Long-Term Endurance and Power Training May Facilitate Motor Unit Size Expansion to Compensate for Declining Motor Unit Numbers in Older Age.

The evidence concerning the effects of exercise in older age on motor unit (MU) numbers, muscle fiber denervation and reinnervation cycles is inconclusive and it remains unknown whether any effects are dependent on the type of exercise undertaken or are localized to highly used muscles. MU characteristics of the vastus lateralis (VL) were assessed using surface and intramuscular electromyography in eighty-five participants, divided into sub groups based on age (young, old) and athletic discipline (control, endurance, power). In a separate study of the biceps brachii (BB), the same characteristics were compared in the favored and non-favored arms in eleven masters tennis players. Muscle size was assessed using MRI and ultrasound. In the VL, the CSA was greater in young compared to old, and power athletes had the largest CSA within their age groups. Motor unit potential (MUP) size was larger in all old compared to young (p < 0.001), with interaction contrasts showing this age-related difference was greater for endurance and power athletes than controls, and MUP size was greater in old athletes compared to old controls. In the BB, thickness did not differ between favored and non-favored arms (p = 0.575), but MUP size was larger in the favored arm (p < 0.001). Long-term athletic training does not prevent age-related loss of muscle size in the VL or BB, regardless of athletic discipline, but may facilitate more successful axonal sprouting and reinnervation of denervated fibers. These effects may be localized to muscles most involved in the exercise.

Failure to expand the motor unit size to compensate for declining motor unit numbers distinguishes sarcopenic from non-sarcopenic older men.

KEY POINTS: The age-related loss of muscle mass is related to the loss of innervating motor neurons and denervation of muscle fibres. Not all denervated muscle fibres are degraded; some may be reinnervated by an adjacent surviving neuron, which expands the innervating motor unit proportional to the numbers of fibres rescued. Enlarged motor units have larger motor unit potentials when measured using electrophysiological techniques. We recorded much larger motor unit potentials in relatively healthy older men compared to young men, but the older men with the smallest muscles (sarcopenia) had smaller motor unit potentials than healthy older men. These findings suggest that healthy older men reinnervate large numbers of muscle fibres to compensate for declining motor neuron numbers, but a failure to do so contributes to muscle loss in sarcopenic men. ABSTRACT: Sarcopenia results from the progressive loss of skeletal muscle mass and reduced function in older age. It is likely to be associated with the well-documented reduction of motor unit numbers innervating limb muscles and the increase in size of surviving motor units via reinnervation of denervated fibres. However, no evidence exists to confirm the extent of motor unit remodelling in sarcopenic individuals. The aim of the present study was to compare motor unit size and number between young (n = 48), non-sarcopenic old (n = 13), pre-sarcopenic (n = 53) and sarcopenic (n = 29) men. Motor unit potentials (MUPs) were isolated from intramuscular and surface EMG recordings. The motor unit numbers were reduced in all groups of old compared with young men (all P < 0.001). MUPs were higher in non-sarcopenic and pre-sarcopenic men compared with young men (P = 0.039 and 0.001 respectively), but not in the vastus lateralis of sarcopenic old (P = 0.485). The results suggest that extensive motor unit remodelling occurs relatively early during ageing, exceeds the loss of muscle mass and precedes sarcopenia. Reinnervation of denervated muscle fibres probably expands the motor unit size in the non-sarcopenic and pre-sarcopenic old, but not in the sarcopenic old. These findings suggest that a failure to expand the motor unit size distinguishes sarcopenic from pre-sarcopenic muscles.

The reliability of methods to estimate the number and size of human motor units and their use with large limb muscles.

PURPOSE: Current methods for estimating muscle motor unit (MU) number provide values which are remarkably similar for muscles of widely differing size, probably because surface electrodes sample from similar and relatively small volumes in each muscle. We have evaluated an alternative means of estimating MU number that takes into account differences in muscle size. METHODS: Intramuscular motor unit potentials (MUPs) were recorded and muscle cross-sectional area (CSA) was measured using MRI to provide a motor unit number estimate (iMUNE). This was compared to the traditional MUNE method, using compound muscle action potentials (CMAP) and surface motor unit potentials (sMUPs) recorded using surface electrodes. Data were collected from proximal and distal regions of the vastus lateralis (VL) in young and old men while test-retest reliability was evaluated with VL, tibialis anterior and biceps brachii. RESULTS: MUPs, sMUPs and CMAPs were highly reliable (r = 0.84-0.91). The traditional MUNE, based on surface recordings, did not differ between proximal and distal sites of the VL despite the proximal CSA being twice the distal CSA. iMUNE, however, gave values that differed between young and old and were proportional to the muscle size. CONCLUSION: When evaluating the contribution that MU loss makes to muscle atrophy, such as in disease or ageing, it is important to have a method such as iMUNE, which takes into account any differences in total muscle size.

Age-related neuromuscular changes affecting human vastus lateralis.

KEY POINTS: Skeletal muscle size and strength decline in older age. The vastus lateralis, a large thigh muscle, undergoes extensive neuromuscular remodelling in healthy ageing, as characterized by a loss of motor neurons, enlargement of surviving motor units and instability of neuromuscular junction transmission. The loss of motor axons and changes to motor unit potential transmission precede a clinically-relevant loss of muscle mass and function. ABSTRACT: The anterior thigh muscles are particularly susceptible to muscle loss and weakness during ageing, although how this is associated with changes to neuromuscular structure and function in terms of motor unit (MU) number, size and MU potential (MUP) stability remains unclear. Intramuscular (I.M.) and surface electromyographic signals were recorded from the vastus lateralis (VL) during voluntary contractions held at 25% maximal knee extensor strength in 22 young (mean ± SD, 25.3 ± 4.8 years) and 20 physically active older men (71.4 ± 6.2 years). MUP size, firing rates, phases, turns and near fibre (NF) jiggle were determined and MU number estimates (MUNEs) were made by comparing average surface MUP with maximal electrically-evoked compound muscle action potentials. Quadriceps cross-sectional area was measured by magnetic resonance imaging. In total, 379 individual MUs were sampled in younger men and 346 in older men. Compared to the MU in younger participants, those in older participants had 8% lower firing rates and larger MUP size (+25%), as well as increased complexity, as indicated by phases (+13%), turns (+20%) and NF jiggle (+11%) (all P < 0.0005). The MUNE values (derived from the area of muscle in range of the surface-electrode) in older participants were ∼70% of those in the young (P < 0.05). Taking into consideration the 30% smaller cross-sectional area of the VL, the total number of MUs in the older muscles was between 50% and 60% lower compared to in young muscles (P < 0.0005). A large portion of the VL MU pool is lost in older men and those recruited during moderate intensity contractions were enlarged and less stable. These MU changes were evident before clinically relevant changes to muscle function were apparent; nevertheless, the changes in MU number and size are probably a prelude to future movement problems.

Decomposition of intramuscular EMG signals using a knowledge -based certainty classifier algorithm.

An automated system for resolving an intramuscular electromyographic (EMG) signal into its constituent motor unit potential trains (MUPTs) is presented. The system is intended mainly for clinical applications where several physiological parameters for each motor unit (MU), such as the motor unit potential (MUP) template and mean firing rate, are required. The system decomposes an EMG signal off-line by filtering the signal, detecting MUPs, and then grouping the detected MUPs using a clustering and a supervised classification algorithm. Both the clustering and supervised classification algorithms use MUP shape and MU firing pattern information to group MUPs into several MUPTs. Clustering is partially based on the K-means clustering algorithm. Supervised classification is implemented using a certainty-based classifier technique that employs a knowledge-based system to merge trains, detect and correct invalid trains, as well as adjust the assignment threshold for each train. The accuracy (93.2%±5.5%), assignment rate (93.9%±2.6%), and error in estimating the number of MUPTs (0.3±0.5) achieved for 10 simulated EMG signals comprised of 3-11 MUPTs are encouraging for using the system for decomposing various EMG signals.

Probabilistic muscle characterization using QEMG: application to neuropathic muscle.

Clinicians who use electromyographic (EMG) signals to help determine the presence or absence of abnormality in a muscle often, with varying degrees of success, evaluate sets of motor unit potentials (MUPs) qualitatively and/or quantitatively to characterize the muscle in a clinically meaningful way. The resulting muscle characterization can be improved using automated analysis. As such, the intent of this study was to evaluate the performance of automated, conventional Means/Outlier and Probabilistic methods in converting MUP statistics into a concise, and clinically relevant, muscle characterization. Probabilistic methods combine the set of MUP characterizations, derived using Pattern Discovery (PD), of all MUPs detected from a muscle into a characterization measure that indicates normality or abnormality. Using MUP data from healthy control subjects and patients with known neuropathic disorders, a Probabilistic method that used Bayes' rule to combine MUP characterizations into a Bayesian muscle characterization (BMC) achieved a categorization accuracy of 79.7% compared to 76.4% using the Mean method (P > 0.1) for biceps muscles and 94.6% accuracy for the BMC method compared to 85.8% using the Mean method (P < 0.01) for first dorsal interosseous muscles. The BMC method can facilitate the determination of "possible," "probable," or "definite" levels for a given muscle categorization (e.g., neuropathic) whereas the conventional Means and Outlier methods support only a dichotomous "normal" or "abnormal" decision. This work demonstrates that the BMC method can provide information that may be more useful in supporting clinical decisions than that provided by the conventional Means or Outlier methods.

Decision support for QEMG.

For clinicians to use quantitative electromyography (QEMG) to help determine the presence or absence of neuromuscular disease, they must manually interpret an exhaustive set of motor unit potential (MUP) or interference pattern statistics to formulate a clinically useful muscle characterization. A new method is presented for automatically categorizing a set of quantitative electromyographic (EMG) data as characteristic of data acquired from a muscle affected by a myopathic, normal or neuropathic disease process, based on discovering patterns of MUP feature values. From their numbers of occurrence in a set of training data, representative of each muscle category, discovered patterns of MUP feature values are expressed as conditional probabilities of detecting such MUPs in each category of muscle. The conditional probabilities of each MUP in a set of MUPs acquired from an examined muscle are combined using Bayes' rule to estimate conditional probabilities of the examined muscle being of each category type. Using simulated and clinical data, the ability of a "pattern discovery" based Bayesian (PD-based Bayesian) method to correctly categorize sets of test MUP data was compared to conventional methods which use data means and outliers. The simulated data were created by modeling the effects of myopathic and neuropathic diseases using a physiologically based EMG signal simulator. The clinical data was from controls and patients with known neuropathic disorders. PD-based Bayesian muscle characterization had an accuracy of 84.4% compared to 51.9% for the means and outlier based method when using all MUP features considered. PD-based Bayesian methods can accurately characterize a muscle. PD-based Bayesian muscle characterization automatically maximizes both sensitivity and specificity and provides transparent rationalizations for its characterizations. This leads to the expectation that clinicians using PD-based Bayesian muscle characterization will be provided with improved decision support compared to that provided by the status quo means and outlier based methods.

Bayesian characterization of external anal sphincter muscles using quantitative electromyography.

OBJECTIVES: Based on the analysis of electromyographic (EMG) data muscles are often characterized as normal or affected by a neuromuscular disease process. The objective of this work was to compare the accuracy of Bayesian muscle characterization to conventional means and outlier analysis of motor unit potential (MUP) feature values. METHODS: Quantitative MUP data from the external anal sphincter muscles of control subjects and patients were used to compare the sensitivity, specificity, and accuracy of the methods under examination. RESULTS: The results demonstrated that Bayesian muscle characterization achieved similar accuracy to combined means and outlier analysis. Thickness and number of turns were the most discriminative MUP features for characterizing the external anal sphincter (EAS) muscles studied in this work. CONCLUSIONS: Although, Bayesian muscle characterization achieved similar accuracy to combined means and outlier analysis, Bayesian muscle characterization can facilitate the determination of "possible", "probable", or "definite" levels of pathology, whereas the conventional means and outlier methods can only provide a dichotomous "normal" or "abnormal" decision. Therefore, Bayesian muscle characterization can be directly used to support clinical decisions related to initial diagnosis as well as treatment and management over time. Decisions are based on facts and not impressions giving electromyography a more reliable role in the diagnosis, management, and treatment of neuromuscular disorders. SIGNIFICANCE: Bayesian muscle characterization can help make electrophysiological examinations more accurate and objective.

Motor unit potential characterization using "pattern discovery".

Typically in clinical practice, electromyographers use qualitative auditory and visual analysis of electromyographic (EMG) signals to help infer if a neuromuscular disorder is present and if it is neuropathic or myopathic. Quantitative EMG methods exist that can more accurately measure feature values but require qualitative interpretation of a large number of statistics. Electrophysiological characterization of a neuromuscular system can be improved through the quantitative interpretation of EMG statistics. The aim of the present study was to compare the accuracy of pattern discovery (PD) characterization of motor unit potentials (MUPs) to other classifiers commonly used in the medical field. In addition, a demonstration of PD's transparency is provided. The transparency of PD characterization is a result of observing statistically significant events known as patterns. Using clinical MUP data from normal subjects and patients with known neuropathic disorders, PD achieved an error rate of 30.3% versus 29.8% for a Naïve Bayes classifier, 30.1% for a Decision Tree and 29% for discriminant analysis. Similar results were found for simulated EMG data. PD characterization succeeded in interpreting the information extracted from MUPs and transforming it into knowledge that is consistent with the literature and that can be valuable for the capture and transparent expression of clinically useful knowledge.

EMG signal decomposition: how can it be accomplished and used?

Electromyographic (EMG) signals are composed of the superposition of the activity of individual motor units. Techniques exist for the decomposition of an EMG signal into its constituent components. Following is a review and explanation of the techniques that have been used to decompose EMG signals. Before describing the decomposition techniques, the fundamental composition of EMG signals is explained and after, potential sources of information from and various uses of decomposed EMG signals are described.

Fatigue effects on motor unit activity during submaximal contractions.

OBJECTIVE: To examine motor unit changes during the development of fatigue in healthy subjects. DESIGN: Automated decomposition-enhanced spike-triggered averaging was used to characterize motor unit size and firing rate in the dominant vastus medialis during maintained contractions at 10% and 30% of maxima voluntary contraction (MVC). SETTING: Academic outpatient neuromuscular clinic. PARTICIPANTS: Healthy laboratory personnel. MAIN OUTCOME MEASURES: Surface electromyogram, surface-detected motor unit action potential amplitude (S-MUAP), mean firing rate, force (MVC), motor unit index. RESULTS: Surface electromyogram values and S-MUAP amplitudes increased during both 10% and 30% MVC fatiguing contractions, while mean firing rates decreased. A motor unit index, indicating the degree of motor unit pool activation, increased similarly to S-MUAP size, implying that new and larger units were recruited to maintain the contraction. Repeated contractions led to earlier motor unit changes and fatigue. CONCLUSION: During submaximal fatiguing contractions, additional motor units are activated to maintain strength. These changes begin early, within the first minute, particularly after a previous fatiguing effort.

The relationship of motor unit size, firing rate and force.

OBJECTIVE: Using a clinical electromyographic (EMG) protocol, motor units were sampled from the quadriceps femoris during isometric contractions at fixed force levels to examine how average motor unit size and firing rate relate to force generation. METHODS: Mean firing rates (mFRs) and sizes (mean surface-detected motor unit action potential (mS-MUAP) area) of samples of active motor units were assessed at various force levels in 79 subjects. RESULTS: MS-MUAP size increased linearly with increased force generation, while mFR remained relatively constant up to 30% of a maximal force and increased appreciably only at higher force levels. A relationship was found between muscle force and mS-MUAP area (r2 = 0.67), mFR (r2 = 0.38), and the product of mS-MUAP area and mFR (mS-MUAP x mFR) (r2 = 0.70). CONCLUSIONS: The results support the hypothesis that motor units are recruited in an orderly manner during forceful contractions, and that in large muscles only at higher levels of contraction ( > 30% MVC) do mFRs increase appreciably. MS-MUAP and mFR can be assessed using clinical EMG techniques and they may provide a physiological basis for analyzing the role of motor units during muscle force generation.

Detecting single fiber contributions to motor unit action potentials.

The ability to detect muscle fiber action potential (MFAP) contributions to motor unit action potentials (MUAPs) measured using single fiber (SF) and concentric needle (CN) electrodes was studied using simulated MUAPs. Various MFAP-acceleration thresholds were used to define significant fiber contributions. Attempts to detect the significant MFAP contributions, by locating peaks in filtered MUAPs or MUAP accelerations using various MUAP-based thresholds, were then made. Considering filtered MUAPs and a significant contribution threshold of 7.5 kV/s2, and using fiber-density peak-detection criteria, at best 46% and 50% of significant MFAP contributions were detected for the SF and CN MUAPs, respectively. Considering MUAP accelerations and a significant contribution threshold of 7.5 kV/s2, 80% and 84% of significant MFAP contributions could be detected, respectively. Most significant contributions were created from fibers located within approximately 350 microm of the electrode. The results suggest that significant peaks, defined using MUAP-based thresholds, within the acceleration of CN MUAPs can strongly correspond to individual fiber activity and may be useful for measuring fiber density and neuromuscular jitter.

Decomposition and quantitative analysis of clinical electromyographic signals.

Procedures for the quantitative analysis of clinical electromyographic (EMG) signals detected simultaneously using selective or micro and non-selective or macro electrodes are presented. The procedures first involve the decomposition of the micro signals and then the quantitative analysis of the resulting motor unit action potential trains (MUAPTs) in conjunction with the associated macro signal. The decomposition procedures consist of a series of algorithms that are successively and iteratively applied to resolve a composite micro EMG signal into its constituent MUAPTs. The algorithms involve the detection of motor unit action potentials (MUAPs), MUAP clustering and supervised classification and they use shape and firing pattern information along with data dependent assignment criteria to obtain robust performance across a variety of EMG signals. The accuracy, extent and speed with which a set of 10 representative 20-30 s, concentric needle detected, micro signals could be decomposed are reported and discussed. The decomposition algorithms had a maximum and average error rate of 2.5% and 0.7%, respectively, on average assigned 88.7% of the detected MUAPs and took between 4 to 8 s. Quantitative analysis techniques involving average micro and macro MUAP shapes, the variability of micro MUAPs shapes and motor unit firing patterns are described and results obtained from analysis of the data set used to evaluate the decomposition algorithms are summarized and discussed.

A longitudinal study of the pathophysiological changes in single human thenar motor units in amyotrophic lateral sclerosis.

The sequence of pathophysiological changes in amyotrophic lateral sclerosis (ALS) at the single motor unit (MU) level is not well understood. Using a recently described technique, a comprehensive range of physiological properties in two thenar MUs in ALS were intensively studied. In the first MU, despite a marked decline in the ability of the subject to voluntarily recruit the MU, the physiological properties of this MU remained remarkably stable over a 2-year period. In contrast, the physiological properties of the other MU declined rapidly over 5 months despite the fact that this MU could be recruited with ease throughout the study period. These differences between the progressively dysfunctional changes in these two MUs illustrates the value of such longitudinal studies of specific MUs in improving our understanding of the evolution of changes in single motoneurons in ALS. The broader application of longitudinally tracking the pathophysiological changes of the surviving MUs may prove to be a sensitive measure of disease progression and in evaluating the effectiveness of treatments.

Firing rate analysis using decompostion-enhanced spike triggered averaging in the quadriceps femoris.

Electromyographic signals detected from the quadriceps femoris during various constant force contractions were decomposed to identify individual motor unit discharges and mean firing rates (FRs). Subject and group mean FRs were calculated for each force level. Mean FR values and FR variability increased with force. Individual, subject, and group mean FRs showed slight increases until 30% of maximum voluntary contraction and larger increases thereafter. Findings are discussed in relation to motor unit recruitment, frequency modulation, and fatigue.

Robust supervised classification of motor unit action potentials.

A certainty-based classification algorithm is described, which comprises part of a clinically used EMG signal decomposition system. This algorithm classifies a candidate motor unit action potential (MUAP) to the motor unit potential trian (MUAPT) that produces the greatest estimated certainty, provided this maximal certainty is above a given threshold. The algorithm is iterative, such that the certainty with which assignments are made increases with each pass through the data, and it has specific stopping criteria. The performance and sensitivity (to the assignment threshold) of the Certainty algorithm and an iterative minimum Euclidean distance (MED) algorithm are compared by classifying sets of MUAPs detected in real concentric needle-detected EMG signals, using a range of assignment thresholds for each algorithm. With regard to MUAP assignment and error rates, the Certainty algorithm consistently provides better mean results and, more importantly, less variable results than the MED algorithm. The Certainty algorithm can provide mean assignment and error rates of 80.8 and 1.5%, respectively, with a maximum error rate of 3.2%; the MED algorithm can provide mean assignment and error rates of 80.3 and 3.3%, respectively, with a maximum error rate of 6.5%. The Certainty algorithm is relatively insensitive to the certainty threshold used, can consistently differentiate between similarly shaped MUAPs from different MUAPTs, and can make correct classifications despite biological shape variability, background noise and signal shape nonstationarity.

Decomposition-enhanced spike-triggered averaging: contraction level effects.

Decomposition-enhanced spike-triggered averaging (DE-STA) was applied to the vastus medialis muscle to examine size distributions of surface-detected motor-unit action potentials (S-MUAPs) at various force levels. Using DE-STA, 15-20 S-MUAPs were identified during 5%, 10%, 20%, and 30% of maximum voluntary contraction. Average S-MUAPs showed increase in peak to peak (and negative peak) amplitude with force (In microV): 5% = 37.9 +/- 6.1 (16.6 +/- 2.5), 10% = 44.0 +/- 4.0 (20.4 +/- 1.8), 20% = 80.7 +/- 9.3 (41.3 +/- 4.5), and 30% = 102.5 +/- 10.3 (53.6 +/- 5.0). Test-retest variability of peak to peak (and negative peak amplitude) between repeated trials was 0.10 (0.14), 0.14 (0.14), 0.17 (0.15), and 0.21 (0.20) at 5%, 10%, 20%, and 30% respectively. A relationship was found between the S-MUAP amplitude and force (r2 = 0.78, df = 90, F = 160, P < 0.001). Increase in average S-MUAP amplitude with force suggests that STA performed only at low levels of contraction may result in a biased sampling and small average S-MUAP amplitudes.

Supervised mutual-information based feature selection for motor unit action potential classification.

A new supervised mutual information-based feature selection method is presented. Using real motor unit action potential (MUAP) data from 10 EMG signals, the performances of 32 time-sample feature sets, feature subsets selected using first- and second-order mutual information and features obtained using linear discriminant analysis (LDA) and principal component analysis (PCA) were evaluated using a minimum Euclidean distance (MED) classifier. The evaluation showed that by using only 20 first-order features or only 15 second-order features mean error rates and error rate variations equivalent to using all 32 samples or LDA or PCA could be obtained. The computational cost of first-order feature selection was considerably less than LDA, PCA and second-order feature selection. The performance of first-order features was further evaluated using a more robust classifier. Unlike the MED classifier, the robust classifier only assigned a candidate MUAP if the assignment was sufficiently certain. For the robust classifier the average error rates using 20 features were similar to using the full feature set, yet higher assignment rates were obtained. Results from both evaluations suggest that the sets of first-order features were an efficient representation of lower dimension, which provided high accuracy classification with reduced computational requirements.

Resolving superimposed motor unit action potentials.

A new algorithm to resolve superimposed motor unit action potentials (MUAPs) is described, which uses a reduced search space and is based on the peel off approach. Knowledge specific to the problem domain, such as temporal relationships between and within motor unit action potential trains and MUAP energy information, is used to reduce the search space of motor units, possibly contributing to a superposition. The algorithm is tested using real electromyographic signals, and it demonstrates robust performance across the signals tested. For the signals tested, the average total resolution rate is 94%, the average correct resolution rate is 99.2% and the average error rate is 0.85%.