Associations between quantitative sleep EEG and subsequent cognitive decline in older women

Linking sleep microstructure, blood markers of inflammation and metabolism, and cognition: mediation analysis in the osteoporotic fractures in men study

The sleep electroencephalographic (EEG) microstructure is explicitly related to brain functions, such as sleep spindle and memory consolidation. On the other hand, given the crosstalk between the central nervous system and the body, including inflammation regulation and metabolic systems, there is a gap in understanding the bidirectional relationship between sleep and these systems that contribute to cognitive health. We used data from the Osteoporotic Fractures in Men (MrOS) Study, a six-site cohort study of community-dwelling men 65 years or older. We analyzed 1898 participants who participated in the Sleep Visit and Visit 2. We analyzed 41 sleep EEG microstructures and 9 blood markers. The outcome was the Modified Mini-Mental State Examination (3MS) in Visit 2. We used partial Spearman's correlation to investigate the pairwise associations and performed 3MS prediction. We then performed mediation analyses using each blood marker as the exposure and each sleep EEG microstructure as the mediator, and then the other way around. Sleep EEG microstructures were more strongly correlated with 3MS than blood markers in general. The best Pearson's correlation between the actual and predicted 3MS scores was 0.45 (95% confidence interval 0.38-0.47) using sleep EEG microstructures and covariates, which provided little improvement compared to using covariates alone (0.43, 0.39-0.48). Leptin and 3MS score were associated (Spearman's ρ = 0.053, p = 0.02) while adjusting for covariates. The association between leptin and 3MS score was mediated by fast spindle density (indirect effect = 0.030, p = 0.02) and spindle-slow oscillation (SO) overlap (indirect effect = 0.029, p = 0.02). No blood marker mediated the association between sleep EEG microstructure and 3MS. Instead, the following sleep EEG microstructures had significant direct associations with 3MS independent of the blood markers: theta power during N1 and REM (direct effect = - 0.57 and - 0.46, p = 0.0002 and 0.007, respectively), spindle density (direct effect = 0.39, p = 0.006), and spindle-SO coupling overlap (direct effect = 0.29, p = 0.01). The blood markers of inflammation and metabolism were less predictive of and indirectly associated with global cognition compared to the sleep EEG microstructures. Future work is needed to confirm these results in an experimental setting.

Predicting future risk of developing cognitive impairment using ambulatory sleep EEG: Integrating univariate analysis and multivariate information theory approach

BackgroundEarly identification of individuals at risk for cognitive impairment is crucial, as the preclinical phase offers an opportunity for interventions to slow disease progression and improve outcomes.ObjectiveWhile sleep electroencephalography (EEG) has shown significant promise in detecting cognitive impairment, this study aims to 1) develop and validate overnight EEG biomarkers for the prediction of future cognitive impairment risk, 2) assess their predictive performance within 5 years, and 3) explore the feasibility of using wearable, low-density EEG devices for convenient at-home monitoring.MethodsOvernight polysomnography was performed on 281 cognitively normal women in the Study of Osteoporotic Fractures (SOF). Cognitive reassessments were conducted approximately five years later. Features such as relative EEG power across different frequency bands and channel interactions, quantified using generalized mutual information measures, were extracted and used as inputs for machine learning models. Binary classification models distinguished participants who developed cognitive impairment from those who remained cognitively normal. Optimal feature subsets and frequency bands for classiffiation were identifed, with additional analyses testing the contribution of demographic data, sleep macrostructure, and APOE genotype.ResultsThe optimal model, utilizing univariate and multivariate EEG features, achieved an AUC of 0.76. Features from the N3 sleep stage and gamma band exhibited the largest effect sizes. Adding demographics, sleep macrostructure, and APOE genotype did not enhance performance.ConclusionsOvernight EEG analyses demonstrate a promising, cost-effective approach for early cognitive impairment risk assessment. Larger studies with more diverse populations are required to validate and expand these findings in diverse populations.

Evaluation of Sleep Quality in Clinical Practice

A major problem in clinical sleep medicine is that the most comprehensive and sophisticated investigative tool, the polysomnography, fails to provide a reason for the patient's complaints in all but those with sleep apnea and movement disorders. The reasons why conventional metrics of sleep quality are of limited value are discussed in detail. This is followed by description of several well-established features that have not been implemented in clinical practice because of the impracticality of doing the measurements visually. Automation is pending. Finally, several automated features recently derived from spectral analysis of the electroencephalogram were described.

Sleep spindles and slow oscillations predict cognition and biomarkers of neurodegeneration in mild to moderate Alzheimer's disease

INTRODUCTION

Changes in sleep physiology can predate cognitive symptoms by decades in persons with Alzheimer's disease (AD), but it remains unclear which sleep characteristics predict cognitive and neurodegenerative changes after AD onset.

METHODS

Using data from a prospective cohort of mild to moderate AD (n = 60), we analyzed non-rapid eye movement sleep spindles and slow oscillations (SOs) at baseline and their associations with baseline amyloid beta (Aβ) and tau and with cognition from baseline to 3-year follow-up.

RESULTS

Higher spindle and SO activity predicted significant changes in Aβ and tau at baseline, lower Alzheimer's Disease Assessment Scale Cognitive Subscale (better cognitive performance) score, and higher Mini-Mental State Examination score from baseline to 36 months. Spindles and SOs mediated the effect of phosphorylated tau 181 (pTau181)/Aβ42 on cognition, while pTau181/aβ42 moderated the effect of spindles and SOs on cognition.

DISCUSSION

Our findings demonstrate that spindle and SO activity during sleep constitute predictive and non-invasive biomarkers of neurodegeneration and cognition in AD patients.

HIGHLIGHTS

Sleep spindles predict long-term cognitive performance in AD. Sleep spindle and SOs can be predictive, non-invasive biomarkers for AD. Sleep may be one of the most important modifiable risk factors for AD progression. Sleep microarchitecture is a novel therapeutic target for preserving brain heath. Sleep physiology can provide novel therapeutic targets to slow AD progression.

Effects of early midlife ovarian removal on sleep: Polysomnography-measured cortical arousal, homeostatic drive, and spindle characteristics

Bilateral salpingo-oophorectomy (BSO; removal of ovaries and fallopian tubes) prior to age 48 is associated with elevated risk for both Alzheimer's disease (AD) and sleep disorders such as insomnia and sleep apnea. In early midlife, individuals with BSO show reduced hippocampal volume, function, and hippocampal-dependent verbal episodic memory performance associated with changes in sleep. It is unknown whether BSO affects fine-grained sleep measurements (sleep microarchitecture) and how these changes might relate to hippocampal-dependent memory. We recruited thirty-six early midlife participants with BSO. Seventeen of these participants were taking 17β-estradiol therapy (BSO+ET) and 19 had never taken ET (BSO). Twenty age-matched control participants with intact ovaries (AMC) were also included. Overnight at-home polysomnography recordings were collected, along with subjective sleep quality and hot flash frequency. Multivariate Partial Least Squares (PLS) analysis was used to assess how sleep varied between groups. Compared to AMC, BSO without ET was associated with significantly decreased time spent in non-rapid eye movement (NREM) stage 2 sleep as well as increased NREM stage 2 and 3 beta power, NREM stage 2 delta power, and spindle power and maximum amplitude. Increased spindle maximum amplitude was negatively correlated with verbal episodic memory performance. Decreased sleep latency, increased sleep efficiency, and increased time spent in rapid eye movement sleep were observed for BSO+ET. Findings suggest there is an association between ovarian hormone loss and sleep microarchitecture, which may contribute to poorer cognitive outcomes and be ameliorated by ET.

Assessing Risk of Health Outcomes From Brain Activity in Sleep: A Retrospective Cohort Study

Background and Objectives

Patterns of electrical activity in the brain (EEG) during sleep are sensitive to various health conditions even at subclinical stages. The objective of this study was to estimate sleep EEG-predicted incidence of future neurologic, cardiovascular, psychiatric, and mortality outcomes.

Methods

This is a retrospective cohort study with 2 data sets. The Massachusetts General Hospital (MGH) sleep data set is a clinic-based cohort, used for model development. The Sleep Heart Health Study (SHHS) is a community-based cohort, used as the external validation cohort. Exposure is good, average, or poor sleep defined by quartiles of sleep EEG-predicted risk. The outcomes include ischemic stroke, intracranial hemorrhage, mild cognitive impairment, dementia, atrial fibrillation, myocardial infarction, type 2 diabetes, hypertension, bipolar disorder, depression, and mortality. Diagnoses were based on diagnosis codes, brain imaging reports, medications, cognitive scores, and hospital records. We used the Cox survival model with death as the competing risk.

Results

There were 8673 participants from MGH and 5650 from SHHS. For all outcomes, the model-predicted 10-year risk was within the 95% confidence interval of the ground truth, indicating good prediction performance. When comparing participants with poor, average, and good sleep, except for atrial fibrillation, all other 10-year risk ratios were significant. The model-predicted 10-year risk ratio closely matched the observed event rate in the external validation cohort.

Discussion

The incidence of health outcomes can be predicted by brain activity during sleep. The findings strengthen the concept of sleep as an accessible biological window into unfavorable brain and general health outcomes.

Sleep Biomarkers for Predicting Cognitive Decline and Alzheimer's Disease: A Systematic Review of Longitudinal Studies

BACKGROUND

Sleep disturbances are considered a hallmark of dementia, and strong evidence supports the association between alterations in sleep parameters and cognitive decline in patients with mild cognitive impairment and Alzheimer's disease (AD).

OBJECTIVE

This systematic review aims to summarize the existing evidence on the longitudinal association between sleep parameters and cognitive decline, with the goal of identifying potential sleep biomarkers of AD-related neurodegeneration.

METHODS

Literature search was conducted in PubMed, Web of Science, and Scopus databases from inception to 28 March 2023. Longitudinal studies investigating the association between baseline objectively-measured sleep parameters and cognitive decline were assessed for eligibility.

RESULTS

Seventeen studies were included in the qualitative synthesis. Sleep fragmentation, reduced sleep efficiency, reduced REM sleep, increased light sleep, and sleep-disordered breathing were identified as predictors of cognitive decline. Sleep duration exhibited a U-shaped relation with subsequent neurodegeneration. Additionally, several sleep microstructural parameters were associated with cognitive decline, although inconsistencies were observed across studies.

CONCLUSIONS

These findings suggest that sleep alterations hold promise as early biomarker of cognitive decline, but the current evidence is limited due to substantial methodological heterogeneity among studies. Further research is necessary to identify the most reliable sleep parameters for predicting cognitive impairment and AD, and to investigate interventions targeting sleep that can assist clinicians in the early recognition and treatment of cognitive decline. Standardized procedures for longitudinal studies evaluating sleep and cognition should be developed and the use of continuous sleep monitoring techniques, such as actigraphy or EEG headband, might be encouraged.

Sleep electroencephalography biomarkers of cognition in obstructive sleep apnea

Obstructive sleep apnea has been associated with cognitive impairment and may be linked to disorders of cognitive function. These associations may be a result of intermittent hypoxaemia, sleep fragmentation and changes in sleep microstructure in obstructive sleep apnea. Current clinical metrics of obstructive sleep apnea, such as the apnea-hypopnea index, are poor predictors of cognitive outcomes in obstructive sleep apnea. Sleep microstructure features, which can be identified on sleep electroencephalography of traditional overnight polysomnography, are increasingly being characterized in obstructive sleep apnea and may better predict cognitive outcomes. Here, we summarize the literature on several major sleep electroencephalography features (slow-wave activity, sleep spindles, K-complexes, cyclic alternating patterns, rapid eye movement sleep quantitative electroencephalography, odds ratio product) identified in obstructive sleep apnea. We will review the associations between these sleep electroencephalography features and cognition in obstructive sleep apnea, and examine how treatment of obstructive sleep apnea affects these associations. Lastly, evolving technologies in sleep electroencephalography analyses will also be discussed (e.g. high-density electroencephalography, machine learning) as potential predictors of cognitive function in obstructive sleep apnea.

Quantitative sleep electroencephalogram and cognitive performance in Parkinson's disease with and without rapid eye movement sleep behavior disorder

Introduction

Parkinson's disease (PD) patients with REM sleep behavior disorder (RBD) are at greater risk for cognitive decline and RBD has been associated with alterations in sleep-related EEG oscillations. This study evaluates differences in sleep quantitative EEG (qEEG) and cognition in PD participants with (PD-RBD) and without RBD (PD-no-RBD).

Methods

In this cross-sectional study, polysomnography (PSG)-derived qEEG and a comprehensive level II neuropsychological assessment were compared between PD-RBD (n = 21) and PD-no-RBD (n = 31). Following artifact rejection, qEEG analysis was performed in the frontal and central leads. Measures included Scalp-slow wave (SW) density, spindle density, morphological properties of SW and sleep spindles, SW-spindle phase-amplitude coupling, and spectral power analysis in NREM and REM. The neurocognitive battery had at least two tests per domain, covering five cognitive domains as recommended by the Movement Disorders Society Task Force for PD-MCI diagnosis. Differences in qEEG features and cognitive performance were compared between the two groups. Stepwise linear regression was performed to evaluate predictors of cognitive performance. Multiple comparisons were corrected using the Benjamini-Hochberg method.

Results

Spindle density and SW-spindle co-occurrence percent were lower in participants with PD-RBD compared to PD-no-RBD. The PD-RBD group also demonstrated higher theta spectral power during REM. Sleep spindles and years of education, but not RBD, were predictors of cognitive performance.

Conclusion

PD participants with RBD have alterations in sleep-related qEEG compared to PD participants without RBD. Although PD-RBD participants had worse cognitive performance compared to PD-no-RBD, regression models suggest that lower sleep spindle density, rather than presence of RBD, predicts worse comprehensive cognitive score. Future studies should include longitudinal evaluation to determine whether sleep-related qEEG alterations are associated with more rapid cognitive decline in PD-RBD.

Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment

Medical data are often multi-modal, which are collected from different sources with different formats, such as text, images, and audio. They have some intrinsic connections in meaning and semantics while manifesting disparate appearances. Polysomnography (PSG) datasets are multi-modal data that include hypnogram, electrocardiogram (ECG), and electroencephalogram (EEG). It is hard to measure the associations between different modalities. Previous studies have used PSG datasets to study the relationship between sleep disorders and quality and sleep architecture. We leveraged a new method of deep learning manifold alignment to explore the relationship between sleep architecture and EEG features. Our analysis results agreed with the results of previous studies that used PSG datasets to diagnose different sleep disorders and monitor sleep quality in different populations. The method could effectively find the associations between sleep architecture and EEG datasets, which are important for understanding the changes in sleep stages and brain activity. On the other hand, the Spearman correlation method, which is a common statistical technique, could not find the correlations between these datasets.

Associations of Baseline Sleep Microarchitecture with Cognitive Function After 8 Years in Middle-Aged and Older Men from a Community-Based Cohort Study

Purpose

Prospective studies examining associations between baseline sleep microarchitecture and future cognitive function recruited from small samples with predominantly short follow-up. This study examined sleep microarchitecture predictors of cognitive function (visual attention, processing speed, and executive function) after 8 years in community-dwelling men.

Patients and Methods

Florey Adelaide Male Ageing Study participants (n=477) underwent home-based polysomnography (2010-2011), with 157 completing baseline (2007-2010) and follow-up (2018-2019) cognitive assessments (trail-making tests A [TMT-A] and B [TMT-B] and the standardized mini-mental state examination [SMMSE]). Whole-night F4-M1 sleep EEG recordings were processed following artifact exclusion, and quantitative EEG characteristics were obtained using validated algorithms. Associations between baseline sleep microarchitecture and future cognitive function (visual attention, processing speed, and executive function) were examined using linear regression models adjusted for baseline obstructive sleep apnoea, other risk factors, and cognition.

Results

The final sample included men aged (mean [SD]) 58.9 (8.9) years at baseline, overweight (BMI 28.5 [4.2] kg/m²), and well educated (75.2% ≥Bachelor, Certificate, or Trade), with majorly normal baseline cognition. Median (IQR) follow-up was 8.3 (7.9, 8.6) years. In adjusted analyses, NREM and REM sleep EEG spectral power was not associated with TMT-A, TMT-B, or SMMSE performance (all p>0.05). A significant association of higher N3 sleep fast spindle density with worse TMT-B performance (B=1.06, 95% CI [0.13, 2.00], p=0.026) did not persist following adjustment for baseline TMT-B performance.

Conclusion

In this sample of community-dwelling men, sleep microarchitecture was not independently associated with visual attention, processing speed, or executive function after 8 years.

Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer's disease in independently dwelling adults

INTRODUCTION

Sleep disorder is often the first symptom of age-related cognitive decline associated with Alzheimer's disease (AD) observed in primary care. The relationship between sleep and early AD was examined using a patented sleep mattress designed to record respiration and high frequency movement arousals. A machine learning algorithm was developed to classify sleep features associated with early AD.

METHOD

Community-dwelling older adults (N = 95; 62-90 years) were recruited in a 3-h catchment area. Study participants were tested on the mattress device in the home bed for 2 days, wore a wrist actigraph for 7 days, and provided sleep diary and sleep disorder self-reports during the 1-week study period. Neurocognitive testing was completed in the home within 30-days of the sleep study. Participant performance on executive and memory tasks, health history and demographics were reviewed by a geriatric clinical team yielding Normal Cognition (n = 45) and amnestic MCI-Consensus (n = 33) groups. A diagnosed MCI group (n = 17) was recruited from a hospital memory clinic following diagnostic series of neuroimaging biomarker assessment and cognitive criteria for AD.

RESULTS

In cohort analyses, sleep fragmentation and wake after sleep onset duration predicted poorer executive function, particularly memory performance. Group analyses showed increased sleep fragmentation and total sleep time in the diagnosed MCI group compared to the Normal Cognition group. Machine learning algorithm showed that the time latency between movement arousals and coupled respiratory upregulation could be used as a classifier of diagnosed MCI vs. Normal Cognition cases. ROC diagnostics identified MCI with 87% sensitivity; 89% specificity; and 88% positive predictive value.

DISCUSSION

AD sleep phenotype was detected with a novel sleep biometric, time latency, associated with the tight gap between sleep movements and respiratory coupling, which is proposed as a corollary of sleep quality/loss that affects the autonomic regulation of respiration during sleep. Diagnosed MCI was associated with sleep fragmentation and arousal intrusion.

Sleep and cognition in aging dogs. A polysomnographic study

Introduction

Sleep is fundamental for cognitive homeostasis, especially in senior populations since clearance of amyloid beta (key in the pathophysiology of Alzheimer's disease) occurs during sleep. Some electroencephalographic characteristics of sleep and wakefulness have been considered a hallmark of dementia. Owners of dogs with canine cognitive dysfunction syndrome (a canine analog to Alzheimer's disease) report that their dogs suffer from difficulty sleeping. The aim of this study was to quantify age-related changes in the sleep-wakefulness cycle macrostructure and electroencephalographic features in senior dogs and to correlate them with their cognitive performance.

Methods

We performed polysomnographic recordings in 28 senior dogs during a 2 h afternoon nap. Percentage of time spent in wakefulness, drowsiness, NREM, and REM sleep, as well as latency to the three sleep states were calculated. Spectral power, coherence, and Lempel Ziv Complexity of the brain oscillations were estimated. Finally, cognitive performance was evaluated by means of the Canine Dementia Scale Questionnaire and a battery of cognitive tests. Correlations between age, cognitive performance and sleep-wakefulness cycle macrostructure and electroencephalographic features were calculated.

Results

Dogs with higher dementia scores and with worse performance in a problem-solving task spent less time in NREM and REM sleep. Additionally, quantitative electroencephalographic analyses showed differences in dogs associated with age or cognitive performance, some of them reflecting shallower sleep in more affected dogs.

Discussion

Polysomnographic recordings in dogs can detect sleep-wakefulness cycle changes associated with dementia. Further studies should evaluate polysomnography's potential clinical use to monitor the progression of canine cognitive dysfunction syndrome.

Improvements in cognitive function and quantitative sleep electroencephalogram in obstructive sleep apnea after six months of continuous positive airway pressure treatment

STUDY OBJECTIVES

Untreated obstructive sleep apnea (OSA) is associated with cognitive deficits and altered brain electrophysiology. We evaluated the effect of continuous positive airway pressure (CPAP) treatment on quantitative sleep electroencephalogram (EEG) measures and cognitive function.

METHODS

We studied 167 patients with OSA (age 50 ± 13, AHI 35.0 ± 26.8) before and after 6 months of CPAP. Cognitive tests assessed working memory, sustained attention, visuospatial scanning, and executive function. All participants underwent overnight polysomnography at baseline and after CPAP. Power spectral analysis was performed on EEG data (C3-M2) in a sub-set of 90 participants. Relative delta EEG power and sigma power in NREM and EEG slowing in REM were calculated. Spindle densities (events/min) in N2 were also derived using automated spindle event detection. All outcomes were analysed as change from baseline.

RESULTS

Cognitive function across all cognitive domains improved after six months of CPAP. In our sub-set, increased relative delta power (p < .0001) and reduced sigma power (p = .001) during NREM were observed after the 6-month treatment period. Overall, fast and slow sleep spindle densities during N2 were increased after treatment.

CONCLUSIONS

Cognitive performance was improved and sleep EEG features were enhanced when assessing the effects of CPAP. These findings suggest the reversibility of cognitive deficits and altered brain electrophysiology observed in untreated OSA following six months of treatment.

The association between sleep microarchitecture and cognitive function in middle-aged and older men: a community-based cohort study

STUDY OBJECTIVES

Sleep microarchitecture parameters determined by quantitative power spectral analysis (PSA) of electroencephalograms (EEGs) have been proposed as potential brain-specific markers of cognitive dysfunction. However, data from community samples remains limited. This study examined cross-sectional associations between sleep microarchitecture and cognitive dysfunction in community-dwelling men.

METHODS

Florey Adelaide Male Ageing Study participants (n=477) underwent home-based polysomnography (PSG) (2010-2011). All-night EEG recordings were processed using PSA following artefact exclusion. Cognitive testing (2007-2010) included the inspection time task, trail-making tests A (TMT-A) and B (TMT-B), and Fuld object memory evaluation. Complete case cognition, PSG, and covariate data were available in 366 men. Multivariable linear regression models controlling for demographic, biomedical, and behavioral confounders determined cross-sectional associations between sleep microarchitecture and cognitive dysfunction overall and by age-stratified subgroups.

RESULTS

In the overall sample, worse TMT-A performance was associated with higher NREM theta and REM theta and alpha but lower delta power (all p<0.05). In men ≥65 years, worse TMT-A performance was associated with lower NREM delta but higher NREM and REM theta and alpha power (all p<0.05). Furthermore, in men ≥65 years, worse TMT-B performance was associated with lower REM delta but higher theta and alpha power (all p<0.05).

CONCLUSIONS

Sleep microarchitecture parameters may represent important brain-specific markers of cognitive dysfunction, particularly in older community-dwelling men. Therefore, this study extends the emerging community-based cohort literature on a potentially important link between sleep microarchitecture and cognitive dysfunction. Utility of sleep microarchitecture for predicting prospective cognitive dysfunction and decline warrants further investigation.

Use of a Personal Sleep Self-Monitoring Device for Sleep Self-Management: A Feasibility Study

The purpose of the current study was to establish feasibility of personal sleep monitoring devices (PSMDs) as an intervention for sleep self-management in older adults. This study followed a mixed-methods experimental design based on the World Health Organization's International Classification of Functioning, Disability, and Health, and the proposed conceptual model of symptom management in a social context. Results showed an acceptable recruitment and retention rate of participants, and acceptability of PSMDs by users. Participants were able to meaningfully interpret PSMD data as evidenced by the numeracy evaluation scores, initiate sleep goals, and share their sleep data and goals with friends or relatives. Findings support extending this research protocol to a larger sample. Future studies for sleep health self-management and personally tailored interventions using personal sleep monitoring are recommended. [Journal of Gerontological Nursing, 47(1), 28-34.].

Spontaneous slow oscillation - slow spindle features predict induced overnight memory retention

Study Objectives

Synchronization of neural activity within local networks and between brain regions is a major contributor to rhythmic field potentials such as the EEG. On the other hand, dynamic changes in microstructure and activity are reflected in the EEG, for instance slow oscillation (SO) slope can reflect synaptic strength. SO-spindle coupling is a measure for neural communication. It was previously associated with memory consolidation, but also shown to reveal strong interindividual differences. In studies, weak electric current stimulation has modulated brain rhythms and memory retention. Here, we investigate whether SO-spindle coupling and SO slope during baseline sleep are associated with (predictive of) stimulation efficacy on retention performance.

Methods

Twenty-five healthy subjects participated in three experimental sessions. Sleep-associated memory consolidation was measured in two sessions, in one anodal transcranial direct current stimulation oscillating at subjects individual SO frequency (so-tDCS) was applied during nocturnal sleep. The third session was without a learning task (baseline sleep). The dependence on SO-spindle coupling and SO-slope during baseline sleep of so-tDCS efficacy on retention performance were investigated.

Results

Stimulation efficacy on overnight retention of declarative memories was associated with nesting of slow spindles to SO trough in deep nonrapid eye movement baseline sleep. Steepness and direction of SO slope in baseline sleep were features indicative for stimulation efficacy.

Conclusions

Findings underscore a functional relevance of activity during the SO up-to-down state transition for memory consolidation and provide support for distinct consolidation mechanisms for types of declarative memories.

Daytime-to-Nighttime Sleep Ratios and Cognitive Impairment in Older Intensive Care Unit Survivors

BACKGROUND

Sleep duration and proportion of daytime versus nighttime sleep may affect cognitive function in older patients in the transition out of the intensive care unit.

OBJECTIVE

To explore the relationship between the daytime-to-nighttime sleep ratio and cognitive impairment in older intensive care unit survivors.

METHODS

The study enrolled 30 older adults within 24 to 48 hours after intensive care unit discharge. All participants were functionally independent before admission and underwent mechanical ventilation in the intensive care unit. Actigraphy was used to estimate daytime (6 AM to 9:59 PM) and nighttime (10 PM to 5:59 AM) total sleep duration. Daytime-to-nighttime sleep ratios were calculated by dividing the proportion of daytime sleep by the proportion of nighttime sleep. The National Institutes of Health Toolbox Cognition Battery Dimensional Change Card Sort Test (DCCST) was used to assess cognition. Associations between sleep and cognition were explored using multivariate regression after adjusting for covariates.

RESULTS

The mean (SD) daytime sleep duration was 7.55 (4.30) hours (range, 0.16-14.21 hours), and the mean (SD) nighttime sleep duration was 4.99 (1.95) hours (range, 0.36-7.21 hours). The mean (SD) daytime-to-nighttime sleep ratio was 0.71 (0.30) (range, 0.03-1.10). Greater daytime sleep duration (β = -0.351, P = .008) and higher daytime-to-nighttime sleep ratios (β = -0.373, P = .008) were negatively associated with DCCST scores.

CONCLUSIONS

The daytime-to-nighttime sleep ratio was abnormally high in the study population, revealing an altered sleep/wake cycle. Higher daytime-to-nighttime sleep ratios were associated with worse cognition, suggesting that proportionally greater daytime sleep may predict cognitive impairment.

Sleep, Mild Cognitive Impairment, and Interventions for Sleep Improvement: An Integrative Review

Sleep disturbance in mild cognitive impairment (MCI) is associated with progression to Alzheimer's disease (AD), more severe AD symptoms, and worse health outcomes. The aim of this review was to examine the relationship between sleep and MCI, and the effectiveness of sleep improvement interventions for older adults with MCI or AD. An integrative review was conducted using four databases, and findings were analyzed using an iterative process. Findings from 24 studies showed that alterations in sleep increased the risk of MCI and that the sleep quality of individuals with MCI or AD was poorer than healthy controls. Changes in brain anatomy were also observed in healthy older adults with sleep disturbances. Examined interventions were shown to be effective in improving sleep. Screening for sleep disturbances in individuals with MCI/AD is crucial to mitigate neurodegenerative or neurobehavioral risks in this population.

Slow-Wave Sleep and MRI Markers of Brain Aging in a Community-Based Sample

OBJECTIVE

To test the hypothesis that reduced slow-wave sleep, or N3 sleep, which is thought to underlie the restorative functions of sleep, is associated with MRI markers of brain aging, we evaluated this relationship in the community-based Framingham Heart Study Offspring cohort using polysomnography and brain MRI.

METHODS

We studied 492 participants (age 58.8 ± 8.8 years, 49.4% male) free of neurological diseases who completed a brain MRI scan and in-home overnight polysomnography to assess slow-wave sleep (absolute duration and percentage of total sleep). Volumes of total brain, total cortical, frontal cortical, subcortical gray matter, hippocampus, and white matter hyperintensities were investigated as a percentage of intracranial volume, and the presence of covert brain infarcts was evaluated. Linear and logistic regression models were adjusted for age, age squared, sex, time interval between polysomnography and MRI (3.3 ± 1.0 years), APOE ε4 carrier status, stroke risk factors, sleeping pill use, body mass index, and depression.

RESULTS

Less slow-wave sleep was associated with lower cortical brain volume (absolute duration, β [standard error] = 0.20 [0.08], p = 0.015; percentage, 0.16 [0.08], p = 0.044), lower subcortical brain volume (percentage, 0.03 [0.02], p = 0.034), and higher white matter hyperintensities volume (absolute duration, -0.12 [0.05], p = 0.010; percentage, -0.10 [0.04], p = 0.033). Slow-wave sleep duration was not associated with hippocampal volume or the presence of covert brain infarcts.

CONCLUSION

Loss of slow-wave sleep might facilitate accelerated brain aging, as evidence by its association with MRI markers suggestive of brain atrophy and injury. Alternatively, subtle injuries and accelerated aging might reduce the ability of the brain to produce slow-wave sleep.

Sleep Disturbance Forecasts β-Amyloid Accumulation across Subsequent Years

Experimental sleep-wake disruption in rodents and humans causally modulates β-amyloid (Aβ) dynamics (e.g., [1-3]). This leads to the hypothesis that, beyond cross-sectional associations, impaired sleep structure and physiology could represent prospective biomarkers of the speed with which Aβ accumulates over time. Here, we test the hypothesis that initial baseline measures of non-rapid eye movement (NREM) sleep slow-wave activity (SWA) and sleep quality (efficiency) provide future forecasting sensitivity to the rate of Aβ accumulation over subsequent years. A cohort of clinically normal older adults was assessed using objective sleep polysomnography in combination with longitudinal tracking of Aβ accumulation with [11C]PiB positron emission tomography (PET) imaging. Both the proportion of NREM SWA below 1 Hz and the measure of sleep efficiency predicted the speed (slope) of subsequent Aβ deposition over time, and these associations remained robust when taking into account additional cofactors of interest (e.g., age, sex, sleep apnea). Moreover, these measures were specific, such that no other macro- and microphysiological architecture metrics of sleep demonstrated such sensitivity. Our data support the proposal that objective sleep markers could be part of a set of biomarkers that statistically forecast the longitudinal trajectory of cortical Aβ deposition in the human brain. Sleep may therefore represent a potentially affordable, scalable, repeatable, and non-invasive tool for quantifying of Aβ pathological progression, prior to cognitive symptoms of Alzheimer's disease (AD).

Obstructive Sleep Apnea and Its Treatment in Aging: Effects on Alzheimer's disease Biomarkers, Cognition, Brain Structure and Neurophysiology

Here we review the impact of obstructive sleep apnea (OSA) on biomarkers of Alzheimer's disease (AD) pathogenesis, neuroanatomy, cognition and neurophysiology, and present the research investigating the effects of continuous positive airway pressure (CPAP) therapy. OSA is associated with an increase in AD markers amyloid-β and tau measured in cerebrospinal fluid (CSF), by Positron Emission Tomography (PET) and in blood serum. There is some evidence suggesting CPAP therapy normalizes AD biomarkers in CSF but since mechanisms for amyloid-β and tau production/clearance in humans are not completely understood, these findings remain preliminary. Deficits in the cognitive domains of attention, vigilance, memory and executive functioning are observed in OSA patients with the magnitude of impairment appearing stronger in younger people from clinical settings than in older community samples. Cognition improves with varying degrees after CPAP use, with the greatest effect seen for attention in middle age adults with more severe OSA and sleepiness. Paradigms in which encoding and retrieval of information are separated by periods of sleep with or without OSA have been done only rarely, but perhaps offer a better chance to understand cognitive effects of OSA than isolated daytime testing. In cognitively normal individuals, changes in EEG microstructure during sleep, particularly slow oscillations and spindles, are associated with biomarkers of AD, and measures of cognition and memory. Similar changes in EEG activity are reported in AD and OSA, such as "EEG slowing" during wake and REM sleep, and a degradation of NREM EEG microstructure. There is evidence that CPAP therapy partially reverses these changes but large longitudinal studies demonstrating this are lacking. A diagnostic definition of OSA relying solely on the Apnea Hypopnea Index (AHI) does not assist in understanding the high degree of inter-individual variation in daytime impairments related to OSA or response to CPAP therapy. We conclude by discussing conceptual challenges to a clinical trial of OSA treatment for AD prevention, including inclusion criteria for age, OSA severity, and associated symptoms, the need for a potentially long trial, defining relevant primary outcomes, and which treatments to target to optimize treatment adherence.

Toward refining Alzheimer's disease into overlapping subgroups

Alzheimer's disease (AD) is an age-related neurodegenerative disorder characterized by progressive anterograde amnesia, cerebral atrophy, and eventual death. Current treatment has limited efficacy and cannot decelerate the disease progression. Clinical trials targeting the removal of the neuropathological hallmarks of AD, including accumulation of amyloid plaques or neurofibrillary tangles, have failed to modify disease progression. Without new or innovative hypotheses, AD is poised to become a public health crisis within this decade. We present an alternative hypothesis-that AD is the result of multiple interrelated causalities. The intention of this manuscript is to initiate a discussion regarding these multiple causalities and their overlapping similarities. The idea of creating subgroups allows for better identification of biomarkers across a narrower patient population for improved pharmacotherapeutic opportunities. The interrelatedness of many of these proposed subgroups indicates the complexity of this disorder. However, it also supports that no one single factor may initiate the cascade of events.

Dynamic Changes in EEG Power Spectral Densities During NIH-Toolbox Flanker, Dimensional Change Card Sort Test and Episodic Memory Tests in Young Adults

Much is known about electroencephalograph (EEG) patterns during sleep, but until recently, it was difficult to study EEG patterns during conscious, awake behavior. Technological advances such as powerful wireless EEG systems have led to a renewed interest in EEG as a clinical and research tool for studying real-time changes in the brain. We report here the first normative study of EEG activity while healthy young adults completed a series of cognitive tests recently published by the National Institutes of Health Toolbox Cognitive Battery (NIH-TCB), a commonly-used standardized measure of cognition primarily used in clinical populations. In this preliminary study using a wireless EEG system, we examined power spectral density (PSD) in four EEG frequency bands. During baseline and cognitive testing, PSD activity for the lower frequency bands (theta and alpha) was greater, relative to the higher frequency bands (beta and gamma), suggesting participants were relaxed and mentally alert. Alpha, beta and gamma activity was increased during a memory test compared to two other, less demanding executive function tests. Gamma activity was also inversely correlated with performance on the memory test, consistent with the neural efficiency hypothesis which proposes that better cognitive performance may link with lower cortical energy consumption. In summary, our study suggests that cognitive performance is related to the dynamics of EEG activity in a normative young adult population.

Association of sleep spindle characteristics with executive functioning in healthy sedentary middle-aged and older adults

To determine the relationship between sleep spindle characteristics (density, power and frequency), executive functioning and cognitive decline in older adults, we studied a convenience subsample of healthy middle-aged and older participants of the Brain in Motion study. Participants underwent a single night of unattended in-home polysomnography with neurocognitive testing carried out shortly afterwards. Spectral analysis of the EEG was performed to derive spindle characteristics in both central and frontal derivations during non-rapid eye movement (NREM) Stage 2 and 3. Multiple linear regressions were used to examine associations between spindle characteristics and cognitive outcomes, with age, body mass index (BMI), periodic limb movements index (PLMI) and apnea hypopnea index (AHI) as covariates. NREM Stage 2 total spindle density was significantly associated with executive functioning (central: β = .363, p = .016; frontal: β = .408, p = .004). NREM Stage 2 fast spindle density was associated with executive functioning (central: β = .351, p = .022; frontal: β = .380, p = .009) and Montreal Cognitive Assessment score (MoCA, central: β = .285, p = .037; frontal: β = .279, p = .032). NREM Stage 2 spindle frequency was also associated with MoCA score (central: β = .337, p = .013). Greater spindle density and fast spindle density were associated with better executive functioning and less cognitive decline in our study population. Our cross-sectional design cannot infer causality. Longitudinal studies will be required to assess the ability of spindle characteristics to predict future cognitive status.

Advanced polysomnographic analysis for OSA: A pathway to personalized management?

Obstructive sleep apnea (OSA) is a highly heterogeneous disorder, with diverse pathways to disease, expression of disease, susceptibility to co-morbidities and response to therapy, and is ideally suited to precision medicine approaches. Clinically, the content of the information-rich polysomnogram (PSG) is not currently fully utilized in determining patient management. Novel PSG parameters such as hypoxic burden, pulse transit time, cardiopulmonary coupling and the frequency representations of PSG sensor signals could predict a variety of cardiovascular disease, cancer and neurodegeneration co-morbidities. The PSG can also be used to identify key pathophysiological parameters such as loop gain, arousal threshold and muscle compensation which can enhance understanding of the causes of OSA in an individual, and thereby guide choices on therapy. Machine learning methods performing their own parameter extraction coupled with large PSG data sets offer an exciting opportunity for discovering new links between the PSG variables and disease outcomes. By exploiting existing and emerging analytical methods, the PSG may offer a pathway to personalized management for OSA.

Quantitative electroencephalography measures in rapid eye movement and nonrapid eye movement sleep are associated with apnea–hypopnea index and nocturnal hypoxemia in men

Study Objectives

Quantitative electroencephalography (EEG) measures of sleep may identify vulnerability to obstructive sleep apnea (OSA) sequelae, however, small clinical studies of sleep microarchitecture in OSA show inconsistent alterations. We examined relationships between quantitative EEG measures during rapid eye movement (REM) and non-REM (NREM) sleep and OSA severity among a large population-based sample of men while accounting for insomnia.

Methods

All-night EEG (F4-M1) recordings from full in-home polysomnography (Embletta X100) in 664 men with no prior OSA diagnosis (age ≥ 40) were processed following exclusion of artifacts. Power spectral analysis included non-REM and REM sleep computed absolute EEG power for delta, theta, alpha, sigma, and beta frequency ranges, total power (0.5–32 Hz) and EEG slowing ratio.

Results

Apnea–hypopnea index (AHI) ≥10/h was present in 51.2% (severe OSA [AHI ≥ 30/h] 11.6%). In mixed effects regressions, AHI was positively associated with EEG slowing ratio and EEG power across all frequency bands in REM sleep (all p < 0.05); and with beta power during NREM sleep (p = 0.06). Similar associations were observed with oxygen desaturation index (3%). Percentage total sleep time with oxygen saturation <90% was only significantly associated with increased delta, theta, and alpha EEG power in REM sleep. No associations with subjective sleepiness were observed.

Conclusions

In a large sample of community-dwelling men, OSA was significantly associated with increased EEG power and EEG slowing predominantly in REM sleep, independent of insomnia. Further study is required to assess if REM EEG slowing related to nocturnal hypoxemia is more sensitive than standard PSG indices or sleepiness in predicting cognitive decline.

Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population

Objective: Recent research suggests that sleep disorders or changes in sleep stages or EEG waveform precede over time the onset of the clinical signs of pathological cognitive impairment (e.g., Alzheimer's disease). The aim of this study was to identify biomarkers based on EEG power values and spindle characteristics during sleep that occur in the early stages of mild cognitive impairment (MCI) in older adults. Methods: This study was a case-control cross-sectional study with 1-year follow-up of cases. Patients with isolated subjective cognitive complaints (SCC) or MCI were recruited in the Bordeaux Memory Clinic (MEMENTO cohort). Cognitively normal controls were recruited. All participants were recorded with two successive polysomnography 1 year apart. Delta, theta, and sigma absolute spectral power and spindle characteristics (frequency, density, and amplitude) were analyzed from purified EEG during NREM and REM sleep periods during the entire second night. Results: Twenty-nine patients (8 males, age = 71 ± 7 years) and 29 controls were recruited at T0. Logistic regression analyses demonstrated that age-related cognitive impairment were associated with a reduced delta power (odds ratio (OR) 0.072, P < 0.05), theta power (OR 0.018, P < 0.01), sigma power (OR 0.033, P < 0.05), and spindle maximal amplitude (OR 0.002, P < 0.05) during NREM sleep. Variables were adjusted on age, gender, body mass index, educational level, and medication use. Seventeen patients were evaluated at 1-year follow-up. Correlations showed that changes in self-reported sleep complaints, sleep consolidation, and spindle characteristics (spectral power, maximal amplitude, duration, and frequency) were associated with cognitive impairment (P < 0.05). Conclusion: A reduction in slow-wave, theta and sigma activities, and a modification in spindle characteristics during NREM sleep are associated very early with a greater risk of the occurrence of cognitive impairment. Poor sleep consolidation, lower amplitude, and faster frequency of spindles may be early sleep biomarkers of worsening cognitive decline in older adults.

Dynamic Contributions of Slow Wave Sleep and REM Sleep to Cognitive Longevity

Purpose of review

The purpose of this paper was to address how sleep changes with aging, with the broader goal of informing how REM sleep and slow wave activity mechanisms interact to promote cognitive longevity.

Recent findings

We conducted novel analyses based on the National Sleep Research Resource database. Over approximately five years, middle-to-older aged adults, on average, showed dramatically worse sleep fragmentation, a steady decrease in slow wave sleep, and yet a small increase in REM sleep. Averaging across participants, however, masked a major theme: Individuals differ substantially in their longitudinal trajectories for specific components of sleep. We considered this individual variability in light of recent theoretical and empirical work that has shown disrupted sleep and decreased slow wave activity to impair frontal lobe restoration, glymphatic system functioning, and memory consolidation. Based on multiple recent longitudinal studies, we contend that preserved or enhanced REM sleep may compensate for otherwise disrupted sleep in advancing age.

Summary

The scientific community has often debated whether slow wave activity or REM sleep mechanisms are more important to cognitive aging. We propose that a more fruitful approach for future work will be to investigate how REM and slow wave processes dynamically interact to affect cognitive longevity.