Cargando…

Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles

The sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood. The objective of this study was to quantify how much the primary afferent activity of muscle spindles can contribute to shaping muscle coactivation pattern...

Descripción completa

Detalles Bibliográficos
Autores principales: Hardesty, Russell L., Boots, Matthew T., Yakovenko, Sergiy, Gritsenko, Valeriya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326973/
https://www.ncbi.nlm.nih.gov/pubmed/32606297
http://dx.doi.org/10.1038/s41598-020-67403-w
_version_ 1783552442694107136
author Hardesty, Russell L.
Boots, Matthew T.
Yakovenko, Sergiy
Gritsenko, Valeriya
author_facet Hardesty, Russell L.
Boots, Matthew T.
Yakovenko, Sergiy
Gritsenko, Valeriya
author_sort Hardesty, Russell L.
collection PubMed
description The sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood. The objective of this study was to quantify how much the primary afferent activity of muscle spindles can contribute to shaping muscle coactivation patterns during reaching movements with complex dynamics. To achieve this objective, we designed a virtual reality task that guided healthy human participants through a set of planar reaching movements with controlled kinematic and dynamic conditions that were accompanied by variable muscle co-contraction. Next, we approximated the Ia afferent activity using a phenomenological model of the muscle spindle and muscle lengths derived from a musculoskeletal model. The parameters of the spindle model were altered systematically to evaluate the effect of fusimotor drive on the shape of the temporal profile of afferent activity during movement. The experimental and simulated data were analyzed with hierarchical clustering. We found that the pattern of co-activation of agonistic and antagonistic muscles changed based on whether passive forces in each movement played assistive or resistive roles in limb dynamics. The reaching task with assistive limb dynamics was associated with the most muscle co-contraction. In contrast, the simulated Ia afferent profiles were not changing between tasks and they were largely reciprocal with homonymous muscle activity. Simulated physiological changes to the fusimotor drive were not sufficient to reproduce muscle co-contraction. These results largely rule out the static set and α-γ coactivation as the main types of fusimotor drive that transform the monosynaptic Ia afferent feedback into task-dependent co-contraction of antagonistic muscles. We speculate that another type of nonlinear transformation of Ia afferent signals that is independent of signals modulating the activity of α motoneurons is required for Ia afferent-based co-contraction. This transformation could either be applied through a complex nonlinear profile of fusimotor drive that is not yet experimentally observed or through presynaptic inhibition.
format Online
Article
Text
id pubmed-7326973
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-73269732020-07-01 Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles Hardesty, Russell L. Boots, Matthew T. Yakovenko, Sergiy Gritsenko, Valeriya Sci Rep Article The sensorimotor integration during unconstrained reaching movements in the presence of variable environmental forces remains poorly understood. The objective of this study was to quantify how much the primary afferent activity of muscle spindles can contribute to shaping muscle coactivation patterns during reaching movements with complex dynamics. To achieve this objective, we designed a virtual reality task that guided healthy human participants through a set of planar reaching movements with controlled kinematic and dynamic conditions that were accompanied by variable muscle co-contraction. Next, we approximated the Ia afferent activity using a phenomenological model of the muscle spindle and muscle lengths derived from a musculoskeletal model. The parameters of the spindle model were altered systematically to evaluate the effect of fusimotor drive on the shape of the temporal profile of afferent activity during movement. The experimental and simulated data were analyzed with hierarchical clustering. We found that the pattern of co-activation of agonistic and antagonistic muscles changed based on whether passive forces in each movement played assistive or resistive roles in limb dynamics. The reaching task with assistive limb dynamics was associated with the most muscle co-contraction. In contrast, the simulated Ia afferent profiles were not changing between tasks and they were largely reciprocal with homonymous muscle activity. Simulated physiological changes to the fusimotor drive were not sufficient to reproduce muscle co-contraction. These results largely rule out the static set and α-γ coactivation as the main types of fusimotor drive that transform the monosynaptic Ia afferent feedback into task-dependent co-contraction of antagonistic muscles. We speculate that another type of nonlinear transformation of Ia afferent signals that is independent of signals modulating the activity of α motoneurons is required for Ia afferent-based co-contraction. This transformation could either be applied through a complex nonlinear profile of fusimotor drive that is not yet experimentally observed or through presynaptic inhibition. Nature Publishing Group UK 2020-06-30 /pmc/articles/PMC7326973/ /pubmed/32606297 http://dx.doi.org/10.1038/s41598-020-67403-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Hardesty, Russell L.
Boots, Matthew T.
Yakovenko, Sergiy
Gritsenko, Valeriya
Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles
title Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles
title_full Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles
title_fullStr Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles
title_full_unstemmed Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles
title_short Computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles
title_sort computational evidence for nonlinear feedforward modulation of fusimotor drive to antagonistic co-contracting muscles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326973/
https://www.ncbi.nlm.nih.gov/pubmed/32606297
http://dx.doi.org/10.1038/s41598-020-67403-w
work_keys_str_mv AT hardestyrusselll computationalevidencefornonlinearfeedforwardmodulationoffusimotordrivetoantagonisticcocontractingmuscles
AT bootsmatthewt computationalevidencefornonlinearfeedforwardmodulationoffusimotordrivetoantagonisticcocontractingmuscles
AT yakovenkosergiy computationalevidencefornonlinearfeedforwardmodulationoffusimotordrivetoantagonisticcocontractingmuscles
AT gritsenkovaleriya computationalevidencefornonlinearfeedforwardmodulationoffusimotordrivetoantagonisticcocontractingmuscles