Deep sleep maintains learning efficiency of the human brain

Fattinger, Sara; Beukelaar, Toon T. de; Ruddy, Kathy; Volk, Carina; Heyse, Natalie C.; Herbst, Joshua A.; Hahnloser, Richard H. R.; Wenderoth, Nicole; Huber, Reto

doi:10.1038/ncomms15405

Cited by 118 publications

(111 citation statements)

References 54 publications

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“…They are easily applicable to large datasets, and will speed up data processing tremendously. Many of them even work for on‐line data processing and might thus be useful in applications like “closed loop” stimulation during sleep (Fattinger et al., ; Ngo, Martinetz, Born, & Molle, ).…”

Section: Resultsmentioning

confidence: 99%

Automatic artefact detection in single‐channel sleep EEG recordings

Malafeev

Omlin

Wierzbicka

et al. 2018

Journal of Sleep Research

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Quantitative electroencephalogram analysis (e.g. spectral analysis) has become an important tool in sleep research and sleep medicine. However, reliable results are only obtained if artefacts are removed or excluded. Artefact detection is often performed manually during sleep stage scoring, which is time consuming and prevents application to large datasets. We aimed to test the performance of mostly simple algorithms of artefact detection in polysomnographic recordings, derive optimal parameters and test their generalization capacity. We implemented 14 different artefact detection methods, optimized parameters for derivation C3A2 using receiver operator characteristic curves of 32 recordings, and validated them on 21 recordings of healthy participants and 10 recordings of patients (different laboratory) and considered the methods as generalizable. We also compared average power density spectra with artefacts excluded based on algorithms and expert scoring. Analyses were performed retrospectively. We could reliably identify artefact contaminated epochs in sleep electroencephalogram recordings of two laboratories (healthy participants and patients) reaching good sensitivity (specificity 0.9) with most algorithms. The best performance was obtained using fixed thresholds of the electroencephalogram slope, high-frequency power (25-90 Hz or 45-90 Hz) and residuals of adaptive autoregressive models. Artefacts in electroencephalogram data can be reliably excluded by simple algorithms with good performance, and average electroencephalogram power density spectra with artefact exclusion based on algorithms and manual scoring are very similar in the frequency range relevant for most applications in sleep research and sleep medicine, allowing application to large datasets as needed to address questions related to genetics, epidemiology or precision medicine.

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Section: Resultsmentioning

confidence: 99%

Automatic artefact detection in single‐channel sleep EEG recordings

Malafeev

Omlin

Wierzbicka

et al. 2018

Journal of Sleep Research

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“…Another approach to modulate consolidation of specific human memories in a local manner is to perform closed-loop stimulation during sleep that is time-locked with regional oscillations. Such stimulation increases spindle activity locally and enhances motor memory consolidation [45], and can disrupt SWA locally and interfere with motor learning [46]. These studies compared brain activity across two different sessions, where considerable variability exists in sleep, brain activity, and memory processing.…”

Section: Discussionmentioning

confidence: 99%

Local Targeted Memory Reactivation in Human Sleep

Bar

Arzi

Perl

et al. 2019

Preprint

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Memory consolidation can be promoted via Targeted Memory Reactivation (TMR)that re-presents training cues or context during sleep. Whether TMR acts locally or globally on cortical sleep oscillations remains unknown. Here we exploit the unique functional neuroanatomy of olfaction with its ipsilateral stimulus processing to perform local TMR in one brain hemisphere. Participants learned associations between words and locations in left or right visual fields with contextual odor throughout. During post-learning naps, odors were presented to one nostril throughout NREM sleep. We found improved memory for specific words processed in the cued hemisphere (ipsilateral to stimulated nostril). Unilateral odor cues locally modulated slow wave activity (SWA) such that regional SWA increase in the cued hemisphere negatively correlated with select memories for cued words.Moreover, local TMR improved slow wave-spindle coupling specifically in the cued hemisphere. Thus, TMR in human sleep transcends global action by selectively promoting specific memories associated with local sleep oscillations.

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“…Subjects participated in two-night sessions separated by 1 week: non-stimulation (SHAM) and closedloop auditory stimulation in the up-phase (STIM) of real-time detected slow waves. Real-time closed-loop slow-wave detection during sleep stages N2 and N3 was performed as described in Fattinger et al, 2017. In the STIM condition, auditory stimuli (50 ms bursts of 1/f pink noise, inter-stimulus interval ≥ 2s) were played whenever the EEG signal in the C4 electrode (the "target channel") crossed a default threshold of +30 µV (Figure 1, see Materials and Methods).…”

Section: Resultsmentioning

confidence: 99%

“…The manipulation of slow-wave activity during sleep by various techniques was instrumental in establishing causal relationships between sleep and learning-related processes (Fattinger et al, 2017;Del Felice et al, 2015;Landsness et al, 2009;Marshall et al, 2006;Prehn-Kristensen et al, 2014). However, whether cross-frequency coupling is critical for such causal relationships between sleep and learning is unknown.…”

Section: Introductionmentioning

confidence: 99%

Changes in cross-frequency coupling following closed-loop auditory stimulation in non-rapid eye movement sleep

Krugliakova

Volk

Jaramillo

et al. 2019

Preprint

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The activity of different brain networks in non-rapid eye movement (NREM) sleep is regulated locally in an experience-dependent manner, reflecting the extent of the network load during wakefulness. In particular, improved task performance after sleep correlates with the local post-learning power increase of neocortical slow waves and faster oscillations such as sleep spindles and their temporal coupling. Recently, it was demonstrated that by targeting slow waves in a particular region at a particular phase with closed-loop auditory stimulation it is possible to locally manipulate slow-wave activity and interact with training-induced neuroplastic changes. Based on this finding, we tested whether closed-loop auditory stimulation targeting the up-phase of slow-waves over the right sensorimotor area might affect power in delta, theta and sigma bands and coupling between these oscillations within the circumscribed region. We demonstrate that while closed-loop auditory stimulation globally enhances power in delta, theta and sigma bands, changes in cross-frequency coupling of these oscillations were more spatially restricted. In particular, stimulation induced a significant decrease of delta-theta coupling in frontal channels, within the area of the strongest baseline coupling between these frequency bands. In contrast, a significant increase in delta-sigma coupling was observed over the right parietal area, located directly posterior to the target electrode. These findings suggest that closed-loop auditory stimulation locally modulates coupling between delta phase and sigma power in a targeted region, which could be used to manipulate sleep-dependent memory formation within the brain network of interest.

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Deep sleep maintains learning efficiency of the human brain

Cited by 118 publications

References 54 publications

Automatic artefact detection in single‐channel sleep EEG recordings

Automatic artefact detection in single‐channel sleep EEG recordings

Local Targeted Memory Reactivation in Human Sleep

Changes in cross-frequency coupling following closed-loop auditory stimulation in non-rapid eye movement sleep

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