Early alterations in the MCH system link aberrant neuronal activity and sleep disturbances in a mouse model of Alzheimer's disease

Early Alzheimer's disease (AD) is associated with hippocampal hyperactivity and decreased sleep quality. Here we show that homeostatic mechanisms transiently counteract the increased excitatory drive to CA1 neurons in App^NL-G-F mice, but that this mechanism fails in older mice. Spatial transcriptomics analysis identifies Pmch as part of the adaptive response in App^NL-G-F mice. Pmch encodes melanin-concentrating hormone (MCH), which is produced in sleep-active lateral hypothalamic neurons that project to CA1 and modulate memory. We show that MCH downregulates synaptic transmission, modulates firing rate homeostasis in hippocampal neurons and reverses the increased excitatory drive to CA1 neurons in App^NL-G-F mice. App^NL-G-F mice spend less time in rapid eye movement (REM) sleep. App^NL-G-F mice and individuals with AD show progressive changes in morphology of CA1-projecting MCH axons. Our findings identify the MCH system as vulnerable in early AD and suggest that impaired MCH-system function contributes to aberrant excitatory drive and sleep defects, which can compromise hippocampus-dependent functions.

Coupling between the prelimbic cortex, nucleus reuniens, and hippocampus during NREM sleep remains stable under cognitive and homeostatic demands

The interplay between the medial prefrontal cortex and hippocampus during non-rapid eye movement (NREM) sleep contributes to the consolidation of contextual memories. To assess the role of the thalamic nucleus reuniens (Nre) in this interaction, we investigated the coupling of neuro-oscillatory activities among prelimbic cortex, Nre, and hippocampus across sleep states and their role in the consolidation of contextual memories using multi-site electrophysiological recordings and optogenetic manipulations. We showed that ripples are time-locked to the Up state of cortical slow waves, the transition from UP to DOWN state in thalamic slow waves, the troughs of cortical spindles, and the peaks of thalamic spindles during spontaneous sleep, rebound sleep and sleep following a fear conditioning task. In addition, spiking activity in Nre increased before hippocampal ripples, and the phase-locking of hippocampal ripples and thalamic spindles during NREM sleep was stronger after acquisition of a fear memory. We showed that optogenetic inhibition of Nre neurons reduced phase-locking of ripples to cortical slow waves in the ventral hippocampus whilst their activation altered the preferred phase of ripples to slow waves in ventral and dorsal hippocampi. However, none of these optogenetic manipulations of Nre during sleep after acquisition of fear conditioning did alter sleep-dependent memory consolidation. Collectively, these results showed that Nre is central in modulating hippocampus and cortical rhythms during NREM sleep.

Paradoxical somatodendritic decoupling supports cortical plasticity during REM sleep

Rapid eye movement (REM) sleep is associated with the consolidation of emotional memories. Yet, the underlying neocortical circuits and synaptic mechanisms remain unclear. We found that REM sleep is associated with a somatodendritic decoupling in pyramidal neurons of the prefrontal cortex. This decoupling reflects a shift of inhibitory balance between parvalbumin neuron-mediated somatic inhibition and vasoactive intestinal peptide-mediated dendritic disinhibition, mostly driven by neurons from the central medial thalamus. REM-specific optogenetic suppression of dendritic activity led to a loss of danger-versus-safety discrimination during associative learning and a lack of synaptic plasticity, whereas optogenetic release of somatic inhibition resulted in enhanced discrimination and synaptic potentiation. Somatodendritic decoupling during REM sleep promotes opposite synaptic plasticity mechanisms that optimize emotional responses to future behavioral stressors.

Alterations in TRN-anterodorsal thalamocortical circuits affect sleep architecture and homeostatic processes in oxidative stress vulnerable Gclm-/- mice

Schizophrenia is associated with alterations of sensory integration, cognitive processing and both sleep architecture and sleep oscillations in mouse models and human subjects, possibly through changes in thalamocortical dynamics. Oxidative stress (OxS) damage, including inflammation and the impairment of fast-spiking gamma-aminobutyric acid neurons have been hypothesized as a potential mechanism responsible for the onset and development of schizophrenia. Yet, the link between OxS and perturbation of thalamocortical dynamics and sleep remains unclear. Here, we sought to investigate the effects of OxS on sleep regulation by characterizing the dynamics of thalamocortical networks across sleep-wake states in a mouse model with a genetic deletion of the modifier subunit of glutamate-cysteine ligase (Gclm knockout, KO) using high-density electrophysiology in freely-moving mice. We found that Gcml KO mice exhibited a fragmented sleep architecture and impaired sleep homeostasis responses as revealed by the increased NREM sleep latencies, decreased slow-wave activities and spindle rate after sleep deprivation. These changes were associated with altered bursting activity and firing dynamics of neurons from the thalamic reticularis nucleus, anterior cingulate and anterodorsal thalamus. Administration of N-acetylcysteine (NAC), a clinically relevant antioxidant, rescued the sleep fragmentation and spindle rate through a renormalization of local neuronal dynamics in Gclm KO mice. Collectively, these findings provide novel evidence for a link between OxS and the deficits of frontal TC network dynamics as a possible mechanism underlying sleep abnormalities and impaired homeostatic responses observed in schizophrenia.

Association between depressive symptoms and sleep neurophysiology in early adolescence

BACKGROUND

Depression is highly prevalent among adolescents, and depressive symptoms rise rapidly during early adolescence. Depression is often accompanied by subjective sleep complaints and alterations in sleep neurophysiology. In this study, we examine whether depressive symptoms, measured on a continuum, are associated with subjective and objective (sleep architecture and neurophysiology) measures of sleep in early adolescence.

METHODS

High-density sleep EEG, actigraphy, and self-reported sleep were measured in 52 early adolescents (12.31 years; SD: 1.121; 25 female). Depressive symptoms were measured on a continuum using the Center for Epidemiological Studies Depression Scale (CES-D). The association between depressive symptoms and 2 weeks of actigraphy, self-reported sleep, sleep architecture, and sleep neurophysiology (slow wave activity and sigma power) was determined via multiple linear regression with factors age, sex, and pubertal status.

RESULTS

Despite no association between polysomnography measures of sleep quality and depressive symptoms, individuals with more depressive symptoms manifested worse actigraphically measured sleep. Less sleep spindle activity, as reflected in nonrapid eye movement sleep sigma power, was associated with more depressive symptoms over a large cluster encompassing temporal, parietal, and occipital regions. Furthermore, worse subjectively reported sleep quality was also associated with less sigma power over these same areas. Puberty, age, and sex did not impact this association.

CONCLUSIONS

Sleep spindles have been hypothesized to protect sleep against environmental disturbances. Thus, diminished spindle power may be a subtle sign of disrupted sleep and its association with depressive symptoms in early adolescence may signal vulnerability for depression.

Oscillatory patterns in the electroencephalogram at sleep onset

Falling asleep is a gradually unfolding process. We investigated the role of various oscillatory activities including sleep spindles and alpha and delta oscillations at sleep onset (SO) by automatically detecting oscillatory events. We used two datasets of healthy young males, eight with four baseline recordings, and eight with a baseline and recovery sleep after 40 h of sustained wakefulness. We analyzed the 2-min interval before SO (stage 2) and the five consecutive 2-min intervals after SO. The incidence of delta/theta events reached its maximum in the first 2-min episode after SO, while the frequency of them was continuously decreasing from stage 1 onwards, continuing over SO and further into deeper sleep. Interestingly, this decrease of the frequencies of the oscillations were not affected by increased sleep pressure, in contrast to the incidence which increased. We observed an increasing number of alpha events after SO, predominantly frontally, with their prevalence varying strongly across individuals. Sleep spindles started to occur after SO, with first an increasing then a decreasing incidence and a continuous decrease in their frequency. Again, the frequency of the spindles was not altered after sleep deprivation. Oscillatory events revealed derivation dependent aspects. However, these regional aspects were not specific of the process of SO but rather reflect a general sleep related phenomenon. No individual traits of SO features (incidence and frequency of oscillations) and their dynamics were observed. Delta/theta events are important features for the analysis of SO in addition to slow waves.

Dynamic REM Sleep Modulation by Ambient Temperature and the Critical Role of the Melanin-Concentrating Hormone System

Ambient temperature (T_a) warming toward the high end of the thermoneutral zone (TNZ) preferentially increases rapid eye movement (REM) sleep over non-REM (NREM) sleep across species. The control and function of this temperature-induced REM sleep expression have remained unknown. Melanin-concentrating hormone (MCH) neurons play an important role in REM sleep control. We hypothesize that the MCH system may modulate REM sleep as a function of T_a. Here, we show that wild-type (WT) mice dynamically increased REM sleep durations specifically during warm T_a pulsing within the TNZ, compared to both the TNZ cool and baseline constant T_a conditions, without significantly affecting either wake or NREM sleep durations. However, genetically engineered MCH receptor-1 knockout (MCHR1-KO) mice showed no significant changes in REM sleep as a function of T_a, even with increased sleep pressure following a 4-h sleep deprivation. Using MCH-cre mice transduced with channelrhodopsin, we then optogenetically activated MCH neurons time locked with T_a warming, showing an increase in REM sleep expression beyond what T_a warming in yellow fluorescent protein (YFP) control mice achieved. Finally, in mice transduced with archaerhodopsin-T, semi-chronic optogenetic MCH neuronal silencing during T_a warming completely blocked the increase in REM sleep seen in YFP controls. These data demonstrate a previously unknown role for the MCH system in the dynamic output expression of REM sleep during T_a manipulation. These findings are consistent with the energy allocation hypothesis of sleep function, suggesting that endotherms have evolved neural circuits to opportunistically express REM sleep when the need for thermoregulatory defense is minimized.

Heritability of Sleep EEG Topography in Adolescence: Results from a Longitudinal Twin Study

The topographic distribution of sleep EEG power is a reflection of brain structure and function. The goal of this study was to examine the degree to which genes contribute to sleep EEG topography during adolescence, a period of brain restructuring and maturation. We recorded high-density sleep EEG in monozygotic (MZ; n = 28) and dizygotic (DZ; n = 22) adolescent twins (mean age = 13.2 ± 1.1 years) at two time points 6 months apart. The topographic distribution of normalized sleep EEG power was examined for the frequency bands delta (1-4.6 Hz) to gamma 2 (34.2-44 Hz) during NREM and REM sleep. We found highest heritability values in the beta band for NREM and REM sleep (0.44 ≤ h2 ≤ 0.57), while environmental factors shared amongst twin siblings accounted for the variance in the delta to sigma bands (0.59 ≤ c2 ≤ 0.83). Given that both genetic and environmental factors are reflected in sleep EEG topography, our results suggest that topography may provide a rich metric by which to understand brain function. Furthermore, the frequency specific parsing of the influence of genetic from environmental factors on topography suggests functionally distinct networks and reveals the mechanisms that shape these networks.

Developmental Changes in Sleep Oscillations during Early Childhood

Although quantitative analysis of the sleep electroencephalogram (EEG) has uncovered important aspects of brain activity during sleep in adolescents and adults, similar findings from preschool-age children remain scarce. This study utilized our time-frequency method to examine sleep oscillations as characteristic features of human sleep EEG. Data were collected from a longitudinal sample of young children (n = 8; 3 males) at ages 2, 3, and 5 years. Following sleep stage scoring, we detected and characterized oscillatory events across age and examined how their features corresponded to spectral changes in the sleep EEG. Results indicated a developmental decrease in the incidence of delta and theta oscillations. Spindle oscillations, however, were almost absent at 2 years but pronounced at 5 years. All oscillatory event changes were stronger during light sleep than slow-wave sleep. Large interindividual differences in sleep oscillations and their characteristics (e.g., "ultrafast" spindle-like oscillations, theta oscillation incidence/frequency) also existed. Changes in delta and spindle oscillations across early childhood may indicate early maturation of the thalamocortical system. Our analytic approach holds promise for revealing novel types of sleep oscillatory events that are specific to periods of rapid normal development across the lifespan and during other times of aberrant changes in neurobehavioral function.

Global field synchronization in gamma range of the sleep EEG tracks sleep depth: Artifact introduced by a rectangular analysis window

BACKGROUND

Investigating functional connectivity between brain networks has become an area of interest in neuroscience. Several methods for investigating connectivity have recently been developed, however, these techniques need to be applied with care. We demonstrate that global field synchronization (GFS), a global measure of phase alignment in the EEG as a function of frequency, must be applied considering signal processing principles in order to yield valid results.

NEW METHOD

Multichannel EEG (27 derivations) was analyzed for GFS based on the complex spectrum derived by the fast Fourier transform (FFT). We examined the effect of window functions on GFS, in particular of non-rectangular windows.

RESULTS

Applying a rectangular window when calculating the FFT revealed high GFS values for high frequencies (>15Hz) that were highly correlated (r=0.9) with spectral power in the lower frequency range (0.75-4.5Hz) and tracked the depth of sleep. This turned out to be spurious synchronization. With a non-rectangular window (Tukey or Hanning window) these high frequency synchronization vanished. Both, GFS and power density spectra significantly differed for rectangular and non-rectangular windows.

COMPARISON WITH EXISTING METHOD(S)

Previous papers using GFS typically did not specify the applied window and may have used a rectangular window function. However, the demonstrated impact of the window function raises the question of the validity of some previous findings at higher frequencies.

CONCLUSIONS

We demonstrated that it is crucial to apply an appropriate window function for determining synchronization measures based on a spectral approach to avoid spurious synchronization in the beta/gamma range.

Relation of Heart Rate and its Variability during Sleep with Age, Physical Activity, and Body Composition in Young Children

Background: Recent studies have claimed a positive effect of physical activity and body composition on vagal tone. In pediatric populations, there is a pronounced decrease in heart rate with age. While this decrease is often interpreted as an age-related increase in vagal tone, there is some evidence that it may be related to a decrease in intrinsic heart rate. This factor has not been taken into account in most previous studies. The aim of the present study was to assess the association between physical activity and/or body composition and heart rate variability (HRV) independently of the decline in heart rate in young children.

Methods: Anthropometric measurements were taken in 309 children aged 2–6 years. Ambulatory electrocardiograms were collected over 14–18 h comprising a full night and accelerometry over 7 days. HRV was determined of three different night segments: (1) over 5 min during deep sleep identified automatically based on HRV characteristics; (2) during a 20 min segment starting 15 min after sleep onset; (3) over a 4-h segment between midnight and 4 a.m. Linear models were computed for HRV parameters with anthropometric and physical activity variables adjusted for heart rate and other confounding variables (e.g., age for physical activity models).

Results: We found a decline in heart rate with increasing physical activity and decreasing skinfold thickness. HRV parameters decreased with increasing age, height, and weight in HR-adjusted regression models. These relationships were only found in segments of deep sleep detected automatically based on HRV or manually 15 min after sleep onset, but not in the 4-h segment with random sleep phases.

Conclusions: Contrary to most previous studies, we found no increase of standard HRV parameters with age, however, when adjusted for heart rate, there was a significant decrease of HRV parameters with increasing age. Without knowing intrinsic heart rate correct interpretation of HRV in growing children is impossible.

Interindividual differences in the dynamics of the homeostatic process are trait-like and distinct for sleep versus wakefulness

The sleep homeostatic Process S reflects the build-up of sleep pressure during waking and its dissipation during sleep. Process S is modelled as a saturating exponential function during waking and a decreasing exponential function during sleep. Slow wave activity is a physiological marker for non-rapid eye movement (non-REM) sleep intensity and serves as an index of Process S. There is considerable interindividual variability in the sleep homeostatic responses to sleep and sleep deprivation. The aim of this study was to investigate whether interindividual differences in Process S are trait-like. Polysomnographic recordings of 8 nights (12-h sleep opportunities, 22:00-10:00 hours) interspersed with three 36-h periods of sustained wakefulness were performed in 11 healthy young adults. Empirical mean slow wave activity per non-REM sleep episode at episode mid-points were used for parameter estimation. Parameters of Process S were estimated using different combinations of consecutive sleep recordings, resulting in two to three sets of parameters per subject. Intraclass correlation coefficients were calculated to assess whether the parameters were stable across the study protocol and they showed trait-like variability among individuals. We found that the group-average time constants of the build-up and dissipation of Process S were 19.2 and 2.7 h, respectively. Intraclass correlation coefficients ranged from 0.48 to 0.56, which reflects moderate trait variability. The time constants of the build-up and dissipation varied independently among subjects, indicating two distinct traits. We conclude that interindividual differences in the parameters of the dynamics of the sleep homeostatic Process S are trait-like.

Global field synchronization reveals rapid eye movement sleep as most synchronized brain state in the human EEG

Sleep is characterized by a loss of consciousness, which has been attributed to a breakdown of functional connectivity between brain regions. Global field synchronization (GFS) can estimate functional connectivity of brain processes. GFS is a frequency-dependent measure of global synchronicity of multi-channel EEG data. Our aim was to explore and extend the hypothesis of disconnection during sleep by comparing GFS spectra of different vigilance states. The analysis was performed on eight healthy adult male subjects. EEG was recorded during a baseline night, a recovery night after 40 h of sustained wakefulness and at 3 h intervals during the 40 h of wakefulness. Compared to non-rapid eye movement (NREM) sleep, REM sleep showed larger GFS values in all frequencies except in the spindle and theta bands, where NREM sleep showed a peak in GFS. Sleep deprivation did not affect GFS spectra in REM and NREM sleep. Waking GFS values were lower compared with REM and NREM sleep except for the alpha band. Waking alpha GFS decreased following sleep deprivation in the eyes closed condition only. Our surprising finding of higher synchrony during REM sleep challenges the view of REM sleep as a desynchronized brain state and may provide insight into the function of REM sleep.

Development of nap neurophysiology: preliminary insights into sleep regulation in early childhood

Although all young children nap, the neurophysiological features and associated developmental trajectories of daytime sleep remain largely unknown. Longitudinal studies of napping physiology are fundamental to understanding sleep regulation during early childhood, a sensitive period in brain and behaviour development and a time when children transition from a biphasic to a monophasic sleep-wakefulness pattern. We investigated daytime sleep in eight healthy children with sleep electroencephalography (EEG) assessments at three longitudinal points: 2 years (2.5-3.0 years), 3 years (3.5-4.0 years) and 5 years (5.5-6.0 years). At each age, we measured nap EEG during three randomized conditions: after 4 h (morning nap), 7 h (afternoon nap) and 10 h (evening nap) duration of prior wakefulness. Developmental changes in sleep were most prevalent in the afternoon nap (e.g. decrease in sleep duration by 30 min from 2 to 3 years and by 20 min from 3 to 5 years). In contrast, nap sleep architecture (% of sleep stages) remained unchanged across age. Maturational changes in non-rapid eye movement sleep EEG power were pronounced in the slow wave activity (SWA, 0.75-4.5 Hz), theta (4.75-7.75 Hz) and sigma (10-15 Hz) frequency ranges. These findings indicate that the primary marker of sleep depth, SWA, is less apparent in daytime naps as children mature. Moreover, our fundamental data provide insight into associations between sleep regulation and functional modifications in the central nervous system during early childhood.

Development of Brain EEG Connectivity across Early Childhood: Does Sleep Play a Role?

Sleep has beneficial effects on brain function and learning, which are reflected in plastic changes in the cortex. Early childhood is a time of rapid maturation in fundamental skills-e.g., language, cognitive control, working memory-that are predictive of future functioning. Little is currently known about the interactions between sleep and brain maturation during this developmental period. We propose coherent electroencephalogram (EEG) activity during sleep may provide unique insight into maturational processes of functional brain connectivity. Longitudinal sleep EEG assessments were performed in eight healthy subjects at ages 2, 3 and 5 years. Sleep EEG coherence increased across development in a region- and frequency-specific manner. Moreover, although connectivity primarily decreased intra-hemispherically across a night of sleep, an inter-hemispheric overnight increase occurred in the frequency range of slow waves (0.8-2 Hz), theta (4.8-7.8 Hz) and sleep spindles (10-14 Hz), with connectivity changes of up to 20% across a night of sleep. These findings indicate sleep EEG coherence reflects processes of brain maturation-i.e., programmed unfolding of neuronal networks-and moreover, sleep-related alterations of brain connectivity during the sensitive maturational window of early childhood.

Entrainment of the human circadian clock to the natural light-dark cycle

The electric light is one of the most important human inventions. Sleep and other daily rhythms in physiology and behavior, however, evolved in the natural light-dark cycle [1], and electrical lighting is thought to have disrupted these rhythms. Yet how much the age of electrical lighting has altered the human circadian clock is unknown. Here we show that electrical lighting and the constructed environment is associated with reduced exposure to sunlight during the day, increased light exposure after sunset, and a delayed timing of the circadian clock as compared to a summer natural 14 hr 40 min:9 hr 20 min light-dark cycle camping. Furthermore, we find that after exposure to only natural light, the internal circadian clock synchronizes to solar time such that the beginning of the internal biological night occurs at sunset and the end of the internal biological night occurs before wake time just after sunrise. In addition, we find that later chronotypes show larger circadian advances when exposed to only natural light, making the timing of their internal clocks in relation to the light-dark cycle more similar to earlier chronotypes. These findings have important implications for understanding how modern light exposure patterns contribute to late sleep schedules and may disrupt sleep and circadian clocks.

Topographical aspects in the dynamics of sleep homeostasis in young men: individual patterns

BACKGROUND

Sleep homeostasis refers to the increase of sleep pressure during waking and the decrease of sleep intensity during sleep. Electroencephalography (EEG) slow-wave activity (SWA; EEG power in the 0.75-4.5 Hz range) is a marker of non-rapid eye movement (NREM) sleep intensity and can be used to model sleep homeostasis (Process S). SWA shows a frontal predominance, and its increase after sleep deprivation is most pronounced in frontal areas. The question arises whether the dynamics of the homeostatic Process S also show regional specificity. Furthermore, the spatial distribution of SWA is characteristic for an individual and may reflect traits of functional anatomy. The aim of the current study was to quantify inter-individual variation in the parameters of Process S and investigate their spatial distribution. Polysomnographic recordings obtained with 27 EEG derivations of a baseline night of sleep and a recovery night of sleep after 40 h of sustained wakefulness were analyzed. Eight healthy young subjects participated in this study. Process S was modeled by a saturating exponential function during wakefulness and an exponential decline during sleep. Empirical mean SWA per NREM sleep episode at episode midpoint served for parameter estimation at each derivation. Time constants were restricted to a physiologically meaningful range.

RESULTS

For both, the buildup and decline of Process S, significant topographic differences were observed: The decline and buildup of Process S were slowest in fronto-central areas while the fastest dynamics were observed in parieto-occipital (decrease) and frontal (buildup) areas. Each individual showed distinct spatial patterns in the parameters of Process S and the parameters differed significantly between individuals.

CONCLUSIONS

For the first time, topographical aspects of the buildup of Process S were quantified. Our data provide an additional indication of regional differences in sleep homeostasis and support the notion of local aspects of sleep regulation.

Inter-individual differences in the dynamics of sleep homeostasis

STUDY OBJECTIVES

The two-process model posits that sleep is regulated by 2 independent processes, a circadian Process C and a homeostatic Process S. EEG slow-wave activity (SWA) is a marker of NREM sleep intensity and is used as an indicator of sleep homeostasis. So far, parameters of the two-process model have been derived mainly from average data. Our aim was to quantify inter-individual differences.

DESIGN

Polysomnographic recordings (analysis of existing data).

SETTING

Sound attenuated sleep laboratory.

PATIENTS OR PARTICIPANTS

Eight healthy young males.

INTERVENTIONS

40-h sustained wakefulness.

MEASUREMENTS AND RESULTS

Process S was modeled by a saturating exponential function during wakefulness and an exponential decline during sleep. Empirical mean SWA (derivation C3A2) per NREM sleep episode at episode midpoint were used for parameter estimation. Parameters were estimated simultaneously by minimizing the mean square error between data and simulations of Process S. This approach was satisfactory for average data and most individual data. We further improved our methodological approach by limiting the time constants to a physiologically meaningful range. This allowed a satisfactory fit also for the one individual whose parameters were beyond a physiological range. The time constants of the buildup of Process S ranged from 14.1 h to 26.4 h and those of the decline from 1.2 h to 2.9 h with similar inter-individual variability of the buildup and decline of Process S.

CONCLUSIONS

We established a robust method for parameter estimation of Process S on an individual basis.