Regional differences in cortical electroencephalogram (EEG) slow wave activity and interhemispheric EEG asymmetry in the fur seal

Slow wave sleep (SWS) in the northern fur seal (Callorhinus ursinus) is characterized by a highly expressed interhemispheric electroencephalogram (EEG) asymmetry, called ‘unihemispheric’ or ‘asymmetrical’ SWS. The aim of this study was to examine the regional differences in slow wave activity (SWA; power in the range of 1.2–4.0 Hz) within one hemisphere and differences in the degree of interhemispheric EEG asymmetry within this species. Three seals were implanted with 10 EEG electrodes, positioned bilaterally (five in each hemisphere) over the frontal, occipital and parietal cortex. The expression of interhemispheric SWA asymmetry between symmetrical monopolar recordings was estimated based on the asymmetry index [AI = (L?R)/(L+R), where L and R are the power in the left and right hemispheres, respectively]. Our findings indicate an anterior–posterior gradient in SWA during asymmetrical SWS in fur seals, which is opposite to that described for other mammals, including humans, with a larger SWA recorded in the parietal and occipital cortex. Interhemispheric EEG asymmetry in fur seals was recorded across the entire dorsal cerebral cortex, including sensory (visual and somatosensory), motor and associative (parietal or suprasylvian) cortical areas. The expression of asymmetry was greatest in occipital–lateral and parietal derivations and smallest in frontal–medial derivations. Regardless of regional differences in SWA, the majority (90%) of SWS episodes with interhemispheric EEG asymmetry meet the criteria for ‘unihemispheric SWS’ (one hemisphere is asleep while the other is awake). The remaining episodes can be described as episodes of bilateral SWS with a local activation in one cerebral hemisphere.     

Anatomical markers of sleep slow wave activity derived from structural magnetic resonance images

Sleep studies often observe differences in slow wave activity (SWA) during non-rapid eye movement sleep between subjects. This study investigates to what extent these absolute differences in SWA can be explained with differences in grey matter volume, white matter volume or the thickness of skull and outer liquor rooms. To do this, we selected the 10-min interval showing maximal SWA of 20 young adult subjects and correlated these values lobe-wise with grey matter, skull and liquor thickness and globally with white matter as well as segments of the corpus callosum. Whereas grey matter, skull thickness and liquor did not correlate significantly with maximal slow wave activity, there were significant correlations with the anterior parts of the corpus callosum and with one other white matter region. In contrast, electroencephalogram power of higher frequencies correlates positively with grey matter volumes and cortical surface area. We discuss the possible role of white matter tracts on the synchronization of slow waves across the cortex.     

Redistribution of slow wave activity of sleep during pharmacological treatment of depression with paroxetine but not with nefazodone

It has been suggested that increase in delta sleep ratio (DSR), a marker for the relative distribution of slow wave activity (SWA) over night time, is associated with clinical response to antidepressant treatment. We examined this index and its relationship to rapid eye movement (REM) suppression before and during long-term treatment with nefazodone, which does not suppress REM sleep, and paroxetine which does. The effect of serotonin (5-HT_2A) receptor blockade on the evolution of SWA during treatment was also investigated. In a double-blind, randomised, parallel group, 8-week study in 29 depressed patients, sleep electroencephalograms were performed at home at baseline, on night 3 and 10, and at 8 weeks of treatment with either paroxetine or nefazodone. SWA was automatically analysed and a modified DSR (mDSR) was derived, being the ratio of amount of SWA in the first 90 min of sleep to that in the second plus third 90-min periods. At baseline, the pattern of SWA over night time was similar to other reports of depressed patients. mDSR improved over the course of treatment; there was no difference between remitters and non-remitters but there was a significant drug effect and a significant drug × time effect with paroxetine patients having a much higher mDSR after treatment, regardless of clinical status. SWA and REM during antidepressant treatment appear to be interdependent and neither of them alone is likely to predict response to treatment. Higher mDSR did not predict therapeutic response. 5-HT_2A blockade by nefazodone does not increase SWA above normal levels.     

The aging slow wave: a shifting amalgam of distinct slow wave and spindle coupling subtypes define slow wave sleep across the human lifespan

Study ObjectivesSlow wave and spindle coupling supports memory consolidation, and loss of coupling is linked with cognitive decline and neurodegeneration. Coupling is proposed to be a possible biomarker of neurological disease, yet little is known about the different subtypes of coupling that normally occur throughout human development and aging. Here we identify distinct subtypes of spindles within slow wave upstates and describe their relationships with sleep stage across the human lifespan.MethodsCoupling within a cross-sectional cohort of 582 subjects was quantified from stages N2 and N3 sleep across ages 6–88 years old. Results were analyzed across the study population via mixed model regression. Within a subset of subjects, we further utilized coupling to identify discrete subtypes of slow waves by their coupled spindles.ResultsTwo different subtypes of spindles were identified during the upstates of (distinct) slow waves: an “early-fast” spindle, more common in stage N2 sleep, and a “late-fast” spindle, more common in stage N3. We further found stages N2 and N3 sleep contain a mixture of discrete subtypes of slow waves, each identified by their unique coupled-spindle timing and frequency. The relative contribution of coupling subtypes shifts across the human lifespan, and a deeper sleep phenotype prevails with increasing age.ConclusionsDistinct subtypes of slow waves and coupled spindles form the composite of slow wave sleep. Our findings support a model of sleep-dependent synaptic regulation via discrete slow wave/spindle coupling subtypes and advance a conceptual framework for the development of coupling-based biomarkers in age-associated neurological disease.     

Slow wave activity and slow oscillations in sleepwalkers and controls: effects of 38 h of sleep deprivation

Sleepwalkers have been shown to have an unusually high number of arousals from slow wave sleep and lower slow wave activity (SWA) power during the night than controls. Because sleep deprivation increases the frequency of slow wave sleep (SWS) arousals in sleepwalkers, it may also affect the expression of the homeostatic process to a greater extent than shown previously. We thus investigated SWA power as well as slow wave oscillation (SWO) density in 10 sleepwalkers and nine controls at baseline and following 38 h of sleep deprivation. There was a significant increase in SWA during participants' recovery sleep, especially during their second non‐rapid eye movement (NREM) period. SWO density was similarly increased during recovery sleep's first two NREM periods. A fronto‐central gradient in SWA and SWO was also present on both nights. However, no group differences were noted on any of the 2 nights on SWA or SWO. This unexpected result may be related to the heterogeneity of sleepwalkers as a population, as well as our small sample size. SWA pressure after extended sleep deprivation may also result in a ceiling effect in both sleepwalkers and controls.     

EEG sigma and slow‐wave activity during NREM sleep correlate with overnight declarative and procedural memory consolidation

Previous studies suggest that sleep‐specific brain activity patterns such as sleep spindles and electroencephalographic slow‐wave activity contribute to the consolidation of novel memories. The generation of both sleep spindles and slow‐wave activity relies on synchronized oscillations in a thalamo‐cortical network that might be implicated in synaptic strengthening (spindles) and downscaling (slow‐wave activity) during sleep. This study further examined the association between electroencephalographic power during non‐rapid eye movement sleep in the spindle (sigma, 12–16 Hz) and slow‐wave frequency range (0.1–3.5 Hz) and overnight memory consolidation in 20 healthy subjects (10 men, 27.1 ± 4.6 years). We found that both electroencephalographic sigma power and slow‐wave activity were positively correlated with the pre–post‐sleep consolidation of declarative (word list) and procedural (mirror‐tracing) memories. These results, although only correlative in nature, are consistent with the view that processes of synaptic strengthening (sleep spindles) and synaptic downscaling (slow‐wave activity) might act in concert to promote synaptic plasticity and the consolidation of both declarative and procedural memories during sleep.     

Evidence for differential human slow-wave activity regulation across the brain

The regulation of the timing of sleep is thought to be linked to the temporal dynamics of slow‐wave activity [SWA, electroencephalogram (EEG) spectral power in the ～0.75–4.5 Hz range] in the cortical non‐rapid eye movement (NREM) sleep EEG. In the two‐process model of sleep regulation, SWA was used as a direct indication of sleep debt, or Process S. Originally, estimation of the latter was performed in a gross way, by measuring average SWA across NREM–REM sleep cycles, fitting an exponential curve to the values thus obtained and estimating its time constant. In later studies, SWA was assumed to be proportional to the instantaneous decay rate of Process S, rather than taken as a direct reflection of S. Following up on this, we extended the existing model of SWA dynamics in which the effects of intrusions of REM sleep and wakefulness were incorporated. For each subject, a ‘gain constant’ can be estimated that quantifies the efficiency of SWA in dissipating S. As the course of SWA is variable across cortical locations, local differences are likely to exist in the rate of discharge of S, eventually leading to different levels of S in different cortical regions. In this study, we estimate the extent of local differences of SWA regulation on the basis of the extended model of SWA dynamics, for 26 locations on the scalp. We observed higher efficiency of SWA in dissipation of S in frontal EEG derivations, suggesting that SWA regulation has a clear local aspect. This result further suggests that the process involved in (local) SWA regulation cannot be identical to the Process S involved (with Process C) in effectual determination of sleep timing – a single behaviour that cannot vary between locations on the scalp. We therefore propose to distinguish these two representations and characterize the former, purely SWA‐related, as ‘Process Z’, which then is different for different locations on the scalp. To demonstrate those differences, we compare the gain constants derived for the medial EEG derivations (Fz, Cz, Pz, Oz) with each other and with the decay rate derived from SWA values per NREM–REM sleep cycle.     

Seasonal covariation of the circadian phases of rectal temperature and slow wave sleep onset

There is a scarcity of well-controlled studies of the seasonal variation in circadian rhythmicity. In the present study, the circadian phase of rectal temperature and the onset of slow wave sleep were studied in a series of twelve 24-h experiments, one each month of the year, for six healthy subjects under controlled conditions in a climatic chamber. In winter, as compared with summer, the average circadian rhythm of rectal temperature was phase delayed by 45 min, and the average onset of slow wave sleep was phase delayed by 40 min. The temporal relationship between the circadian phase of rectal temperature and the timing of slow wave sleep was maintained throughout the year. Habitual rising and retiring times covaried as well. Furthermore, the circadian rhythm of rectal temperature followed the timing of the photoperiod across the year, but had a much smaller range of seasonal variation. Apparently, the seasonal variation in the photoperiodic zeitgeber is largely compensated for by the stabilizing influence of secondary zeitgebers. However, in healthy subjects some effect of photoperiodic variation can still be observed.     

Combining different models of sleep regulation

SUMMARY  Different models have been proposed to account for processes underlying the regulation of sleep and alertness. Specific models have addressed sleep homeostasis, the nonREM-REM sleep cycle, the circadian sleep/wake rhythm, and changes of daytime alertness. We show that the different models are not mutually exclusive but that they can be integrated as 'modules' in a combined model.     

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.     

Cortical thinning and sleep slow wave activity reductions mediate age-related improvements in cognition during mid-late adolescence

Study ObjectivesGains in cognitive test performance that occur during adolescence are associated with brain maturation. Cortical thinning and reduced sleep slow wave activity (SWA) are markers of such developmental changes. Here we investigate whether they mediate age-related improvements in cognition.Methods109 adolescents aged 15–19 years (49 males) underwent magnetic resonance imaging, polysomnography (PSG), and a battery of cognitive tasks within a 2-month time window. Cognitive tasks assessed nonverbal intelligence, sustained attention, speed of processing and working memory and executive function. To minimize the effect of sleep history on SWA and cognitive performance, PSG and test batteries were administered only after at least 8 nights of 9-h time-in-bed (TIB) sleep opportunity.ResultsAge-related improvements in speed of processing (r = 0.33, p = 0.001) and nonverbal intelligence (r = 0.24, p = 0.01) domains were observed. These cognitive changes were associated with reduced cortical thickness, particularly in bilateral temporoparietal regions (rs = −0.21 to −0.45, ps < 0.05), as well as SWA (r = −0.35, p < 0.001). Serial mediation models found that ROIs in the middle/superior temporal cortices, together with SWA mediated the age-related improvement observed on cognition.ConclusionsDuring adolescence, age-related improvements in cognition are mediated by reductions in cortical thickness and sleep SWA.     

Sleep homeostasis and cortical synchronization: III. A high-density EEG study of sleep slow waves in humans

STUDY OBJECTIVES: The mechanisms responsible for the homeostatic decrease of slow-wave activity (SWA, defined in this study as electroencephalogram [EEG] power between 0.5 and 4.0 Hz) during sleep are unknown. In agreement with a recent hypothesis, in the first of 3 companion papers, large-scale computer simulations of the sleeping thalamocortical system showed that a decrease in cortical synaptic strength is sufficient to account for the decline in SWA. In the model, the reduction in SWA was accompanied by decreased incidence of high-amplitude slow waves, decreased wave slopes, and increased number of waves with multiple peaks. In a second companion paper in the rat, local field potential recordings during early and late sleep confirmed the predictions of the model. Here, we investigated the model's predictions in humans by using all-night high-density (hd)-EEG recordings to explore slow-wave parameters over the entire cortical mantle. DESIGN: 256-channel EEG recordings in humans over the course of an entire night's sleep. SETTING: Sound-attenuated sleep research room PATIENTS OR PARTICIPANTS: Seven healthy male subjects INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: During late sleep (non-rapid eye movement [NREM] episodes 3 and 4, toward morning), when compared with early sleep (NREM sleep episodes 1 and 2, at the beginning of the night), the analysis revealed (1) reduced SWA, (2) fewer large-amplitude slow waves, (3) decreased wave slopes, (4) more frequent multipeak waves. The decrease in slope between early and late sleep was present even when waves were directly matched by wave amplitude and slow-wave power in the background EEG. Finally, hd-EEG showed that multipeak waves have multiple cortical origins. CONCLUSIONS: In the human EEG, the decline of SWA during sleep is accompanied by changes in slow-wave parameters that were predicted by a computer model simulating a homeostatic reduction of cortical synaptic strength.     

Gender-dependent age effects on sleep EEG power density in major depression

Summary Introduction   In the present study, we evaluated the impact of age and gender on EEG spectral power of non rapid eye movement (NREM) sleep in Major depression and hypothesized a gender-dependent age effect as previously observed in healthy controls (more prominent decline of delta activity in male than in female subjects).
Patients and Methods   We spectrally analyzed the NREM sleep EEG of 11 male and 11 female depressed patients, who were carefully pair-matched with regard to age and clinical parameters and were free of any psychoactive drugs for at least 1 week.
Results   Whereas male and female patients did not differ significantly in averaged spectral power, we found a significant decline of delta activity as the main age effect on sleep in men, but not in women.
Conclusion   This clear gender-dependent age effect on the sleep EEG in Major depression contributes to a better understanding of sleep changes with aging and is methodologically important for future sleep studies in psychiatric disorders.     

The pattern of slow wave activity in spontaneously occurring long sleep

SUMMARY  The aim of the present study was to estimate the time course of slow wave activity (SWA) in naturally occurring long sleep episodes (ad lib). Sixteen male shift workers were subjected to 24 h ambulatory polysomnography in connection with an afternoon shift. The EEG was subjected to spectral analysis (FFT) as well as to traditional sleep stage scoring. SWA (0.5-4.5 Hz band, both nonREM and REM sleep) declined exponentially and reached an asymptote by the fifth or sixth sleep cycle. However, half the subjects showed a reduced SWA in the first cycle, with a subsequent recovery in the second cycle. The SWA reduction of the first cycle was associated with a reduced REM-latency and it was suggested that uncontrolled external influences of the real life settings may have affected SWA in the first cycle. It was concluded that the decline of SWA across time may deviate from an exponential shape under real life conditions.     

The sleep EEG topography in children and adolescents shows sex differences in language areas

The topographic distribution of slow wave activity (SWA, EEG power between 0.75 and 4.5 Hz) during non-rapid eye movement (NREM) sleep was proposed to mirror cortical maturation with a typical age-related pattern. Here, we examined whether sex differences occur in SWA topography of children and adolescents (22 age-matched subjects, 11 boys, mean age 13.4 years, range: 8.7–19.4, and 11 girls, mean age 13.4 years, range: 9.1–19.0 years). In females, SWA during the first 60 min of NREM sleep was higher over bilateral cortical areas that are related to language functions, while in males SWA was increased over the right prefrontal cortex, a region also involved in spatial abilities. We conclude that cortical areas governing functions in which one sex outperforms the other exhibit increased sleep SWA and, thus, may indicate maturation of sex-specific brain function and higher cortical plasticity during development.     

Variations in slow wave activity during sleep in the rat

Scoring of human electroencephalogram (EEG) recordings usually includes subdivisions of non-rapid eye movement (NREM) sleep based on amount of slow wave activity. This procedure has revealed relationships between slow wave activity and many other variables. In animals, however, few experimenters have described variations in slow wave activity within NREM sleep. The present study quantifies, by filtering and integration techniques, variations in amount of slow wave activity during NREM sleep in the rat. Slow wave activity was found to be greatest at the start of the light period; the diurnal variation of slow wave activity within NREM sleep was correlated with variations in amount of NREM sleep. An amplitude criterion was used to define NREM sleep, but overall EEG amplitude during NREM sleep did not show the same diurnal variation as slow wave activity. The results indicate the value of measuring variations in slow wave amplitude during NREM sleep in animals in addition to overall EEG amplitude.     

EEG sleep, depression, and aging

—To date little attention has been paid to the posssible age-dependent relationships of EEG sleep measures in depression or to the implications of such relationships for diagnostic sensitivity and specificity. In a study of 108 patients with major depressive disorders (67 inpatients, 41 outpatients), age was shown to be a very powerful determinant of electroencephalographic (EEG) sleep patterns. Thus, among other sleep variables, sleep efficiency, delta sleep percent, and REM latency all showed significant linear declines with increasing age. Similar trends were seen in both inpatients and outpatients. Some variables were without age trends (age-stable), including sleep latency, REM sleep percent, and REM activity. These findings confirm those of an earlier report from our laboratory [45] and suggest that age-corrected sleep variables can be developed for clinical diagnostic application. Thus, using normative data from Gillin et al. [19] for comparison, a sensitivity level of 65% for age-corrected REM latency was demonstrated, together with a specificity of 95% and a diagnostic confidence of 92%. Data from a pilot study comparing EEG sleep measures in depression and dementia are also presented; these data suggest the potential utility of EEG sleep measures in the differential diagnosis of these two disorders, especially in patients with mixed symptoms. Additional areas for further research are reviewed with enumeration of specific testable hypotheses.     

Heterogeneity in EEG sleep findings in adolescent depression: unipolar versus bipolar clinical course

Background: EEG sleep measures in child and adolescent subjects with depression have shown considerable variability regarding group differences between depressed and control subjects. This investigation was designed to assess whether some of the observed variability is related to undifferentiated unipolar and bipolar disorders in a sample that was reported previously. Methods: Twenty-eight adolescents who met criteria for unipolar major depression and 35 controls with no lifetime psychiatric disorder participated in a cross-sectional sleep polysomnography study. Approximately 7 years later, follow-up clinical evaluations were conducted in 94% of the original cohort. Clinical course during the interval period was assessed without knowledge of subjects’ initial diagnostic and psychobiological status. Re-analysis of the original sleep data were performed with the added information of longitudinal clinical course. Results: Depressed subjects who had a unipolar course showed reduced REM latency, higher REM density, and more REM sleep (specifically in the early part of the night) compared with depressed adolescents who converted to bipolar disorder and controls who remained free from psychopathology at follow-up. In contrast to the unipolar group, depressed subjects who would later switch to bipolar disorder had demonstrated more stage 1 sleep and diminished stage 4 sleep. Conclusions: These preliminary results indicate that some of the observed variability in EEG sleep measures in adolescent depression appear to be confounded by latent bipolar illness. The findings also suggest that sleep regulatory changes associated with unipolar versus bipolar mood disorders may be different.     

EEG sleep, depression, and aging

—To date little attention has been paid to the posssible age-dependent relationships of EEG sleep measures in depression or to the implications of such relationships for diagnostic sensitivity and specificity. In a study of 108 patients with major depressive disorders (67 inpatients, 41 outpatients), age was shown to be a very powerful determinant of electroencephalographic (EEG) sleep patterns. Thus, among other sleep variables, sleep efficiency, delta sleep percent, and REM latency all showed significant linear declines with increasing age. Similar trends were seen in both inpatients and outpatients. Some variables were without age trends (age-stable), including sleep latency, REM sleep percent, and REM activity. These findings confirm those of an earlier report from our laboratory [45] and suggest that age-corrected sleep variables can be developed for clinical diagnostic application. Thus, using normative data from Gillin et al. [19] for comparison, a sensitivity level of 65% for age-corrected REM latency was demonstrated, together with a specificity of 95% and a diagnostic confidence of 92%. Data from a pilot study comparing EEG sleep measures in depression and dementia are also presented; these data suggest the potential utility of EEG sleep measures in the differential diagnosis of these two disorders, especially in patients with mixed symptoms. Additional areas for further research are reviewed with enumeration of specific testable hypotheses.     

Prototype algorithm for automated determination of gastric slow wave characteristics

An algorithm for determining the frequency and propagation time of the gastric slow wave has been designed for integration into a demand gastric pacing system. The algorithm analyses the serosal activity in both the time and frequency domains, and the results are compared to produce a conclusion only when the values are within 5% of each other. Thus, the probability of inappropriate intervention is reduced, at the expense of unidentified segments. The system is verified by comparing the conclusions produced by the algorithm with conclusions from hand analysis of seven canine and one human serosal recordings. The algorithm correctly identifies the slow-wave frequency in the distal portion of the stomach for 90% of the segments, while producing no incorrect results. Slow-wave propagation times in the antrum are correctly identified for 84% of the segments, with no incorrect identifications.