Evidence map›Paper›PMID 31191258›Full record

ArticleFrontiers in neural circuits2019

Neural Correlates of Anesthesia in Newborn Mice and Humans.

Mattia Chini, Sabine Gretenkord, Johanna K Kostka, Jastyn A Pöpplau, Laura Cornelissen, Charles B Berde, Ileana L Hanganu-Opatz, Sebastian H Bitzenhofer

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neural circuits, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
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

19 citing papers in PubMed, 37 citations in OpenAlex.

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  5. Development of Prefrontal Circuits and Cognitive Abilities.Cold Spring Harbor perspectives in biology · 2024
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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

8 authors at 4 institutions in 2 countries.

Mattia ChiniDevelopmental Neurophysiology, Institute of Neuroanatomy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Sabine GretenkordDevelopmental Neurophysiology, Institute of Neuroanatomy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Johanna K KostkaDevelopmental Neurophysiology, Institute of Neuroanatomy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Jastyn A PöpplauDevelopmental Neurophysiology, Institute of Neuroanatomy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Laura CornelissenDepartment of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, United States.
Charles B BerdeDepartment of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, United States.
Ileana L Hanganu-OpatzDevelopmental Neurophysiology, Institute of Neuroanatomy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Sebastian H BitzenhoferDevelopmental Neurophysiology, Institute of Neuroanatomy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
University Medical Center Hamburg-Eppendorf · DEBoston Children's Hospital · USHarvard University · USUniversität Hamburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring the hypnotic component of anesthesia during surgeries is critical to prevent intraoperative awareness and reduce adverse side effects. For this purpose, electroencephalographic (EEG) methods complementing measures of autonomic functions and behavioral responses are in use in clinical practice. However, in human neonates and infants existing methods may be unreliable and the correlation between brain activity and anesthetic depth is still poorly understood. Here, we characterized the effects of different anesthetics on brain activity in neonatal mice and developed machine learning approaches to identify electrophysiological features predicting inspired or end-tidal anesthetic concentration as a proxy for anesthetic depth. We show that similar features from EEG recordings can be applied to predict anesthetic concentration in neonatal mice and humans. These results might support a novel strategy to monitor anesthetic depth in human newborns.

Indexed as

AlgorithmsAnesthesiaAnestheticsAnimalsAnimals, NewbornBrainElectroencephalographyFemaleHumansInfantInfant, NewbornMachine LearningMaleMiceMice, Inbred C57BLAnestheticsanesthesiadevelopmentEEGhumanLFPmachine learningmousenetwork dynamics

Identifiers

PMID31191258
PMCPMC6538977
OpenAlexW2946610633

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.