Evidence map›Paper›PMID 38135170›Full record

ArticleNeuroImage2024

Mapping general anesthesia states based on electro-encephalogram transition phases.

V Loison, Y Voskobiynyk, B Lindquist, D Necula, D Longrois, J Paz, D Holcman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage, 2024. 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. Review
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

7 authors.

V LoisonGroup of Data Modeling and Computational Biology, Institut de Biologie (IBENS), École Normale Supérieure CNRS, Université PSL Paris, France.
Y VoskobiynykGladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA.
B LindquistGladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA.
D NeculaGladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA.
D LongroisDépartement d'Anesthésie-Réanimation, Hôpital Bichat-Claude Bernard, Assistance Publique-Hôpitaux de Paris, Paris, France.
J PazGladstone Institutes, USA; Gladstone Institute of Neurological Disease, University of California, San Francisco, USA.
D HolcmanGroup of Data Modeling and Computational Biology, Institut de Biologie (IBENS), École Normale Supérieure CNRS, Université PSL Paris, France; DAMPT, University of Cambridge and Churchill College, CB30DS, Cambridge, UK. Electronic address: david.holcman@ens.fr.

Funding

NINDS Research Education Programs for Residents and Fellows in Neurology, Neurosurgery, Neuropathology, Neuroradiology and Emergency Medicine (R25)R25NS070680 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JOSEPHSON, SCOTT ANDREW · 2010 to 2023
$6.8M
NINDS NIH HHS R25 NS070680
6 · The paper itself

Abstract

Cortical electro-encephalography (EEG) served as the clinical reference for monitoring unconsciousness during general anesthesia. The existing EEG-based monitors classified general anesthesia states as underdosed, adequate, or overdosed, lacking predictive power due to the absence of transition phases among these states. In response to this limitation, we undertook an analysis of the EEG signal during isoflurane-induced general anesthesia in mice. Adopting a data-driven approach, we applied signal processing techniques to track θ- and δ-band dynamics, along with iso-electric suppressions. Combining this approach with machine learning, we successfully developed an automated algorithm. The findings of our study revealed that the dampening of the δ-band occurred several minutes before the onset of significant iso-electric suppression episodes. Furthermore, a distinct γ-frequency oscillation was observed, persisting for several minutes during the recovery phase subsequent to isoflurane-induced overdose. As a result of our research, we generated a map summarizing multiple brain states and their transitions, offering a tool for predicting and preventing overdose during general anesthesia. The transition phases identified, along with the developed algorithm, have the potential to be generalized, enabling clinicians to prevent inadequate anesthesia and, consequently, tailor anesthetic regimens to individual patients.

Indexed as

IsofluraneAnesthesia, GeneralAnimalsBrainElectroencephalographyHumansMiceUnconsciousnessIsofluraneClassificationElectro-encephalographyGeneral AnesthesiaIRASAIso-electric suppressionIsofluraneMachine LearningSpectral decompositionState chart

Identifiers

PMID38135170
PMCPMC10792552

What OpenQuestion holds

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Registered trials

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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.