scholarly journals Order matters: sleep spindles contribute to memory consolidation only when followed by rapid-eye-movement sleep

SLEEP ◽  
2022 ◽  
Author(s):  
Mélanie Strauss ◽  
Lucie Griffon ◽  
Pascal Van Beers ◽  
Maxime Elbaz ◽  
Jason Bouziotis ◽  
...  
Keyword(s):  
Eye Movement ◽  
Memory Consolidation ◽  
Learning Task ◽  
Sleep Cycle ◽  
Sleep Stages ◽  
Sleep Spindles ◽  
Rapid Eye Movement ◽  
Sequential Hypothesis ◽  
Falling Asleep ◽  
Negative Effect

Abstract Sleep is known to benefit memory consolidation, but little is known about the contribution of sleep stages within the sleep cycle. The sequential hypothesis proposes that memories are first replayed during non-rapid-eye-movement (NREM or N) sleep and then integrated into existing networks during rapid-eye-movement (REM or R) sleep, two successive critical steps for memory consolidation. However, it lacks experimental evidence as N always precedes R sleep in physiological conditions. We tested this sequential hypothesis in patients with central hypersomnolence disorder, including patients with narcolepsy who present the unique, anti-physiological peculiarity of frequently falling asleep in R sleep before entering N sleep. Patients performed a visual perceptual learning task before and after daytime naps stopped after one sleep cycle, starting in N or R sleep and followed by the other stage (i.e. N-R vs. R-N sleep sequence). We compared over-nap changes in performance, reflecting memory consolidation, depending on the sleep sequence during the nap. Thirty-six patients who slept for a total of 67 naps were included in the analysis. Results show that sleep spindles are associated with memory consolidation only when N is followed by R sleep, that is in physiologically ordered N-R naps, thus providing support to the sequential hypothesis in humans. In addition, we found a negative effect of rapid-eye-movements in R sleep on perceptual consolidation, highlighting the complex role of sleep stages in the balance to remember and to forget.

scholarly journals Contribution of REM sleep and N2 sleep spindles to procedural memory consolidation in Vipassana meditators and non-meditating controls.

2017 ◽  
Author(s):  
Elizaveta Solomonova ◽  
Simon Dubé ◽  
Cloé Blanchette-Carrière ◽  
Arnaud Samson-Richer ◽  
Michelle Carr ◽  
...  
Keyword(s):  
Eye Movement ◽  
Rem Sleep ◽  
Memory Consolidation ◽  
Nrem Sleep ◽  
Procedural Memory ◽  
Sleep Stages ◽  
Sleep Spindles ◽  
Meditation Practice ◽  
Rapid Eye Movement ◽  
Vipassana Meditation

Study objectives: Rapid eye movement (REM) sleep, non-rapid eye movement (NREM) sleep, and sleep spindles are all implicated in the consolidation of procedural memories. The relative contributions of sleep stages and sleep spindles was previously shown to depend on individual differences in task processing. Experience with Vipassana meditation is one such individual difference that has not been investigated in relation to sleep. Vipassana meditation is a form of mental training that enhances proprioceptive and somatic awareness and alters attentional style. The goal was thus to examine a potential moderating role for Vipassana meditation experience on sleep-dependent procedural memory consolidation.Methods: Groups of Vipassana meditation practitioners (N=20) and matched meditation-naïve controls (N=20) slept for a single daytime nap in the laboratory. Before and after the nap they completed a procedural task on the Wii Fit balance platform.Results: Meditators performed slightly better on the task before the nap, but the two groups improved similarly after sleep. The groups showed different patterns of sleep-dependent procedural memory consolidation: in meditators task learning was negatively correlated with density of fast and positively correlated with density of slow occipital spindles, while in controls task improvement was associated with increases in REM sleep. Meditation practitioners had a lower density of sleep spindles, especially in occipital regions.Conclusions: Results suggest that neuroplastic changes associated with sustained meditation practice may alter overall sleep architecture and reorganize sleep-dependent patterns of memory consolidation. The lower density of spindles in meditators may mean that meditation practice compensates for some of the memory functions of sleep.

scholarly journals EEG connectivity across sleep cycles and age

SLEEP ◽  
2019 ◽  
Author(s):  
Maude Bouchard ◽  
Jean-Marc Lina ◽  
Pierre-Olivier Gaudreault ◽  
Jonathan Dubé ◽  
Nadia Gosselin ◽  
...  
Keyword(s):  
Eye Movement ◽  
Rem Sleep ◽  
Verbal Memory ◽  
Sleep Cycle ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Older Individuals ◽  
Age Related ◽  
Eeg Connectivity ◽  
Sleep Cycles

Abstract Study Objectives In young adults, sleep is associated with important changes in cerebral connectivity during the first cycle of non-rapid eye movement (NREM) sleep. Our study aimed to evaluate how electroencephalography (EEG) connectivity during sleep differs between young and older individuals, and across the sleep cycles. Methods We used imaginary coherence to estimate EEG connectivity during NREM and rapid eye movement (REM) sleep in 30 young (14 women; 20–30 years) and 29 older (18 women; 50–70 years) individuals. We also explored the association between coherence and cognitive measures. Results Older individuals showed lower EEG connectivity in stage N2 but higher connectivity in REM and stage N3 compared to the younger cohort. Age-related differences in N3 were driven by the first sleep cycle. EEG connectivity was lower in REM than N3, especially in younger individuals. Exploratory analyses, controlling for the effects of age, indicated that higher EEG connectivity in delta during N2 was associated with higher processing speed, whereas, during REM sleep, lower EEG connectivity in delta and sigma was associated with higher verbal memory performance and a higher global averaged intelligence quotient score. Conclusion Our results indicated that age modifies sleep EEG connectivity but the direction and the magnitude of these effects differ between sleep stages and cycles. Results in N3 and REM point to a reduced ability of the older brains to disconnect as compared to the younger ones. Our results also support the notion that cerebral functional connectivity during sleep may be associated with cognitive functions.

scholarly journals Sleep’s impact on emotional memory: A meta-analysis of whole-night, nap, and REM sleep effects

2019 ◽  
Author(s):  
Sarah K. Schäfer ◽  
Benedikt E. Wirth ◽  
Marlene Staginnus ◽  
Nicolas Becker ◽  
Tanja Michael ◽  
...  
Keyword(s):  
Eye Movement ◽  
Slow Wave ◽  
Memory Consolidation ◽  
Slow Wave Sleep ◽  
Meta Analysis ◽  
Emotional Memory ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Neutral Material

SummaryNumerous studies have shown that sleep enhances the consolidation of episodic memory. However, it remains unclear whether this consolidation benefit is moderated by the emotional valence of the learned material. To clarify whether sleep selectively enhances the consolidation of emotional material, we conducted a meta-analysis including N = 1,059 observations. Overall, our results do not support this hypothesis. When only studies with a sleep-group wake-group comparison were included in the analysis (k = 22), the retention advantage for emotional over neutral material was not significantly different between sleep and wake groups. When studies initially lacking a wake-control group were included in the analysis after statistical estimation of wake-group parameters, the retention advantage for emotional material was significantly larger in wake-groups than in sleep-groups (k = 34). Interestingly, however, an additional analysis of k = 8 studies investigating the selective effects of rapid-eye-movement sleep and slow-wave sleep on emotional memory consolidation provided evidence for a selective enhancement of emotional over neutral memory consolidation after rapid-eye-movement sleep compared to slow-wave sleep. These results suggest that sleep does not generally enhance emotional memory consolidation over neutral memory consolidation. However, specific sleep stages might preferentially enhance consolidation of emotional and neutral material, respectively.

Dyscoordination of Non-Rapid Eye Movement Sleep Oscillations in Autism Spectrum Disorder

SLEEP ◽  
2022 ◽  
Author(s):  
Dimitrios Mylonas ◽  
Sasha Machado ◽  
Olivia Larson ◽  
Rudra Patel ◽  
Roy Cox ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Eye Movement ◽  
Memory Consolidation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Thalamic Reticular Nucleus ◽  
Reticular Nucleus ◽  
Sleep Spindles ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep

Abstract Study Objectives Converging evidence from neuroimaging, sleep, and genetic studies suggests that dysregulation of thalamocortical interactions mediated by the thalamic reticular nucleus (TRN) contribute to autism spectrum disorder (ASD). Sleep spindles assay TRN function, and their coordination with cortical slow oscillations (SOs) indexes thalamocortical communication. These oscillations mediate memory consolidation during sleep. In the present study, we comprehensively characterized spindles and their coordination with SOs in relation to memory and age in children with ASD. Methods Nineteen children and adolescents with ASD, without intellectual disability, and 18 typically developing (TD) peers, aged 9-17, completed a home polysomnography study with testing on a spatial memory task before and after sleep. Spindles, SOs, and their coordination were characterized during stages 2 (N2) and 3 (N3) non-rapid eye movement sleep. Results ASD participants showed disrupted SO-spindle coordination during N2 sleep. Spindles peaked later in SO upstates and their timing was less consistent. They also showed a spindle density (#/min) deficit during N3 sleep. Both groups showed significant sleep-dependent memory consolidation, but its relations with spindle density differed. While TD participants showed the expected positive correlations, ASD participants showed the opposite. Conclusions The disrupted SO-spindle coordination and spindle deficit provide further evidence of abnormal thalamocortical interactions and TRN dysfunction in ASD. The inverse relations of spindle density with memory suggest a different function for spindles in ASD than TD. We propose that abnormal sleep oscillations reflect genetically mediated disruptions of TRN-dependent thalamocortical circuit development that contribute to the manifestations of ASD and are potentially treatable.

scholarly journals Manipulating neural activity and sleep-dependent memory consolidation

Neuroforum ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Maryam Ghorbani ◽  
Lisa Marshall
Keyword(s):  
Eye Movement ◽  
Neural Activity ◽  
Memory Consolidation ◽  
Nrem Sleep ◽  
Neural Oscillations ◽  
Rapid Eye Movement ◽  
Hippocampus Dependent Memory

AbstractSleep contributes actively to the consolidation of many forms of memory. This review describes the neural oscillations of non-rapid eye movement (NREM) sleep, the structures underlying these oscillations and their relation to hippocampus-dependent memory consolidation. A main focus lies on the relation between inter- and intraregional interactions and their electrophysiological representation. Methods for modulating neural oscillations with the intent of affecting memory consolidation are presented.

Detection of the Sleep Stages Throughout Non-Obtrusive Measures of Inter-Beat Fluctuations and Motion: Night and Day Sleep of Female Shift Workers

2017 ◽  
Vol 16 (04) ◽  
pp. 1750033 ◽  
Author(s):  
Martin O. Mendez ◽  
Elvia R. Palacios-Hernandez ◽  
Alfonso Alba ◽  
Juha M. Kortelainen ◽  
Mirja L. Tenhunen ◽  
...  
Keyword(s):  
Eye Movement ◽  
Performance Measures ◽  
Night Shift ◽  
Sleep Stages ◽  
Support Vector ◽  
Rapid Eye Movement ◽  
Sleep Staging ◽  
Kappa Index ◽  
Extensive Evaluation ◽  
Night And Day

Automatic sleep staging based on inter-beat fluctuations and motion signals recorded through a pressure bed sensor during sleep is presented. The analysis of the sleep was based on the three major divisions of the sleep time: Wake, non-rapid eye movement (nREM) and rapid eye movement (REM) sleep stages. Twelve sleep recordings, from six females working alternate shift, with their respective annotations were used in the study. Six recordings were acquired during the night and six during the day after a night shift. A Time-Variant Autoregressive Model was used to extract features from inter-beat fluctuations which later were fed to a Support Vector Machine classifier. Accuracy, Kappa index, and percentage in wake, REM and nREM were used as performance measures. Comparison between the automatic sleep staging detection and the standard clinical annotations, shows mean values of [Formula: see text]% for accuracy [Formula: see text] for kappa index, and mean errors of 5% for sleep stages. The performance measures were similar for night and day sleep recordings. In this sample of recordings, the results suggest that inter-beat fluctuations and motions acquired in non-obtrusive way carried valuable information related to the sleep macrostructure and could be used to support to the experts in extensive evaluation and monitoring of sleep.

High-throughput visual assessment of sleep stages in mice using machine learning

SLEEP ◽  
2021 ◽  
Author(s):  
Brian Geuther ◽  
Mandy Chen ◽  
Raymond J Galante ◽  
Owen Han ◽  
Jie Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Movement ◽  
High Throughput ◽  
Data Augmentation ◽  
Sleep Stage ◽  
Visual Assessment ◽  
Video Data ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep

Abstract Study Objectives Sleep is an important biological process that is perturbed in numerous diseases, and assessment its substages currently requires implantation of electrodes to carry out electroencephalogram/electromyogram (EEG/EMG) analysis. Although accurate, this method comes at a high cost of invasive surgery and experts trained to score EEG/EMG data. Here, we leverage modern computer vision methods to directly classify sleep substages from video data. This bypasses the need for surgery and expert scoring, provides a path to high-throughput studies of sleep in mice. Methods We collected synchronized high-resolution video and EEG/EMG data in 16 male C57BL/6J mice. We extracted features from the video that are time and frequency-based and used the human expert-scored EEG/EMG data to train a visual classifier. We investigated several classifiers and data augmentation methods. Results Our visual sleep classifier proved to be highly accurate in classifying wake, non-rapid eye movement sleep (NREM), and rapid eye movement sleep (REM) states, and achieves an overall accuracy of 0.92 +/- 0.05 (mean +/- SD). We discover and genetically validate video features that correlate with breathing rates, and show low and high variability in NREM and REM sleep, respectively. Finally, we apply our methods to non-invasively detect that sleep stage disturbances induced by amphetamine administration. Conclusions We conclude that machine learning based visual classification of sleep is a viable alternative to EEG/EMG based scoring. Our results will enable non-invasive high-throughput sleep studies and will greatly reduce the barrier to screening mutant mice for abnormalities in sleep.

Abstract P417: Relationship Between Blood Pressure Variability and Sleep Architecture

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Xiaoyue Liu ◽  
Jeongok G Logan ◽  
Younghoon Kwon ◽  
Jennifer Lobo ◽  
Hyojung Kang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Eye Movement ◽  
Rem Sleep ◽  
Sleep Time ◽  
Sleep Architecture ◽  
Sleep Stages ◽  
Apnea Hypopnea Index ◽  
Rapid Eye Movement ◽  
Sleep Studies ◽  
The Relationship

Introduction: Blood pressure (BP) variability (BPV) is a novel marker for cardiovascular disease (CVD) independent of high BP. Sleep architecture represents the structured pattern of sleep stages consisting of rapid eye movement (REM) and non-rapid eye movement (NREM), and it is an important element in the homeostatic regulation of sleep. Currently, little is known regarding whether BPV is linked to sleep stages. Our study aimed to examine the relationship between sleep architecture and BPV. Methods: We analyzed in-lab polysomnographic studies collected from individuals who underwent diagnostic sleep studies at a university hospital from 2010 to 2017. BP measures obtained during one year prior to the sleep studies were included. BPV was computed using the coefficient of variation for all individuals who had three or more systolic and diastolic BP data. We conducted linear regression analysis to assess the relationship of systolic BPV (SBPV) and diastolic BPV (DBPV) with the sleep stage distribution (REM and NREM sleep time), respectively. Covariates that can potentially confound the relationships were adjusted in the models, including age, sex, race/ethnicity, body mass index, total sleep time, apnea-hypopnea index, mean BP, and history of medication use (antipsychotics, antidepressants, and antihypertensives) during the past two years before the sleep studies. Results: Our sample (N=3,565; male = 1,353) was racially and ethnically diverse, with a mean age 54 ± 15 years and a mean BP of 131/76 ± 13.9/8.4 mmHg. Among the sleep architecture measures examined, SBPV showed an inverse relationship with REM sleep time after controlling for all covariates ( p = .033). We subsequently categorized SBPV into four quartiles and found that the 3 rd quartile (mean SBP SD = 14.9 ± 2.1 mmHg) had 3.3 fewer minutes in REM sleep compared to the 1 st quartile ( p = .02). However, we did not observe any relationship between DBPV and sleep architecture. Conclusion: Greater SBPV was associated with lower REM sleep time. This finding suggests a possible interplay between BPV and sleep architecture. Future investigation is warranted to clarify the directionality, mechanism, and therapeutic implications.

scholarly journals Nonrapid eye movement sleep electroencephalographic oscillations in idiopathic rapid eye movement sleep behavior disorder: a study of sleep spindles and slow oscillations

SLEEP ◽  
2020 ◽  
Author(s):  
Jun-Sang Sunwoo ◽  
Kwang Su Cha ◽  
Jung-Ick Byun ◽  
Jin-Sun Jun ◽  
Tae-Joon Kim ◽  
...  
Keyword(s):  
Eye Movement ◽  
Rem Sleep ◽  
Rem Sleep Behavior Disorder ◽  
Behavior Disorder ◽  
Nrem Sleep ◽  
Sleep Spindles ◽  
Rapid Eye Movement ◽  
Sleep Behavior ◽  
Slow Oscillations ◽  
Lower Amplitude

Abstract Study Objectives We investigated electroencephalographic (EEG) slow oscillations (SOs), sleep spindles (SSs), and their temporal coordination during nonrapid eye movement (NREM) sleep in patients with idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD). Methods We analyzed 16 patients with video-polysomnography-confirmed iRBD (age, 65.4 ± 6.6 years; male, 87.5%) and 10 controls (age, 62.3 ± 7.5 years; male, 70%). SSs and SOs were automatically detected during stage N2 and N3. We analyzed their characteristics, including density, frequency, duration, and amplitude. We additionally identified SO-locked spindles and examined their phase distribution and phase locking with the corresponding SO. For inter-group comparisons, we used the independent samples t-test or Wilcoxon rank-sum test, as appropriate. Results The SOs of iRBD patients had significantly lower amplitude, longer duration (p = 0.005 for both), and shallower slope (p < 0.001) than those of controls. The SS power of iRBD patients was significantly lower than that of controls (p = 0.002), although spindle density did not differ significantly. Furthermore, SO-locked spindles of iRBD patients prematurely occurred during the down-to-up-state transition of SOs, whereas those of controls occurred at the up-state peak of SOs (p = 0.009). The phase of SO-locked spindles showed a positive correlation with delayed recall subscores (p = 0.005) but not with tonic or phasic electromyography activity during REM sleep. Conclusions In this study, we found abnormal EEG oscillations during NREM sleep in patients with iRBD. The impaired temporal coupling between SOs and SSs may reflect early neurodegenerative changes in iRBD.

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