Anders Fagergren

Validation of a New Biomechanical Model to Measure Muscle Tone in Spastic Muscles

Background. There is no easy and reliable method to measure spasticity, although it is a common and important symptom after a brain injury. Objective. The aim of this study was to develop and validate a new method to measure spasticity that can be easily used in clinical practice. Methods. A biomechanical model was created to estimate the components of the force resisting passive hand extension, namely (a) inertia (IC), (b) elasticity (EC), (c) viscosity (VC), and (d) neural components (NC). The model was validated in chronic stroke patients with varying degree of hand spasticity. Electromyography (EMG) was recorded to measure the muscle activity induced by the passive stretch. Results. The model was validated in 3 ways: (a) NC was reduced after an ischemic nerve block, (b) NC correlated with the integrated EMG across subjects and in the same subject during the ischemic nerve block, and (c) NC was velocity dependent. In addition, the total resisting force and NC correlated with the modified Ashworth score. According to the model, the neural and nonneural components varied between patients. In most of the patients, but not in all, the NC dominated. Conclusions. The results suggest that the model allows valid measurement of spasticity in the upper extremity of chronic stroke patients and that it can be used to separate the neural component induced by the stretch reflex from resistance caused by altered muscle properties.

Precision grip force dynamics: a system identification approach

A linear model of the dynamics of the human precision grip is presented. The transfer function is identified as representing the peripheral motor subsystem. from the motoneuron pool to the final production of a grip force between the tip of the index finger and the thumb. The transfer function captures the limiting isometric muscle dynamics that, e.g., cortical motor areas have to act through. When identifying the transfer function the authors introduce a novel technique, common subsystem identification. This characterizes a specific subsystem in a complex biomechanical system. This technique requires data from two functionally different experiments that both involve the subsystem of interest. Two transfer functions, one for each experiment, are then estimated using a linear black box technique. The common mathematical factors, represented by poles and zeros, are used to form a new transfer function. It is concluded that this transfer function represents the common biological subsystem involved in both experiments. Here, the authors use one active and one reactive isometric grip force experiment to capture the subsystem of interest, i.e., the motoneuron pool, motor units, muscles, tendons and fingertip tissue. The characteristics of the dynamics are in agreement with previously published experiments on human neuro-muscular systems. The model, H(s)=280/(s/sup 2/+22s+280), is well suited for the representation of a force producing end-effector in simulations including a control system with sensory feedback.

Cortical Activity in Relation to Velocity Dependent Movement Resistance in the Flexor Muscles of the Hand After Stroke

Background. The role of spinal networks in spasticity is well investigated, but little is known about possible cortical contributions to hypertonicity across a joint. Objective. The authors hypothesized that there are cortical activation correlates to spasticity in stroke patients with increased muscle tone of the wrist flexors. Methods. Stroke patients and controls were scanned using event-related functional magnetic resonance imaging (fMRI) during slow and fast passive movements of the hand with simultaneous recording of passive movement resistance (PMR). Results. Control participants had velocity-dependent activity (greater for slow than fast movements) of 2 types, in areas that were also more active in passive movement than rest (eg, relative increase in activation in contralateral S1 and M1 was greater for slow than fast) and in areas that were also more active in rest than passive movement (eg, relative decrease in activation in occipital areas and ipsilateral precentral gyrus was greater for fast than slow). In the patient group, with large interindividual variation of spasticity, we found an association between PMR and the velocity-dependent activity in ipsilateral S1 (area 3b) extending into M1 (area 4a), contralateral cingulate cortex, supplementary motor area (SMA), Brodmann Area 45 (BA 45), and cerebellum. Post hoc testing also revealed a similar correlation in S1 and M1 bilaterally in controls and showed that patients activated ipsilateral S1 and M1 more than controls in the velocity-dependent condition. Conclusions. The findings suggest the possibility of ipsilateral sensory and motor cortical involvement in spasticity after stroke, which warrant further investigation.

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

Quantifying neural and non-neural contributions to increased joint resistance in spasticity is essential for a better understanding of its pathophysiological mechanisms and evaluating different intervention strategies. However, direct measurement of spasticity-related manifestations, e.g., motoneuron and biophysical properties in humans, is extremely challenging. In this vein, we developed a forward neuromusculoskeletal model that accounts for dynamics of muscle spindles, motoneuron pools, muscle activation and musculotendon of wrist flexors and relies on the joint angle and resistant torque as the only input measurement variables. By modeling the stretch reflex pathway, neural and non-neural related properties of the spastic wrist flexors were estimated during the wrist extension test. Joint angle and resistant torque were collected from 17 persons with chronic stroke and healthy controls using NeuroFlexor, a motorized force measurement device during the passive wrist extension test. The model was optimized by tuning the passive and stretch reflex-related parameters to fit the measured torque in each participant. We found that persons with moderate and severe spasticity had significantly higher stiffness than controls. Among subgroups of stroke survivors, the increased neural component was mainly due to a lower muscle spindle rate at 50% of the motoneuron recruitment. The motoneuron pool threshold was highly correlated to the motoneuron pool gain in all subgroups. The model can describe the overall resistant behavior of the wrist joint during the test. Compared to controls, increased resistance was predominantly due to higher elasticity and neural components. We concluded that in combination with the NeuroFlexor measurement, the proposed neuromusculoskeletal model and optimization scheme served as suitable tools for investigating potential parameter changes along the stretch-reflex pathway in persons with spasticity.

A neuromusculoskeletal model to simulate the isokinetic ankle dorsiflexion test of spasticity

Spasticity is a motor disorder characterized by a velocity-dependent increase in tonic stretch reflexes [1], commonly seen in many neurological disorders. Clinically, spasticity is measured by an examiner rotating a joint and simultaneously estimating the resistance according to an ordinal scale. However, the limited reliability of the measurement and the impossibility to discriminate between the underlying neural (stretch reflex) and non-neural (i.e. muscle mechanics) contributions have been the motivation to develop methods describing resistance joint torque quantitatively. The aim of this preliminary study is to develop a forward neuromusculoskeletal model consisting of the explicit musculotendon, muscle spindle, and motoneuron pool, which can simulate the passive isokinetic ankle dorsiflexion test of spasticity.