Increased Alpha (8–12 Hz) Activity during Slow Wave Sleep as a Marker for the Transition from Implicit Knowledge to Explicit Insight

2012 ◽  
Vol 24 (1) ◽  
pp. 119-132 ◽  
Author(s):  
Juliana Yordanova ◽  
Vasil Kolev ◽  
Ullrich Wagner ◽  
Jan Born ◽  
Rolf Verleger
Keyword(s):  
Slow Wave ◽  
Explicit Knowledge ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Implicit Knowledge ◽  
Specific Power ◽  
Specific Marker ◽  
Knowledge Representations ◽  
Beta Power ◽  
The Brain

The number reduction task (NRT) allows us to study the transition from implicit knowledge of hidden task regularities to explicit insight into these regularities. To identify sleep-associated neurophysiological indicators of this restructuring of knowledge representations, we measured frequency-specific power of EEG while participants slept during the night between two sessions of the NRT. Alpha (8–12 Hz) EEG power during slow wave sleep (SWS) emerged as a specific marker of the transformation of presleep implicit knowledge to postsleep explicit knowledge (ExK). Beta power during SWS was increased whenever ExK was attained after sleep, irrespective of presleep knowledge. No such EEG predictors of insight were found during Sleep Stage 2 and rapid eye movement sleep. These results support the view that it is neuronal memory reprocessing during sleep, in particular during SWS, that lays the foundations for restructuring those task-related representations in the brain that are necessary for promoting the gain of ExK.

scholarly journals Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication

2021 ◽  
Author(s):  
Cassie J Hilditch ◽  
Kanika Bansal ◽  
Ravi Chachad ◽  
Lily R Wong ◽  
Nicholas G Bathurst ◽  
...  
Keyword(s):  
Long Range ◽  
Slow Wave ◽  
Path Length ◽  
Slow Wave Sleep ◽  
Mechanistic Explanation ◽  
Clustering Coefficient ◽  
Sleep Inertia ◽  
The Awakening ◽  
Beta Power ◽  
The Brain

Sleep inertia is the brief period of impaired alertness and performance experienced immediately after waking. While the neurobehavioral symptoms of sleep inertia are well-described, less is known about the neural mechanisms underlying this phenomenon. A better understanding of the neural processes during sleep inertia may offer insight into the cognitive impairments observed and the awakening process generally. We observed brain activity following abrupt awakening from slow wave sleep during the biological night. Using electroencephalography (EEG) and a network science approach, we evaluated power, clustering coefficient, and path length across frequency bands under both a control condition and a blue-enriched light intervention condition in a within-subject design. We found that under control conditions, the awakening brain is typified by an immediate reduction in global theta, alpha, and beta power. Simultaneously, we observed a decrease in the clustering coefficient and an increase in path length within the delta band. Exposure to blue-enriched light immediately after awakening ameliorated these changes, but only for clustering. Our results suggest that long-range network communication within the brain is crucial to the waking process and that the brain may prioritize these long-range connections during this transitional state. Our study highlights a novel neurophysiological signature of the awakening brain and provides a potential mechanistic explanation for the effect of light in improving performance after waking.

scholarly journals The Aging Slow Wave: A Shifting Amalgam of Distinct Slow Wave and Spindle Coupling Subtypes Define Slow Wave Sleep Across the Human Lifespan

SLEEP ◽  
2021 ◽  
Author(s):  
Brice V McConnell ◽  
Eugene Kronberg ◽  
Peter D Teale ◽  
Stefan H Sillau ◽  
Grace M Fishback ◽  
...  
Keyword(s):  
Slow Wave ◽  
Neurological Disease ◽  
Mixed Model ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Slow Waves ◽  
Cross Sectional ◽  
Relative Contribution ◽  
Human Lifespan ◽  
Development And Aging

Abstract Study Objectives Slow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan. Methods Coupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6-88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles. Results Two different subtypes of spindles were identified during the upstates of (distinct) slow waves: an “early-fast” spindle, more common in stage N2 sleep, and a “late-fast” spindle, more common in stage N3. We further found stages N2 and N3 sleep contain a mixture of discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails with increasing age. Conclusions Distinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep. Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease.

nCPAP Treatment of Obstructive Sleep Apnea Increases Slow Wave Sleep in Prefrontal EEG

2007 ◽  
Vol 38 (3) ◽  
pp. 148-154 ◽  
Author(s):  
Veera Eskelinen ◽  
Toomas Uibu ◽  
Sari-Leena Himanen
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Treatment Period ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Eeg ◽  
Sleep Stages ◽  
Obstructive Sleep ◽  
Sleep Stage Scoring

According to standard sleep stage scoring, sleep EEG is studied from the central area of parietal lobes. However, slow wave sleep (SWS) has been found to be more powerful in frontal areas in healthy subjects. Obstructive sleep apnea syndrome (OSAS) patients often suffer from functional disturbances in prefrontal lobes. We studied the effects of nasal Continuous Positive Airway Pressure (nCPAP) treatment on sleep EEG, and especially on SWS, in left prefrontal and central locations in 12 mild to moderate OSAS patients. Sleep EEG was recorded by polysomnography before treatment and after a 3 month nCPAP treatment period. Recordings were classified into sleep stages. No difference was found in SWS by central sleep stage scoring after the nCPAP treatment period, but in the prefrontal lobe all night S3 sleep stage increased during treatment. Furthermore, prefrontal SWS increased in the second and decreased in the fourth NREM period. There was more SWS in prefrontal areas both before and after nCPAP treatment, and SWS increased significantly more in prefrontal than central areas during treatment. Regarding only central sleep stage scoring, nCPAP treatment did not increase SWS significantly. Frontopolar recording of sleep EEG is useful in addition to central recordings in order to better evaluate the results of nCPAP treatment.

036 The Effect of Exercise on Sleep Architecture and Memory Consolidation during a Daytime Nap

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A16-A16
Author(s):  
Megan Collins ◽  
Erin Wamsley ◽  
Hailey Napier ◽  
Madeline Ray
Keyword(s):  
Slow Wave ◽  
Memory Consolidation ◽  
Slow Wave Sleep ◽  
Declarative Memory ◽  
Sleep Stage ◽  
Sleep Time ◽  
Learning Task ◽  
Memory Retention ◽  
Verbal Information ◽  
Significant Difference

Abstract Introduction Slow wave sleep (SWS) is thought to especially benefit declarative memory (i.e., memory for facts and events). As such, recent studies have used various methods to experimentally increase the amount of slow wave sleep that participants obtain, with the goal of assessing how SWS affects declarative memory consolidation. Studies dating back decades have reported that exercising before sleep may increase time spent in SWS. Thus, the aim of the current project was to determine whether exercising after learning verbal information enhances slow wave sleep during a subsequent nap and/or enhances memory for verbal information. Methods Participants who exercised regularly were recruited to attend two 2.5hr laboratory sessions. During each session, they trained on a paired associates learning task and then completed either a 20min cardiovascular exercise routine or a 20min stretching routine. Following a 1hr nap opportunity, participants were tested on their memory. PSG was recorded during the nap, and scored following AASM criteria. Participants were excluded from analysis if they failed to sleep for at least 10 min. Following exclusions, n=30 participants were included in analysis. Results Contrary to our hypotheses, there was no significant difference between the exercise and stretching conditions for minutes spent in slow wave sleep (p=.16), % time spent in slow wave sleep (p=.22), or raw improvement in paired associated performance (p=.23). The amount of SWS obtained during the nap did not correlate with performance in either condition (SWS min vs. memory in exercise condition: r28=.10, p=.60; sleep condition: r28=-.06, p=.74). Exercise did not affect time spent in any other sleep stage, nor did it affect total sleep time. Conclusion Contrary to our hypotheses and the results of prior research, we were unable to detect a significant effect of exercise on slow wave sleep. Also contrary to our hypotheses, exercise did not affect memory retention across the nap interval. These null results could indicate that there is no effect of exercise on nap sleep and/or associated memory retention. However, it could also be that we lacked sufficient power to detect effects that were smaller than expected. Support (if any):

Changes in sleep quality of athletes under normobaric hypoxia equivalent to 2,000-m altitude: a polysomnographic study

2007 ◽  
Vol 103 (6) ◽  
pp. 2005-2011 ◽  
Author(s):  
Masako Hoshikawa ◽  
Sunao Uchida ◽  
Takayuki Sugo ◽  
Yasuko Kumai ◽  
Yoshiteru Hanai ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Normobaric Hypoxia ◽  
Fast Fourier Transformation ◽  
Urinary Catecholamines ◽  
Arterial Blood ◽  
Subjective Sleepiness

This study evaluated the sleep quality of athletes in normobaric hypoxia at a simulated altitude of 2,000 m. Eight male athletes slept in normoxic condition (NC) and hypoxic conditions equivalent to those at 2,000-m altitude (HC). Polysomnographic recordings of sleep included the electroencephalogram (EEG), electrooculogram, chin surface electromyogram, and electrocardiogram. Thoracic and abdominal motion, nasal and oral airflow, and arterial blood oxygen saturation (SaO2) were also recorded. Standard visual sleep stage scoring and fast Fourier transformation analyses of the EEG were performed on 30-s epochs. Subjective sleepiness and urinary catecholamines were also monitored. Mean SaO2 decreased and respiratory disturbances increased with HC. The increase in respiratory disturbances was significant, but the increase was small and subclinical. The duration of slow-wave sleep (stage 3 and 4) and total delta power (<3 Hz) of the all-night non-rapid eye movement sleep EEG decreased for HC compared with NC. Subjective sleepiness and amounts of urinary catecholamines did not differ between the conditions. These results indicate that acute exposure to normobaric hypoxia equivalent to that at 2,000-m altitude decreased slow-wave sleep in athletes, but it did not change subjective sleepiness or amounts of urinary catecholamines.

scholarly journals Excessive Fragmentary Myoclonus: Time of Night and Sleep Stage Distributions

1993 ◽  
Vol 20 (2) ◽  
pp. 142-146 ◽  
Author(s):  
Otavio Lins ◽  
Michelle Castonguay ◽  
Wayne Dunham ◽  
Sonya Nevsimalova ◽  
Roger Broughton
Keyword(s):  
Sleep Disorders ◽  
Patient Group ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Onset ◽  
Sleep Fragmentation ◽  
New Technique ◽  
A New Technique

ABSTRACT:Excessive fragmentary myoclonus during sleep consists of high amounts of brief twitch-like movements occurring asynchronously and asymmetrically in different body areas and has been reported to occur in association with a number of sleep disorders. It was analyzed using a new technique of quantification, the fragmentary myoclonus index (FMI). The FMI exhibited high rates in all stages of sleep but with a somewhat lower frequency in slow wave sleep explaining, as well, a significantly lower rate in the first hour after sleep onset compared to later hours. There was no evidence for greater sleep fragmentation or lighter sleep compared to a matched patient group in whom it had not been noted.

scholarly journals The Aging Slow Wave: A Shifting Amalgam of Distinct Slow Wave and Spindle Coupling Subtypes Define Slow Wave Sleep Across the Human Lifespan

2020 ◽  
Author(s):  
Brice V. McConnell ◽  
Eugene Kronberg ◽  
Peter D. Teale ◽  
Grace M. Fishback ◽  
Rini I. Kaplan ◽  
...  
Keyword(s):  
Slow Wave ◽  
Cognitive Aging ◽  
Neurological Disease ◽  
Mixed Model ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Slow Waves ◽  
Cross Sectional ◽  
Relative Contribution ◽  
Human Lifespan

AbstractStudy ObjectivesSlow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan.MethodsCoupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6-88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles.ResultsTwo different subtypes of spindles were identified during the upstates of (distinct) slow waves: an “early-fast” spindle, more common in stage N2 sleep, and a “late-fast” spindle, more common in stage N3. We further found stages N2 and N3 sleep are composed of two discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails during old age.ConclusionsDistinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep. Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease.Statement of SignificanceSlow waves of nonrapid eye movement sleep couple with sleep spindles in a process hypothesized to support memory functions. This coupling has recently gained interest as a possible biomarker of cognitive aging and onset of Alzheimer’s disease. Most studies have been limited by an assumption that all slow waves (and coupled spindles) are fundamentally the same physiological events. Here we demonstrate that distinct subtypes of slow waves and their coupled spindles can be identified in human sleep. A mixture of different slow wave and spindle subtypes shifts in composition during lighter versus deeper sleep, and aging favors the deep sleep subtypes. These data should inform any future attempts to use slow wave sleep as a biomarker or clinical interventional target.

D-C potential changes in rabbit brain during slow-wave and paradoxical sleep

1964 ◽  
Vol 207 (6) ◽  
pp. 1379-1386 ◽  
Author(s):  
H. Kawamura ◽  
C. H. Sawyer
Keyword(s):  
Reticular Formation ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Paradoxical Sleep ◽  
Reference Electrode ◽  
Reference Points ◽  
Rabbit Brain ◽  
Positive Side ◽  
The Brain ◽  
Stimulation Of

A study has been made of d-c potential changes in the brain during various states of sleep and wakefulness in the unrestrained unanesthetized rabbit with chronically implanted electroencephalogram and d-c electrodes. In preliminary acute experiments with an occipital bone reference electrode, the direction of the d-c potential change induced by stimulation of the midbrain reticular formation was the same in cortical and subcortical sites. With frontal and occipital bones as reference points the cortical d-c potential changes were similar to one another, but arousal stimuli readily elicited positive d-c shifts with the frontal reference electrode. In the chronic rabbit with an occipital reference electrode, cortical and hypothalamic d-c potentials shifted strongly to the positive side during slow-wave sleep and after injection of pentobarbital; from these conditions stimulation of the reticular formation induced a marked negative shift. During paradoxical sleep both cortical and subcortical d-c electrodes showed negative shifts similar to those seen during an arousal reaction. Grooming and eating elicited strong positive shifts in both cortical and hypothalamic d-c electrodes.

scholarly journals Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep

2022 ◽  
Vol 15 ◽  
Author(s):  
Caglar Cakan ◽  
Cristiana Dimulescu ◽  
Liliia Khakimova ◽  
Daniela Obst ◽  
Agnes Flöel ◽  
...  
Keyword(s):  
Slow Wave ◽  
Traveling Waves ◽  
Large Scale ◽  
Slow Wave Sleep ◽  
Brain Area ◽  
Optimization Approach ◽  
Whole Brain ◽  
Slow Oscillations ◽  
Lower Amplitude ◽  
The Brain

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.

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