Evidence map›Paper›PMID 40414876›Full record

ArticleScientific reports2025

Similarities and differences between natural sleep and urethane anesthesia.

Jurij Brankačk, Yevgenij Yanovsky, Adriano B L Tort, Andreas Draguhn

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Dynamic Respiration-Neural Coupling in Substantia Nigra across Sleep and Anesthesia.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jurij BrankačkInstitute for Physiology and Pathophysiology, Heidelberg University, 69120, Heidelberg, Germany. jurij.brankack@web.de.
Yevgenij YanovskyInstitute for Physiology and Pathophysiology, Heidelberg University, 69120, Heidelberg, Germany.
Adriano B L TortBrain Institute, Federal University of Rio Grande do Norte, Natal, RN, 59078-900, Brazil.
Andreas DraguhnInstitute for Physiology and Pathophysiology, Heidelberg University, 69120, Heidelberg, Germany. andreas.draguhn@physiologie.uni-heidelberg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Slow oscillations dominate the EEG or local field potential (LFP) of mammals during specific periods within natural sleep and anesthesia. Such similarities have led to the use of anesthesia as a model to study sleep and state-dependent changes of consciousness. Previous research has documented the similarities between the activated state of urethane anesthesia and natural REM sleep, particularly with respect to network oscillations in the theta (θ) frequency domain. Likewise, the deactivated states, characterized by large amplitude slow waves in both urethane anesthesia and non-REM sleep, have generally been regarded as similar. Here, we report striking differences between slow oscillations in the mouse parietal cortex during the deactivated state of urethane anesthesia and natural non-REM sleep. These differences are notable in the LFP, the underlying current sources, and in the modulation of unit activity. Our data show that slow network oscillations in natural sleep and anesthesia are generated by different mechanisms, despite phenomenological similarities.

Indexed as

Anesthetics, IntravenousSleepUrethaneAnesthesiaAnimalsElectroencephalographyMaleMiceMice, Inbred C57BLParietal LobeSleep, REMAnesthetics, IntravenousUrethaneParietal cortexSleepSlow oscillationUrethane anesthesiaδ waves

Identifiers

PMID40414876
PMCPMC12104350

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.