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

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7907
Author(s):  
Andrew J. Tweedell ◽  
Matthew S. Tenan
Keyword(s):  
Open Source ◽  
Motor Unit ◽  
Motor Neurons ◽  
Time Domain ◽  
Cross Correlation ◽  
Motor Units ◽  
Motor Unit Action Potential ◽  
Cross Correlation Analysis ◽  
Motor Unit Synchronization ◽  
The Cross

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.

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

2019 ◽  
Author(s):  
Andrew J Tweedell ◽  
Matthew S Tenan
Keyword(s):  
Open Source ◽  
Motor Unit ◽  
Motor Neurons ◽  
Time Domain ◽  
Cross Correlation ◽  
Visual Analysis ◽  
Detection Methods ◽  
Common Input ◽  
Cross Correlation Analysis ◽  
Motor Unit Synchronization

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.

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

2019 ◽  
Author(s):  
Andrew J Tweedell ◽  
Matthew S Tenan
Keyword(s):  
Open Source ◽  
Motor Unit ◽  
Motor Neurons ◽  
Time Domain ◽  
Cross Correlation ◽  
Visual Analysis ◽  
Detection Methods ◽  
Common Input ◽  
Cross Correlation Analysis ◽  
Motor Unit Synchronization

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.

Sensitivity of the cross-correlaton between simulated surface EMGs for two muscles to detect motor unit synchronization

2007 ◽  
Vol 102 (3) ◽  
pp. 1193-1201 ◽  
Author(s):  
Kevin G. Keenan ◽  
Dario Farina ◽  
François G. Meyer ◽  
Roberto Merletti ◽  
Roger M. Enoka
Keyword(s):  
Motor Unit ◽  
Cross Correlation ◽  
Subcutaneous Fat ◽  
Motor Units ◽  
Thin Layers ◽  
Cross Correlation Analysis ◽  
Motor Unit Synchronization ◽  
Simulated Surface ◽  
Fat Thickness ◽  
The Cross

The purpose of the study was to evaluate the use of cross-correlation analysis between simulated surface electromyograms (EMGs) of two muscles to quantify motor unit synchronization. The volume conductor simulated a cylindrical limb with two muscles and bone, fat, and skin tissues. Models of two motor neuron pools were used to simulate 120 s of surface EMG that were detected over both muscles. Short-term synchrony was established using a phenomenological model that aligned the discharge times of selected motor units within and across muscles to simulate physiological levels of motor unit synchrony. The correlation between pairs of surface EMGs was estimated as the maximum of the normalized cross-correlation function. After imposing four levels of motor unit synchrony across muscles, five parameters were varied concurrently in the two muscles to examine their influence on the correlation between the surface EMGs: 1) excitation level (5, 10, 15, and 50% of maximum); 2) muscle size (350 and 500 motor units); 3) fat thickness (1 and 4 mm); 4) skin conductivity (0.1 and 1 S/m); and 5) mean motor unit conduction velocity (2.5 and 4 m/s). Despite a constant and high level of motor unit synchronization among pairs of motor units across the two muscles, the cross-correlation index ranged from 0.08 to 0.56, with variation in the five parameters. For example, cross-correlation of EMGs from pairs of hand muscles, each having thin layers of subcutaneous fat and mean motor unit conduction velocities of 4 m/s, may be relatively insensitive to the level of synchronization across muscles. In contrast, cross-correlation of EMGs from pairs of leg muscles, with larger fat thickness, may exhibit a different sensitivity. These results indicate that cross correlation of the surface EMGs from two muscles provides a limited measure of the level of synchronization between motor units in the two muscles.

Coherence at 16-32 Hz Can Be Caused by Short-Term Synchrony of Motor Units

2005 ◽  
Vol 94 (1) ◽  
pp. 105-118 ◽  
Author(s):  
Chet T. Moritz ◽  
Evangelos A. Christou ◽  
François G. Meyer ◽  
Roger M. Enoka
Keyword(s):  
Motor Unit ◽  
Motor Neurons ◽  
Time Domain ◽  
Motor Units ◽  
Motor Unit Synchronization ◽  
The Cross ◽  
Highly Correlated ◽  
The Time Domain ◽  
Coherence Spectrum

Time- and frequency-domain measures of discharge times for pairs of motor units are used to infer the proportion of common synaptic input received by motor neurons. The physiological mechanisms that can produce the experimentally observed peaks in the cross-correlation histogram and the coherence spectrum are uncertain. The present study used a computational model to impose synchronization on the discharge times of motor units. Randomly selected discharge times of a unit that was being synchronized to a reference unit were aligned with some of the discharge times of the reference unit, provided the original discharge time was within 30 ms of the discharge by the reference unit. All time-domain measures (indexes CIS, E, and k′) were sensitive to changes in the level of imposed motor-unit synchronization ( P < 0.01). In addition, synchronization caused a peak between 16 and 32 Hz in the coherence spectrum. The shape of the cross-correlogram determined the frequency at which the peak occurred in the coherence spectrum. Further, the magnitude of the coherence peak was highly correlated with the time-domain measures of motor-unit synchronization ( r2 > 0.80), with the highest correlation occurring for index E ( r2 = 0.98). Thus the peak in the 16- to 32-Hz band of the coherence spectrum can be caused by the time that individual discharges are advanced or delayed to produce synchrony. Although the in vivo processes that adjust the timing of motor-unit discharges are not fully understood, these results suggest that they may not depend entirely on an oscillatory drive by the CNS.

A simulation study to examine the use of cross-correlation as an estimate of surface EMG cross talk

2003 ◽  
Vol 94 (4) ◽  
pp. 1324-1334 ◽  
Author(s):  
Madeleine M. Lowery ◽  
Nikolay S. Stoykov ◽  
Todd A. Kuiken
Keyword(s):  
Correlation Function ◽  
Motor Unit ◽  
Cross Talk ◽  
Cross Correlation ◽  
Surface Emg ◽  
Cross Correlation Function ◽  
Cross Correlation Analysis ◽  
Motor Unit Synchronization ◽  
Common Drive ◽  
The Cross

Cross-correlation between surface electromyogram (EMG) signals is commonly used as a means of quantifying EMG cross talk during voluntary activation. To examine the reliability of this method, the relationship between cross talk and the cross-correlation between surface EMG signals was examined by using model simulation. The simulation results illustrate an increase in cross talk with increasing subcutaneous fat thickness. The results also indicate that the cross-correlation function decays more rapidly with increasing distance from the active fibers than cross talk, which was defined as the normalized EMG amplitude during activation of a single muscle. The influence of common drive and short-term motor unit synchronization on the cross-correlation between surface EMG signals was also examined. While common drive did not alter the maximum value of the cross-correlation function, the correlation increased with increasing motor unit synchronization. It is concluded that cross-correlation analysis is not a suitable means of quantifying cross talk or of distinguishing between cross talk and coactivation during voluntary contraction. Furthermore, it is possible that a high correlation between surface EMG signals may reflect an association between motor unit firing times, for example due to motor unit synchronization.

scholarly journals Motor unit control and force fluctuation during fatigue

2009 ◽  
Vol 107 (1) ◽  
pp. 235-243 ◽  
Author(s):  
Paola Contessa ◽  
Alexander Adam ◽  
Carlo J. De Luca
Keyword(s):  
Motor Unit ◽  
Coefficient Of Variation ◽  
Cross Correlation ◽  
Motor Units ◽  
Vastus Lateralis Muscle ◽  
Force Output ◽  
Endurance Time ◽  
Firing Rates ◽  
Force Fluctuation ◽  
The Cross

During isometric contractions, the fluctuation of the force output of muscles increases as the muscle fatigues, and the contraction is sustained to exhaustion. We analyzed motor unit firing data from the vastus lateralis muscle to investigate which motor unit control parameters were associated with the increased force fluctuation. Subjects performed a sequence of isometric constant-force contractions sustained at 20% maximal force, each spaced by a 6-s rest period. The contractions were performed until the mean value of the force output could not be maintained at the desired level. Intramuscular EMG signals were detected with a quadrifilar fine-wire sensor. The EMG signals were decomposed to identify all of the firings of several motor units by using an artificial intelligence-based set of algorithms. We were able to follow the behavior of the same motor units as the endurance time progressed. The force output of the muscle was filtered to remove contributions from the tracking task. The coefficient of variation of the force was found to increase with endurance time ( P < 0.001, R2 = 0.51). We calculated the coefficient of variation of the firing rates, the synchronization of pairs of motor unit firings, the cross-correlation value of the firing rates of pairs of motor units, the cross-correlation of the firing rates of motor units and the force, and the number of motor units recruited during the contractions. Of these parameters, only the cross-correlation of the firing rates ( P < 0.01, R2 = 0.10) and the number of recruited motor units ( P = 0.042, R2 = 0.22) increased significantly with endurance time for grouped subjects. A significant increase ( P < 0.001, R2 = 0.16) in the cross-correlation of the firing rates and force was also observed. It is suggested that the increase in the cross-correlation of the firing rates is likely due to a decrease in the sensitivity of the proprioceptive feedback from the spindles.

scholarly journals Common Input to Motor Neurons Innervating the Same and Different Compartments of the Human Extensor Digitorum Muscle

2004 ◽  
Vol 91 (1) ◽  
pp. 57-62 ◽  
Author(s):  
Douglas A. Keen ◽  
Andrew J. Fuglevand
Keyword(s):  
Motor Unit ◽  
Motor Neurons ◽  
Cross Correlation ◽  
Motor Units ◽  
Extensor Digitorum ◽  
Short Term ◽  
Common Input ◽  
Active Motor ◽  
Voluntary Contractions ◽  
Input Strength

Short-term synchronization of active motor units has been attributed in part to last-order divergent projections that provide common synaptic input across motor neurons. The extent of synchrony thus allows insight as to how the inputs to motor neurons are distributed. Our particular interest relates to the organization of extrinsic finger muscles that give rise distally to multiple tendons, which insert onto all the fingers. For example, extensor digitorum (ED) is a multi-compartment muscle that extends digits 2–5. Given the unique architecture of ED, it is unclear if synaptic inputs are broadly distributed across the entire pool of motor neurons innervating ED or segregated to supply subsets of motor neurons innervating different compartments. Therefore the purpose of this study was to evaluate the degree of motor-unit synchrony both within and across compartments of ED. One hundred and forty-five different motor-unit pairs were recorded in the human ED of nine subjects during weak voluntary contractions. Cross-correlation histograms were generated for all of the motor-unit pairs and the degree of synchronization between two units was assessed using the index of common input strength (CIS). The degree of synchrony for motor-unit pairs within the same compartment (CIS = 0.7 ± 0.3; mean ± SD) was significantly greater than for motor-unit pairs in different compartments (CIS = 0.4 ± 0.22). Consequently, last-order synaptic projections are not distributed uniformly across the entire pool of motor neurons innervating ED but are segregated to supply subsets of motor neurons innervating different compartments.

Amplitude cancellation reduces the size of motor unit potentials averaged from the surface EMG

2006 ◽  
Vol 100 (6) ◽  
pp. 1928-1937 ◽  
Author(s):  
Kevin G. Keenan ◽  
Dario Farina ◽  
Roberto Merletti ◽  
Roger M. Enoka
Keyword(s):  
Motor Unit ◽  
Conduction Velocity ◽  
Motor Units ◽  
Surface Emg ◽  
Excitation Level ◽  
Bipolar Electrode ◽  
Emg Signal ◽  
Motor Unit Synchronization ◽  
Rate Coding ◽  
Simulated Surface

The purpose of the study was to evaluate the influence of selected physiological parameters on amplitude cancellation in the simulated surface electromyogram (EMG) and the consequences for spike-triggered averages of motor unit potentials derived from the interference and rectified EMG signals. The surface EMG was simulated from prescribed recruitment and rate coding characteristics of a motor unit population. The potentials of the motor units were detected on the skin over a hand muscle with a bipolar electrode configuration. Averages derived from the EMG signal were generated using the discharge times for each of the 24 motor units with lowest recruitment thresholds from a population of 120 across three conditions: 1) excitation level; 2) motor unit conduction velocity; and 3) motor unit synchronization. The area of the surface-detected potential was compared with potentials averaged from the interference, rectified, and no-cancellation EMGs. The no-cancellation EMG comprised motor unit potentials that were rectified before they were summed, thereby preventing cancellation between the opposite phases of the potentials. The percent decrease in area of potentials extracted from the rectified EMG was linearly related to the amount of amplitude cancellation in the interference EMG signal, with the amount of cancellation influenced by variation in excitation level and motor unit conduction velocity. Motor unit synchronization increased potentials derived from both the rectified and interference EMG signals, although cancellation limited the increase in area for both potentials. These findings document the influence of amplitude cancellation on motor unit potentials averaged from the surface EMG and the consequences for using the procedure to characterize motor unit properties.

Analyzing the Impact of COVID-19 on the Cross-Correlations between Financial Search Engine Data and Movie Box Office

2020 ◽  
pp. 2150021
Author(s):  
Renyu Wang ◽  
Yujie Xie ◽  
Hong Chen ◽  
Guozhu Jia
Keyword(s):  
Financial Market ◽  
Cross Correlation ◽  
Multifractal Spectrum ◽  
Movie Industry ◽  
Box Office ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross ◽  
Google Search ◽  
The Impact

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market.

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