Evidence map›Paper›PMID 39915775›Full record

ArticleBMC medicine2025

Assessing brain-muscle networks during motor imagery to detect covert command-following.

Emilia Fló, Daniel Fraiman, Jacobo Diego Sitt

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In one paragraph

Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

3 authors.

Emilia FlóSorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Paris, France. emilia.flo.rama@gmail.com.ORCID http://orcid.org/0000-0002-1986-2802
Daniel FraimanDepartamento de Matemática y Ciencias, Universidad de San Andrés, Buenos Aires, Argentina.
Jacobo Diego SittSorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, Paris, France. jacobo.sitt@inserm.fr.

Funding

Agencia Nacional de Investigación e Innovación POS_EXT_2018_1_153765TWCF Funding Consciousness Research through Registered Reports initiative
6 · The paper itself

Abstract

backgroundIn this study, we evaluated the potential of a network approach to electromyography and electroencephalography recordings to detect covert command-following in healthy participants. The motivation underlying this study was the development of a diagnostic tool that can be applied in common clinical settings to detect awareness in patients that are unable to convey explicit motor or verbal responses, such as patients that suffer from disorders of consciousness (DoC).

methodsWe examined the brain and muscle response during movement and imagined movement of simple motor tasks, as well as during resting state. Brain-muscle networks were obtained using non-negative matrix factorization (NMF) of the coherence spectra for all the channel pairs. For the 15/38 participants who showed motor imagery, as indexed by common spatial filters and linear discriminant analysis, we contrasted the configuration of the networks during imagined movement and resting state at the group level, and subject-level classifiers were implemented using as features the weights of the NMF together with trial-wise power modulations and heart response to classify resting state from motor imagery.

resultsKinesthetic motor imagery produced decreases in the mu-beta band compared to resting state, and a small correlation was found between mu-beta power and the kinesthetic imagery scores of the Movement Imagery Questionnaire-Revised Second version. The full-feature classifiers successfully distinguished between motor imagery and resting state for all participants, and brain-muscle functional networks did not contribute to the overall classification. Nevertheless, heart activity and cortical power were crucial to detect when a participant was mentally rehearsing a movement.

conclusionsOur work highlights the importance of combining EEG and peripheral measurements to detect command-following, which could be important for improving the detection of covert responses consistent with volition in unresponsive patients.

Indexed as

BrainImaginationMuscle, SkeletalAdultElectroencephalographyElectromyographyFemaleHumansMaleMovementYoung AdultBrain-muscle networksDisorders of consciousnessECGEEGEMGMotor imagery

Identifiers

PMID39915775
PMCPMC11803995

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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.