title: The number and choice of muscles impact the results of muscle synergy analyses.
abstract: One theory for how humans control movement is that muscles are activated in weighted groups or synergies. Studies have shown that electromyography (EMG) from a variety of tasks can be described by a low-dimensional space thought to reflect synergies. These studies use algorithms, such as nonnegative matrix factorization, to identify synergies from EMG. Due to experimental constraints, EMG can rarely be taken from all muscles involved in a task. However, it is unclear if the choice of muscles included in the analysis impacts estimated synergies. The aim of our study was to evaluate the impact of the number and choice of muscles on synergy analyses. We used a musculoskeletal model to calculate muscle activations required to perform an isometric upper-extremity task. Synergies calculated from the activations from the musculoskeletal model were similar to a prior experimental study. To evaluate the impact of the number of muscles included in the analysis, we randomly selected subsets of between 5 and 29 muscles and compared the similarity of the synergies calculated from each subset to a master set of synergies calculated from all muscles. We determined that the structure of synergies is dependent upon the number and choice of muscles included in the analysis. When five muscles were included in the analysis, the similarity of the synergies to the master set was only 0.57 ± 0.54; however, the similarity improved to over 0.8 with more than ten muscles. We identified two methods, selecting dominant muscles from the master set or selecting muscles with the largest maximum isometric force, which significantly improved similarity to the master set and can help guide future experimental design. Analyses that included a small subset of muscles also over-estimated the variance accounted for (VAF) by the synergies compared to an analysis with all muscles. Thus, researchers should use caution using VAF to evaluate synergies when EMG is measured from a small subset of muscles.
authors: Mechanical Engineering, University of Washington Seattle, WA, USA ; Sensorimotor Performance Program, Rehabilitation Institute of Chicago Chicago, IL, USA.
authors: Katherine M
authors: KM
authors: Steele
authors: Matthew C
authors: MC
authors: Tresch
authors: Eric J
authors: EJ
authors: Perreault
keywords: electromyography; muscle synergy; musculoskeletal model; nonnegative matrix factorization; simulation
references: J Mot Behav. 1984 Jun;16(2):135-70
references: 14713663
references: Front Comput Neurosci. 2013 Feb 26;7:8
references: 23471195
references: PLoS Comput Biol. 2012;8(5):e1002434
references: 22570602
references: J Biomech. 2009 Jun 19;42(9):1282-7
references: 19394023
references: Med Biol Eng Comput. 2013 Feb;51(1-2):187-96
references: 23124815
references: J Neurosci. 2007 Oct 10;27(41):11149-61
references: 17928457
references: Brain Res Brain Res Rev. 2002 Oct;40(1-3):66-79
references: 12589907
references: J Neurophysiol. 2009 Mar;101(3):1235-57
references: 19091930
references: J Biomech. 2012 Aug 9;45(12):2157-63
references: 22727468
references: IEEE Trans Biomed Eng. 2007 Nov;54(11):1940-50
references: 18018689
references: J Neurophysiol. 2013 Feb;109(3):768-81
references: 23155178
references: J Neurophysiol. 2010 Feb;103(2):844-57
references: 20007501
references: J Neurophysiol. 2005 Jan;93(1):609-13
references: 15342720
references: Nature. 1999 Oct 21;401(6755):788-91
references: 10548103
references: Ann Biomed Eng. 2005 Jun;33(6):829-40
references: 16078622
references: J Neurophysiol. 2009 Jul;102(1):59-68
references: 19369362
references: J Neurophysiol. 2012 Apr;107(8):2123-42
references: 22279190
references: Proc Natl Acad Sci U S A. 2005 Feb 22;102(8):3076-81
references: 15708969
references: J Neurosci. 2006 Jul 26;26(30):7791-810
references: 16870725
references: J Neurophysiol. 2006 Apr;95(4):2199-212
references: 16394079
references: Neuroscientist. 2006 Aug;12(4):339-48
references: 16840710
references: Exp Brain Res. 2003 Oct;152(3):281-92
references: 12904934
references: Proc Natl Acad Sci U S A. 2012 Sep 4;109(36):14652-6
references: 22908288
references: Nat Neurosci. 1999 Feb;2(2):162-7
references: 10195201
references: J Neurophysiol. 2006 Sep;96(3):1530-46
references: 16775203