Alterations in upper limb muscle synergy structure in chronic stroke survivors

Abstract

Previous studies in neurologically intact subjects have shown that motor coordination can be described by task-dependent combinations of a few muscle synergies, defined here as a fixed pattern of activation across a set of muscles. Arm function in severely impaired stroke survivors is characterized by stereotypical postural and movement patterns involving the shoulder and elbow. Accordingly, we hypothesized that muscle synergy composition is altered in severely impaired stroke survivors. Using an isometric force matching protocol, we examined the spatial activation patterns of elbow and shoulder muscles in the affected arm of 10 stroke survivors (Fugl-Meyer <25/66) and in both arms of six age-matched controls. Underlying muscle synergies were identified using non-negative matrix factorization. In both groups, muscle activation patterns could be reconstructed by combinations of a few muscle synergies (typically 4). We did not find abnormal coupling of shoulder and elbow muscles within individual muscle synergies. In stroke survivors, as in controls, two of the synergies were comprised of isolated activation of the elbow flexors and extensors. However, muscle synergies involving proximal muscles exhibited consistent alterations following stroke. Unlike controls, the anterior deltoid was coactivated with medial and posterior deltoids within the shoulder abductor/extensor synergy and the shoulder adductor/flexor synergy in stroke was dominated by activation of pectoralis major, with limited anterior deltoid activation. Recruitment of the altered shoulder muscle synergies was strongly associated with abnormal task performance. Overall, our results suggest that an impaired control of the individual deltoid heads may contribute to poststroke deficits in arm function.

Muscle synergies after stroke are correlated with perilesional high gamma

“…Four of the five muscle synergies from our subject were remarkably similar to recently published muscle synergies from the affected and the unaffected upper arm of stroke patients (Fig. 2B and C reproduced from Roh et al,6 see Fig. S1 for synergy 5).…”

“…S1 for synergy 5). While our subject's elbow‐related synergies were not significantly different from the controls in the Roh et al study,6 the shoulder‐related synergies showed abnormal co‐activation that was most consistent with synergies from the stroke group (Fig. 2D).…”

“…Data from −5 to +4 seconds relative to movement onset were used for analysis. Nonnegative matrix factorization (NMF) was used to calculate synergies 6. NMF was performed using the MATLAB function, Natick, Massachusetts, U.S.A with 100 replicates to repeat the factorization using random starting values, to produce five weights, the minimum for synergies to have a >90% “variance accounted for.” We compared the cross‐correlation coefficient (CC) of ECoG relative to the muscle synergy across time‐shifts (“lags”); ECoG activity in each frequency band and synergy activation time‐series were correlated as a function of the temporal lag using the Matlab function xcorr .…”

“…Each electrode's CC was normalized using the CC values from “baseline” electrodes that contained activity not significantly related to the temporal course of synergy activation (see Data S1). Finally, we quantified the similarity between the sets of synergies in our subject with that of a cohort of control and stroke patients from a recently published paper6; we calculated an inner product column similarity measure using the matcorr function. Bootstrapping was then used to determine the chance level of similarity (Data S1) 6…”

“…These synergies can be measured by creating lower dimensional representations of electromyography (EMG) signals. After stroke, however, there is a distinct change in the modular recruitment of muscles, thus resulting in abnormal synergies in the most impaired subjects 4, 5, 6. While the neural basis of such abnormal synergies is poorly understood, a commonly held view is that descending cortical inputs can shape subcortical circuits to organize and dynamically control the weighting of synergies 7.…”