Spatial and Temporal Muscle Synergies Provide a Dual Characterization of Low-dimensional and Intermittent Control of Upper-limb Movements.


Journal

Neuroscience
ISSN: 1873-7544
Titre abrégé: Neuroscience
Pays: United States
ID NLM: 7605074

Informations de publication

Date de publication:
15 03 2023
Historique:
received: 07 09 2022
revised: 16 01 2023
accepted: 18 01 2023
pubmed: 29 1 2023
medline: 7 3 2023
entrez: 28 1 2023
Statut: ppublish

Résumé

Muscle synergy analysis investigates the neurophysiological mechanisms that the central nervous system employs to coordinate muscles. Several models have been developed to decompose electromyographic (EMG) signals into spatial and temporal synergies. However, using multiple approaches can complicate the interpretation of results. Spatial synergies represent invariant muscle weights modulated with variant temporal coefficients; temporal synergies are invariant temporal profiles that coordinate variant muscle weights. While non-negative matrix factorization allows to extract both spatial and temporal synergies, the comparison between the two approaches was rarely investigated targeting a large set of multi-joint upper-limb movements. Spatial and temporal synergies were extracted from two datasets with proximal (16 subjects, 10M, 6F) and distal upper-limb movements (30 subjects, 21M, 9F), focusing on their differences in reconstruction accuracy and inter-individual variability. We showed the existence of both spatial and temporal structure in the EMG data, comparing synergies with those from a surrogate dataset in which the phases were shuffled preserving the frequency content of the original data. The two models provide a compact characterization of motor coordination at the spatial or temporal level, respectively. However, a lower number of temporal synergies are needed to achieve the same reconstruction R

Identifiants

pubmed: 36708799
pii: S0306-4522(23)00029-5
doi: 10.1016/j.neuroscience.2023.01.017
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

100-122

Informations de copyright

Copyright © 2023 IBRO. Published by Elsevier Ltd. All rights reserved.

Auteurs

Cristina Brambilla (C)

Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Lecco, Italy.

Manfredo Atzori (M)

Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), CH-3960 Sierre, Switzerland; Department of Neuroscience, University of Padova, via Belzoni 160, 35121 Padova, Italy.

Henning Müller (H)

Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), CH-3960 Sierre, Switzerland; Medical Informatics, University of Geneva, Rue Gabrielle-Perret-Gentil 4, 1205 Geneva, Switzerland.

Andrea d'Avella (A)

Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy; Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy. Electronic address: adavella@unime.it.

Alessandro Scano (A)

Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Lecco, Italy. Electronic address: alessandro.scano@stiima.cnr.it.

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Classifications MeSH