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dc.contributor.author
Fló, Emilia
dc.contributor.author
Fraiman Borrazás, Daniel Edmundo

dc.contributor.author
Sitt, Jacobo Diego

dc.date.available
2025-09-02T13:02:03Z
dc.date.issued
2025-02
dc.identifier.citation
Fló, Emilia; Fraiman Borrazás, Daniel Edmundo; Sitt, Jacobo Diego; Assessing brain-muscle networks during motor imagery to detect covert command-following; BioMed Central; Bmc Medicine; 23; 1; 2-2025; 1-22
dc.identifier.issn
1741-7015
dc.identifier.uri
http://hdl.handle.net/11336/270149
dc.description.abstract
Background: In 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 sufer from disorders of consciousness (DoC). Methods: We 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 flters and linear discriminant analysis, we contrasted the confguration of the networks during imagined movement and resting state at the group level, and subject-level classifers 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. Results: Kinesthetic 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 classifers successfully distinguished between motor imagery and resting state for all participants, and brain-muscle functional networks did not contribute to the overall classifcation. Nevertheless, heart activity and cortical power were crucial to detect when a participant was mentally rehearsing a movement. Conclusions: Our 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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
BioMed Central

dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
Brain-muscle networks
dc.subject
Motor imagery
dc.subject
EEG
dc.subject
Disorders of consciousness
dc.subject.classification
Otras Ciencias Físicas

dc.subject.classification
Ciencias Físicas

dc.subject.classification
CIENCIAS NATURALES Y EXACTAS

dc.title
Assessing brain-muscle networks during motor imagery to detect covert command-following
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2025-09-02T12:38:21Z
dc.journal.volume
23
dc.journal.number
1
dc.journal.pagination
1-22
dc.journal.pais
Reino Unido

dc.journal.ciudad
Londres
dc.description.fil
Fil: Fló, Emilia. Sorbonne University; Francia
dc.description.fil
Fil: Fraiman Borrazás, Daniel Edmundo. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Sitt, Jacobo Diego. Sorbonne University; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Bmc Medicine

dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-025-03846-0
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1186/s12916-025-03846-0
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