New directions in sleep and memory research: the role of autonomic activity

Respiration modulates sleep oscillations and memory reactivation in humans

The beneficial effect of sleep on memory consolidation relies on the precise interplay of slow oscillations and spindles. However, whether these rhythms are orchestrated by an underlying pacemaker has remained elusive. Here, we tested the relationship between respiration, which has been shown to impact brain rhythms and cognition during wake, sleep-related oscillations and memory reactivation in humans. We re-analysed an existing dataset, where scalp electroencephalography and respiration were recorded throughout an experiment in which participants (N = 20) acquired associative memories before taking a nap. Our results reveal that respiration modulates the emergence of sleep oscillations. Specifically, slow oscillations, spindles as well as their interplay (i.e., slow-oscillation_spindle complexes) systematically increase towards inhalation peaks. Moreover, the strength of respiration - slow-oscillation_spindle coupling is linked to the extent of memory reactivation (i.e., classifier evidence in favour of the previously learned stimulus category) during slow-oscillation_spindles. Our results identify a clear association between respiration and memory consolidation in humans and highlight the role of brain-body interactions during sleep.

Peripheral blood flow estimated by laser doppler flowmetry provides additional information about sleep state beyond that provided by pulse rate variability

Pulse rate variability (PRV), derived from Laser Doppler flowmetry (LDF) or photoplethysmography, has recently become widely used for sleep state assessment, although it cannot identify all the sleep stages. Peripheral blood flow (BF), also estimated by LDF, may be modulated by sleep stages; however, few studies have explored its potential for assessing sleep state. Thus, we aimed to investigate whether peripheral BF could provide information about sleep stages, and thus improve sleep state assessment. We performed electrocardiography and simultaneously recorded BF signals by LDF from the right-index finger and ear concha of 45 healthy participants (13 women; mean age, 22.5 ± 3.4 years) during one night of polysomnographic recording. Time- and frequency-domain parameters of peripheral BF, and time-domain, frequency-domain, and non-linear indices of PRV and heart rate variability (HRV) were calculated. Finger-BF parameters in the time and frequency domains provided information about different sleep stages, some of which (such as the difference between N1 and rapid eye movement sleep) were not revealed by finger-PRV. In addition, finger-PRV patterns and HRV patterns were similar for most parameters. Further, both finger- and ear-BF results showed 0.2-0.3 Hz oscillations that varied with sleep stages, with a significant increase in N3, suggesting a modulation of respiration within this frequency band. These results showed that peripheral BF could provide information for different sleep stages, some of which was complementary to the information provided by PRV. Furthermore, the combination of peripheral BF and PRV may be more advantageous than HRV alone in assessing sleep states and related autonomic nervous activity.

A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning

The vast majority of sleep disturbances are caused by various types of sleep arousal. To diagnose sleep disorders and prevent health problems such as cardiovascular disease and cognitive impairment, sleep arousals must be accurately detected. Consequently, sleep specialists must spend considerable time and effort analyzing polysomnography (PSG) recordings to determine the level of arousal during sleep. The development of an automated sleep arousal detection system based on PSG would considerably benefit clinicians. We quantify the EEG-ECG by using Lyapunov exponents, fractals, and wavelet transforms to identify sleep stages and arousal disorders. In this paper, an efficient hybrid-learning method is introduced for the first time to detect and assess arousal incidents. Modified drone squadron optimization (mDSO) algorithm is used to optimize the support vector machine (SVM) with radial basis function (RBF) kernel. EEG-ECG signals are preprocessed samples from the SHHS sleep dataset and the PhysioBank challenge 2018. In comparison to other traditional methods for identifying sleep disorders, our physiological signals correlation innovation is much better than similar approaches. Based on the proposed model, the average error rate was less than 2%-7%, respectively, for two-class and four-class issues. Additionally, the proper classification of the five sleep stages is determined to be accurate 92.3% of the time. In clinical trials of sleep disorders, the hybrid-learning model technique based on EEG-ECG signal correlation features is effective in detecting arousals.

Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?

Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.

Competitive dynamics underlie cognitive improvements during sleep

Sleep facilitates both long-term episodic memory consolidation and short-term working memory functioning. However, the mechanism by which the sleeping brain performs both complex feats and which sleep features are associated with these processes remain unclear. Using a pharmacological approach, we demonstrate that long-term and working memory are served by distinct offline neural mechanisms and that these mechanisms are mutually antagonistic. We propose a sleep switch model in which the brain toggles between the two memory processes via a complex interaction at the synaptic, systems, and mechanistic level with implications for research on cognitive disturbances observed in neurodegenerative disorders such as Alzheimer’s and Parkinson's disease, both of which involve the decline of sleep.

We provide evidence that human sleep is a competitive arena in which cognitive domains vie for limited resources. Using pharmacology and effective connectivity analysis, we demonstrate that long-term memory and working memory are served by distinct offline neural mechanisms that are mutually antagonistic. Specifically, we administered zolpidem to increase central sigma activity and demonstrated targeted suppression of autonomic vagal activity. With effective connectivity, we determined the central activity has greater causal influence over autonomic activity, and the magnitude of this influence during sleep produced a behavioral trade-off between offline long-term and working memory processing. These findings suggest a sleep switch mechanism that toggles between central sigma-dependent long-term memory and autonomic vagal-dependent working memory processing.

Dietary glycemic index and glycemic load in association with sleep duration: YaHS-TAMYZ and Shahedieh observational studies

BACKGROUND & AIMS

Current data on the association between dietary glycemic index (GI) and glycemic load (GL) and sleep duration are limited and controversial. The present cross-sectional study aimed to investigate the relationship between dietary GI and GL with sleep duration in a large number of adults living in central Iran.

METHODS

Baseline data from two Iranian cohorts were pooled for the current analysis: the YaHS-TAMYZ (n = 9962) and Shahedieh (n = 9977) studies. Demographic data as well as dietary intakes, sleep duration, and possible confounders, were collected by interview. Anthropometric measurements were also conducted by trained interviewers. Participants were categorized based on sleep duration [short (<6 h), normal (6-8 h), and long (>8 h)]. Multivariable logistic regression was used to determine the relationship between dietary GI/GL and the likelihood of short and long sleep duration.

RESULTS

In total, 10,451 participants aged 20-70 years were included in the analyses. After controlling for all possible confounders, no significant association was observed between dietary GI and short or long sleep duration (P > 0.05). Individuals in the highest quartile of dietary GL had a higher chance for long sleep duration compared to participants in the lowest quartile (OR = 1.41; 95% CI: 1.14, 1.74, P_trend = 0.001). No significant association was observed between dietary GL and short sleep duration.

CONCLUSION

Dietary GL is positively associated with the likelihood of long sleep duration. Future prospective studies are required to confirm the current findings.

Exercising the Sleepy-ing Brain: Exercise, Sleep, and Sleep Loss on Memory

ABSTRACT

We examine the novel hypothesis that physical exercise and sleep have synergistic effects on memory. Exercise can trigger mechanisms that can create an optimal brain state during sleep to facilitate memory processing. The possibility that exercise could counteract the deleterious effects of sleep deprivation on memory by protecting neuroplasticity is also discussed.

Age-related losses in cardiac autonomic activity during a daytime nap

In healthy, young individuals, a reduction in cardiovascular output and a shift from sympathetic to parasympathetic (vagal) dominance is observed from wake into stages of nocturnal and daytime sleep. This cardiac autonomic profile, measured by heart rate variability (HRV), has been associated with significant benefits for cardiovascular health. Aging is associated with decreased nighttime sleep quality and lower parasympathetic activity during both sleep and resting. However, it is not known whether age-related dampening of HRV extends to daytime sleep, diminishing the cardiovascular benefits of naps in the elderly. Here, we investigated this question by comparing the autonomic activity profile between young and older healthy adults during a daytime nap and a similar period of wakefulness (quiet wake; QW). For each condition, from the electrocardiogram (ECG), we obtained beat-to-beat HRV intervals (RR), root mean square of successive differences between adjacent heart-beat-intervals (RMSSD), high-frequency (HF), low-frequency (LF) power, and total power (TP), HF normalized units (HFnu ), and the LF/HF ratio. As previously reported, young subjects showed a parasympathetic dominance during NREM, compared with REM, prenap rest, and WASO. Moreover, older, compared to younger, adults showed significantly lower vagally mediated HRV (measured by RMSSD, HF, HFnu ) during NREM. Interestingly, however, no age-related differences were detected during prenap rest or QW. Altogether, our findings suggest a sleep-specific reduction in parasympathetic modulation that is unique to NREM sleep in older adults.

Autonomic Activity during a Daytime Nap Facilitates Working Memory Improvement

Recent investigations have implicated the parasympathetic branch of the autonomic nervous system in higher-order executive functions. These actions are purported to occur through autonomic nervous system's modulation of the pFC, with parasympathetic activity during wake associated with working memory (WM) ability. Compared with wake, sleep is a period with substantially greater parasympathetic tone. Recent work has reported that sleep may also contribute to improvement in WM. Here, we examined the role of cardiac parasympathetic activity during sleep on WM improvement in healthy young adults. Participants were tested in an operation span task in the morning and evening, and during the intertest period, participants experienced either a nap or wake. We measured high-frequency heart rate variability as an index of cardiac, parasympathetic activity during both wake and sleep. Participants showed the expected boost in parasympathetic activity during nap, compared with wake. Furthermore, parasympathetic activity during sleep, but not wake, was significantly correlated with WM improvement. Together, these results indicate that the natural boost in parasympathetic activity during sleep may benefit gains in prefrontal executive function in young adults. We present a conceptual model illustrating the interaction between sleep, autonomic activity, and prefrontal brain function and highlight open research questions that will facilitate understanding of the factors that contribute to executive abilities in young adults as well as in cognitive aging.

Autonomic/central coupling benefits working memory in healthy young adults

Working memory (WM) is an executive function that can improve with training. However, the precise mechanism for this improvement is not known. Studies have shown greater WM gains after a period of sleep than a similar period of wake, and correlations between WM improvement and slow wave activity (SWA; 0.5-1 Hz) during slow wave sleep (SWS). A different body of literature has suggested an important role for autonomic activity during wake for WM. A recent study from our group reported that the temporal coupling of Autonomic/CentralEvents (ACEs) during sleep was associated with memory consolidation. We found that heart rate bursts (HR bursts) during non-rapid eye movement (NREM) sleep are accompanied by increases in SWA and sigma (12-15 Hz) power, as well as increases in the high-frequency (HF) component of the RR interval, reflecting vagal rebound. In addition, ACEs predict long-term, episodic memory improvement. Building on these previous results, we examined whether ACEs also contribute to gains in WM. We tested 104 young adults in an operation span task (OSPAN) in the morning and evening, with either a nap (n = 53; with electroencephalography (EEG) and electrocardiography (ECG)) or wake (n = 51) between testing sessions. We identified HR bursts in the ECG and replicated the increases in SWA and sigma prior to peak of the HR burst, as well as vagal rebound after the peak. Furthermore, we showed sleep-dependent WM improvement, which was predicted by ACE activity. Using regression analyses, we discovered that significantly more variance in WM improvement could be explained with ACE variables than with overall sleep activity not time-locked with ECG. These results provide the first evidence that coordinated autonomic and central events play a significant role in sleep-related WM improvement and implicate the potential of autonomic interventions during sleep for cognitive enhancement.