Alterations of Elastic Property of Spastic Muscle With Its Joint Resistance Evaluated From Shear Wave Elastography and Biomechanical Model

Abstract

This study aims to quantify passive muscle stiffness of spastic wrist flexors in stroke survivors using shear wave elastography (SWE) and to correlate with neural and non-neural contributors estimated from a biomechanical model to hyper-resistance measured during passive wrist extension. Fifteen hemiplegic individuals after stroke with Modified Ashworth Scale (MAS) score larger than one were recruited. SWE were used to measure Young's modulus of flexor carpi radialis muscle with joint from 0° (at rest) to 50° flexion (passive stretch condition), with 10° interval. The neural (NC) and non-neural components i.e., elasticity component (EC) and viscosity component (VC) of the wrist joint were analyzed from a motorized mechanical device NeuroFlexor® (NF). Combining with a validated biomechanical model, the neural reflex and muscle stiffness contribution to the increased resistance can be estimated. MAS and Fugl-Meyer upper limb score were also measured to evaluate the spasticity and motor function of paretic upper limb. Young's modulus was significantly higher in the paretic side of flexor carpi radialis than that of the non-paretic side ( p < 0.001) and it increased significantly from 0° to 50° of the paretic side ( p < 0.001). NC, EC, and VC on the paretic side were higher than the non-paretic side ( p < 0.05). There was moderate significant positive correlation between the Young's Modulus and EC ( r = 0.565, p = 0.028) and VC ( r = 0.645, p = 0.009) of the paretic forearm flexor muscle. Fugl-Meyer of the paretic forearm flexor has a moderate significant negative correlation with NC ( r = −0.578, p = 0.024). No significant correlation between MAS and shear elastic modulus or NF components was observed. This study demonstrated the feasibility of combining SWE and NF as a non-invasive approach to assess spasticity of paretic muscle and joint in stroke clinics. The neural and non-neural components analysis as well as correlation findings of muscle stiffness of SWE might provide understanding of mechanism behind the neuromuscular alterations in stroke survivors and facilitate the design of suitable intervention for them.

The Effects of Extracorporeal Shock Wave Therapy on Spastic Muscle of the Wrist Joint in Stroke Survivors: Evidence From Neuromechanical Analysis

“…The NF was validated in people with Parkinson's disease, cerebral palsy and stroke (Gäverth et al, 2014 ; Zetterberg et al, 2015 ; Kachmar et al, 2016 ). Our previously published study confirmed the feasibility to assess muscle stiffness, elasticity and viscosity characteristic of upper extremity spastic muscle in patients with stroke using the NF technique (Leng et al, 2019 ). A systematic review published recently encouraged the application of measuring technique to, including myotonometer, to assess the changes of muscle properties induced by ESW in spastic muscle (Dymarek et al, 2020 ).…”

“…The range of wrist movement was 50° with a starting position at −20° palmar flexion and an end position at 30° extension (7, 21). Three components of NC, EC and VC were calculated based on a biomechanical model which was described in detailed in previous study (Lindberg et al, 2011 ; Pennati et al, 2016 ; Leng et al, 2019 ). EC represents the length-dependent resistant force and VC represents the viscosity-dependent resistant force.…”

Changes in Muscle Stress and Sarcomere Adaptation in Mice Following Ischemic Stroke

“…This premise was supported by the differences in muscle density among the tested groups in our current study. The non-invasive ultrasonography method used in the patients with stroke also demonstrated the similar findings on the passive mechanical property changes after the neurological insults ( Jakubowski et al, 2017 ; Leng et al, 2019 ). However, future research involving histological analyses of myofilament and cytoskeleton proteins will be needed to better understand how changes in muscle compositions post-stroke may potentially alter muscle stress/tension.…”

A spasticity assessment method for voluntary movement using data fusion and machine learning

“…For example, the inertial sensor or the exoskeleton machine can be used to provide biomechanical information, such as the counter torque and the angle of joint motion in the process of passive stretching [14], [15], [16]. In addition, H-reflex and H/M ratio [17] are determined based on electromyography signals and using Shear Wave Elastography [18], [19] to directly measure muscle stiffness. Although the mechanical signalbased approach has good interpretability, the absence of muscle activation analysis makes it difficult to identify the disturbance caused by contractures.…”