Examining Sleep Modulation by Drosophila Ellipsoid Body Neurons.

Recent work in Drosophila has uncovered several neighboring classes of sleep-regulatory neurons within the central complex. However, the logic of connectivity and network motifs remains limited by the incomplete examination of relevant cell types. Using a recent genetic-anatomic classification of ellipsoid body ring neurons, we conducted a thermogenetic screen in female flies to assess sleep/wake behavior and identified two wake-promoting drivers that label ER3d neurons and two sleep-promoting drivers that express in ER3m cells. We then used intersectional genetics to refine driver expression patterns. Activation of ER3d cells shortened sleep bouts, suggesting a key role in sleep maintenance. While sleep-promoting drivers from our mini-screen label overlapping ER3m neurons, intersectional strategies cannot rule out sleep regulatory roles for additional neurons in their expression patterns. Suppressing GABA synthesis in ER3m neurons prevents postinjury sleep, and GABAergic ER3d cells are required for thermogenetically induced wakefulness. Finally, we use an activity-dependent fluorescent reporter for putative synaptic contacts to embed these neurons within the known sleep-regulatory network. ER3m and ER3d neurons may receive connections from wake-active Helicon/ExR1 cells, and ER3m neurons likely inhibit ER3d neurons. Together, these data suggest a neural mechanism by which previously uncharacterized circuit elements stabilize sleep-wake states.

Deep brain stimulation of the subthalamic nucleus improves sleep in Parkinson disease patients: A systematic review and meta-analysis.

BACKGROUND: Sleep disorders significantly affect the quality of life in Parkinson disease (PD) patients. Deep brain stimulation of the subthalamic nucleus has been reported to improve motor symptoms and decrease medication usage. However, the impact of subthalamic nucleus deep brain stimulation (STN-DBS) on sleep quality in PD patients remains to be definitively determined. This systematic review and meta-analysis, conducted following the preferred reporting items for systematic reviews and meta-analyses guidelines, aimed to clarify the effect of STN-DBS on sleep quality in PD patients. METHODS: A rigorous literature search identified 6 studies, including 1 randomized controlled trial and 5 self-controlled trials, totaling 154 patients who underwent deep brain stimulation, providing 308 pairs of data for analysis. Parkinson disease sleep scale was the primary measure of interest, while the Movement Disorder Society-sponsored revision of the unified Parkinson disease rating scale was documented in all trials. Study quality was assessed using the Newcastle-Ottawa scale. RESULTS: STN-DBS significantly improved Parkinson disease sleep scale scores (mean difference = 20.41, 95% CI: [13.03, 27.79], I² = 60.8%, P < .001), indicating enhanced sleep quality. Furthermore, a significant reduction in movement disorder society unified Parkinson disease rating scale part III scores postoperatively (mean difference = -12.59, 95% CI: [-14.70, -10.49], I² = 89.9%, P < .001) suggested improved motor function. PD medication usage was also significantly reduced postoperatively (mean difference = -314.71, 95% CI: [-468.13, -161.28], I² = 52.9%, P < .001). A sensitivity analysis confirmed the robustness of the main findings. The sample size was adequate, allowing for conclusive inferences. CONCLUSION: The present study, which comprises a comprehensive systematic review and meta-analysis, offers compelling evidence that STN-DBS can ameliorate sleep quality, augment motor function, and curtail medication consumption among individuals afflicted with PD.

An Examination of the Associations between Nutritional Composition, Social Jet Lag and Temporal Sleep Variability in Young Adults.

While dietary intake has previously been related to various indices of poor sleep (e.g., short sleep duration, poor sleep quality), to date, few studies have examined chrononutrition from the perspectives of the relationship between dietary intake and social jet lag and temporal sleep variability. Moreover, recently it has been suggested that previous methods of measuring social jet lag have the potential to lead to large overestimations. Together, this precludes a clear understanding of the role of nutritional composition in the pathophysiology of poor sleep, via social jet lag and temporal sleep variability, or vice versa. The aim of the present study was to determine the relationships between nutrient intake and social jet lag (using a revised index, taking account of intention to sleep and sleep onset and offset difficulties), and temporal sleep variability. Using a cross-sectional survey, 657 healthy participants (mean age 26.7 ± 6.1 years), without sleep disorders, were recruited via an online platform and completed measures of weekly dietary intake, social jet lag, temporal sleep variability, stress/sleep reactivity and mood. Results showed limited associations between nutritional composition and social jet lag. However, levels of temporal sleep variability were predicted by consumption of polyunsaturated fats, sodium, chloride and total energy intake. The results suggest further examinations of specific nutrients are warranted in a first step to tailoring interventions to manage diet and temporal variabilities in sleep patterns.

Association of sleep behaviors, insulin resistance surrogates, and the risk of hypertension in Chinese adults with type 2 diabetes mellitus.

Objective: Our aim was to evaluate the association between midday napping, combined sleep quality, and insulin resistance surrogates and the risk of hypertension in patients with type 2 diabetes mellitus (T2DM). Methods: Data were collected using a standardized questionnaire. Binary logistic regression was performed to estimate the odds ratio (OR) and 95% confidence interval (CI) for the risk of hypertension. Systolic and diastolic blood pressure were grouped as categorical variables and unpaired two-sided Student's t-test and Spearman correlation analysis were performed to estimate the association between different blood pressure levels and insulin resistance surrogates. Results: The overall prevalence rate of hypertension was 50%. Age (OR = 1.056, 95% CI:1.044-1.068), poor sleep quality (OR = 1.959, 95% CI:1.393-2.755), hyperlipidemia (OR = 1.821, 95% CI:1.462-2.369), family history of hypertension (OR = 2.811, 95% CI:2.261-3.495), and obesity (OR = 5.515, 95% CI:1.384-21.971) were significantly associated with an increased risk of hypertension. Midday napping for 1-30 min was negatively correlated with the risk of hypertension (OR = 0.534, 95% CI:0.305-0.936, P <0.05). Conclusion: Poor sleep quality and obesity are independent risk factors for hypertension. Midday napping (1-30 min) is associated with a decreased risk of hypertension in patients with T2DM.

CAPSCNet: A novel scattering network for automated identification of phasic cyclic alternating patterns of human sleep using multivariate EEG signals.

The Cyclic Alternating Pattern (CAP) can be considered a physiological marker of sleep instability. The CAP can examine various sleep-related disorders. Certain short events (A and B phases) manifest related to a specific physiological process or pathology during non-rapid eye movement (NREM) sleep. These phases unexpectedly modify EEG oscillations; hence, manual detection is challenging. Therefore, it is highly desirable to have an automated system for detecting the A-phases (AP). Deep convolution neural networks (CNN) have shown high performance in various healthcare applications. A variant of the deep neural network called the Wavelet Scattering Network (WSN) has been used to overcome the specific limitations of CNN, such as the need for a large amount of data to train the model. WSN is an optimized network that can learn features that help discriminate patterns hidden inside signals. Also, WSNs are invariant to local perturbations, making the network significantly more reliable and effective. It can also help improve performance on tasks where data is minimal. In this study, we proposed a novel WSN-based CAPSCNet to automatically detect AP using EEG signals. Seven dataset variants of cyclic alternating pattern (CAP) sleep cohort is employed for this study. Two electroencephalograms (EEG) derivations, namely: C4-A1 and F4-C4, are used to develop the CAPSCNet. The model is examined using healthy subjects and patients tormented by six different sleep disorders, namely: sleep-disordered breathing (SDB), insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, periodic leg movement disorder (PLM) and rapid eye movement behavior disorder (RBD) subjects. Several different machine-learning algorithms were used to classify the features obtained from the WSN. The proposed CAPSCNet has achieved the highest average classification accuracy of 83.4% using a trilayered neural network classifier for the healthy data variant. The proposed CAPSCNet is efficient and computationally faster.

Re-examining the role of the dorsal fan-shaped body in promoting sleep in Drosophila.

The needs fulfilled by sleep are unknown, though the effects of insufficient sleep are manifold. To better understand how the need to sleep is sensed and discharged, much effort has gone into identifying the neural circuits involved in regulating arousal, especially those that promote sleep. In prevailing models, the dorsal fan-shaped body (dFB) plays a central role in this process in the fly brain. In the present study we manipulated various properties of the dFB including its electrical activity, synaptic output, and endogenous gene expression. In each of these experimental contexts we were unable to identify any effect on sleep that could be unambiguously mapped to the dFB. Furthermore, we found evidence that sleep phenotypes previously attributed to the dFB were caused by genetic manipulations that inadvertently targeted the ventral nerve cord. We also examined expression of two genes whose purported effects have been attributed to functions within a specific subpopulation of dFB neurons. In both cases we found little to no expression in the expected cells. Collectively, our results cast doubt on the prevailing hypothesis that the dFB plays a central role in promoting sleep.

Impact of sleep on chronobiology of micturition among healthy older adults.

Nocturia (waking to void) is prevalent among older adults. Disruption of the well-described circadian rhythm in urine production with higher nighttime urine output is its most common cause. In young adults, their circadian rhythm is modulated by the 24-h secretory pattern of hormones that regulate salt and water excretion, including antidiuretic hormone (ADH), renin, angiotensin, aldosterone, and atrial natriuretic peptide (ANP). The pattern of hormone secretion is less clear in older adults. We investigated the effect of sleep on the 24-h secretion of these hormones in healthy older adults. Thirteen participants aged ≥65 yr old underwent two 24-h protocols at a clinical research center 6 wk apart. The first used a habitual wake-sleep protocol, and the second used a constant routine protocol that removed the influence of sleep, posture, and diet. To assess hormonal rhythms, plasma was collected at 8:00 am, 12:00 pm, 4:00 pm, and every 30 min from 7:00 pm to 7:00 am. A mixed-effects regression model was used to compare subject-specific and mean trajectories of hormone secretion under the two conditions. ADH, aldosterone, and ANP showed a diurnal rhythm that peaked during sleep in the wake-sleep protocol. These nighttime elevations were significantly attenuated within subjects during the constant routine. We conclude that sleep has a masking effect on circadian rhythm amplitude of ADH, aldosterone, and ANP: the amplitude of each is increased in the presence of sleep and reduced in the absence of sleep. Disrupted sleep could potentially alter nighttime urine output in healthy older adults via this mechanism.NEW & NOTEWORTHY Nocturia (waking to void) is the most common cause of sleep interruption among older adults, and increased nighttime urine production is its primary etiology. We showed that in healthy older adults sleep affects the 24-h secretory rhythm of hormones that regulate salt-water balance, which potentially alters nighttime urine output. Further studies are needed to elucidate the impact of chronic insomnia on the secretory rhythms of these hormones.

Multivariate prediction of cognitive performance from the sleep electroencephalogram.

Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross-validated regularized regression to link sleep EEG features to cognitive performance in cross-sectional analyses. In independent validation samples 2.5-10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics account for more covariance between sleep EEG and cognition than health variables, and consequently reduce this association by a greater degree, but even with the strictest covariate sets a statistically significant association is present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r = 0.283), with the strongest effect ascribed to spindle-frequency activity. This association becomes weaker after adjusting for demographic (r = 0.186) and health variables (r = 0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.

Automated sleep classification with chronic neural implants in freely behaving canines.

Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.

The function of REM and NREM sleep on memory distortion and consolidation.

During rapid eye movement (REM) sleep, newly consolidated memories can be distorted to adjust the existing memory base in memory integration. However, only a few studies have demonstrated the role of REM sleep in memory distortion. The present study aims to clarify the role of REM sleep in the facilitation of memory distortion, that is, hindsight bias, compared to non-rapid eye movement (NREM) sleep and wake states. The split-night paradigm was used to segregate REM and NREM sleep. The hypotheses are (1) hindsight bias-memory distortion-is more substantial during REM-rich sleep (late-night sleep) than during NREM-rich sleep (early-night sleep); (2) memory stabilization is more substantial during NREM-rich sleep (early-night sleep) than during REM-rich sleep (late-night sleep); and (3) memory distortion takes longer time than memory stabilization. The results of the hindsight bias test show that more memory distortions were observed after the REM condition in comparison to the NREM condition. Contrary to the hindsight bias, the correct response in the word-pair association test was observed more in the NREM than in the REM condition. The difference in the hindsight bias index between the REM and NREM conditions was identified only one week later. Comparatively, the difference in correct responses in the word-pair association task between the conditions appeared three hours later and one week later. The present study found that (1) memory distortion occurs more during REM-rich sleep than during NREM-rich sleep, while memory stabilization occurs more during NREM-rich sleep than during REM-rich sleep. Moreover, (2) the newly encoded memory could be stabilized immediately after encoding, but memory distortion occurs over several days. These results suggest that the roles of NREM and REM sleep in memory processes could be different.

Accelerometer-derived sleep measures in idiopathic dystonia: A UK Biobank cohort study.

BACKGROUND: Sleep disturbance is an increasingly recognized non-motor trait in dystonia, with varying findings reported to date. Here, we examine sleep in a UK Biobank derived dystonia cohort using subjective self-reported sleep symptoms and objective accelerometer-derived sleep measures, with comparison to a control population. METHODS: A total of 241 dystonia cases were compared to 964 matched controls in analysis of self-reported sleep symptoms and changes in sleep architecture using wrist-worn triaxial accelerometers. RESULTS: Dystonia participants had poorer self-reported sleep patterns compared to controls. Accelerometery measurements demonstrated later sleep times, reduced time in bed, and shifts in circadian rhythm. No association was observed with pain, and only limited relationships with psychiatric symptoms. DISCUSSION: This study demonstrates the utility of accelerometers in longer term evaluation of sleep in dystonia, for measurement of disturbance and response to treatment. Compared to controls, altered sleep and circadian rhythm were more common in dystonia patients which may contribute to the clinical phenotype.

Sleep architecture and Nusinersen therapy in children with Spinal Muscular Atrophy type 1.

BACKGROUND: Spinal muscular atrophy (SMA) is a severe neuromuscular disorder, the phenotype of the disease is caused by the mutation of the SMN1 (survival motor neuron 1) gene which encodes for the SMN protein. Innovative treatments for SMA have become available and the first molecule approved is Nusinersen, an antisense oligonucleotide that increases the production of SMN protein. Nusinersen has been shown to be associated with a significant motor improvement and an increase of the event-free survival. For these reasons the aim of the present study is to assess if Nusinersen is able modify sleep architecture and microstructure and to improve sleep structure in these patients. METHODS: Sixteen patients affected by SMA1 were enrolled in the study (4 boys, 12 girls; median age 72.5 months, intelligence quotient range 24-84). All patients underwent complete nocturnal PSG before the start of the treatment trough intrathecal injections with Nusinersen (T0) and after the fifth infusion (day 180, T180). PSG recordings were visually scored and interpreted according to the indications of the American Academy of Sleep Medicine (AASM) and and microstructure by means of the Cyclic Alternating Pattern (CAP). RESULTS: After 6 months therapy we found a significantly reduced sleep latency and a significantly increased sleep efficiency. Regarding sleep microstructure parameters (CAP), we did not find any significant change after therapy however, it is worth mentioning that a moderate effect size was observed for the increase in CAP A3 index. CONCLUSIONS: We observed short-term effects of Nusinersen on sleep with an improvement in sleep efficiency and reduction in sleep onset latency; regarding sleep microstructure, a moderate effect size was found for the number of CAP A3 subtypes that slightly increased, possibly indicating a slightly higher arousability. This finding points at a probably overall better sleep pattern organization associated with the treatment, but they need to be confirmed by larger studies with patients treated earlier in life and for a longer period.

Evaluation of hypersomnolence: From symptoms to diagnosis, a multidimensional approach.

Hypersomnolence is a major public health issue given its high frequency, its impact on academic/occupational functioning and on accidentology, as well as its heavy socio-economic burden. The positive and aetiological diagnosis is crucial, as it determines the therapeutic strategy. It must consider the following aspects: i) hypersomnolence is a complex concept referring to symptoms as varied as excessive daytime sleepiness, excessive need for sleep, sleep inertia, or drowsiness, all of which warrant specific dedicated investigations; ii) the boundary between physiological and abnormal hypersomnolence is blurred, since most symptoms can be encountered in the general population to varying degrees without being considered as pathological, meaning that their severity, frequency, context of occurrence and related impairment need to be carefully assessed; iii) investigation of hypersomnolence relies on scales/questionnaires as well as behavioural and neurophysiological tests, which measure one or more dimensions, keeping in mind the possible discrepancy between objective and subjective assessment; iv) aetiological reasoning is driven by knowledge of the main sleep regulation mechanisms, epidemiology, and associated symptoms. The need to assess hypersomnolence is growing, both for its management, and for assessing the efficacy of treatments. The landscape of tools available for investigating hypersomnolence is constantly evolving, in parallel with research into sleep physiology and technical advances. These investigations face the challenges of reconciling subjective perception and objective data, making tools accessible to as many people as possible and predicting the risk of accidents.

Sleep-associated insulin resistance promotes neurodegeneration.

Lifestyle modification can lead to numerous health issues closely associated with sleep. Sleep deprivation and disturbances significantly affect inflammation, immunity, neurodegeneration, cognitive depletion, memory impairment, neuroplasticity, and insulin resistance. Sleep significantly impacts brain and memory formation, toxin excretion, hormonal function, metabolism, and motor and cognitive functions. Sleep restriction associated with insulin resistance affects these functions by interfering with the insulin signalling pathway, neurotransmission, inflammatory pathways, and plasticity of neurons. So, in this review, We discuss the evidence that suggests that neurodegeneration occurs via sleep and is associated with insulin resistance, along with the insulin signalling pathways involved in neurodegeneration and neuroplasticity, while exploring the role of hormones in these conditions.

Sleep and epilepsy: A clinical and pathophysiological overview.

The interaction between sleep and epilepsy is complex. A better understanding of the mechanisms linking sleep and epilepsy appears increasingly important as it may improve diagnosis and therapeutic strategies in patients with epilepsy. In this narrative review, we aim to (i) provide an overview of the physiological and pathophysiological processes linking sleep and epilepsy; (ii) present common sleep disorders in patients with epilepsy; (iii) discuss how sleep and sleep disorders should be considered in new therapeutic approaches to epilepsy such as neurostimulation; and (iv) present the overall nocturnal manifestations and differential diagnosis between epileptic seizures and parasomnia.

The multifaceted aspects of sleep and sleep-wake disorders following stroke.

Sleep-wake disorders (SWD) are acknowledged risk factors for both ischemic stroke and poor cardiovascular and functional outcome after stroke. SWD are frequent following stroke, with sleep apnea (SA) being the most frequent SWD affecting more than half of stroke survivors. While sleep disturbances and SWD are frequently reported in the acute phase, they may persist in the chronic phase after an ischemic stroke. Despite the frequency and risk associated with SWD following stroke, screening for SWD remains rare in the clinical setting, due to challenges in the assessment of post-stroke SWD, uncertainty regarding the optimal timing for their diagnosis, and a lack of clear treatment guidelines (i.e., when to treat and the optimal treatment strategy). However, little evidence support the feasibility of SWD treatment even in the acute phase of stroke and its favorable effect on long-term cardiovascular and functional outcomes. Thus, sleep health recommendations and SWD treatment should be systematically embedded in secondary stroke prevention strategy. We therefore propose that the management of SWD associated with stroke should rely on a multidisciplinary approach, with an integrated diagnostic, treatment, and follow-up strategy. The challenges in the field are to improve post-stroke SWD diagnosis, prognosis and treatment, through a better appraisal of their pathophysiology and temporal evolution.

How we sleep: From brain states to processes.

All our lives, we alternate between wakefulness and sleep with direct consequences on our ability to interact with our environment, the dynamics and contents of our subjective experience, and our brain activity. Consequently, sleep has been extensively characterised in terms of behavioural, phenomenological, and physiological changes, the latter constituting the gold standard of sleep research. The common view is thus that sleep represents a collection of discrete states with distinct neurophysiological signatures. However, recent findings challenge such a monolithic view of sleep. Indeed, there can be sharp discrepancies in time and space in the activity displayed by different brain regions or networks, making it difficult to assign a global vigilance state to such a mosaic of contrasted dynamics. Viewing sleep as a multidimensional continuum rather than a succession of non-overlapping and mutually exclusive states could account for these local aspects of sleep. Moving away from the focus on sleep states, sleep can also be investigated through the brain processes that are present in sleep, if not necessarily specific to sleep. This focus on processes rather than states allows to see sleep for what it does rather than what it is, avoiding some of the limitations of the state perspective and providing a powerful heuristic to understand sleep. Indeed, what is sleep if not a process itself that makes up wake up every morning with a brain cleaner, leaner and less cluttered.

A common neuronal ensemble in nucleus accumbens regulates pain-like behaviour and sleep.

A comorbidity of chronic pain is sleep disturbance. Here, we identify a dual-functional ensemble that regulates both pain-like behaviour induced by chronic constrictive injury or complete Freund's adjuvant, and sleep wakefulness, in the nucleus accumbens (NAc) in mice. Specifically, a select population of NAc neurons exhibits increased activity either upon nociceptive stimulation or during wakefulness. Experimental activation of the ensemble neurons exacerbates pain-like (nociceptive) responses and reduces NREM sleep, while inactivation of these neurons produces the opposite effects. Furthermore, NAc ensemble primarily consists of D1 neurons and projects divergently to the ventral tegmental area (VTA) and preoptic area (POA). Silencing an ensemble innervating VTA neurons selectively increases nociceptive responses without affecting sleep, whereas inhibiting ensemble-innervating POA neurons decreases NREM sleep without affecting nociception. These results suggest a common NAc ensemble that encodes chronic pain and controls sleep, and achieves the modality specificity through its divergent downstream circuit targets.

New bands in the sleep stages of spider monkeys (Ateles geoffroyi): Electroencephalographic correlations and spatial distribution.

The study of electroencephalographic (EEG) signals in nonhuman primates has led to important discoveries in neurophysiology and sleep behavior. Several studies have analyzed digital EEG data from primate species with prehensile tails, like the spider monkey, and principal component analysis has led to the identification of new EEG bands and their spatial distribution during sleep and wakefulness in these monkeys. However, the spatial location of the EEG correlations of these new bands during the sleep-wake cycle in the spider monkey has not yet been explored. Thus, the objective of this study was to determine the spatial distribution of EEG correlations in the new bands during wakefulness, rapid eye movement (REM) sleep, and non-REM sleep in this species. EEG signals were obtained from the scalp of six monkeys housed in experimental conditions in a laboratory setting. Regarding the 1-21 Hz band, a significant correlation between left frontal and central regions was recorded during non-REM 2 sleep. In the REM sleep, a significant correlation between these cortical areas was seen in two bands: 1-3 and 3-13 Hz. This reflects a modification of the degree of coupling between the cortical areas studied, associated with the distinct stages of sleep. The intrahemispheric EEG correlation found between left perceptual and motor regions during sleep in the spider monkey could indicate activation of a neural circuit for the processing of environmental information that plays a critical role in monitoring the danger of nocturnal predation.

Neurofluid coupling during sleep and wake states.

BACKGROUND: In clinical populations, the movement of cerebrospinal fluid (CSF) during sleep is a growing area of research with potential mechanistic connections in both neurodegenerative (e.g., Alzheimer's Disease) and neurodevelopmental disorders. However, we know relatively little about the processes that influence CSF movement. To inform clinical intervention targets this study assesses the coupling between (a) real-time CSF movement, (b) neuronal-driven movement, and (c) non-neuronal systemic physiology driven movement. METHODS: This study included eight young, healthy volunteers, with concurrently acquired neurofluid dynamics using functional Magnetic Resonance Imaging (MRI), neural activity using Electroencephalography (EEG), and non-neuronal systemic physiology with peripheral functional Near-Infrared Spectroscopy (fNIRS). Neuronal and non-neuronal drivers were assessed temporally; wherein, EEG measured slow wave activity that preceded CSF movement was considered neuronally driven. Similarly, slow wave oscillations (assessed via fNIRS) that coupled with CSF movement were considered non-neuronal systemic physiology driven. RESULTS AND CONCLUSIONS: Our results document neural contributions to CSF movement were only present during light NREM sleep but low-frequency non-neuronal oscillations were strongly coupled with CSF movement in all assessed states - awake, NREM-1, NREM-2. The clinical/research implications of these findings are two-fold. First, neuronal-driven oscillations contribute to CSF movement outside of deep sleep (NREM-3); therefore, interventions aimed at increasing CSF movement may yield meaningful increases with the promotion of NREM sleep more generally - a focus on NREM S3 may not be needed. Second, non-neuronal systemic oscillations contribute across wake and sleep stages; therefore, interventions may increase CSF movement by manipulating systemic physiology.