Identifying Representative Synergy Matrices for Describing Muscular Activation Patterns During Multidirectional Reaching in the Horizontal Plane

Abstract

Muceli S, Boye AT, d'Avella A, Farina D. Identifying representative synergy matrices for describing muscular activation patterns during multidirectional reaching in the horizontal plane. J Neurophysiol 103: 1532-1542, 2010. First published January 13, 2010 doi:10.1152/jn.00559.2009. Muscle synergies have been proposed as a simplifying principle of generation of movements based on a low-dimensional control by the CNS. This principle may be useful for movement restoration by, e.g., functional electrical stimulation (FES), if a limited set of synergies can describe several functional tasks. This study investigates the possibility of describing a multijoint reaching task of the upper limb by a linear combination of one set of muscle synergies common to multiple directions. Surface electromyographic (EMG) signals were recorded from 12 muscles of the dominant upper limb of eight healthy men during single-joint movements and a multijoint reaching task in 12 directions in the horizontal plane. The movement kinematics was recorded by a motion analysis system. Muscle synergies were extracted with nonnegative matrix factorization of the EMG envelopes. Synergies were computed either from the single-joint movements to describe the two degrees of freedom independently or from the multijoint movements. On average, the multijoint reaching task could be accurately described in all the directions (coefficient of determination Ͼ0.85) by a linear combination of either four synergies extracted from the individual degrees of freedom or three synergies extracted from multijoint movements in at least three reaching directions. These results indicate that a large set of multijoint movements can be generated by a synergy matrix of limited dimensionality and common to all directions if the synergies are extracted from a representative number of directions. The linear combination of synergies may thus be used in strategies for restoring functions, such as FES.

Impulses of activation but not motor modules are preserved in the locomotion of subacute stroke patients

“…This conclusion was substantiated by both the computation of the degree of similarity between modules and the result that the healthy modules could reconstruct the muscular activation pattern of each individual subject. This result confirms previous observations during reaching with the upper limb (Cheung et al 2009;d'Avella et al 2005;Muceli et al 2010). The motor modules of stroke patients differed from those of controls, although the activation signals were similar.…”

“…This complexity may be reduced by the activation of motor modules (also referred to as loadings or muscle synergies) in the spinal cord by means of a small number of activation signals (also referred to as factors or primitives or activation coefficients). This hypothesis has been confirmed in animal and human studies during a variety of tasks (Bizzi et al 2002d'Avella et al 2006Hart and Giszter 2010;Muceli et al 2010;Wakeling and Horn 2009), including human locomotion (Ivanenko et al 2004;Monaco et al 2010;Prentice et al 1998). A small number of motor modules (usually 4 or 5) have been identified to describe the muscle activation patterns of human locomotion (Cappellini et al 2006;Jo et al 2008;Ivanenko et al 2004;Monaco et al 2010;Prentice et al 1998).…”

“…The matrices of motor modules extracted from each individual were compared among individuals of the same group and between the two body sides by computing the scalar product between pairs of motor modules. This corresponds mathematically to the product of pairs of columns of the matrix S, normalized by the product of the norms of the columns (d'Avella et al 2003;Muceli et al 2010). Because vectors of modules are nonnegative, this operation provides a value that ranges between 0 and 1.…”

“…To address the first aim of the study (i.e., whether the modularity of control persists and whether motor modules are shared between the control and patient group), the muscle activation pattern of each stroke patient was reconstructed with the matrix of motor modules extracted from the entire data set of control subjects. For this analysis, a modified version of the NMF algorithm (Muceli et al 2010), referred to in the following as nonnegative reconstruction (NNR), was used. With NNR, the matrix S of motor modules was fixed, and at each iteration, the activation signal matrix P was updated with the Euclidean update rules described by Lee and Seung (2001).…”

Fatigue-related adaptations in muscle coordination during a cyclic exercise in humans

“…Before electrode application, the skin was shaved and cleaned with a mixture of alcohol and ether to minimize impedance. Each electrode was placed longitudinally with respect to the underlying muscle fibers arrangement and all were located according to the recommendations of surface EMG for non-invasive assessment of muscles (SENIAM) (Hermens et al, 2000) for all the muscles, except for the LD (de Sèze and Cazalets, 2008), Br (Muceli et al, 2010) and FD (Zipp, 1982), which are not referenced by SENIAM. The wires connected to the electrodes were well secured with adhesive tape to avoid movement-induced artefacts.…”