Neural and non-neural related properties in the spastic wrist flexors: An optimization study

AAPM&R consensus guidance on spasticity assessment and management

BACKGROUND

The American Academy of Physical Medicine and Rehabilitation (AAPM&R) conducted a comprehensive review in 2021 to identify opportunities for enhancing the care of adult and pediatric patients with spasticity. A technical expert panel (TEP) was convened to develop consensus-based practice recommendations aimed at addressing gaps in spasticity care.

OBJECTIVE

To develop consensus-based practice recommendations to identify and address gaps in spasticity care.

METHODS

The Spasticity TEP engaged in a 16-month virtual meeting process, focusing on formulating search terms, refining research questions, and conducting a structured evidence review. Evidence quality was assessed by the AAPM&R Evidence, Quality and Performance Committee (EQPC), and a modified Delphi process was employed to achieve consensus on recommendation statements and evidence grading. The Strength of Recommendation Taxonomy (SORT) guided the rating of individual studies and the strength of recommendations.

RESULTS

The TEP approved five recommendations for spasticity management and five best practices for assessment and management, with one recommendation unable to be graded due to evidence limitations. Best practices were defined as widely accepted components of care, while recommendations required structured evidence reviews and grading. The consensus guidance statement represents current best practices and evidence-based treatment options, intended for use by PM&R physicians caring for patients with spasticity.

CONCLUSION

This consensus guidance provides clinicians with practical recommendations for spasticity assessment and management based on the best available evidence and expert opinion. Clinical judgment should be exercised, and recommendations tailored to individual patient needs, preferences, and risk profiles. The accompanying table summarizes the best practice recommendations for spasticity assessment and management, reflecting principles with little controversy in care delivery.

Identification of Neural and Non-Neural Origins of Joint Hyper-Resistance Based on a Novel Neuromechanical Model

Joint hyper-resistance is a common symptom in neurological disorders. It has both neural and non-neural origins, but it has been challenging to distinguish different origins based on clinical tests alone. Combining instrumented tests with parameter identification based on a neuromechanical model may allow us to dissociate the different origins of joint hyper-resistance in individual patients. However, this requires that the model captures the underlying mechanisms. Here, we propose a neuromechanical model that, in contrast to previously proposed models, accounts for muscle short-range stiffness (SRS) and its interaction with muscle tone and reflex activity. We collected knee angle trajectories during the pendulum test in 15 children with cerebral palsy (CP) and 5 typically developing children. We did the test in two conditions - hold and pre-movement - that have been shown to alter knee movement. We modeled the lower leg as an inverted pendulum actuated by two antagonistic Hill-type muscles extended with SRS. Reflex activity was modeled as delayed, linear feedback from muscle force. We estimated neural and non-neural parameters by optimizing the fit between simulated and measured knee angle trajectories during the hold condition. The model could fit a wide range of knee angle trajectories in the hold condition. The model with personalized parameters predicted the effect of pre-movement demonstrating that the model captured the underlying mechanism and subject-specific deficits. Our model may help with the identification of neural and non-neural origins of joint hyper-resistance and thereby opens perspectives for improved diagnosis and treatment selection in children with spastic CP, but such applications require further studies to establish the method's reliability.

Rigidity in Parkinson's disease: evidence from biomechanical and neurophysiological measures

Although rigidity is a cardinal motor sign in patients with Parkinson's disease (PD), the instrumental measurement of this clinical phenomenon is largely lacking, and its pathophysiological underpinning remains still unclear. Further advances in the field would require innovative methodological approaches able to measure parkinsonian rigidity objectively, discriminate the different biomechanical sources of muscle tone (neural or visco-elastic components), and finally clarify the contribution to 'objective rigidity' exerted by neurophysiological responses, which have previously been associated with this clinical sign (i.e. the long-latency stretch-induced reflex). Twenty patients with PD (67.3 ± 6.9 years) and 25 age- and sex-matched controls (66.9 ± 7.4 years) were recruited. Rigidity was measured clinically and through a robotic device. Participants underwent robot-assisted wrist extensions at seven different angular velocities randomly applied, when ON therapy. For each value of angular velocity, several biomechanical (i.e. elastic, viscous and neural components) and neurophysiological measures (i.e. short and long-latency reflex and shortening reaction) were synchronously assessed and correlated with the clinical score of rigidity (i.e. Unified Parkinson's Disease Rating Scale-part III, subitems for the upper limb). The biomechanical investigation allowed us to measure 'objective rigidity' in PD and estimate the neuronal source of this phenomenon. In patients, 'objective rigidity' progressively increased along with the rise of angular velocities during robot-assisted wrist extensions. The neurophysiological examination disclosed increased long-latency reflexes, but not short-latency reflexes nor shortening reaction, in PD compared with control subjects. Long-latency reflexes progressively increased according to angular velocities only in patients with PD. Lastly, specific biomechanical and neurophysiological abnormalities correlated with the clinical score of rigidity. 'Objective rigidity' in PD correlates with velocity-dependent abnormal neuronal activity. The observations overall (i.e. the velocity-dependent feature of biomechanical and neurophysiological measures of objective rigidity) would point to a putative subcortical network responsible for 'objective rigidity' in PD, which requires further investigation.

Quantitative assessment of spasticity: a narrative review of novel approaches and technologies

Spasticity is a complex neurological disorder, causing significant physical disabilities and affecting patients' independence and quality of daily lives. Current spasticity assessment methods are questioned for their non-standardized measurement protocols, limited reliabilities, and capabilities in distinguishing neuron or non-neuron factors in upper motor neuron lesion. A series of new approaches are developed for improving the effectiveness of current clinical used spasticity assessment methods with the developing technology in biosensors, robotics, medical imaging, biomechanics, telemedicine, and artificial intelligence. We investigated the reliabilities and effectiveness of current spasticity measures employed in clinical environments and the newly developed approaches, published from 2016 to date, which have the potential to be used in clinical environments. The new spasticity scales, taking advantage of quantified information such as torque, or echo intensity, the velocity-dependent feature and patients' self-reported information, grade spasticity semi-quantitatively, have competitive or better reliability than previous spasticity scales. Medical imaging technologies, including near-infrared spectroscopy, magnetic resonance imaging, ultrasound and thermography, can measure muscle hemodynamics and metabolism, muscle tissue properties, or temperature of tissue. Medical imaging-based methods are feasible to provide quantitative information in assessing and monitoring muscle spasticity. Portable devices, robotic based equipment or myotonometry, using information from angular, inertial, torque or surface EMG sensors, can quantify spasticity with the help of machine learning algorithms. However, spasticity measures using those devices are normally not physiological sound. Repetitive peripheral magnetic stimulation can assess patients with severe spasticity, which lost voluntary contractions. Neuromusculoskeletal modeling evaluates the neural and non-neural properties and may gain insights into the underlying pathology of spasticity muscles. Telemedicine technology enables outpatient spasticity assessment. The newly developed spasticity methods aim to standardize experimental protocols and outcome measures and enable quantified, accurate, and intelligent assessment. However, more work is needed to investigate and improve the effectiveness and accuracy of spasticity assessment.

Examining the role of intrinsic and reflexive contributions to ankle joint hyper-resistance treated with botulinum toxin-A

BACKGROUND

Spasticity, i.e. stretch hyperreflexia, increases joint resistance similar to symptoms like hypertonia and contractures. Botulinum neurotoxin-A (BoNT-A) injections are a widely used intervention to reduce spasticity. BoNT-A effects on spasticity are poorly understood, because clinical measures, e.g. modified Ashworth scale (MAS), cannot differentiate between the symptoms affecting joint resistance. This paper distinguishes the contributions of the reflexive and intrinsic pathways to ankle joint hyper-resistance for participants treated with BoNT-A injections. We hypothesized that the overall joint resistance and reflexive contribution decrease 6 weeks after injection, while returning close to baseline after 12 weeks.

METHODS

Nine participants with spasticity after spinal cord injury or after stroke were evaluated across three sessions: 0, 6 and 12 weeks after BoNT-A injection in the calf muscles. Evaluation included clinical measures (MAS, Tardieu Scale) and motorized instrumented assessment using the instrumented spasticity test (SPAT) and parallel-cascade (PC) system identification. Assessments included measures for: (1) overall resistance from MAS and fast velocity SPAT; (2) reflexive resistance contribution from Tardieu Scale, difference between fast and slow velocity SPAT and PC reflexive gain; and (3) intrinsic resistance contribution from slow velocity SPAT and PC intrinsic stiffness/damping.

RESULTS

Individually, the hypothesized BoNT-A effect, the combination of a reduced resistance (week 6) and return towards baseline (week 12), was observed in the MAS (5 participants), fast velocity SPAT (2 participants), Tardieu Scale (2 participants), SPAT (1 participant) and reflexive gain (4 participants). On group-level, the hypothesis was only confirmed for the MAS, which showed a significant resistance reduction at week 6. All instrumented measures were strongly correlated when quantifying the same resistance contribution.

CONCLUSION

At group-level, the expected joint resistance reduction due to BoNT-A injections was only observed in the MAS (overall resistance). This observed reduction could not be attributed to an unambiguous group-level reduction of the reflexive resistance contribution, as no instrumented measure confirmed the hypothesis. Validity of the instrumented measures was supported through a strong association between different assessment methods. Therefore, further quantification of the individual contributions to joint resistance changes using instrumented measures across a large sample size are essential to understand the heterogeneous response to BoNT-A injections.

Quantitative measurement of resistance force and subsequent attenuation during passive isokinetic extension of the wrist in patients with mild to moderate spasticity after stroke

Background

Spasticity is evaluated by measuring the increased resistance to passive movement, primarily by manual methods. Few options are available to measure spasticity in the wrist more objectively. Furthermore, no studies have investigated the force attenuation following increased resistance. The aim of this study was to conduct a safe quantitative evaluation of wrist passive extension stiffness in stroke survivors with mild to moderate spastic paresis using a custom motor-controlled device. Furthermore, we wanted to clarify whether the changes in the measured values could quantitatively reflect the spastic state of the flexor muscles involved in the wrist stiffness of the patients.

Materials and methods

Resistance forces were measured in 17 patients during repetitive passive extension of the wrist at velocities of 30, 60, and 90 deg/s. The Modified Ashworth Scale (MAS) in the wrist and finger flexors was also assessed by two skilled therapists and their scores were averaged (i.e., average MAS) for analysis. Of the fluctuation of resistance, we focused on the damping just after the peak forces and used these for our analysis. A repeated measures analysis of variance was conducted to assess velocity-dependence. Correlations between MAS and damping parameters were analyzed using Spearman’s rank correlation.

Results

The damping force and normalized value calculated from damping part showed significant velocity-dependent increases.

There were significant correlations (ρ = 0.53–0.56) between average MAS for wrist and the normalized value of the damping part at 90 deg/s. The correlations became stronger at 60 deg/s and 90 deg/s when the MAS for finger flexors was added to that for wrist flexors (ρ = 0.65–0.68).

Conclusions

This custom-made isokinetic device could quantitatively evaluate spastic changes in the wrist and finger flexors simultaneously by focusing on the damping part, which may reflect the decrease in resistance we perceive when manually assessing wrist spasticity using MAS.

Trial registration UMIN Clinical Trial Registry, as UMIN000030672, on July 4, 2018

Do Muscle Changes Contribute to the Neurological Disorder in Spastic Paresis?

Background

At the onset of stroke-induced hemiparesis, muscle tissue is normal and motoneurones are not overactive. Muscle contracture and motoneuronal overactivity then develop. Motor command impairments are classically attributed to the neurological lesion, but the role played by muscle changes has not been investigated.

Methods

Interaction between muscle and command disorders was explored using quantified clinical methodology-the Five Step Assessment. Six key muscles of each of the lower and upper limbs in adults with chronic poststroke hemiparesis were examined by a single investigator, measuring the angle of arrest with slow muscle stretch (XV1) and the maximal active range of motion against the resistance of the tested muscle (XA). The coefficient of shortening CSH = (XN-XV1)/XN (XN, normally expected amplitude) and of weakness CW = (XV1-XA)/XV1) were calculated to estimate the muscle and command disorders, respectively. Composite CSH (CCSH) and CW (CCW) were then derived for each limb by averaging the six corresponding coefficients. For the shortened muscles of each limb (mean CSH > 0.10), linear regressions explored the relationships between coefficients of shortening and weakness below and above their median coefficient of shortening.

Results

A total of 80 persons with chronic hemiparesis with complete lower limb assessments [27 women, mean age 47 (SD 17), time since lesion 8.8 (7.2) years], and 32 with upper limb assessments [18 women, age 32 (15), time since lesion 6.4 (9.3) years] were identified. The composite coefficient of shortening was greater in the lower than in the upper limb (0.12 ± 0.04 vs. 0.08 ± 0.04; p = 0.0002, while the composite coefficient of weakness was greater in the upper limb (0.28 ± 0.12 vs. 0.15 ± 0.06, lower limb; p < 0.0001). In the lower limb shortened muscles, the coefficient of weakness correlated with the composite coefficient of shortening above the 0.15 median CSH (R = 0.43, p = 0.004) but not below (R = 0.14, p = 0.40).

Conclusion

In chronic hemiparesis, muscle shortening affects the lower limb particularly, and, beyond a threshold of severity, may alter descending commands. The latter might occur through chronically increased intramuscular tension, and thereby increased muscle afferent firing and activity-dependent synaptic sensitization at the spinal level.

Quantifying neural and non-neural components of wrist hyper-resistance after stroke: Comparing two instrumented assessment methods

Patients with poor upper limb motor recovery after stroke are likely to develop increased resistance to passive wrist extension, i.e., wrist hyper-resistance. Quantification of the underlying neural and non-neural elastic components is of clinical interest. This cross-sectional study compared two methods: a commercially available device (NeuroFlexor®) with an experimental EMG-based device (Wristalyzer) in 43 patients with chronic stroke. Spearman's rank correlation coefficients (r) between components, modified Ashworth scale (MAS) and range of passive wrist extension (PRoM) were calculated with 95% confidence intervals. Neural as well as elastic components assessed by both devices were associated (r = 0.61, 95%CI: 0.38-0.77 and r = 0.53, 95%CI: 0.28-0.72, respectively). The neural component assessed by the NeuroFlexor® associated significantly with the elastic components of NeuroFlexor® (r = 0.46, 95%CI: 0.18-0.67) and Wristalyzer (r = 0.36, 95%CI: 0.06-0.59). The neural component assessed by the Wristalyzer was not associated with the elastic components of both devices. Neural and elastic components of both devices associated similarly with the MAS (r = 0.58, 95%CI: 0.34-0.75 vs. 0.49, 95%CI: 0.22-0.69 and r = 0.51, 95%CI: 0.25-0.70 vs. 0.30, 95%CI: 0.00-0.55); elastic components associated with PRoM (r = -0.44, 95%CI: -0.65- -0.16 vs. -0.74, 95%CI: -0.85- -0.57 for NeuroFlexor® and Wristalyzer respectively). Results demonstrate that both methods perform similarly regarding the quantification of neural and elastic wrist hyper-resistance components and have an added value when compared to clinical assessment with the MAS alone. The added value of EMG in the discrimination between neural and non-neural components requires further investigation.

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

Background: This study combined neuromechanical modeling analysis, muscle tone measurement from mechanical indentation and electrical impedance myography to assess the neural and peripheral contribution to spasticity post stroke at wrist joint. It also investigated the training effects and explored the underlying mechanism of radial extracorporeal shock wave (rESW) on spasticity.

Methods: People with first occurrence of stroke were randomly allocated to rESW intervention or control group. The intervention group received one session of rESW therapy, followed by routine therapy which was the same frequency and intensity as the control group. Outcome measures were: (1) NeuroFlexor method measured neural component (NC), elastic component (EC) and viscosity component (VC), and (2) myotonometer measured muscle tone (F) and stiffness (S), (3) electrical impedance myography measured resistance (R), reactance (X) and phase angle (θ); (4) modified Asworth scale; (5) Fugl Meyer Upper limb scale. All outcome measures were recorded at baseline, immediately post rESW and at 1-week follow-up. The differences between the paretic and non-paretic side were assessed by t-test. The effectiveness of rESW treatment were analyzed by repeated-measures one-way analysis of variance (ANOVA) at different time points.

Results: Twenty-seven participants completed the study. NC, EC, and VC of the Neuroflexor method, F and S from myotonometer were all significantly higher on the paretic side than those from the non-paretic side. R, X, and θ from electrical impedance were significantly lower on the paretic side than the non-paretic side. Immediately after rESW intervention, VC, F, and S were significantly reduced, and X was significantly increased. The clinical scores showed improvements immediate post rESW and at 1-week follow-up.

Conclusions: The observed changes in upper limb muscle properties adds further support to the theory that both the neural and peripheral components play a role in muscle spasticity. ESW intervention may be more effective in addressing the peripheral component of spasticity in terms of muscle mechanical properties changes. The clinical management of post stroke spasticity should take into consideration of both the neural and non-neural factors in order to identify optimal intervention regime.

Quantitative Modeling of Spasticity for Clinical Assessment, Treatment and Rehabilitation

Spasticity, a common symptom in patients with upper motor neuron lesions, reduces the ability of a person to freely move their limbs by generating unwanted reflexes. Spasticity can interfere with rehabilitation programs and cause pain, muscle atrophy and musculoskeletal deformities. Despite its prevalence, it is not commonly understood. Widely used clinical scores are neither accurate nor reliable for spasticity assessment and follow up of treatments. Advancement of wearable sensors, signal processing and robotic platforms have enabled new developments and modeling approaches to better quantify spasticity. In this paper, we review quantitative modeling techniques that have been used for evaluating spasticity. These models generate objective measures to assess spasticity and use different approaches, such as purely mechanical modeling, musculoskeletal and neurological modeling, and threshold control-based modeling. We compare their advantages and limitations and discuss the recommendations for future studies. Finally, we discuss the focus on treatment and rehabilitation and the need for further investigation in those directions.

Advanced quantitative estimation methods for spasticity: a literature review

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.

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

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.

A spasticity model based on feedback from muscle force explains muscle activity during passive stretches and gait in children with cerebral palsy

Muscle spasticity is characterized by exaggerated stretch reflexes and affects about 85% of the children with cerebral palsy. However, the mechanisms underlying spasticity and its influence on gait are not well understood. Here, we first aimed to model the response of spastic hamstrings and gastrocnemii in children with cerebral palsy to fast passive stretches. Then, we evaluated how the model applied to gait. We developed three models based on exaggerated proprioceptive feedback. The first model relied on feedback from muscle fiber length and velocity (velocity-related model), the second model relied on feedback from muscle fiber length, velocity, and acceleration (acceleration-related model), and the third model relied on feedback from muscle force and its first time derivative (force-related model). The force-related model better reproduced measured hamstrings and gastrocnemii activity during fast passive stretches (coefficients of determination (R2): 0.73 ± 0.10 and 0.60 ± 0.13, respectively, and root mean square errors (RMSE): 0.034 ± 0.031 and 0.009 ± 0.007, respectively) than the velocity-related model (R2: 0.46 ± 0.15 and 0.07 ± 0.13, and RMSE: 0.053 ± 0.051 and 0.015 ± 0.009), and the acceleration-related model (R2: 0.47 ± 0.15 and 0.09 ± 0.14, and RMSE: 0.052 ± 0.050 and 0.015 ± 0.008). Additionally, the force-related model predicted hamstrings and gastrocnemii activity that better correlated with measured activity during gait (cross correlations: 0.82 ± 0.09 and 0.85 ± 0.06, respectively) than the activity predicted by the velocity-related model (cross correlations: 0.49 ± 0.17 and 0.71 ± 0.22) and the acceleration-related model (cross correlations: 0.51 ± 0.16 and 0.67 ± 0.20). Our results therefore suggest that force encoding in muscle spindles in combination with altered feedback gains and thresholds underlie activity of spastic muscles during passive stretches and gait. Our model of spasticity opens new perspectives for studying movement impairments due to spasticity through simulation.

Changes in the Neural and Non-neural Related Properties of the Spastic Wrist Flexors After Treatment With Botulinum Toxin A in Post-stroke Subjects: An Optimization Study

Quantifying neural and non-neural contributions to the joint resistance in spasticity is essential for a better evaluation of different intervention strategies such as botulinum toxin A (BoTN-A). However, direct measurement of muscle mechanical properties and spasticity-related parameters in humans is extremely challenging. The aim of this study was to use a previously developed musculoskeletal model and optimization scheme to evaluate the changes of neural and non-neural related properties of the spastic wrist flexors during passive wrist extension after BoTN-A injection. Data of joint angle and resistant torque were collected from 21 chronic stroke patients before, and 4 and 12 weeks post BoTN-A injection using NeuroFlexor, which is a motorized force measurement device to passively stretch wrist flexors. The model was optimized by tuning the passive and stretch-related parameters to fit the measured torque in each participant. It was found that stroke survivors exhibited decreased neural components at 4 weeks post BoNT-A injection, which returned to baseline levels after 12 weeks. The decreased neural component was mainly due to the increased motoneuron pool threshold, which is interpreted as a net excitatory and inhibitory inputs to the motoneuron pool. Though the linear stiffness and viscosity properties of wrist flexors were similar before and after treatment, increased exponential stiffness was observed over time which may indicate a decreased range of motion of the wrist joint. Using a combination of modeling and experimental measurement, valuable insights into the treatment responses, i.e., transmission of motoneurons, are provided by investigating potential parameter changes along the stretch reflex pathway in persons with chronic stroke.