Post-stroke spasticity seriously affects patients’ quality of life. Spasticity is considered to involve both neural and non-neural factors. Current clinical scales, such as the Modified Ashworth Scale and the Modified Tardieu Scale, lack reliability and reproducibility. These scales are also unable to identify the neural and non-neural contributions to spasticity. Surface electromyography and biomechanical and myotonometry measurement methods for post-stroke spasticity are discussed in this report. Surface electromyography can provide neural information, while myotonometry can estimate muscular properties. Both the neural and non-neural contributions can be estimated by biomechanical measurement. These laboratory methods can quantitatively assess spasticity. They can provide more valuable information for further study on treatment and rehabilitation than clinical scales.

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ObjectiveIncreased muscle co-contraction of the agonist and antagonist muscles during voluntary movement is commonly observed in the upper limbs of stroke survivors. Much remain to be understood about the underlying mechanism. The aim of the study is to investigate the correlation between increased muscle co-contraction and the function of the corticospinal tract (CST).MethodsNine stroke survivors and nine age-matched healthy individuals were recruited. All the participants were instructed to perform isometric maximal voluntary contraction (MVC) and horizontal task which consist of sponge grasp, horizontal transportation, and sponge release. We recorded electromyography (EMG) activities from four muscle groups during the MVC test and horizontal task in the upper limbs of stroke survivors. The muscle groups consist of extensor digitorum (ED), flexor digitorum (FD), triceps brachii (TRI), and biceps brachii (BIC). The root mean square (RMS) of EMG was applied to assess the muscle activation during horizontal task. We adopted a co-contraction index (CI) to evaluate the degree of muscle co-contraction. CST function was evaluated by the motor-evoked potential (MEP) parameters, including resting motor threshold, amplitude, latency, and central motor conduction time. We employed correlation analysis to probe the association between CI and MEP parameters.ResultsThe RMS, CI, and MEP parameters on the affected side showed significant difference compared with the unaffected side of stroke survivors and the healthy group. The result of correlation analysis showed that CI was significantly correlated with MEP parameters in stroke survivors.ConclusionThere existed increased muscle co-contraction and impairment in CST functionality on the affected side of stroke survivors. The increased muscle co-contraction was correlated with the impairment of the CST. Intervention that could improve the excitability of the CST may contribute to the recovery of muscle discoordination in the upper limbs of stroke survivors.

Spasticity is a major contributor to pain, disabilities and many secondary complications after stroke. Investigating the effect of spasticity on neuromuscular function in stroke patients may facilitate the development of its clinical treatment, while the underlying mechanism of spasticity still remains unclear. The aim of this study is to explore the difference in the neuromuscular response to passive stretch between healthy subjects and stroke patients with spasticity. Five healthy subjects and three stroke patients with spastic elbow flexor were recruited to complete the passive stretch at four angular velocities (10°/s, 60°/s, 120°/s, and 180°/s) performed by an isokinetic dynamometer. Meanwhile, the 64-channel electromyography (EMG) signals from biceps brachii muscle were recorded. The root mean square (RMS) and fuzzy entropy (FuzzyEn) of EMG recordings of each channel were calculated, and the relationship between the average value of RMS and FuzzyEn over 64-channel was examined. The two groups showed similar performance from results that RMS increased and FuzzyEn decreased with the increment of stretch velocity, and the RMS was negatively correlated with FuzzyEn. The difference is that stroke patients showed higher RMS and lower FuzzyEn during quick stretch than the healthy group. Furthermore, compared with the healthy group, distinct variations of spatial distribution within the spastic muscle were found in the EMG activity of stroke patients. These results suggested that a large number of motor units were recruited synchronously in the presence of spasticity, and this recruitment pattern was non-uniform in the whole muscle. Using a combination of RMS and FuzzyEn calculated from high-density EMG (HD-EMG) recordings can provide an innovative insight into the physiological mechanism underlying spasticity, and FuzzyEn could potentially be used as a new indicator for spasticity, which would be beneficial to clinical intervention and further research on spasticity.