Abstract
Muscle networks describe the functional connectivity between muscles quantified through the decomposition of intermuscular coherence (IMC) to identify shared frequencies at which certain muscles are co-modulated by common neural input. Efforts have been devoted to characterizing muscle networks in healthy subjects but stroke-linked alterations to muscle networks remain unexplored. Muscle networks were assessed for eight key upper extremity muscles during isometric force generation in stroke survivors with mild, moderate, and severe impairment and compared against healthy controls to identify stroke-specificalterations in muscle connectivity. Coherence matrices were decomposed using non-negative matrix factorization. The variance accounted for thresholding was then assessed to identify the number of muscle networks. Results showed that the number of muscle networks decreased in stroke survivors compared to age-matched healthy controls (four networks in the healthy control group) as the severity of post-stroke motor impairment increased (three in the mild- and two in the moderate- and severe-strokegroups). Statistically significant reductions of IMC in the synergistic deltoid muscles in the alpha-band in stroke patients versus healthy controls ( p < 0.05) were identified. This study represents the first effort, to the best of our knowledge, to assess stroke-linked alterations in functional intermuscular connectivity using muscle network analysis. The findings revealed a pattern of alterations to muscle networks in stroke survivors compared to healthy controls, as a result of the loss of brain function associated with the stroke. These alterations in muscle networks reflected underlying pathophysiology. These findings can help better understand the motor impairment and motor control in stroke and may advance rehabilitation efforts for stroke by identifying the impaired neuromuscular coordination among multiple muscles in the frequency domain.
Highlights
S TROKE is one of the leading causes of disabilities in the US [1], often leading to abnormal motor coordination with the muscles of the affected arm [2]–[4]
The non-negative matrix factorization (NNMF) algorithm applied to the electromyographic (EMG) data collected from stroke survivors with varying levels of motor impairment identified that alterations in proximal muscle synergies were evident in a lesser severity of stroke impairment, but still most pronounced in the severe stroke [17]
The most qualitatively distinguishable difference in the coherence patterns between healthy controls and stroke survivors were observed for the AD-MD, MD-PD, and TRIlat-TRIlong muscle pairs
Summary
S TROKE is one of the leading causes of disabilities in the US [1], often leading to abnormal motor coordination with the muscles of the affected arm [2]–[4]. Alterations in the composition of muscle synergy patterns of isometric force generation have been identified by applying NNMF in chronic stroke patients with severe impairment, compared to age-matched healthy controls [16]. The NNMF algorithm applied to the electromyographic (EMG) data collected from stroke survivors with varying levels of motor impairment identified that alterations in proximal muscle synergies were evident in a lesser severity of stroke impairment, but still most pronounced in the severe stroke [17]. The alteration in the proximal muscle synergies implies a potential lack of ability for post-stroke survivors to selectively activate deltoid muscles under isometric conditions. While muscle synergies can represent the anatomical intermuscular coordination patterns in neuromuscular control, they cannot quantify the functional connectivity among muscles such as coherence can [22]
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