Cortical source localization of sleep-stage specific oscillatory activity

Abstract

The oscillatory features of non-REM sleep states have been a subject of intense research over many decades. However, a systematic spatial characterization of the spectral features of cortical activity in each sleep state is not available yet. Here, we used magnetoencephalography (MEG) and electroencephalography (EEG) recordings during night sleep. We performed source reconstruction based on the individual subject's anatomical magnetic resonance imaging (MRI) scans and spectral analysis on each non-REM sleep epoch in eight standard frequency bands, spanning the complete spectrum, and computed cortical source reconstructions of the spectral contrasts between each sleep state in comparison to the resting wakefulness. Despite not distinguishing periods of high and low activity within each sleep stage, our results provide new information about relative overall spectral changes in the non-REM sleep stages. Brain activity both during wakefulness and sleep is characterized by fluctuations in neuronal responses and rhythmic activation at various time scales. Sleep occurs periodically following a rather strict circadian rhythm and is itself a highly dynamic event, characterized by re-occurring and alternating phases of circa 80-120 minutes each, during which different polysomnographic events can be recorded 1. In each sleep stage, the brain is characterized by specific patterns of oscillatory activity. These oscillatory patterns of the sleeping brain have been described mostly using EEG. Our aim is to exploit the higher localization accuracy of magnetoencephalography (MEG) to shed new light on the spatial distribution of oscillatory features in non-REM sleep stages. The first light sleep stage (N1) is a state of drowsiness and of early loss of consciousness, physiologically characterized by a decreasing low voltage EEG frequency (2-7 Hz) 2. The following, second sleep stage (N2) is characterized by the occurrence of sleep spindles and K-complexes in the EEG signal. Spindles are rhythmic bursts of EEG activity that oscillate at a frequency between 12-15 Hz (classically named as sigma band). They have a waxing and a waning component and last for about 1 sec at a time 3,4. K-complexes are variable patterns of sudden bursts consisting mostly of a high voltage diphasic slow wave, especially in N2. Their brief negative peak in the EEG seems to be a signature of neuronal hyperpolarization, while its initial positive component depends on excitation of neurons. Often K-complexes co-occur with sleep spindles or may even trigger them 5. They are either spontaneous or occur in response to sudden sensory stimuli 6. As sleep deepens, subjects enter a third sleep stage, N3. This is characterized by a reduction of sleep spindles and by the emergence of low-frequency, high-amplitude fluctuations (delta waves) 7,8. In the past, some studies further distinguished between N3 and another N4 state, based on some arbitrary percentage of slow wave oscillations (e.g., >20% and >50%, respectively), but for the purpose of this study we consider them...

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“…They found a significant reduction in relative theta (4-8 Hz), alpha (8)(9)(10)(11)(12), and sigma (12-14 Hz) powers after long (30-40 s) apneas compared to moderate (20-30 s) and short (10-20 s) apneas [14]. However, they did not find any significant differences in relative post-event delta (0.5-4 Hz) or beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) powers between any of the apnea duration groups [14].…”

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Bi-Temporal Anodal Transcranial Direct Current Stimulation during Slow-Wave Sleep Boosts Slow-Wave Density but Not Memory Consolidation

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Advances in Electrical Source Imaging: A Review of the Current Approaches, Applications and Challenges

“…In view of the fact that EEG has been consistently employed to study sleep-related cycles and disorders, source localization can efficiently identify the main areas that are involved in the sleep onset transition and the activity alterations in the brain regions affected by sleep deprivation recovery [67,68]. Furthermore, ESI is effective in detecting the areas and the associated activity during different NREM sleep stages with the use of MEG and EEG recordings [69], the areas responsible for generating sleep spindles [70] and slow wave generation [71]. Interestingly, source localization has been applied for sleepwalking and sleep terror origin decoding, used not only to estimate the brain areas that were affected by the sleep arousal disorder, but also for computing the decrement of slow wave activity power [72].…”