Reduced Regional NREM Sleep Slow-Wave Activity Is Associated With Cognitive Impairment in Parkinson Disease

Lack variation of low slow-wave activity over time in the frontal region in NREM sleep may be associated with dyskinesia in Parkinson's disease

OBJECTIVE

Levodopa-induced dyskinesia (DYS) adversely affects the quality of life of Parkinson's disease (PD) patients. However, few studies have focused on the relationship between DYS and sleep and electroencephalography (EEG). Our study aimed to establish the objective physiological indicators assessed by polysomnography (PSG) that are associated with DYS in PD patients.

METHODS

We enrolled 122 patients with PD, divided into two groups: PD with DYS (n = 27) and PD without DYS group (non-DYS, n = 95). The demographics and clinical characteristics and sleep assessment in the two groups were collected. More importantly, overnight six-channel PSG parameters were compared in the two groups. We also compared different bands and brain regions of average power spectral density within each group.

RESULTS

Compared with the non-DYS group, the DYS group tended to have a significantly higher percentage of nonrapid eye movement sleep (NREM). Gender, levodopa equivalent daily dose (LEDD), rapid eye movement (REM) sleep (min), and the NREM percentage were positively correlated with the occurrence of DYS. After adjusting for gender, disease duration, LEDD, taking amantadine or not, and Montreal Cognitive Assessment (MoCA), NREM%, N3%, and REM (min), the percentage of NREM sleep (p = 0.035), female (p = 0.002), and LEDD (p = 0.005), and REM sleep time (min) (p = 0.012) were still associated with DYS. There was no significant difference in whole-night different bands of average power spectral density between two groups. There was no significant difference in normalized average power spectral density of slow wave activity (SWA) (0.5-2 Hz, 0.5-4 Hz, and 2-4 Hz) of early and late NREM sleep in the DYS group. Dynamic normalized average power spectral density of SWA of low-frequency (0.5-2 Hz) reduction in the frontal region (p = 0.013) was associated with DYS in logistic regression after adjusting for confounding factors.

CONCLUSION

PD patients with DYS have substantial sleep structure variations. Higher NREM percentage and less REM percentage were observed in PD patients with DYS. Dynamic normalized average power spectral density of low-frequency (0.5-2 Hz) SWA reduction in the frontal area could be a new electrophysiological marker of DYS in PD.

Changes in slow-wave sleep characteristics in Parkinson's disease patients with mild-moderate depression

INTRODUCTION

Depression and sleep disturbances are commonly seen non-motor symptoms in patients with Parkinson's disease (PD). This study used polysomnography to examine the relationship between mild-moderate depression in PD and sleep characteristics, particularly slow wave activities (SWA).

METHODS

59 PD patients were split into two groups: nd-PD (n = 27) (patients with PD without depression) and d-PD (n = 32) (patients with PD with mild-moderate depression). Their clinical features, polysomnography parameters, and demographics were evaluated. Early and late sleep SWA spectrum densities and overnight SWA decline in different brain regions were particularly analyzed.

RESULTS

Non-rapid eye movement 3 (N3) sleep duration and percentage were greater in the d-PD group. N3 percentage was linked to depression (p = 0.014). During late sleep, higher SWA (0.5-4Hz) in the frontal and central regions, higher low-SWA (0.5-2Hz) in the whole brain, central and occipital regions, and higher high-SWA (2-4Hz) in the frontal region was observed in the d-PD group. During early sleep, there was also higher low-SWA (0.5-2Hz) in the occipital region. Patients in d-PD group exhibited reduced overnight high-SWA (2-4Hz) decline (Δhigh-SWA) in the whole brain and occipital regions. Δhigh-SWA(2-4Hz) in the occipital region were associated with depression (p = 0.049).

CONCLUSION

PD patients with mild-moderate depression have impaired slow wave sleep, exhibiting as increased N3 sleep, SWA, and reduced overnight SWA decline. This implies that synaptic strength reduction during sleep and impaired synaptic homeostasis regulation may be associated with depression in PD. Reduced overnight high-SWA decline in the occipital region may serve as a novel electrophysiological biomarker for indicating depression in PD.

Sleep spindle density and temporal clustering are associated with sleep-dependent memory consolidation in Parkinson's disease

STUDY OBJECTIVES

Sleep is required for successful memory consolidation. Sleep spindles, bursts of oscillatory activity occurring during non-rapid eye movement sleep, are known to be crucial for this process and, recently, it has been proposed that the temporal organization of spindles into clusters might additionally play a role in memory consolidation. In Parkinson's disease, spindle activity is reduced, and this reduction has been found to be predictive of cognitive decline. However, it remains unknown whether alterations in sleep spindles in Parkinson's disease are predictive of sleep-dependent cognitive processes such as memory consolidation, leaving open questions about the possible mechanisms linking sleep and a more general cognitive state in Parkinson's patients.

METHODS

The current study sought to fill this gap by recording overnight polysomnography and measuring overnight declarative memory consolidation in a sample of 35 patients with Parkinson's. Memory consolidation was measured using a verbal paired-associates task administered before and after the night of recorded sleep.

RESULTS

We found that lower sleep spindle density at frontal leads during non-rapid eye movement stage 3 was associated with worse overnight declarative memory consolidation. We also found that patients who showed less temporal clustering of spindles exhibited worse declarative memory consolidation.

CONCLUSIONS

These results suggest alterations to sleep spindles, which are known to be a consequence of Parkinson's disease, might represent a mechanism by which poor sleep leads to worse cognitive function in Parkinson's patients.

CITATION

Lahlou S, Kaminska M, Doyon J, Carrier J, Sharp M. Sleep spindle density and temporal clustering are associated with sleep-dependent memory consolidation in Parkinson's disease. J Clin Sleep Med. 2024;20(7):1153-1162.

Cognitive training and promoting a healthy lifestyle for individuals with isolated REM sleep behavior disorder: study protocol of the delayed-start randomized controlled trial CogTrAiL-RBD

BACKGROUND

Isolated REM sleep behavior disorder (iRBD) is an early α-synucleinopathy often accompanied by incipient cognitive impairment. As executive dysfunctions predict earlier phenotypic conversion from iRBD to Parkinson's disease and Lewy body dementia, cognitive training focusing on executive functions could have disease-modifying effects for individuals with iRBD.

METHODS

The study CogTrAiL-RBD investigates the short- and long-term effectiveness and the feasibility and underlying neural mechanisms of a cognitive training intervention for individuals with iRBD. The intervention consists of a 5-week digital cognitive training accompanied by a module promoting a healthy, active lifestyle. In this monocentric, single-blinded, delayed-start randomized controlled trial, the intervention's effectiveness will be evaluated compared to an initially passive control group that receives the intervention in the second, open-label phase of the study. Eighty individuals with iRBD confirmed by polysomnography will be consecutively recruited from the continuously expanding iRBD cohort at the University Hospital Cologne. The evaluation will focus on cognition and additional neuropsychological and motor variables. Furthermore, the study will examine the feasibility of the intervention, effects on physical activity assessed by accelerometry, and interrogate the intervention's neural effects using magnetic resonance imaging and polysomnography. Besides, a healthy, age-matched control group (HC) will be examined at the first assessment time point, enabling a cross-sectional comparison between individuals with iRBD and HC.

DISCUSSION

This study will provide insights into whether cognitive training and psychoeducation on a healthy, active lifestyle have short- and long-term (neuro-)protective effects for individuals with iRBD.

TRIAL REGISTRATION

The study was prospectively registered in the German Clinical Trial Register (DRKS00024898) on 2022-03-11, https://drks.de/search/de/trial/DRKS00024898 .

PROTOCOL VERSION

V5 2023-04-24.

Correlation of slow-wave sleep with motor and nonmotor progression in Parkinson's disease

OBJECTIVE

This study aimed to explore the association between slow-wave sleep and the progression of motor and nonmotor symptoms in patients with PD.

METHODS

Data were collected from the Parkinson's Progression Markers Initiative study. Slow-wave sleep, also known as deep non-rapid eye movement (DNREM) sleep, was objectively assessed using the Verily Study Watch. Motor function was assessed using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III score, Hoehn and Yahr stage, freezing of gait, motor fluctuations, and dyskinesia severity. Comprehensive assessments were conducted on nonmotor symptoms, including depression, anxiety, global cognitive function, and autonomic dysfunction. Statistical analyses involved repeated-measures analysis of variance and linear regression.

RESULTS

A total of 102 patients with PD were included in the study, with a median follow-up duration of 3.4 years. In the long DNREM sleep duration group (n = 55), better motor function (DNREM × time interaction: F(1,100) = 4.866, p = 0.030), less severe sexual dysfunction (p = 0.026), and improved activities of daily living (p = 0.033) were observed at the last follow-up visit compared with the short DNREM sleep duration group (n = 47). Reduced DNREM sleep duration is a risk factor for motor progression (β = -0.251, p = 0.021; 95% confidence interval = -0.465 to -0.038).

INTERPRETATION

The findings suggest an association between longer DNREM sleep duration and slower motor and nonmotor progression in patients with PD.

Slow-wave sleep dysfunction in mild parkinsonism is associated with excessive beta and reduced delta oscillations in motor cortex

Increasing evidence suggests slow-wave sleep (SWS) dysfunction in Parkinson's disease (PD) is associated with faster disease progression, cognitive impairment, and excessive daytime sleepiness. Beta oscillations (8-35 Hz) in the basal ganglia thalamocortical (BGTC) network are thought to play a role in the development of cardinal motor signs of PD. The role cortical beta oscillations play in SWS dysfunction in the early stage of parkinsonism is not understood, however. To address this question, we used a within-subject design in a nonhuman primate (NHP) model of PD to record local field potentials from the primary motor cortex (MC) during sleep across normal and mild parkinsonian states. The MC is a critical node in the BGTC network, exhibits pathological oscillations with depletion in dopamine tone, and displays high amplitude slow oscillations during SWS. The MC is therefore an appropriate recording site to understand the neurophysiology of SWS dysfunction in parkinsonism. We observed a reduction in SWS quantity (p = 0.027) in the parkinsonian state compared to normal. The cortical delta (0.5-3 Hz) power was reduced (p = 0.038) whereas beta (8-35 Hz) power was elevated (p = 0.001) during SWS in the parkinsonian state compared to normal. Furthermore, SWS quantity positively correlated with delta power (r = 0.43, p = 0.037) and negatively correlated with beta power (r = -0.65, p < 0.001). Our findings support excessive beta oscillations as a mechanism for SWS dysfunction in mild parkinsonism and could inform the development of neuromodulation therapies for enhancing SWS in people with PD.

A classification-based generative approach to selective targeting of global slow oscillations during sleep

Background

Given sleep's crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability.

Objective/Hypothesis

This study targets a specific type of SOs, widespread on the electrode manifold in a short delay ("global SOs"), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile.

Methods

We introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance.

Results

Our study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization.

Conclusion

Our research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.

Spatiotemporal convolution sleep network based on graph attention mechanism with automatic feature extraction

BACKGROUND AND OBJECTIVE

Graph neural networks (GNNs) are widely used for automatic sleep staging. However, the majority of GNNs are based on spectral approaches, as far as we know, which heavily depend on the Laplacian eigenbasis determined by the graph structure with a large computing cost.

METHODS

We introduced a non-spectral approach named graph attention networks v2 (GATv2) as the core of our network to extract spatial information (S-GATv2 in our work), which is more flexible and intuitive than the routined spectral method. Meanwhile, to resolve the issue of weak generalization of using traditional feature extraction, the multi-convolutional layers are implemented to automatically extract features. In this work, the proposed spatiotemporal convolution sleep network (ST-GATv2) consists of multi-convolution layers and a GATv2 block. Of note, the graph attention technique to the time domain was applied to construct temporal GATv2 (T-GATv2), which intends to capture the connection between two channels in the adjacent sleep stages. Besides, the modified function is further proposed to capture the hidden changing trend information by the difference in the feature's value of the two adjacent stages.

RESULTS

In our experiment, we used the SS3 datasets in the MASS as our test datasets to compare with other advanced models. Our result reveals our model achieves the highest accuracy at 89.0 %. Besides, the proposed T-GATv2 block and modified function bring an approximate 0.5 % improvement in Kappa and F1-score.

CONCLUSIONS

Our results support the potential of graph attention mechanisms and creative blocks (T-GATv2 and modified function) in sleep classification. We suggest the proposed ST-GATv2 model as an effective tool in sleep staging in either healthy or diseased states.

Mapping sleep's oscillatory events as a biomarker of Alzheimer's disease

INTRODUCTION

Memory-associated neural circuits produce oscillatory events including theta bursts (TBs), sleep spindles (SPs), and slow waves (SWs) in sleep electroencephalography (EEG). Changes in the "coupling" of these events may indicate early Alzheimer's disease (AD) pathogenesis.

METHODS

We analyzed 205 aging adults using single-channel sleep EEG, cerebrospinal fluid (CSF) AD biomarkers, and Clinical Dementia Rating® (CDR®) scale. We mapped SW-TB and SW-SP neural circuit coupling precision to amyloid positivity, cognitive impairment, and CSF AD biomarkers.

RESULTS

Cognitive impairment correlated with lower TB spectral power in SW-TB coupling. Cognitively unimpaired, amyloid positive individuals demonstrated lower precision in SW-TB and SW-SP coupling compared to amyloid negative individuals. Significant biomarker correlations were found in oscillatory event coupling with CSF Aβ42 /Aβ40 , phosphorylated- tau181 , and total-tau.

DISCUSSION

Sleep-dependent memory processing integrity in neural circuits can be measured for both SW-TB and SW-SP coupling. This breakdown associates with amyloid positivity, increased AD pathology, and cognitive impairment.

HIGHLIGHTS

At-home sleep EEG is a potential biomarker of neural circuits linked to memory. Circuit precision is associated with amyloid positivity in asymptomatic aging adults. Levels of CSF amyloid and tau also correlate with circuit precision in sleep EEG. Theta burst EEG power is decreased in very early mild cognitive impairment. This technique may enable inexpensive wearable EEGs for monitoring brain health.

Excessive cortical beta oscillations are associated with slow-wave sleep dysfunction in mild parkinsonism

Increasing evidence associates slow-wave sleep (SWS) dysfunction with neurodegeneration. Using a within-subject design in the nonhuman primate model of Parkinson's disease (PD), we found that reduced SWS quantity in mild parkinsonism was accompanied by elevated beta and reduced delta power during SWS in the motor cortex. Our findings support excessive beta oscillations as a mechanism for SWS dysfunction and will inform development of neuromodulation therapies for enhancing SWS in PD.

Wake and non-rapid eye movement sleep dysfunction is associated with colonic neuropathology in Parkinson's disease

Study Objectives

The body-first Parkinson's disease (PD) hypothesis suggests initial gut Lewy body pathology that propagates to the pons before reaching the substantia nigra, and subsequently progresses to the diencephalic and cortical levels. This disease course may also be the most likely in PD with rapid eye movement sleep behavior disorder (RBD).

Objectives

We aimed to explore the potential association between colonic phosphorylated alpha-synuclein histopathology (PASH) and diencephalic or cortical dysfunction evidenced by non-rapid eye movement (NREM) sleep and wakefulness polysomnographic markers.

Methods

In a study involving 43 patients with PD who underwent clinical examination, rectosigmoidoscopy, and polysomnography, we detected PASH on colonic biopsies using whole-mount immunostaining. We performed a visual semi-quantitative and automated quantification of spindle and slow wave features of NREM sleep, and the wake dominant frequency, and then determined Braak and Arizona stage classifications for PD severity based on sleep and wake electroencephalographic features.

Results

The visual analysis aligned with the automated quantified spindle characteristics and the wake dominant frequency. Altered NREM sleep and wake parameters correlated with markers of PD severity, colonic PASH, and RBD diagnosis. Colonic PASH frequency also increased in parallel to presumed PD Braak and Arizona stage classifications.

Conclusions

Colonic PASH in PD is strongly associated with widespread brain sleep and wake dysfunction, pointing toward likely extensive diffusion of the pathological process in the presumptive body-first PD phenotype. Visual and automated analyses of polysomnography signals provide useful markers to gauge covert brain dysfunction in PD.

Statement of Significance

The presence of gut synucleinopathy in Parkinson's disease can be linked to the body-first hypothesis in its pathophysiology. This study, performed in a cohort of 43 patients with Parkinson's disease that underwent clinical assessment, rectosigmoidoscopy and polysomnography, provides evidence that colonic neuropathology in Parkinson's disease is associated with widespread brain dysfunction, as evaluated by wake and non-rapid eye movement sleep polysomnographic markers. Our results support the assumption of an extensive diffusion of the pathological process to diencephalic and neocortical structures in the presumptive body-first phenotype. They also suggest the use of routine polysomnography in phenotyping patients with Parkinson's disease. Future studies should investigate the brain diffusion pattern and its sleep markers in the hypothesized brain-first phenotype of Parkinson's disease.

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 in Parkinson's Disease: A Case-Control Study

BACKGROUND

Sleep disorders are common in Parkinson's disease (PD) and include alterations in sleep-related EEG oscillations.

OBJECTIVE

This case-control study tested the hypothesis that patients with PD would have a lower density of Scalp-Slow Wave (SW) oscillations and higher slow-to-fast frequencies ratio in rapid eye movement (REM) sleep than non-PD controls. Other sleep-related quantitative EEG (qEEG) features were also examined, including SW morphology, sleep spindles, and Scalp-SW spindle phase-amplitude coupling.

METHODS

Polysomnography (PSG)-derived sleep EEG was compared between PD participants (n = 56) and non-PD controls (n = 30). Following artifact rejection, sleep qEEG analysis was performed in frontal and central leads. Measures included SW density and morphological features of SW and sleep spindles, SW-spindle phase-amplitude coupling, and spectral power analysis in Non-REM (NREM) and REM. Differences in qEEG features between PD and non-PD controls were compared using two-tailed Welch's t-tests, and correction for multiple comparisons was performed per the Benjamini-Hochberg method.

RESULTS

SW density was lower in PD than in non-PD controls (F = 13.5, p' = 0.003). The PD group also exhibited higher ratio of slow REM EEG frequencies (F = 4.23, p' = 0.013), higher slow spindle peak frequency (F = 24.7, p' <  0.002), and greater SW-spindle coupling angle distribution non-uniformity (strength) (F = 7.30, p' = 0.034).

CONCLUSION

This study comprehensively evaluates sleep qEEG including SW-spindle phase amplitude coupling in PD compared to non-PD controls. These findings provide novel insights into how neurodegenerative disease disrupts electrophysiological sleep rhythms. Considering the role of sleep oscillatory activity on neural plasticity, future studies should investigate the influence of these qEEG markers on cognition in PD.

Sleep-Related Changes Prior to Cognitive Dysfunction

PURPOSE OF REVIEW

The aim of this review is to summarize the current evidence on the relationship between sleep and cognition and present available data reporting the impact that sleep alterations may have on cognitive functions.

RECENT FINDINGS

Research findings support the idea that sleep is involved in cognitive processes and that altered sleep homeostasis or circadian rhythms may lead to clinical and biochemical changes associated with cognitive impairment. Evidence is particularly solid for the association between specific sleep architecture and circadian alterations and Alzheimer's disease. Sleep changes, as early manifestations or possible risk factors for neurodegeneration and cognitive decline, may be appropriate targets for interventions aiming to reduce the likelihood of dementia.

Topographic-dynamic reorganisation model of dreams (TRoD) - A spatiotemporal approach

Dreams are one of the most bizarre and least understood states of consciousness. Bridging the gap between brain and phenomenology of (un)conscious experience, we propose the Topographic-dynamic Re-organization model of Dreams (TRoD). Topographically, dreams are characterized by a shift towards increased activity and connectivity in the default-mode network (DMN) while they are reduced in the central executive network, including the dorsolateral prefrontal cortex (except in lucid dreaming). This topographic re-organization is accompanied by dynamic changes; a shift towards slower frequencies and longer timescales. This puts dreams dynamically in an intermediate position between awake state and NREM 2/SWS sleep. TRoD proposes that the shift towards DMN and slower frequencies leads to an abnormal spatiotemporal framing of input processing including both internally- and externally-generated inputs (from body and environment). In dreams, a shift away from temporal segregation to temporal integration of inputs results in the often bizarre and highly self-centric mental contents as well as hallucinatory-like states. We conclude that topography and temporal dynamics are core features of the TroD, which may provide the connection of neural and mental activity, e.g., brain and experience during dreams as their "common currency".

Mapping Sleep's Oscillatory Events as a Biomarker of Alzheimer's Disease

Objective

Memory-associated neural circuits produce oscillatory events within single-channel sleep electroencephalography (EEG), including theta bursts (TBs), sleep spindles (SPs) and multiple subtypes of slow waves (SWs). Changes in the temporal "coupling" of these events are proposed to serve as a biomarker for early stages of Alzheimer's disease (AD) pathogenesis.

Methods

We analyzed data from 205 aging adults, including single-channel sleep EEG, cerebrospinal fluid (CSF) AD-associated biomarkers, and Clinical Dementia Rating® (CDR®) scale. Individual SW events were sorted into high and low transition frequencies (TF) subtypes. We utilized time-frequency spectrogram locations within sleep EEG to "map" the precision of SW-TB and SW-SP neural circuit coupling in relation to amyloid positivity (by CSF Aβ 42 /Aβ 40 threshold), cognitive impairment (by CDR), and CSF levels of AD-associated biomarkers.

Results

Cognitive impairment was associated with lower TB spectral power in both high and low TF SW-TB coupling (p<0.001, p=0.001). Cognitively unimpaired, amyloid positive aging adults demonstrated lower precision of the neural circuits propagating high TF SW-TB (p<0.05) and low TF SW-SP (p<0.005) event coupling, compared to cognitively unimpaired amyloid negative individuals. Biomarker correlations were significant for high TF SW-TB coupling with CSF Aβ 42 /Aβ 40 (p=0.005), phosphorylated-tau 181 (p<0.005), and total-tau (p<0.05). Low TF SW-SP coupling was also correlated with CSF Aβ 42 /Aβ 40 (p<0.01).

Interpretation

Loss of integrity in neural circuits underlying sleep-dependent memory processing can be measured for both SW-TB and SW-SP coupling in spectral time-frequency space. Breakdown of sleep's memory circuit integrity is associated with amyloid positivity, higher levels of AD-associated pathology, and cognitive impairment.

Early onset of sleep/wake disturbances in a progressive macaque model of Parkinson's disease

Parkinsonian patients often experience sleep/wake disturbances, which may appear at an early stage of the disease; however, these disturbances have not been fully described. To better understand the evolution of these disturbances with respect to disease progression, we aimed to characterize these clinical signs in a progressive nonhuman primate model of Parkinson's disease. Three adult macaques (Macaca fascicularis) were equipped with a polysomnographic telemetry system allowing the characterization of sleep/wake behavior via long-term neurophysiological recordings and underwent a modified multiple sleep latency test. Experiments were first performed in a healthy state and then during the progressive induction of a parkinsonian syndrome by intramuscular injections of low doses of MPTP. We observed an early onset of significant sleep/wake disturbances (i.e., before the appearance of motor symptoms). These disturbances resulted in (i) a disorganization of nighttime sleep with reduced deep sleep quality and (ii) an excessive daytime sleepiness characterized by sleep episodes occurring more rapidly in the morning and spreading through the middle of the day. The present study suggests that nighttime and daytime sleep/wake disturbances may appear early in the disease and should be considered in the development of biomarkers in further studies.

LRRK2 Deficiency Aggravates Sleep Deprivation-Induced Cognitive Loss by Perturbing Synaptic Pruning in Mice

Mutations of the leucine-rich repeat kinase 2 (LRRK2) gene are associated with pronounced sleep disorders or cognitive dysfunction in neurodegenerative diseases. However, the effects of LRRK2 deficiency on sleep rhythms and sleep deprivation-related cognitive changes, and the relevant underlying mechanism, remain unrevealed. In this study, Lrrk2-/- and Lrrk2+/+ mice were subjected to normal sleep (S) or sleep deprivation (SD). Sleep recording, behavioral testing, Golgi-cox staining, immunofluorescence, and real-time PCR were employed to evaluate the impacts of LRRK2 deficiency on sleep behaviors and to investigate the underlying mechanisms. The results showed that after SD, LRRK2-deficient mice displayed lengthened NREM and shortened REM, and reported decreased dendritic spines, increased microglial activation, and synaptic endocytosis in the prefrontal cortex. Meanwhile, after SD, LRRK2 deficiency aggravated cognitive impairments, especially in the recall memory cued by fear conditioning test. Our findings evidence that LRRK2 modulates REM/NREM sleep and its deficiency may exacerbate sleep deprivation-related cognitive disorders by perturbing synaptic plasticity and microglial synaptic pruning in mice.

Nine-day continuous recording of EEG and 2-hour of high-density EEG under chronic sleep restriction in mice

This work provides an EEG dataset collected from nine mice during the sleep deprivation (SD) paradigm for the sleep science community. It includes 9-day of continuous recording of the frontal and parietal EEG, accelerometer, and 2-hour of high-density EEG (HD-EEG) under SD and SD-free conditions. Eighteen hours of SD were conducted on 5 consecutive days. The HD-EEG data were saved in the EEGLAB format and stored as the brain imaging data structure (BIDS). These datasets can be used to (i) compare mouse HD-EEG to human HD-EEG, (ii) track oscillatory activities of the sleep EEG (e.g., slow waves, spindles) across the cortical regions under different conditions of sleep pressure, and (iii) investigate the cortical traveling waves in the mouse brain. We also provided Python code for basic analyses of this dataset, including the detection of slow waves and sleep spindles. We hope that our dataset will reveal hidden activities during sleep and lead to a better understanding of the functions and mechanisms of sleep.

An Attention-Guided Spatiotemporal Graph Convolutional Network for Sleep Stage Classification

Sleep staging has been widely used as an approach in sleep diagnoses at sleep clinics. Graph neural network (GNN)-based methods have been extensively applied for automatic sleep stage classifications with significant results. However, the existing GNN-based methods rely on a static adjacency matrix to capture the features of the different electroencephalogram (EEG) channels, which cannot grasp the information of each electrode. Meanwhile, these methods ignore the importance of spatiotemporal relations in classifying sleep stages. In this work, we propose a combination of a dynamic and static spatiotemporal graph convolutional network (ST-GCN) with inter-temporal attention blocks to overcome two shortcomings. The proposed method consists of a GCN with a CNN that takes into account the intra-frame dependency of each electrode in the brain region to extract spatial and temporal features separately. In addition, the attention block was used to capture the long-range dependencies between the different electrodes in the brain region, which helps the model to classify the dynamics of each sleep stage more accurately. In our experiments, we used the sleep-EDF and the subgroup III of the ISRUC-SLEEP dataset to compare with the most current methods. The results show that our method performs better in accuracy from 4.6% to 5.3%, in Kappa from 0.06 to 0.07, and in macro-F score from 4.9% to 5.7%. The proposed method has the potential to be an effective tool for improving sleep disorders.

Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease

Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson's disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient's sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole-Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.

Aberrant waste disposal in neurodegeneration: why improved sleep could be the solution

Sleep takes up a large percentage of our lives and the full functions of this state are still not understood. However, over the last 10 years a new and important function has emerged as a mediator of brain clearance. Removal of toxic metabolites and proteins from the brain parenchyma generated during waking activity and high levels of synaptic processing is critical to normal brain function and only enabled during deep sleep. Understanding of this process is revealing how impaired sleep contributes an important and likely causative role in the accumulation and aggregation of aberrant proteins such as β-amyloid and phosphorylated tau, as well as inflammation and neuronal damage. We are also beginning to understand how brain slow-wave activity interacts with vascular function allowing the flow of CSF and interstitial fluid to drain into the body's lymphatic system. New methodology is enabling visualization of this process in both animals and humans and is revealing how these processes break down during ageing and disease. With this understanding we can begin to envisage novel therapeutic approaches to the treatment of neurodegeneration, and how reversing sleep impairment in the correct manner may provide a way to slow these processes and improve brain function.