Dynamic- and Frequency-Specific Regulation of Sleep Oscillations by Cortical Potassium Channels.

Primary electroencephalographic (EEG) features of sleep arise in part from thalamocortical neural assemblies, and cortical potassium channels have long been thought to play a critical role. We have exploited the regionally dynamic nature of sleep EEG to develop a novel screening strategy and used it to conduct an adeno-associated virus (AAV)-mediated RNAi screen for cellular roles of 31 different voltage-gated potassium channels in modulating cortical EEG features across the circadian sleep-wake cycle. Surprisingly, a majority of channels modified only electroencephalographic frequency bands characteristic of sleep, sometimes diurnally or even in specific vigilance states. Confirming our screen for one channel, we show that depletion of the KCa1.1 (or "BK") channel reduces EEG power in slow-wave sleep by slowing neuronal repolarization. Strikingly, this reduction completely abolishes transcriptomic changes between sleep and wake. Thus, our data establish an unexpected connection between transcription and EEG power controlled by specific potassium channels. We postulate that additive dynamic roles of individual potassium channels could integrate different influences upon sleep and wake within single neurons.

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.

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.

Stimulation of the brain with radiofrequency electromagnetic field pulses affects sleep-dependent performance improvement.

BACKGROUND: Sleep-dependent performance improvements seem to be closely related to sleep spindles (12-15 Hz) and sleep slow-wave activity (SWA, 0.75-4.5 Hz). Pulse-modulated radiofrequency electromagnetic fields (RF EMF, carrier frequency 900 MHz) are capable to modulate these electroencephalographic (EEG) characteristics of sleep. OBJECTIVE: The aim of our study was to explore possible mechanisms how RF EMF affect cortical activity during sleep and to test whether such effects on cortical activity during sleep interact with sleep-dependent performance changes. METHODS: Sixteen male subjects underwent 2 experimental nights, one of them with all-night 0.25-0.8 Hz pulsed RF EMF exposure. All-night EEG was recorded. To investigate RF EMF induced changes in overnight performance improvement, subjects were trained for both nights on a motor task in the evening and the morning. RESULTS: We obtained good sleep quality in all subjects under both conditions (mean sleep efficiency > 90%). After pulsed RF EMF we found increased SWA during exposure to pulse-modulated RF EMF compared to sham exposure (P < 0.05) toward the end of the sleep period. Spindle activity was not affected. Moreover, subjects showed an increased RF EMF burst-related response in the SWA range, indicated by an increase in event-related EEG spectral power and phase changes in the SWA range. Notably, during exposure, sleep-dependent performance improvement in the motor sequence task was reduced compared to the sham condition (-20.1%, P = 0.03). CONCLUSION: The changes in the time course of SWA during the exposure night may reflect an interaction of RF EMF with the renormalization of cortical excitability during sleep, with a negative impact on sleep-dependent performance improvement.

Triangular relationship between sleep spindle activity, general cognitive ability and the efficiency of declarative learning.

EEG sleep spindle activity (SpA) during non-rapid eye movement (NREM) sleep has been reported to be associated with measures of intelligence and overnight performance improvements. The reticular nucleus of the thalamus is generating sleep spindles in interaction with thalamocortical connections. The same system enables efficient encoding and processing during wakefulness. Thus, we examined if the triangular relationship between SpA, measures of intelligence and declarative learning reflect the efficiency of the thalamocortical system. As expected, SpA was associated with general cognitive ability, e.g. information processing speed. SpA was also associated with learning efficiency, however, not with overnight performance improvement in a declarative memory task. SpA might therefore reflect the efficiency of the thalamocortical network and can be seen as a marker for learning during encoding in wakefulness, i.e. learning efficiency.

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.

The functional Val158Met polymorphism of COMT predicts interindividual differences in brain alpha oscillations in young men.

Individual patterns of the electroencephalogram (EEG) in wakefulness and sleep are among the most heritable traits in humans, yet distinct genetic and neurochemical mechanisms underlying EEG phenotypes are largely unknown. A functional polymorphism in the gene encoding catechol-O-methyltransferase (COMT), an enzyme playing an important role in cortical dopamine metabolism, causes a common substitution of methionine (Met) for valine (Val) at codon 158 of COMT protein. Val allele homozygotes exhibit higher COMT activity and lower dopaminergic signaling in prefrontal cortex than Met/Met homozygotes. Evidence suggests that this polymorphism affects executive functions in healthy individuals. We hypothesized that it also modulates functional aspects of EEG in wakefulness and sleep. EEG recordings were conducted twice on separate occasions in 10 Val/Val and 12 Met/Met allele carriers (all men) in wakefulness, and in baseline and recovery sleep before and after 40 h prolonged waking. During sleep deprivation, subjects received placebo and modafinil in randomized, cross-over manner. We show that the Val158Met polymorphism predicts stable and frequency-specific, interindividual variation in brain alpha oscillations. Alpha peak frequency in wakefulness was 1.4 Hz slower in Val/Val genotype than in Met/Met genotype. Moreover, Val/Val allele carriers exhibited less 11-13 Hz activity than Met/Met homozygotes in wakefulness, rapid-eye-movement (REM) sleep, and non-REM sleep. This difference was resistant against the effects of sleep deprivation and modafinil. The data demonstrate that mechanisms involving COMT contribute to interindividual differences in brain alpha oscillations, which are functionally related to executive performance such as counting tendency on a random number generation task in young adults.

Adenosinergic mechanisms contribute to individual differences in sleep deprivation-induced changes in neurobehavioral function and brain rhythmic activity.

Large individual differences characterize the changes induced by sleep deprivation on neurobehavioral functions and rhythmic brain activity. To investigate adenosinergic mechanisms in these differences, we studied the effects of prolonged waking and the adenosine receptor antagonist caffeine on sustained vigilant attention and regional electroencephalogram (EEG) power in the ranges of theta activity (6.25-8.25 Hz) in waking and the slow oscillation (<1 Hz) in sleep. Activity in these frequencies is functionally related to sleep deprivation. In 12 subjectively caffeine-sensitive and 10 -insensitive young men, psychomotor vigilance task (PVT) performance and EEG were assessed at 3 h intervals before, during, and after one night without sleep. After 11 and 23 h waking, subjects received 200 mg caffeine and placebo in double-blind, cross-over manner. In the placebo condition, sleep deprivation impaired PVT speed more in caffeine-sensitive than in caffeine-insensitive men. This difference was counteracted by caffeine. Theta power in waking increased more in a frontal EEG derivation than in a posterior derivation. Caffeine attenuated this power gradient in caffeine sensitive subjects. Sleep loss also differently affected the power distribution <1 Hz in non-rapid eye movement sleep between caffeine sensitive and insensitive subjects. Also, this difference was mirrored by the action of caffeine. The effects of sleep deprivation and caffeine on sustained attention and regional EEG power in waking and sleep were inversely related. These findings suggest that adenosinergic mechanisms contribute to individual differences in waking-induced impairment of neurobehavioral performance and functional aspects of EEG topography associated with sleep deprivation.

Sleep deprivation impairs object recognition in mice.

Many studies in animals and humans suggest that sleep facilitates learning, memory consolidation, and retrieval. Moreover, sleep deprivation (SD) incurred after learning, impaired memory in humans, mice, rats, and hamsters. We investigated the importance of sleep and its timing in an object recognition task in OF1 mice subjected to 6h SD either immediately after the acquisition phase (0-6 SD) or 6h later (7-12 SD), and in corresponding undisturbed controls. Motor activity was continuously recorded with infrared sensors. All groups explored two familiar, previously encountered objects to a similar extent, both at the end of the acquisition phase and 24h later during the test phase, indicating intact familiarity detection. During the test phase 0-6 SD mice failed to discriminate between the single novel and the two familiar objects. In contrast, the 7-12 SD group and the two control groups explored the novel object significantly longer than the two familiar objects. Plasma corticosterone levels determined after SD did not differ from time-matched undisturbed controls, but were significantly below the level measured after learning alone. ACTH did not differ between the groups. Therefore, it is unlikely that stress contributed to the memory impairment. We conclude that the loss of sleep and the activities the mice engaged in during the SD, impaired recognition memory retrieval, when they occurred immediately after acquisition. The delayed SD enabled memory consolidation during the 6h when the mice were allowed to sleep, and had no detrimental effect on memory. Neither SD schedule impaired object familiarity processing, suggesting that only specific cognitive abilities were sensitive to the intervention. Sleep may either actively promote memory formation, or alternatively, sleep may provide optimal conditions of non-interference for consolidation.

Sleep inertia: performance changes after sleep, rest and active waking.

Napping benefits and sustains subsequent performance. Prophylactic naps have been recommended as a means to maintain performance during extended wakefulness, as required during shiftwork. However, napping may cause short-term performance impairments, because awakening from sleep is followed by sleep inertia, a period of hypovigilance and impaired cognitive and behavioral performance. We investigated sleep inertia after an afternoon nap. Healthy 18-28 year-olds (n=50, not sleep deprived) were assigned to sleep, active wake or rest groups for a 2-h experimental phase with polysomnography starting either at 14:00 or 16:00 for half of each group. Before (baseline, 12:30 or 14:30) and in five sessions during the hour after the experimental phase (16:00-17:00 or 18:00-19:00), subjects completed an addition task, an auditory reaction time task, and the Stanford Sleepiness Scale. In session one, addition speed in the sleep group was reduced compared with baseline and with active wake controls, whereas calculation accuracy did not change. Addition speed in the sleep and rest groups increased substantially from session one to session two and reached a level similar to that of the active wake group by the fifth session. In the first session, auditory reaction speed of the sleep group was reduced compared with baseline and with rest controls but did not differ from the active wake group. The slowest reaction times showed significant recovery after 20 min. The groups reported similar increases in subjective sleepiness after the experimental period. These findings provide evidence for performance slowing and recovery during the hour following a 2-h nap opportunity. They highlight the importance of employing multiple control groups and various objective and subjective measures to assess sleep inertia.

Inhibitory control of intrinsic hippocampal oscillations?

An oscillatory mode of activity is a basic operational mode of the hippocampus. Such activity involves the concurrent expression of several rhythmic processes, of which theta (4-15 Hz) and gamma (20-80 Hz) oscillations are prominent and considered to be important for cognitive processing. In an experimental model that preserves the intrinsic network oscillator, exhibiting the dependency on cholinergic inputs and consequent expression of concurrent theta and gamma oscillations, we investigate the intrinsic mechanisms underlying such integrated hippocampal network responses. This experimental framework is used here to examine the currently prevailing dogma, that interneurons control hippocampal oscillations. The spontaneous response of individual pyramidal cells (in areas CA3 and CA1) and interneurons (area CA3), during oscillatory activity, was monitored intracellularly. Particular attention was given to the initiation of interneuron discharge during oscillations, to the impact of the synaptic output of discharging interneurons on the oscillatory activity, and to the time at which interneurons discharge in relation to the oscillatory cycles. Analysis of the spontaneous patterns of activity in individual interneurons and their outcome, during the oscillatory activity, revealed that interneuron activity is incompatible with initiating, pacing or determining the oscillatory frequencies, although contributing to the apparent rhythmic patterns. Moreover, our results show that non-interneuronal members of the network control interneuron activity. We therefore suggest that the activity of the excitatory cells, i.e., principle cells, is critical toward the initiation, pacing and synchronization of intrinsic hippocampal network oscillations.