Modeling of the H-reflex facilitation during ramp and hold contractions

Wilmink, Ronnie J. H.; Slot, Peter Jacob; Sinkjær, Thomas

doi:10.1007/bf00161092

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…These curves provided good fits to the data (average r 2 = 0.61 for HRup and 0.62 for HRdown subjects). The ascending limb of the H‐reflex recruitment curve has been described as sigmoidal, and a sigmoidal curve generally provides a good fit when the H max value is already calculated and available . However, in this study, the H‐reflex and M‐wave measurements from 6 sessions were pooled to generate the H‐reflex/M‐wave recruitment curve and H max , and the corresponding M‐wave size had to be estimated.…”

Section: Methodsmentioning

confidence: 99%

Soleus H‐reflex operant conditioning changes the H‐reflex recruitment curve

Thompson¹,

Chen²,

Wolpaw³

2012

Muscle and Nerve

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Introduction Operant conditioning can gradually change the human soleus H-reflex. The protocol conditions the reflex near M-wave threshold. This study examined its impact on the reflexes at other stimulus strengths. Methods H-reflex recruitment curves were obtained before and after a 24-session exposure to an up-conditioning (HRup) or down-conditioning (HRdown) protocol and were compared. Results In both HRup and HRdown subjects, conditioning affected the entire H-reflex recruitment curve. In 5 of 6 HRup and 3 of 6 HRdown subjects, conditioning elevated (HRup) or depressed (HRdown), respectively, the entire curve. In the other HRup subject or the other 3 HRdown subjects, the curve was shifted to the left or to the right, respectively. Discussion H-reflex conditioning does not simply change the H-reflex to a stimulus of particular strength; it also changes the H-reflexes to stimuli of different strengths. Thus, it is likely to affect many actions in which this pathway participates.

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Section: Methodsmentioning

confidence: 99%

Soleus H‐reflex operant conditioning changes the H‐reflex recruitment curve

Thompson¹,

Chen²,

Wolpaw³

2012

Muscle and Nerve

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“…However, the synaptic weight for a given MNPMC input-force relation can be computed in none of them. The model of Slot and Sinkjaer (1994) and Wilmink et al (1996), which resembles most the present one, has the following deficiencies with regard to our objectives:…”

Section: Introductionmentioning

confidence: 97%

Computer simulation of the motoneuron pool-muscle complex. I. Input system and motoneuron pool

Nussbaumer

Rüegg

Studer³

et al. 2002

Biological Cybernetics

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The aim of the present study was to simulate the input system and the motoneuron (MN) pool of the MN pool-muscle complex (MNPMC). Input fibers, which can originate from command centers in the central nervous system or from sensory organs, activate the MN pool. They generate sequences of action potentials, the frequency of which is proportional to a time-dependent activation factor (which is an input to the model). Different connection patterns between the input fibers and motor units (MUs) are allowed. For simplicity and since no precise experimental data are available, 70 input fibers and 4 boutons per fiber and MN are simulated (this corresponds approximately to the monosynaptic group-Ia input of the cat medial gastrocnemius muscle). Each bouton generates the same conductance change in the postsynaptic membrane. The MNs are modeled with a single compartment and a homogeneous membrane. According to experimental data, the membrane leakage conductance and capacitance are MU dependent. Since the precise relation is unknown: (a) the computed relation between MU contraction force and the MN leakage conductance was taken from a steady-state MNPMC model, and (b) the capacitance was assumed to be proportional to the leakage conductance. The MN membrane includes time- and voltage-dependent ionic channels (fast and slow K(+) and low- and high-threshold Ca(2+) channels). The density and time constant of the slow K(+) channels and the density of the Ca(2+) channels were fitted to approximate afterhyperpolarization characteristics and frequency-injected current relations of type-identified cat MNs. If the membrane reaches a voltage threshold the MNs generate action potentials, which were simulated by voltage pulses. The activation of the MN pool of the human first dorsal interosseus muscle was simulated with injected and synaptic currents in order to illustrate the size principle, synaptic noise, and other features of muscle activation. It is concluded that the present model reproduces the main properties of the input-output relations of different MN types within a muscle. Together with the simulation of the muscle force and the surface EMG, which will be published in subsequent papers, it will be a powerful tool for reproducing experiments on the motor system and investigating functional mechanisms of motor control.

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A sigmoid function is the best fit for the ascending limb of the Hoffmann reflex recruitment curve

2007

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The Hoffmann (H)-reflex has been studied extensively as a measure of spinal excitability. Often, researchers compare the H-reflex between experimental conditions with values determined from a recruitment curve (RC). An RC is obtained experimentally by varying the stimulus intensity to a nerve and recording the peak-to-peak amplitudes of the evoked H-reflex and direct motor (M)-wave. The values taken from an RC may provide different information with respect to a change in reflex excitability. Therefore, it is important to obtain a number of RC parameters for comparison. RCs can be obtained with a measure of current (HCRC) or without current (HMRC). The ascending limb of the RC is then fit with a mathematical analysis technique in order to determine parameters of interest such as the threshold of activation and the slope of the function. The purpose of this study was to determine an unbiased estimate of the specific parameters of interest in an RC through mathematical analysis. We hypothesized that a standardized analysis technique could be used to ascertain important points on an RC, regardless of data presentation methodology (HCRC or HMRC). For both HCRC and HMRC produced using 40 randomly delivered stimuli, six different methods of mathematical analysis [linear regression, polynomial, smoothing spline, general least squares model with custom logistic (sigmoid) equation, power, and logarithmic] were compared using goodness of fit statistics (r-square, RMSE). Behaviour and robustness of selected curve fits were examined in various applications including RCs generated during movement and somatosensory conditioning from published data. Results show that a sigmoid function is the most reliable estimate of the ascending limb of an H-reflex recruitment curve for both HCRC and HMRC. Further, the parameters of interest change differentially with respect to the presentation methodology and the analysis technique. In conclusion, the sigmoid function is a reliable analysis technique which mimics the physiologically based prediction of the input/output relation of the ascending limb of the recruitment curve. Therefore, the sigmoid function should be considered an acceptable and preferable analytical tool for H-reflex recruitment curves obtained with reference to stimulation current or M-wave amplitude.

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Modeling of the H-reflex facilitation during ramp and hold contractions

Cited by 5 publications

References 11 publications

Soleus H‐reflex operant conditioning changes the H‐reflex recruitment curve

Soleus H‐reflex operant conditioning changes the H‐reflex recruitment curve

Computer simulation of the motoneuron pool-muscle complex. I. Input system and motoneuron pool

A sigmoid function is the best fit for the ascending limb of the Hoffmann reflex recruitment curve

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