Key pointsr 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.r 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.r 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.r 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.Abstract We analysed the transformation of synaptic input to the pool of motor neurons into the neural drive to the muscle. The aim was to explain the relations between common oscillatory signals sent to motor neurons and the effective component of the neural signal sent to muscles as output of the spinal cord circuitries. The approach is based on theoretical derivations, computer simulations, and experiments. It is shown theoretically that for frequencies smaller than the average discharge rates of the motor neurons, the pool of motor neurons determines a pure amplification of the frequency components common to all motor neurons, so that the common input is transmitted almost undistorted and the non-common components are strongly attenuated. The effective neural drive to the muscle thus mirrors the common synaptic input to motor neurons. The simulations with three models of motor neuron confirmed the theoretical results by showing that the coherence function between common input components and the neural drive to the muscle tends to 1 when increasing the number of active motor neurons. This result, which was relatively insensitive to the type of model used, was also supported experimentally by observing that, in the low-pass signal bandwidth, the peak in coherence between groups of motor units of the abductor digiti minimi muscle of five healthy subjects tended to 1 when increasing the number of motor units. These results have implications for our understanding of the neural control of muscles as well as for methods used for estimating the strength of common input to populations of motor neurons.

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.

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.

Pathological tremors are involuntary oscillatory movements which cannot be fully attenuated using conventional treatments. For this reason, several studies have investigated the use of neuromuscular electrical stimulation for tremor suppression. In a recent study, however, we found that electrical stimulation below the motor threshold also suppressed tremor, indicating involvement of afferent pathways. In this study, we further explored this possibility by systematically investigating how tremor suppression by afferent stimulation depends on the stimulation settings. In this way, we aimed at identifying the optimal stimulation strategy, as well as to elucidate the underlying physiological mechanisms of tremor suppression. Stimulation strategies varying the stimulation intensity and pulse timing were tested in nine tremor patients using either intramuscular or surface stimulation. Significant tremor suppression was observed in six patients (tremor suppression > 75% was observed in three patients) and the average optimal suppression level observed across all subjects was 52%. The efficiency for each stimulation setting, however, varied substantially across patients and it was not possible to identify a single set of stimulation parameters that yielded positive results in all patients. For example, tremor suppression was achieved both with stimulation delivered in an out-of-phase pattern with respect to the tremor, and with random timing of the stimulation. Overall, these results indicate that low-current stimulation of afferent fibers is a promising approach for tremor suppression, but that further research is required to identify how the effect can be maximized in the individual patient.