Proving the Carry-over Effect of Slow-Wave Sleep by Mathematical Models

1991 ◽  
Vol 3 (2) ◽  
pp. 88-92
Author(s):  
Toshinori Kobayashi ◽  
◽  
Yoichi Tsuji ◽  
Yoshinobu Iguchi
Keyword(s):  
Mathematical Models ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Early Morning ◽  
Night Sleep ◽  
Sleep Episode ◽  
Carry Over ◽  
Daytime Nap ◽  
The Brain ◽  
Carry Over Effect

In order to study the psychophysiological function of ""slow wave sleep (SWS), we are trying to identify the control mechanism of SWS. It is well known that the amount of SWS found in a sleep episode depends upon the length of wakefulness prior to the sleep episode. But Karakan et al. (1970) and Miyashita et al (1978) reported that SWS of a sleep episode was also influenced by SWS of the preceding sleep episode. So, we examined the hypothesis that SWS of a sleep episode depended not only on prior wakefulness to the sleep episode but also on SWS of the preceding sleep episode by the use of mathematical models and the experiment. Two models were prepared to examine the hypothesis: one is MODEL (CO), in which SWS of a sleep episode depends upon both prior wakefulness and SWS carried over from the preceding sleep episode, the other is MODEL (nCO), in which SWS of a sleep episode depends on only prior wakefulness to the sleep episode. Four pairs of night sleep and dayti,me naps were recorded in the experiment for eight healthy university students (aged 18 to 25) as follows: (1) Morning nap (0900-1300) was recorded after the mid night sleep (23000300) or early morning sleep (0300-0700), and (2) evening nap (1700-2100) after mid night sleep or early morning sleep. We compared SWS of night sleep and daytime naps estimated by two models with those obtained by the experiment. There was close agreement between SWS estimated by MODEL (CO) and that obtained by the experiment. This result indicates that there is carry over of SWS from night sleep to daytime nap. So, SWS of a sleep episode depends on both prior wakefulness to the sleep episode and SWS carried over from the preceding sleep episode. SWS is accumulated in proportion to the length of wakefulness prior to a sleep episode during waking and is released according to sleep progression during sleep. When SWS is relatively large compared with the length of sleep episode, all SWS is not completely released in the sleep episode. A part of SWS remains in the brain. The remainder of SWS is carried over to the following sleep episode. When SWS is considered as an index of a kind of fatigue in the brain, it is accumulated during waking and is restored during sleep. When the fatigue is not fully restored in a sleep episode, it carries over into the following sleep episode.

Slow wave sleep (SWS) distribution across night sleep episode in the elderly

1998 ◽  
Vol 10 (6) ◽  
pp. 445-448 ◽  
Author(s):  
P. Lombardo ◽  
G. Formicola ◽  
S. Gori ◽  
C. Gneri ◽  
R. Massetani ◽  
...  
Keyword(s):  
Slow Wave ◽  
Slow Wave Sleep ◽  
The Elderly ◽  
Night Sleep ◽  
Sleep Episode

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 Targeted memory reactivation of face-name learning depends on ample and undisturbed slow-wave sleep

2022 ◽  
Vol 7 (1) ◽  
Author(s):  
Nathan W. Whitmore ◽  
Adrianna M. Bassard ◽  
Ken A. Paller
Keyword(s):  
Slow Wave ◽  
Slow Wave Sleep ◽  
Social Contexts ◽  
Background Music ◽  
Sleep Disruption ◽  
Memory Functions ◽  
Face Memory ◽  
Memory Reactivation ◽  
Two Factors ◽  
Daytime Nap

AbstractFace memory, including the ability to recall a person’s name, is of major importance in social contexts. Like many other memory functions, it may rely on sleep. We investigated whether targeted memory reactivation during sleep could improve associative and perceptual aspects of face memory. Participants studied 80 face-name pairs, and then a subset of spoken names with associated background music was presented unobtrusively during a daytime nap. This manipulation preferentially improved name recall and face recognition for those reactivated face-name pairs, as modulated by two factors related to sleep quality; memory benefits were positively correlated with the duration of stage N3 sleep (slow-wave sleep) and negatively correlated with measures of sleep disruption. We conclude that (a) reactivation of specific face-name memories during sleep can strengthen these associations and the constituent memories, and that (b) the effectiveness of this reactivation depends on uninterrupted N3 sleep.

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.

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.

Improvement of the night sleep quality by electrocutaneous subthreshold stimulation synchronized with the slow wave sleep

2017 ◽  
Vol 474 (1) ◽  
pp. 132-134 ◽  
Author(s):  
Yu. V. Gulyaev ◽  
A. S. Bugaev ◽  
P. A. Indursky ◽  
V. M. Shakhnarovich ◽  
V. V. Dementienko
Keyword(s):  
Sleep Quality ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Night Sleep

scholarly journals The hypothesis of the functional disintegration of the brain due to the persistence of benign epileptiform patterns on the electroencephalogram

2021 ◽  
Vol 16 (3) ◽  
pp. 34-45
Author(s):  
I. A. Sadekov ◽  
A. V. Polyakov ◽  
I. V. Sadekova ◽  
E. A. Tupikina ◽  
V. Yu. Kochmar ◽  
...  
Keyword(s):  
Nervous System ◽  
Slow Wave ◽  
Slow Wave Sleep ◽  
Neuropsychiatric Disorders ◽  
Epileptiform Discharge ◽  
Functional Disorders ◽  
The Brain ◽  
Developing Nervous System

In this work, we have analyzed the results of observation of 200 children aged from 3 to 15 years old, who had various neuropsychiatric disorders in combination with benign childhood epileptiform patterns on the electroencephalogram. A hypothesis has been put forward about functional disorders of the developing nervous system with prolonged persistence of benign focal epileptiform discharge of childhood on electroencephalogram, mainly in slow-wave sleep. The possibilities of therapeutic correction of these disorders are discussed.

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.

scholarly journals IL-1 is a mediator of increases in slow-wave sleep induced by CRH receptor blockade

2000 ◽  
Vol 279 (3) ◽  
pp. R793-R802 ◽  
Author(s):  
Fang-Chia Chang ◽  
Mark R. Opp
Keyword(s):  
Hpa Axis ◽  
Slow Wave ◽  
Tumor Necrosis ◽  
Slow Wave Sleep ◽  
Central Administration ◽  
Regulatory Systems ◽  
The Central Nervous System ◽  
Factor Α ◽  
The Brain ◽  
Necrosis Factor

We hypothesize that corticotropin-releasing hormone (CRH), a regulator of the hypothalamic-pituitary-adrenal (HPA) axis, is involved in sleep-wake regulation on the basis of observations that the CRH receptor antagonist astressin, after a delay of several hours, reduces waking and increases slow-wave sleep (SWS) in rats. This delay suggests a cascade of events that begins with the HPA axis and culminates with actions on sleep regulatory systems in the central nervous system. One candidate mediator in the brain for these actions is interleukin (IL)-1. IL-1 promotes sleep, and glucocorticoids inhibit IL-1 synthesis. In this study, central administration of 12.5 μg astressin into rats before dark onset reduced corticosterone 4 h after injection and increased mRNA expression for IL-1α and IL-1β but not for IL-6 or tumor necrosis factor-α in the brain 6 h after injection. To determine directly whether IL-1 is involved in astressin-induced alterations in sleep-wake behavior, we then pretreated rats with 20 μg anti-IL-1β antibodies before injecting astressin. The increase in SWS and the reduction in waking that occur after astressin are abolished when animals are pretreated with anti-IL-1β. These data indicate that IL-1 is a mediator of astressin-induced alterations in sleep-wake behavior.

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