Sleep Spindles Research Articles

Associations of mental resilience with the elevated NREM beta power and the enhanced sleep spindle characteristics in healthy adolescents

Associations of mental resilience with the elevated NREM beta power and the enhanced sleep spindle characteristics in healthy adolescents

Right hippocampus volume correlates with sleep spindle generation probability in good sleepers: a neuroloop gain analysis study in older adults

Right hippocampus volume correlates with sleep spindle generation probability in good sleepers: a neuroloop gain analysis study in older adults

Desktop app for detecting sleep spindles from sleep EEG data

Desktop app for detecting sleep spindles from sleep EEG data

NREM nap differences in children with and without visual impairment: the role of fast sleep spindles

NREM nap differences in children with and without visual impairment: the role of fast sleep spindles

Sleep spindle dynamics in stroke patients and controls: an exploratory analysis

Sleep spindle dynamics in stroke patients and controls: an exploratory analysis

Sleep spindles characteristics in children with OSA and their relation to cognition

Sleep spindles characteristics in children with OSA and their relation to cognition

Correlation of sleep spindle structures and apnea/hypopnea index in adult survivors of childhood Hodgkin lymphoma

Correlation of sleep spindle structures and apnea/hypopnea index in adult survivors of childhood Hodgkin lymphoma

A personalized semi-automatic sleep spindle detection (PSASD) framework

BackgroundSleep spindles are distinct electroencephalogram (EEG) patterns of brain activity that have been posited to play a critical role in development, learning, and neurological disorders. Manual scoring for sleep spindles is labor-intensive and tedious but could supplement automated algorithms to resolve challenges posed with either approaches alone. New methodsA Personalized Semi-Automatic Sleep Spindle Detection (PSASD) framework was developed to combine the strength of automated detection algorithms and visual expertise of human scorers. The underlying model in the PSASD framework assumes a generative model for EEG sleep spindles as oscillatory components, optimized to EEG amplitude, with remaining signals distributed into transient and low-frequency components. ResultsA single graphical user interface (GUI) allows both manual scoring of sleep spindles (model training data) and verification of automatically detected spindles. A grid search approach allows optimization of parameters to balance tradeoffs between precision and recall measures. Comparison with existing methodsPSASD outperformed DETOKS in F1-score by 19% and 4% on the DREAMS and P-DROWS-E datasets, respectively. It also outperformed YASA in F1-score by 25% in the P-DROWS-E dataset. Further benchmarking analysis showed that PSASD outperformed four additional widely used sleep spindle detectors in F1-score in the P-DROWS-E dataset. Titration analysis revealed that four 30-second epochs are sufficient to fine-tune the model parameters of PSASD. Associations of frequency, duration, and amplitude of detected sleep spindles matched those previously reported with automated approaches. ConclusionsOverall, PSASD improves detection of sleep spindles in EEG data acquired from both younger healthy and older adult patient populations.

Semi-Automatic Analysis of Specific Electroencephalographic Patterns during NREM2 Sleep in a Pediatric Population after SARS-CoV-2 Infection.

The post-COVID-19 condition is defined by the World Health Organization as the persistence of symptoms or development of new symptoms three months after the initial SARS-CoV-2 infection, lasting for at least two months without a clear explanation. Neuropsychiatric disorders associated with this condition include asthenia, memory and concentration problems, and sleep disturbances. Our study aims to investigate sleep patterns following SARS-CoV-2 infection using EEG findings and a sleep quality questionnaire completed by parents (Sleep Disturbance Scale for Children-SDSC). Notably, our investigation is based on a convenience sample. The patients in our sample, aged 1 to 14 years, are not currently taking any medications; rather, they are undergoing follow-up assessments at the Child Neuropsychiatry department of the University Hospital of Messina for neurodevelopmental evaluations. Specifically, we are analyzing amplitude and power spectrum data in the first five minutes of NREM2 sleep, calculated from EEG recordings obtained via bipolar leads within three months after the onset of the disease. These results will be compared with controls performed on the same subjects in the six months preceding the infection. The focus of the study was sleep spindles, which are generated by the thalamocortical systems and play a role in sleep modulation, memory, and learning. Preliminary analysis suggests a predominant increase in the slow component of the spindles in the right-frontal lead.

A visual paired associate learning (vPAL) paradigm to study memory consolidation during sleep.

Sleep improves the consolidation and long-term stability of newly formed memories and associations. Most research on human declarative memory and its consolidation during sleep uses word-pair associations requiring exhaustive learning. In the present study, we present the visual paired association learning (vPAL) paradigm, in which participants learn new associations between images of celebrities and animals. The vPAL is based on a one-shot exposure that resembles learning in natural conditions. We tested if vPAL can reveal a role for sleep in memory consolidation by assessing the specificity of memory recognition, and the cued recall performance, before and after sleep. We found that a daytime nap improved the stability of recognition memory and discrimination abilities compared to identical intervals of wakefulness. By contrast, cued recall of associations did not exhibit significant sleep-dependent effects. High-density electroencephalography during naps further revealed an association between sleep spindle density and stability of recognition memory. Thus, the vPAL paradigm opens new avenues for future research on sleep and memory consolidation across ages and heterogeneous populations in health and disease.

Two-Stage Atomic Decomposition of Multichannel EEG and the Previously Undetectable Sleep Spindles.

We propose a two-step procedure for atomic decomposition of multichannel EEGs, based upon multivariate matching pursuit and dipolar inverse solution, from which atoms representing relevant EEG structures are selected according to prior knowledge. We detect sleep spindles in 147 polysomnographic recordings from the Montreal Archive of Sleep Studies. Detection is compared with human scorers and two state-of-the-art algorithms, which find only about a third of the structures conforming to the definition of sleep spindles and detected by the proposed method. We provide arguments supporting the thesis that the previously undetectable sleep spindles share the same properties as those marked by human experts and previously applied methods, and were previously omitted only because of unfavorable local signal-to-noise ratios, obscuring their visibility to both human experts and algorithms replicating their markings. All detected EEG structures are automatically parametrized by their time and frequency centers, width duration, phase, and spatial location of an equivalent dipolar source within the brain. It allowed us, for the first time, to estimate the spatial gradient of sleep spindles frequencies, which not only confirmed quantitatively the well-known prevalence of higher frequencies in posterior regions, but also revealed a significant gradient in the sagittal plane. The software used in this study is freely available.

Odor cueing of declarative memories during sleep enhances coordinated spindles and slow oscillations

Long-term memories are formed by repeated reactivation of newly encoded information during sleep. This process can be enhanced by using memory-associated reminder cues like sounds and odors. While auditory cueing has been researched extensively, few electrophysiological studies have exploited the various benefits of olfactory cueing. We used high-density electroencephalography in an odor-cueing paradigm that was designed to isolate the neural responses specific to the cueing of declarative memories. We show widespread cueing-induced increases in the duration and rate of sleep spindles. Higher spindle rates were most prominent over centro-parietal areas and largely overlapping with a concurrent increase in the amplitude of slow oscillations (SOs). Interestingly, greater SO amplitudes were linked to a higher likelihood of coupling a spindle and coupled spindles expressed during cueing were more numerous in particular around SO up states. We thus identify temporally and spatially coordinated enhancements of sleep spindles and slow oscillations as a candidate mechanism behind cueing-induced memory processing. Our results further demonstrate the feasibility of studying neural activity patterns linked to such processing using olfactory cueing during sleep.

Temporal association between sleep spindles and ripples in the human anterior and mediodorsal thalamus.

Sleep spindles are major oscillatory components of Non-Rapid Eye Movement (NREM) sleep, reflecting hyperpolarization-rebound sequences of thalamocortical neurons. Reports suggest a link between sleep spindles and several forms of high-frequency oscillations which are considered as expressions of pathological off-line neural plasticity in the central nervous system. Here we investigated the relationship between thalamic sleep spindles and ripples in the anterior and mediodorsal nuclei (ANT and MD) of epilepsy patients. Whole-night LFP from the ANT and MD were co-registered with scalp EEG/polysomnography by using externalized leads in 15 epilepsy patients undergoing a Deep Brain Stimulation protocol. Slow (~12 Hz) and fast (~14 Hz) sleep spindles were present in the human ANT and MD and roughly, 20% of them were associated with ripples. Ripple-associated thalamic sleep spindles were characterized by longer duration and exceeded pure spindles in terms of spindle power as indicated by time-frequency analysis. Furthermore, ripple amplitude was modulated by the phase of sleep spindles within both thalamic nuclei. No signs of pathological processes were correlated with measures of ripple and spindle association, furthermore, the density of ripple-associated sleep spindles in the ANT showed a positive correlation with verbal comprehension. Our findings indicate the involvement of the human thalamus in coalescent spindle-ripple oscillations of NREM sleep.

Temporal cluster-based organization of sleep spindles underlies motor memory consolidation.

Sleep benefits motor memory consolidation, which is mediated by sleep spindle activity and associated memory reactivations during non-rapid eye movement (NREM) sleep. However, the particular role of NREM2 and NREM3 sleep spindles and the mechanisms triggering this memory consolidation process remain unclear. Here, simultaneous electroencephalographic and functional magnetic resonance imaging (EEG-fMRI) recordings were collected during night-time sleep following the learning of a motor sequence task. Adopting a time-based clustering approach, we provide evidence that spindles iteratively occur within clustered and temporally organized patterns during both NREM2 and NREM3 sleep. However, the clustering of spindles in trains is related to motor memory consolidation during NREM2 sleep only. Altogether, our findings suggest that spindles' clustering and rhythmic occurrence during NREM2 sleep may serve as an intrinsic rhythmic sleep mechanism for the timed reactivation and subsequent consolidation of motor memories, through synchronized oscillatory activity within a subcortical-cortical network involved during learning.

Altered sleep behavior strengthens face validity in the ArcAβ mouse model for Alzheimer’s disease

Demographic changes will expand the number of senior citizens suffering from Alzheimer's disease (AD). Key aspects of AD pathology are sleep impairments, associated with onset and progression of AD. AD mouse models may provide insights into mechanisms of AD-related sleep impairments. Such models may also help to establish new biomarkers predicting AD onset and monitoring AD progression. The present study aimed to establish sleep-related face validity of a widely used mouse model of AD (ArcAβ model) by comprehensively characterizing its baseline sleep/wake behavior. Chronic EEG recordings were performed continuously on four consecutive days in freely behaving mice. Spectral and temporal sleep/wake parameters were assessed and analyzed. EEG recordings showed decreased non-rapid eye movement sleep (NREMS) and increased wakefulness in transgenic mice (TG). Vigilance state transitions were different in TG mice when compared to wildtype littermates (WT). During NREMS, TG mice had lower power between 1 and 5 Hz and increased power between 5 and 30 Hz. Sleep spindle amplitudes in TG mice were lower. Our study strongly provides sleep-linked face validity for the ArcAβ model. These findings extend the potential of the mouse model to investigate mechanisms of AD-related sleep impairments and the impact of sleep impairments on the development of AD.

Blindness affects the developmental trajectory of the sleeping brain

Sleep plays a crucial role in brain development, sensory information processing, and consolidation. Sleep spindles are markers of these mechanisms as they mirror the activity of the thalamocortical circuits. Spindles can be subdivided into two groups, slow (10–13 Hz) and fast (13–16 Hz), which are each associated with different functions. Specifically, fast spindles oscillate in the high-sigma band and are associated with sensorimotor processing, which is affected by visual deprivation. However, how blindness influences spindle development has not yet been investigated. We recorded nap video-EEG of 50 blind/severely visually impaired (BSI) and 64 sighted children aged 5 months to 6 years old. We considered aspects of both macro- and micro-structural spindles. The BSI children lacked the evolution of developmental spindles within the central area. Specifically, young BSI children presented low central high-sigma and high-beta (25–30 Hz) event-related spectral perturbation and showed no signs of maturational decrease. High-sigma and high-beta activity in the BSI group correlated with clinical indices predicting perceptual and motor disorders. Our findings suggest that fast spindles are pivotal biomarkers for identifying an early developmental deviation in BSI children. These findings are critical for initial therapeutic intervention.

O02 Assessing SleeP in INfants with moderate-severe atopic Dermatitis by Longitudinal Evaluation (The SPINDLE study)

Abstract Introduction Atopic dermatitis (AD) is associated with sleep disruption due to pruritus, inflammation, increased transepidermal water loss (TEWL) and circadian alterations. Little is known about the impact of AD on sleep in early infancy, a critical time for neurodevelopment. The aim of this study was to deeply characterise the sleep architecture of 6-month-old infants with moderate-severe AD, compared to controls. Methods Overnight movements (defined as 10-15 seconds of movement) were measured for five consecutive nights using a novel wearable movement sensor. Infantile and parental sleep measures were reported by parents. Daytime sleep quality and quantity was measured by electroencephalogram (EEG) polysomnography. AD was assessed using clinical severity scoring, skin barrier assessment (TEWL, natural moisturising factor levels, and filaggrin mutational analysis) and inflammatory cytokine analysis by tape stripping. Results 57 controls and 33 cases were recruited. Average EASI at time of 6-month assessment was 7.2 (range 0.8-34.8). Infants with AD recorded almost twice as many overnight movements as controls (19.2 versus 11.2, p<0.05). Infants with AD slept for 13.1 hours versus 13.8 (p<0.05), and woke 3.5 times versus 1.9 (p<0.05). Infants with AD had differences in EEG sleep measures, including sleep spindle power and frequency. Mothers of cases had worse total sleep time (6.3 versus 7.1 hours, p<0.05), sleep-­onset latency (27.1 versus 12.8 minutes, p<0.05), and sleep ­efficiency. Conclusion This is the most detailed study ever performed on sleep in infants with AD using objective sleep measures, and has revealed significant effects on sleep architecture, sleep duration, and overnight movement.

A robust deep learning detector for sleep spindles and K-complexes: towards population norms

Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that appear during sleep. Large-scale sleep studies would benefit from precise and robust automatic sleep event detectors, capable of adapting the variability in both electroencephalography (EEG) signals and expert annotation rules. We introduce the Sleep EEG Event Detector (SEED), a deep learning system that outperforms existing approaches in SS and KC detection, reaching an F1-score of 80.5% and 83.7%, respectively, on the MASS2 dataset. SEED transfers well and requires minimal fine-tuning for new datasets and annotation styles. Remarkably, SEED substantially reduces the required amount of annotated data by using a novel pretraining approach that leverages the rule-based detector A7. An analysis of 11,224 subjects revealed that SEED's detections provide better estimates of SS population statistics than existing approaches. SEED is a powerful resource for obtaining sleep-event statistics that could be useful for establishing population norms.

Associations Between Sleep Spindle Metrics, Age, Education and Executive Function in Young Adult and Middle-Aged Patients with Obstructive Sleep Apnea.

This study aimed to investigate the association between sleep spindle metrics and executive function in individuals with obstructive sleep apnea (OSA). Furthermore, we examined the association of age and education on executive function. A total of 230 (40.90 ± 8.83 years, F/M = 45/185) participants were enrolled. Overnight electroencephalogram (C3-M2) recording detected sleep spindles by a novel U-Net-type neural network that integrates temporal information with time-frequency images. Sleep spindle metrics, including frequency (Hz), overall density (events/min), fast density (events/min), slow density (events/min), duration (sec) and amplitude (µV), were measured. Executive function was assessed using standardized neuropsychological tests. Associations between sleep spindle metrics, executive function, and demographic factors were analyzed using multivariate linear regression. In fully adjusted linear regression models, higher overall sleep spindle density (TMT-A, B=-1.279, p=0.009; TMT-B, B=-1.813, p=0.008), fast sleep spindle density (TMT-A, B=-1.542, p=0.048; TMT-B, B=-2.187, p=0.036) and slow sleep spindle density (TMT-A, B=-1.731, p=0.037; TMT-B, B=-2.449, p=0.034) were associated with better executive function. And the sleep spindle duration both during N2 sleep time (TMT-A, B=-13.932, p=0.027; TMT-B, B=-19.001, p=0.034) and N3 sleep time (TMT-B, B=-29.916, p=0.009; Stroop-incongruous, B=-21.303, p=0.035) was independently associated with better executive function in this population. Additionally, age and education were found to be highly associated with executive function. Specific sleep spindle metrics, higher overall density, fast density and slow density during N2 sleep time, and longer duration during N2 and N3 sleep time, are independent and sensitive indicators of better executive function in young adult and middle-aged patients with OSA. Further research is needed to explore the underlying mechanisms and clinical implications of these findings.

Risk stratification for phenoconversion in patients with isolated REM sleep behavior disorder. A follow-up study from Turkey.

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is one of the strongest prodromal markers of alpha-synucleinopathies. We aimed to investigate non-invasive clinical and quantitative predictors of phenoconversion from iRBD to parkinsonism. We prospectively followed-up a total of 45 patients (57.8% men) for eight years. Clinical assessments, Sniffin' Sticks Odor Identification Test, Farnsworth-Munsell 100 Hue Color Vision test, Beck Depression Inventory and Rome III Criteria for constipation were performed. Polysomnographic parameters, sleep spindles, electroencephalographic (EEG) spectral analysis, heart rate variability (HRV) were analyzed. Eight patients (17.8%) showed phenoconversion to parkinsonism after a mean duration of 3.2 ± 1 years. Odds ratio for predicting phenoconversion was highest for patients =60 years of age with anosmia and constipation -44.8 (4.5-445.7); kappa = 4.291-. Duration, frequency or density of sleep spindles failed to demonstrate significant correlations. In EEG spectral analysis, lower alpha power in occipital region during wakefulness and REM sleep was significantly correlated with phenoconversion. Slowing in EEG spectrum power, together with age =60 years, anosmia and constipation, resulted in the highest odds ratio -122.5 (9.7-1543.8); kappa = 3.051-. It is of great importance to have a world-wide perspective of phenoconversion rates from iRBD to overt neurodegeneration, since racial and geographical factors may play important modifying roles. Relatively younger age and shorter disease duration may also be confounding factors for lower rate in our study. Neurophysiological biomarkers seem to be important predictors of phenoconversion, though more research is needed to establish subtypes of iRBD with different probabilities of evolution to overt synucleinopathy.