Mechanisms of memory retrieval in slow-wave sleep : memory retrieval in slow-wave sleep

Study Objectives: Memories are strengthened during sleep. The benefits of sleep for memory can be enhanced by re-exposing the sleeping brain to auditory cues; a technique known as targeted memory reactivation (TMR). Prior studies have not assessed the nature of the retrieval mechanisms underpinning TMR: the matching process between auditory stimuli encountered during sleep and previously encoded memories. We carried out two experiments to address this issue. Methods: In Experiment 1, participants associated words with verbal and non-verbal auditory stimuli before an overnight interval in which subsets of these stimuli were replayed in slow-wave sleep. We repeated this paradigm in Experiment 2 with the single difference that the gender of the verbal auditory stimuli was switched between learning and sleep. Results: In Experiment 1, forgetting of cued (vs. non-cued) associations was reduced by TMR with verbal and non-verbal cues to similar extents. In Experiment 2, TMR with identical non-verbal cues reduced forgetting of cued (vs. non-cued) associations, replicating Experiment 1. However, TMR with non-identical verbal cues reduced forgetting of both cued and non-cued associations. Conclusions: These experiments suggest that the memory effects of TMR are influenced by the acoustic overlap between stimuli delivered at training and sleep. Our findings hint at the existence of two processing routes for memory retrieval during sleep. Whereas TMR with acoustically identical cues may reactivate individual associations via simple episodic matching, TMR with non-identical verbal cues may utilise linguistic decoding mechanisms, resulting in widespread reactivation across a broad category of memories

Motor hyperactivity of the iron-deficient rat - an animal model of restless legs syndrome.

BackgroundAbnormal striatal dopamine transmission has been hypothesized to cause restless legs syndrome. Dopaminergic drugs are commonly used to treat restless legs syndrome. However, they cause adverse effects with long-term use. An animal model would allow the systematic testing of potential therapeutic drugs. A high prevalence of restless legs syndrome has been reported in iron-deficient anemic patients. We hypothesized that the iron-deficient animal would exhibit signs similar to those in restless legs syndrome patients.MethodsAfter baseline polysomnographic recordings, iron-deficient rats received pramipexole injection. Then, iron-deficient rats were fed a standard rodent diet, and polysomnographic recording were performed for 2 days each week for 4 weeks.ResultsIron-deficient rats have low hematocrit levels and show signs of restless legs syndrome: sleep fragmentation and periodic leg movements in wake and in slow-wave sleep. Iron-deficient rats had a positive response to pramipexole treatment. After the iron-deficient rats were fed the standard rodent diet, hematocrit returned to normal levels, and sleep quality improved, with increased average duration of wake and slow-wave sleep episodes. Periodic leg movements decreased during both waking and sleep. Hematocrit levels positively correlated with the average duration of episodes in wake and in slow-wave sleep and negatively correlated with periodic leg movements in wake and in sleep. Western blot analysis showed that striatal dopamine transporter levels were higher in iron-deficient rats.ConclusionsThe iron-deficient rat is a useful animal model of iron-deficient anemic restless legs syndrome. © 2017 International Parkinson and Movement Disorder Society

Detecting Slow Wave Sleep Using a Single EEG Signal Channel

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

Rise and shine: The use of polychromatic short-wavelength-enriched light to mitigate sleep inertia at night following awakening from slow-wave sleep

Sleep inertia is the brief period of performance impairment and reduced alertness experienced after waking, especially from slow-wave sleep. We assessed the efficacy of polychromatic short-wavelength-enriched light to improve vigilant attention, alertness and mood immediately after waking from slow-wave sleep at night. Twelve participants (six female, 23.3 ± 4.2 years) maintained an actigraphy-confirmed sleep schedule of 8.5 hr for 5 nights, and 5 hr for 1 night prior to an overnight laboratory visit. In the laboratory, participants were awakened from slow-wave sleep, and immediately exposed to either dim, red ambient light (control) or polychromatic short-wavelength-enriched light (light) for 1 hr in a randomized crossover design. They completed a 5-min Psychomotor Vigilance Task, the Karolinska Sleepiness Scale, and Visual Analogue Scales of mood at 2, 17, 32 and 47 min after waking. Following this testing period, lights were turned off and participants returned to sleep. They were awakened from their subsequent slow-wave sleep period and received the opposite condition. Compared with the control condition, participants exposed to light had fewer Psychomotor Vigilance Task lapses (χ2[1] = 5.285, p = 0.022), reported feeling more alert (Karolinska Sleepiness Scale: F1,77 = 4.955, p = 0.029; Visual Analogue Scalealert: F1,77 = 8.226, p = 0.005), and reported improved mood (Visual Analogue Scalecheerful: F1,77 = 8.615, p = 0.004). There was no significant difference in sleep-onset latency between conditions following the testing period (t10 = 1.024, p = 0.330). Our results suggest that exposure to polychromatic short-wavelength-enriched light immediately after waking from slow-wave sleep at night may help improve vigilant attention, subjective alertness, and mood. Future studies should explore the potential mechanisms of this countermeasure and its efficacy in real-world environments

Slow wave sleep and accelerated forgetting

We investigated whether the benefit of slow wave sleep (SWS) for memory consolidation typically observed in healthy individuals is disrupted in people with accelerated long-term forgetting (ALF) due to epilepsy. SWS is thought to play an active role in declarative memory in healthy individuals and, furthermore, electrographic epileptiform activity is often more prevalent during SWS than during wakefulness or other sleep stages. We studied the relationship between SWS and the benefit of sleep for memory retention using a word-pair associates task. In both the ALF and the healthy control groups, sleep conferred a memory benefit. However, the relationship between the amount of SWS and sleep-related memory benefits differed significantly between the groups. In healthy participants, the amount of SWS correlated positively with sleep-related memory benefits. In stark contrast, the more SWS, the smaller the sleep-related memory benefit in the ALF group. Therefore, contrary to its role in healthy people, SWS-associated brain activity appears to be deleterious for memory in patients with ALF

Overnight weight loss: relationship with sleep structure and heart rate variability

Background: Weight loss can be caused by a loss of body mass due to metabolism and by water loss as unsensible water loss, sweating, or excretion in feces and urine. Although weight loss during sleep is a well-known phenomenon, it has not yet been studied in relation to sleep structure or autonomic tonus during sleep. Our study is proposed to be a first step in assessing the relationship between overnight weight loss, sleep structure, and HRV (heart rate variability) parameters.Methods: Twenty-five healthy volunteers received a 487 kcal meal and 200 ml water before experiment. Volunteers were weighed before and after polysomnography. Absolute and relative weight indices were calculated. Time and frequency domain analysis of heart rate variability was assessed during stages 2, 4, and REM. Nonparametric linear regression analysis was performed between night weight loss parameters, polysomnographic, and HRV ariables. Results: HF correlated positively with weight loss during stage 4. Slow wave sleep duration correlated positively with weight loss and weight loss rate. The duration of Stage 2 correlated negatively with absolute and relative weight loss. Conclusions: Weight loss during sleep is dependent upon sleep stage duration and sleep autonomic tonus. Slow-wave sleep and sleep parasympathetic tonus may be important for weight homeostasis

Slow Wave Sleep and Long Duration Spaceflight

While ground research has clearly shown that preserving adequate quantities of sleep is essential for optimal health and performance, changes in the progression, order and /or duration of specific stages of sleep is also associated with deleterious outcomes. As seen in Figure 1, in healthy individuals, REM and Non-REM sleep alternate cyclically, with stages of Non-REM sleep structured chronologically. In the early parts of the night, for instance, Non-REM stages 3 and 4 (Slow Wave Sleep, or SWS) last longer while REM sleep spans shorter; as night progresses, the length of SWS is reduced as REM sleep lengthens. This process allows for SWS to establish precedence , with increases in SWS seen when recovering from sleep deprivation. SWS is indeed regarded as the most restorative portion of sleep. During SWS, physiological activities such as hormone secretion, muscle recovery, and immune responses are underway, while neurological processes required for long term learning and memory consolidation, also occur. The structure and duration of specific sleep stages may vary independent of total sleep duration, and changes in the structure and duration have been shown to be associated with deleterious outcomes. Individuals with narcolepsy enter sleep through REM as opposed to stage 1 of NREM. Disrupting slow wave sleep for several consecutive nights without reducing total sleep duration or sleep efficiency is associated with decreased pain threshold, increased discomfort, fatigue, and the inflammatory flare response in skin. Depression has been shown to be associated with a reduction of slow wave sleep and increased REM sleep. Given research that shows deleterious outcomes are associated with changes in sleep structure, it is essential to characterize and mitigate not only total sleep duration, but also changes in sleep stages

A continuum model for the dynamics of the phase transition from slow-wave sleep to REM sleep

Previous studies have shown that activated cortical states (awake and rapid eye-movement (REM) sleep), are associated with increased cholinergic input into the cerebral cortex. However, the mechanisms that underlie the detailed dynamics of the cortical transition from slow-wave to REM sleep have not been quantitatively modeled. How does the sequence of abrupt changes in the cortical dynamics (as detected in the electrocorticogram) result from the more gradual change in subcortical cholinergic input? We compare the output from a continuum model of cortical neuronal dynamics with experimentally-derived rat electrocorticogram data. The output from the computer model was consistent with experimental observations. In slow-wave sleep, 0.5–2-Hz oscillations arise from the cortex jumping between “up” and “down” states on the stationary-state manifold. As cholinergic input increases, the upper state undergoes a bifurcation to an 8-Hz oscillation. The coexistence of both oscillations is similar to that found in the intermediate stage of sleep of the rat. Further cholinergic input moves the trajectory to a point where the lower part of the manifold in not available, and thus the slow oscillation abruptly ceases (REM sleep). The model provides a natural basis to explain neuromodulator-induced changes in cortical activity, and indicates that a cortical phase change, rather than a brainstem “flip-flop”, may describe the transition from slow-wave sleep to REM