitute for PSG in diagnosing sleep disorders or for research in which sleep staging is essential.

Study objectiveTo investigate the associations between sleep stage distributions and subsequent decline in cognitive function in older men over time.DesignA population-based prospective substudy of the Osteoporotic Fractures in Men Study.SettingsSix sites in the United States.ParticipantsCommunity-dwelling men aged 67 y or older (n = 2,601), who were free of probable dementia at sleep visit. Follow-up averaged 3.4 y.Measurement and resultsSleep stages were identified by in-home polysomnography at the initial sleep visit (2003-2005). Cognitive outcomes were assessed with the Trail Making Test Part B and Modified Mini-Mental State Examination (3MS) at sleep visit and two follow-up visits. After adjusting for multiple confounders compared with men in the lowest quartile of percent of sleep time spent in Stage N1, those in the highest quartile had a twofold increase in cognitive decline for both cognitive tests (adjusted annualized percent change/y: Trail Making Test Part B Q1 = 1.06, Q4 = 2.45, P = 0.01; 3MS Q1 = -0.27, Q4 = -0.48, P = 0.03). In addition, compared with men in the highest quartile, men in the lowest quartile of percent of sleep time in Stage R revealed more cognitive decline on the 3MS (adjusted annualized percent change/y: Q1 = -0.49, Q4 = -0.22, P = 0.003). These findings were consistent even after further adjustment of total sleep time and sleep disordered breathing. No significant relationships between other sleep stages (N2, N3) and cognitive change were found.ConclusionIncreased time in Stage N1 and less time in Stage R are associated with worsening cognitive performance in older men over time.

RationaleBody habitus is a major determinant of obstructive sleep apnea (OSA). However, many individuals do not have OSA despite being overweight/obese (body mass index > 25 kg/m(2)) for reasons that are not fully elucidated.ObjectivesTo determine the key physiologic traits (upper-airway anatomy/collapsibility, upper-airway muscle responsiveness, chemoreflex control of ventilation, arousability from sleep) responsible for the absence of OSA in overweight/obese individuals.MethodsWe compared key physiologic traits in 18 overweight/obese subjects without apnea (apnea-hypopnea index < 15 events per hour) with 25 overweight/obese matched patients with OSA (apnea-hypopnea index ≥ 15 events per hour) and 11 normal-weight nonapneic control subjects. Traits were measured by repeatedly lowering continuous positive airway pressure to subtherapeutic levels for 3 minutes during non-REM sleep.Measurements and main resultsOverweight/obese subjects without apnea exhibited a less collapsible airway than overweight/obese patients with apnea (critical closing pressure: -3.7 ± 1.9 vs. 0.6 ± 1.2 cm H2O; P = 0.003; mean ± 95% confidence interval), but a more collapsible airway relative to normal-weight control subjects (-8.8 ± 3.1 cm H2O; P < 0.001). Notably, overweight/obese subjects without apnea exhibited a threefold greater upper-airway muscle responsiveness than both overweight/obese patients with apnea (Δgenioglossus EMG/Δepiglottic pressure: -0.49 [-0.22 to -0.79] vs. -0.15 [-0.09 to -0.22] %max/cm H2O; P = 0.008; mean [95% confidence interval]) and normal-weight control subjects (-0.16 [-0.04 to -0.30] %max/cm H2O; P = 0.02). Loop gain was elevated (more negative) in both overweight/obese groups and normal-weight control subjects (P = 0.02). Model-based analysis demonstrated that overweight/obese individuals without apnea rely on both more favorable anatomy and collapsibility and enhanced upper-airway dilator muscle responses to avoid OSA.ConclusionsOverweight/obese individuals without apnea have a moderately compromised upper-airway structure that is mitigated by highly responsive upper-airway dilator muscles to avoid OSA. Elucidating the mechanisms underlying enhanced muscle responses in this population may provide clues for novel OSA interventions.

ObjectivesTo assess how the characteristics of slow waves and spindles change in the falling-asleep process.DesignParticipants undergoing overnight high-density electroencephalographic recordings were awakened at 15- to 30-min intervals. One hundred forty-one falling-asleep periods were analyzed at the scalp and source level.SettingSleep laboratory.ParticipantsSix healthy participants.InterventionsSerial awakenings.ResultsThe number and amplitude of slow waves followed two dissociated, intersecting courses during the transition to sleep: slow wave number increased slowly at the beginning and rapidly at the end of the falling-asleep period, whereas amplitude at first increased rapidly and then decreased linearly. Most slow waves occurring early in the transition to sleep had a large amplitude, a steep slope, involved broad regions of the cortex, predominated over frontomedial regions, and preferentially originated from the sensorimotor and the posteromedial parietal cortex. Most slow waves occurring later had a smaller amplitude and slope, involved more circumscribed parts of the cortex, and had more evenly distributed origins. Spindles were initially sparse, fast, and involved few cortical regions, then became more numerous and slower, and involved more areas.ConclusionsOur results provide evidence for two types of slow waves, which follow dissociated temporal courses in the transition to sleep and have distinct cortical origins and distributions. We hypothesize that these two types of slow waves result from two distinct synchronization processes: (1) a "bottom-up," subcorticocortical, arousal system-dependent process that predominates in the early phase and leads to type I slow waves, and (2) a "horizontal," corticocortical synchronization process that predominates in the late phase and leads to type II slow waves. The dissociation between these two synchronization processes in time and space suggests that they may be differentially affected by experimental manipulations and sleep disorders.