The first-night effect suppresses the strength of slow-wave activity originating in the visual areas during sleep

Tamaki, Masako; Bang, Ji Won; Watanabe, Takeo; Sasaki, Yuka

doi:10.1016/j.visres.2013.10.023

Cited by 22 publications

(21 citation statements)

References 65 publications

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“…Even without any cortical localization, they found both EEG and MEG gamma activity being higher directly before/during eye movement in REM sleep. Finally, Tamaki et al 30 addressed the impact of the environment (First Night Effect -FNE) on slow wave spontaneous oscillatory activity (1-4 Hz) in the visual areas. On a total of ten subjects, they reconstructed brain activity from MEG data during two consecutive sleep sessions.…”

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confidence: 99%

Cortical source localization of sleep-stage specific oscillatory activity

Brancaccio

Tabarelli

Bigica

et al. 2020

Sci Rep

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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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confidence: 99%

Cortical source localization of sleep-stage specific oscillatory activity

Brancaccio

Tabarelli

Bigica

et al. 2020

Sci Rep

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“…Prior to the main experiment, the subjects participated in an adaptation sleep session. It has been shown that when subjects sleep in a sleep laboratory for the first time, sleep quality is degraded due to the first-night effect (FNE) caused by the presence of a new environment (Agnew et al, 1966;Carskadon & Dement, 1981;Tamaki et al, 2014Tamaki et al, , 2016Tamaki et al, 2005;. The adaptation sleep session was thus necessary to mitigate the FNE.…”

Section: Experimental Designmentioning

confidence: 99%

Sleep-dependent offline performance gain in visual perceptual learning is consistent with a learning-dependent model

Tamaki

Sasaki

2020

Preprint

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Are the sleep-dependent offline performance gains of visual perceptual learning (VPL) consistent with a use-dependent or learning-dependent model? Here, we found that a use-dependent model is inconsistent with the offline performance gains in VPL. In two training conditions with matched visual usages, one generated VPL (learning condition), while the other did not (interference condition). The use-dependent model predicts that slow-wave activity (SWA) during posttraining NREM sleep in the trained region increases in both conditions, in correlation with offline performance gains. However, compared with those in the interference condition, sigma activity, not SWA, during NREM sleep and theta activity during REM sleep, source-localized to the trained early visual areas, increased in the learning condition. Sigma activity correlated with offline performance gain. These significant differences in spontaneous activity between the conditions suggest that there is a learning-dependent process during posttraining sleep for the offline performance gains in VPL.

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“…In addition, participants were required to have a regular sleep schedule. Anyone who had a physical or psychiatric disease, was currently under medication, or was suspected to have a sleep disorder was not eligible to participate 74,75 . 20 We first describe common procedures to all experiments and then procedures specific to each experiment.…”

Section: Participantsmentioning

confidence: 99%

Opponent neurochemical and functional processing in NREM and REM sleep in visual learning

Tamaki

Wang

Barnes-Diana

et al. 2019

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Sleep is beneficial for learning. However, whether NREM or REM sleep facilitates learning, whether the learning facilitation results from plasticity increases or stabilization and whether the facilitation results from learning-specific processing are all controversial. Here, after training on a visual task we measured the excitatory and inhibitory neurochemical (E/I) balance, an index of plasticity in human visual areas, for the first time, while subjects slept. Off-line performance gains of presleep learning were associated with the E/I balance increase during NREM sleep, which also occurred without presleep training. In contrast, increased stabilization was associated with decreased E/I balance during REM sleep only after presleep training. These indicate that the above-mentioned issues are not matters of controversy but reflect opposite neurochemical processing for different roles in learning during different sleep stages: NREM sleep increases plasticity leading to performance gains independently of learning, while REM sleep decreases plasticity to stabilize learning in a learning-specific manner.

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The first-night effect suppresses the strength of slow-wave activity originating in the visual areas during sleep

Cited by 22 publications

References 65 publications

Cortical source localization of sleep-stage specific oscillatory activity

Cortical source localization of sleep-stage specific oscillatory activity

Sleep-dependent offline performance gain in visual perceptual learning is consistent with a learning-dependent model

Opponent neurochemical and functional processing in NREM and REM sleep in visual learning

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