motoRneuron: an open-source R toolbox for time-domain motor unit analyses

Motor unit synchronization is the tendency of motor neurons and their associated muscle fibers to discharge near-simultaneously. It has been theorized as a control mechanism for force generation by common excitatory inputs to these motor neurons. Magnitude of synchronization is calculated from peaks in cross-correlation histograms between motor unit discharge trains. However, there are many different methods for detecting these peaks and even more indices for calculating synchronization from them. Methodology is diverse, typically laboratory-specific and requires expensive software, like Matlab or LabView. This lack of standardization makes it difficult to draw definitive conclusions about motor unit synchronization. A free, open-source toolbox, “motoRneuron”, for the R programming language, has been developed which contains functions for calculating time domain synchronization using different methods found in the literature. The objective of this paper is to detail the toolbox’s functionality and present a case study showing how the same synchronization index can differ when different methods are used to compute it. A pair of motor unit action potential trains were collected from the forearm during a isometric finger flexion task using fine wire electromyography. The motoRneuron package was used to analyze the discharge time of the motor units for time-domain synchronization. The primary function “mu_synch” automatically performed the cross-correlation analysis using three different peak detection methods, the cumulative sum method, the z-score method, and a subjective visual method. As function parameters defined by the user, only first order recurrence intervals were calculated and a 1 ms bin width was used to create the cross correlation histogram. Output from the function were six common synchronization indices, the common input strength (CIS), k′, k′ − 1, E, S, and Synch Index. In general, there was a high degree of synchronization between the two motor units. However, there was a varying degree of synchronization between methods. For example, the widely used CIS index, which represents a rate of synchronized discharges, shows a 45% difference between the visual and z-score methods. This singular example demonstrates how a lack of consensus in motor unit synchronization methodologies may lead to substantially differing results between studies. The motoRneuron toolbox provides researchers with a standard interface and software to examine time-domain motor unit synchronization.

The relative strength of common synaptic input to motor neurons is not a determinant of the maximal rate of force development in humans

Correlation between motor unit discharge times, often referred to as motor unit synchronization, is determined by common synaptic input to motor neurons. Although it has been largely speculated that synchronization should influence the rate of force development, the association between the degree of motor unit synchronization and rapid force generation has not been determined. In this study, we examined this association with both simulations and experimental motor unit recordings. The analysis of experimental motor unit discharges from the tibialis anterior muscle of 20 healthy individuals during rapid isometric contractions revealed that the average motor unit discharge rate was associated with the rate of force development. Moreover, the extent of motor unit synchronization was entirely determined by the average motor unit discharge rate ( R > 0.7, P < 0.0001). The simulation model demonstrated that the relative proportion of common synaptic input received by motor neurons, which determines motor unit synchronization, does not influence the rate of force development ( R = 0.03, P > 0.05). Nonetheless, the estimates of correlation between motor unit spike trains were significantly correlated with the rate of force generation ( R > 0.8, P < 0.0001). These results indicate that the average motor unit discharge rate, but not the degree of motor unit synchronization, contributes to most of the variance of human contractile speed among individuals. In addition, estimates of correlation between motor unit discharge times depend strongly on the number of identified motor units and therefore are not indicative of the strength of common input. NEW & NOTEWORTHY It is commonly assumed that motor unit synchronization has an impact on the rate of force development of a muscle. Here we present computer simulations and experimental data of human tibialis anterior motor units during rapid contractions that show that motor unit synchronization is not a determinant of the rate of force production. This conclusion clarifies the neural determinants of rapid force generation.

motoRneuron: an open-source R toolbox for time-domain motor unit analyses

Motor unit synchronization is the tendency of motor neurons and their associated muscle fibers to discharge near-simultaneously. It has been theorized as a control mechanism for force generation by common excitatory inputs to these motor neurons. Magnitude of synchronization is calculated from peaks in cross-correlation histograms between motor unit discharge trains. However, there are many different methods for detecting these peaks and even more indices for calculating synchronization from them. Methodology is typically laboratory-specific and requires expensive software, like Matlab or LabView. This lack of standardization makes it difficult to draw definitive conclusions about motor unit synchronization. To combat this, we have developed a freely available, open-source toolbox, “motoRneuron”, for the R programming language. This toolbox contains functions for calculating time domain synchronization using different methods found in the literature. Our objective is to detail the program’s functionality and provide a clear use-case for implementation. The programs primary function “mu_synch” automatically performs the cross-correlation analysis based on user input. Automated peak detection methods such as the cumulative sum method and the z-score method, as well as subjective, visual analysis are available. Users can also define other parameters like the number of recurrence intervals to be used and histogram bin size. The function outputs six common synchronization indices, the common input strength (CIS), k’, k’-1, E, S, and Synch Index. This toolbox allows for better standardization of techniques and for more comprehensive data mining in the motor control community.

motoRneuron: an open-source R toolbox for time-domain motor unit analyses

Motor unit synchronization is the tendency of motor neurons and their associated muscle fibers to discharge near-simultaneously. It has been theorized as a control mechanism for force generation by common excitatory inputs to these motor neurons. Magnitude of synchronization is calculated from peaks in cross-correlation histograms between motor unit discharge trains. However, there are many different methods for detecting these peaks and even more indices for calculating synchronization from them. Methodology is typically laboratory-specific and requires expensive software, like Matlab or LabView. This lack of standardization makes it difficult to draw definitive conclusions about motor unit synchronization. To combat this, we have developed a freely available, open-source toolbox, “motoRneuron”, for the R programming language. This toolbox contains functions for calculating time domain synchronization using different methods found in the literature. Our objective is to detail the program’s functionality and provide a clear use-case for implementation. The programs primary function “mu_synch” automatically performs the cross-correlation analysis based on user input. Automated peak detection methods such as the cumulative sum method and the z-score method, as well as subjective, visual analysis are available. Users can also define other parameters like the number of recurrence intervals to be used and histogram bin size. The function outputs six common synchronization indices, the common input strength (CIS), k’, k’-1, E, S, and Synch Index. This toolbox allows for better standardization of techniques and for more comprehensive data mining in the motor control community.

New Perspectives for Computer-Aided Discrimination of Parkinson’s Disease and Essential Tremor

Pathological tremor is a common but highly complex movement disorder, affecting ~5% of population older than 65 years. Different methodologies have been proposed for its quantification. Nevertheless, the discrimination between Parkinson’s disease tremor and essential tremor remains a daunting clinical challenge, greatly impacting patient treatment and basic research. Here, we propose and compare several movement-based and electromyography-based tremor quantification metrics. For the latter, we identified individual motor unit discharge patterns from high-density surface electromyograms and characterized the neural drive to a single muscle and how it relates to other affected muscles in 27 Parkinson’s disease and 27 essential tremor patients. We also computed several metrics from the literature. The most discriminative metrics were the symmetry of the neural drive to muscles, motor unit synchronization, and the mean log power of the tremor harmonics in movement recordings. Noteworthily, the first two most discriminative metrics were proposed in this study. We then used decision tree modelling to find the most discriminative combinations of individual metrics, which increased the accuracy of tremor type discrimination to 94%. In summary, the proposed neural drive-based metrics were the most accurate at discriminating and characterizing the two most common pathological tremor types.