Jakob Lund Dideriksen

Motor unit recruitment strategies and muscle properties determine the influence of synaptic noise on force steadiness

Motoneurons receive synaptic inputs from tens of thousands of connections that cause membrane potential to fluctuate continuously (synaptic noise), which introduces variability in discharge times of action potentials. We hypothesized that the influence of synaptic noise on force steadiness during voluntary contractions is limited to low muscle forces. The hypothesis was examined with an analytical description of transduction of motor unit spike trains into muscle force, a computational model of motor unit recruitment and rate coding, and experimental analysis of interspike interval variability during steady contractions with the abductor digiti minimi muscle. Simulations varied contraction force, level of synaptic noise, size of motor unit population, recruitment range, twitch contraction times, and level of motor unit short-term synchronization. Consistent with the analytical derivations, simulations and experimental data showed that force variability at target forces above a threshold was primarily due to low-frequency oscillations in neural drive, whereas the influence of synaptic noise was almost completely attenuated by two low-pass filters, one related to convolution of motoneuron spike trains with motor unit twitches (temporal summation) and the other attributable to summation of single motor unit forces (spatial summation). The threshold force above which synaptic noise ceased to influence force steadiness depended on recruitment range, size of motor unit population, and muscle contractile properties. This threshold was low (<10% of maximal force) for typical values of these parameters. Results indicate that motor unit recruitment and muscle properties of a typical muscle are tuned to limit the influence of synaptic noise on force steadiness to low forces and that the inability to produce a constant force during stronger contractions is mainly attributable to the common low-frequency oscillations in motoneuron discharge rates.

Neuromuscular adjustments that constrain submaximal EMG amplitude at task failure of sustained isometric contractions.

The amplitude of the surface EMG does not reach the level achieved during a maximal voluntary contraction force at the end of a sustained, submaximal contraction, despite near-maximal levels of voluntary effort. The depression of EMG amplitude may be explained by several neural and muscular adjustments during fatiguing contractions, including decreased net neural drive to the muscle, changes in the shape of the motor unit action potentials, and EMG amplitude cancellation. The changes in these parameters for the entire motor unit pool, however, cannot be measured experimentally. The present study used a computational model to simulate the adjustments during sustained isometric contractions and thereby determine the relative importance of these factors in explaining the submaximal levels of EMG amplitude at task failure. The simulation results indicated that the amount of amplitude cancellation in the simulated EMG (∼ 40%) exhibited a negligible change during the fatiguing contractions. Instead, the main determinant of the submaximal EMG amplitude at task failure was a decrease in muscle activation (number of muscle fiber action potentials), due to a reduction in the net synaptic input to motor neurons, with a lesser contribution from changes in the shape of the motor unit action potentials. Despite the association between the submaximal EMG amplitude and reduced muscle activation, the deficit in EMG amplitude at task failure was not consistently associated with the decrease in neural drive (number of motor unit action potentials) to the muscle. This indicates that the EMG amplitude cannot be used as an index of neural drive.

The effective neural drive to muscles is the common synaptic input to motor neurons

Despite the non‐linear property of individual motor neurons, the pool of motor neurons linearizes the relation between their common synaptic input and the neural drive to the muscle, i.e. the ensemble of axonal action potentials reaching the muscle from the spinal cord. In the frequency bandwidth relevant for force generation, the motor neuron pool attenuates the input signals sent independently to each motor neuron and transfers only the common signal components with a pure scaling. The effective neural drive to the muscle tends to exactly replicate, without phase distortion, the common synaptic input to motor neurons for increasing number of active motor neurons. The classic definition and functional meaning of motor unit synchronization are discussed in relation to the role of common input in determining the neural drive to muscle.

Non-invasive characterization of motor unit behaviour in pathological tremor

This paper presents the fully automatic identification of motor unit spike trains from high-density surface electromyograms (EMG) in pathological tremor. First, a mathematical derivation is provided to theoretically prove the possibility of decomposing noise-free high-density surface EMG signals into motor unit spike trains with high correlation, which are typical of tremor contractions. Further, the proposed decomposition method is tested on simulated signals with different levels of noise and on experimental signals from 14 tremor-affected patients. In the case of simulated tremor with central frequency ranging from 5 Hz to 11 Hz and signal-to-noise ratio of 20 dB, the method identified ∼8 motor units per contraction with sensitivity in spike timing identification ≥ 95% and false alarm and miss rates ≤ 5%. In experimental signals, the number of identified motor units varied substantially (range 0-21) across patients and contraction types, as expected. The behaviour of the identified motor units was consistent with previous data obtained by intramuscular EMG decomposition. These results demonstrate for the first time the possibility of a fully non-invasive investigation of motor unit behaviour in tremor-affected patients. The method provides a new means for physiological investigations of pathological tremor.

An integrative model of motor unit activity during sustained submaximal contractions

The purpose of the study was to expand a model of motor unit recruitment and rate coding (30) to simulate the adjustments that occur during a fatiguing contraction. The major new components of the model were the introduction of time-varying parameters for motor unit twitch force, recruitment, discharge rate, and discharge variability, and a control algorithm that estimates the net excitation needed by the motoneuron pool to maintain a prescribed target force. The fatigue-induced changes in motor unit activity in the expanded model are a function of changes in the metabolite concentrations that were computed with a compartment model of the intra- and extracellular spaces. The model was validated by comparing the simulation results with data available from the literature and experimentally recorded in the present study during isometric contractions of the first dorsal interosseus muscle. The output of the model was able to replicate a number of experimental findings, including the time to task failure for a range of target forces, the changes in motor unit discharge rates, the skewness and kurtosis of the interspike interval distributions, discharge variability, and the discharge characteristics of newly recruited motor units. The model output provides an integrative perspective of the adjustments during fatiguing contractions that are difficult to measure experimentally.

A Multimodal Human–Robot Interface to Drive a Neuroprosthesis for Tremor Management

Tremor is the most prevalent movement disorder, and its incidence is increasing with aging. In spite of the numerous therapeutic solutions available, 65% of those suffering from upper limb tremor report serious difficulties during their daily living. This gives rise to research on different treatment alternatives, amongst which wearable robots that apply selective mechanical loads constitute an appealing approach. In this context, the current work presents a multimodal human-robot interface to drive a neuroprosthesis for tremor management. Our approach relies on the precise characterization of the tremor to modulate a functional electrical stimulation system that compensates for it. The neuroprosthesis is triggered by the detection of the intention to move derived from the analysis of electroencephalographic activity, which provides a natural interface with the user. When a prediction is delivered, surface electromyography serves to detect the actual onset of the tremor in the presence of volitional activity. This information in turn triggers the stimulation, which relies on tremor parameters-amplitude and frequency-derived from a pair of inertial sensors that record the kinematics of the affected joint. Surface electromyography also yields a first characterization of the tremor, together with precise information on the preferred stimulation site. Apart from allowing for an optimized performance of the system, our multimodal approach permits the implementation of redundant methods to both enhance the reliability of the system and adapt to the specific needs of different users. Results with a representative group of patients illustrate the performance of the interface presented here and demonstrate its feasibility.

Online Tremor Suppression Using Electromyography and Low-Level Electrical Stimulation

Tremor is one of the most prevalent movement disorders. There is a large proportion of patients (around 25%) in whom current treatments do not attain a significant tremor reduction. This paper proposes a tremor suppression strategy that detects tremor from the electromyographic signals of the muscles from which tremor originates and counteracts it by delivering electrical stimulation to the antagonist muscles in an out of phase manner. The detection was based on the iterative Hilbert transform and stimulation was delivered above the motor threshold (motor stimulation) and below the motor threshold (sensory stimulation). The system was tested on six patients with predominant wrist flexion/extension tremor (four with Parkinson disease and two with Essential tremor) and led to an average tremor reduction in the range of 46%-81% and 35%-48% across five patients when using the motor and sensory stimulation, respectively. In one patient, the system did not attenuate tremor. These results demonstrate that tremor attenuation might be achieved by delivering electrical stimulation below the motor threshold, preventing muscle fatigue and discomfort for the patients, which sets the basis for the development of an alternative treatment for tremor.

A Novel Technology for Motion Capture Using Passive UHF RFID Tags

Although there are several existing methods for human motion capture, they all have important limitations and hence there is the need to explore fundamentally new approaches. Here, we present a method based on a radio frequency identification (RFID) system with passive ultra high frequency (UHF) tags placed on the body segments whose kinematics is to be captured. Dual polarized antennas are used to estimate the inclination of each tag based on the polarization of the tag responses. The method has been validated experimentally for the shank and thigh in the sagittal plane during treadmill walking. The reference segment angles for the validation were obtained by an optoelectronic system. Although the method is in its initial phase of development, the results of the validation are promising and show that the movement information can be extracted from the RFID response signals.

EMG-Based Characterization of Pathological Tremor Using the Iterated Hilbert Transform

The identification and characterization of pathological tremor are necessary for the development of techniques for tremor suppression, for example, based on functional electrical stimulation. For this purpose, the amplitude and phase characteristics of the tremor signal should be estimated by effective detection techniques, either from the kinematics or from muscle recordings. This paper presents an approach for the estimation of the characteristics of pathological tremor from the surface electromyogram (EMG) signal based on the iterated Hilbert transform (IHT). It is shown that the IHT allows an asymptotically exact modeling of the tremor and the voluntary activity components in the surface EMG, and an effective demodulation of the pathological tremor parameters. The method was tested on signals generated by a recent model for tremor generation as well as experimentally recorded from patients affected by pathological tremor. The results showed the ability of the proposed approach to demodulate effectively the tremor amplitude (average correlation with imposed amplitude: R2 = 0.52), the frequency (root mean square error in frequency estimation: 2.6 Hz), and phase, as well as the degree of voluntary activity (correlation with simulated inertial load: R2 = 0.62 ). The application of the method to the experimental data indicated that the estimated tremor component closely resembles inertial measurements of limb movement (peak cross correlation across four patients: 0.62 ± 0.15). Compared to the performance of empirical mode decomposition, the proposed method proved to be more accurate for tremor characterization without a priori knowledge of the tremor characteristics. This method can be used as a part of a control system in strategies for suppression of tremor.

A Model of the Surface Electromyogram in Pathological Tremor

The study developed a novel multiscale model for simulating the surface electromyogram (EMG) of an antagonistic pair of muscles during pathological tremor. By combining and expanding mathematical descriptions from motor units to limb kinematics, the model constitutes the first attempt to simulate the surface EMG and the individual motor unit activity under the influence of descending voluntary command, oscillatory noise in the descending signal, and afferent feedback when controlling a freely moving limb to achieve a predefined angular trajectory. The oscillatory noise was adjusted to simulate various types of pathological tremor. The simulations replicated previously reported experimental results for the power spectral density of the surface EMG, the angular velocity of the limb, and single motor unit activity. The model provides a powerful tool for extracting information about how the surface EMG can be used to describe tremor in various conditions, including different tremor frequencies and intensities, that cannot be achieved solely with experimental approaches.