Evidence map›Paper›PMID 39448600›Full record

ArticleNature communications2024

Measuring the dynamic balance of integration and segregation underlying consciousness, anesthesia, and sleep in humans.

Hyunwoo Jang, George A Mashour, Anthony G Hudetz, Zirui Huang

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Hyunwoo JangNeuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0003-3881-0159
George A MashourNeuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA.
Anthony G HudetzNeuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA.ORCID 0000-0003-2241-1751
Zirui HuangNeuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA. huangzu@med.umich.edu.ORCID 0000-0002-5949-0206

Funding

Neuroimaging of Anesthetic Modulation of Human ConsciousnessR01GM103894 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Zirui Huang, Anthony George Hudetz · 2013 to 2026
$4.0M
NIGMS NIH HHS R01 GM103894U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) 2R01-GM103894
6 · The paper itself

Abstract

Consciousness requires a dynamic balance of integration and segregation in brain networks. We report an fMRI-based metric, the integration-segregation difference (ISD), which captures two key network properties: network efficiency (integration) and clustering (segregation). With this metric, we quantify brain state transitions from conscious wakefulness to unresponsiveness induced by the anesthetic propofol. The observed changes in ISD suggest a profound shift towards the segregation of brain networks during anesthesia. A common unimodal-transmodal sequence of disintegration and reintegration occurs in brain networks during, respectively, loss and return of responsiveness. Machine learning models using integration and segregation data accurately identify awake vs. unresponsive states and their transitions. Metastability (dynamic recurrence of non-equilibrium transient states) is more effectively explained by integration, while complexity (diversity of neural activity) is more closely linked with segregation. A parallel analysis of sleep states produces similar findings. Our results demonstrate that the ISD reliably indexes states of consciousness.

Indexed as

AnesthesiaBrainConsciousnessMagnetic Resonance ImagingPropofolSleepWakefulnessAdultBrain MappingFemaleHumansMachine LearningMaleNerve NetYoung AdultPropofol

Identifiers

PMID39448600
PMCPMC11502666

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.