Ultrasound imaging of dorsal neck muscles with speckle tracking analyses - the relationship between muscle deformation and force.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
23 09 2019
Historique:
received: 29 10 2018
accepted: 29 08 2019
entrez: 25 9 2019
pubmed: 25 9 2019
medline: 31 10 2020
Statut: epublish

Résumé

The development of methods of non-invasive measurement of neck muscle function remains a priority in the clinical sciences. In this study, dorsal neck muscle deformation vs time curves (deformation area) were evaluated against incremental force, recorded from non-invasive real-time ultrasound measurement. The results revealed subject-specific moderate to strong linear or non-linear relationships between deformation and force. Test-retest variability showed strong reliability for all five neck muscles summed together and fair to good reliability for the five muscles evaluated separately. Multivariate statistics were used to analyse the interactions between the dorsal neck muscles during different percentages of maximal voluntary contraction (MVC). Low force (10-20% MVC) was related to muscle shortening; higher force (40-80% MVC) showed combination of shortening and elongation deformation in the muscle interactions. The muscle interactions during isometric MVC test were subject-specific, with different combinations and deformations of the five neck muscles. Force ≥40% MVC were associated with a forward movement of the cervical spine that affected the ultrasound measurement of the dorsal neck muscles. Ultrasound with speckle-tracking analyses may be best used to detect low levels (<40% MVC) of neck muscle activity.

Identifiants

pubmed: 31548564
doi: 10.1038/s41598-019-49916-1
pii: 10.1038/s41598-019-49916-1
pmc: PMC6757103
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

13688

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Auteurs

Gunnel Peterson (G)

Centre for Clinical Research Sörmland, Uppsala University, Eskilstuna, Sweden. gunnel.peterson@liu.se.
Department of Medical and Health Sciences, Division of Physiotherapy, Faculty of Health Sciences, Linköping University, Linköping, Sweden. gunnel.peterson@liu.se.

Shaun O' Leary (SO)

School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia.
Physiotherapy Department, Royal Brisbane and Women's Hospital, Queensland Health, Queensland, Australia.

David Nilsson (D)

Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, Umeå, Sweden.

Katherine Moodie (K)

School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia.

Kylie Tucker (K)

School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, Australia.
The University of Queensland, School of Biomedical Sciences, Brisbane, Australia.

Johan Trygg (J)

Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, Umeå, Sweden.

Anneli Peolsson (A)

Department of Medical and Health Sciences, Division of Physiotherapy, Faculty of Health Sciences, Linköping University, Linköping, Sweden.

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