Activity of single motor units in attention-demanding tasks: firing pattern in the human trapezius muscle

Activity of single motor units in relation to surface electromyography (EMG) was studied in 11 subjects in attention-demanding work tasks with minimal requirement of movement. In 53 verified firing periods, single motor units fired continuously from 30 s to 10 min (duration of the experiment work task) with a stable median firing rate in the range of 8–13 Hz. When the integrated surface EMG were stable, the motor units identified as a rule were continuously active with only small modulations of firing rate corresponding to low-amplitude fluctuations in surface EMG. Marked changes in the surface EMG, either sudden or gradual, were caused by recruitment or derecruitment of motor units, and not by modulations of the motor unit firing rate. Motor unit firing periods (duration 10 s-35 s) in low-level voluntary contractions (approximately 1%–5% EMG_max) performed by the same subjects showed median firing rates (7–12 Hz) similar to the observations in attention-related activation.     

The interpretation of abdominal wall muscle recruitment strategies change when the electrocardiogram (ECG) is removed from the electromyogram (EMG)

The purpose of this study was to determine the effect of the ECG artifact on low-level trunk muscle activation amplitudes and assess the effectiveness of two methods used to remove the ECG. Simulations were performed and percent error in root mean square (RMS) amplitudes were calculated from uncontaminated and contaminated EMG signals at various ECG to EMG ratios. Two methods were used to remove the ECG: (1) filtering by adaptive sampling (FAS) and (2) Butterworth high pass filter at 30 Hz (BW-30 Hz HPF). The percent error was also calculated between the ECG removed and the uncontaminated EMG RMS amplitudes. Next, the BW-30 Hz HPF method was used to remove the ECG from 3-bilateral external oblique (EO) muscle sites collected from 30 healthy subjects performing a one handed lift and replace task. Two separate ANOVA models assessed the effects of ECG on the statistical interpretation of EO recruitment strategies. One model included EMG data that contained the ECG and the other model included EMG data after the ECG was removed. Large percent errors were observed when the ECG was not removed. These errors increased with larger ECG to EMG ratios. Both removal methods reduced the errors to below 10%, but the BW-30 Hz HPF method was more time efficient in removing the ECG artifact. Different statistical findings were observed among the muscle sites for the ECG contaminated model compared to the ECG removed model, which resulted in different conclusions concerning neuromuscular control.     

Removing ECG contamination from EMG recordings: a comparison of ICA-based and other filtering procedures

Trunk muscle electromyography (EMG) is often contaminated by the electrocardiogram (ECG), which hampers data analysis and potentially yields misinterpretations. We propose the use of independent component analysis (ICA) for removing ECG contamination and compared it with other procedures previously developed to decontaminate EMG. To mimic realistic contamination while having uncontaminated reference signals, we employed EMG recordings from peripheral muscles with different activation patterns and superimposed distinct ECG signals that were recorded during rest at conventional locations for trunk muscle EMG. ICA decomposition was performed with and without a separately collected ECG signal as part of the data set and contaminated ICA modes representing ECG were identified automatically. Root mean squared relative errors and correlations between the linear envelopes of uncontaminated and contaminated EMG were calculated to assess filtering effects on EMG amplitude. Changes in spectral content were quantified via mean power frequencies. ICA-based filtering largely preserved the EMG's spectral content. Performance on amplitude measures was especially successful when a separate ECG recording was included. That is, the ICA-based filtering can produce excellent results when EMG and ECG are indeed statistically independent and when mode selection is flexibly adjusted to the data set under study.     

Elimination of electrocardiogram contamination from electromyogram signals: An evaluation of currently used removal techniques.

Trunk electromyographic signals (EMG) are often contaminated with heart muscle electrical activity (ECG) due to the proximity of the collection sites to the heart and the volume conduction characteristics of the ECG through the torso. Few studies have quantified ECG removal techniques relative to an uncontaminated EMG signal (gold standard or criterion measure), or made direct comparisons between different methods for a given set of data. Understanding the impacts of both untreated contaminated EMG and ECG elimination techniques on the amplitude and frequency parameters is vital given the widespread use of EMG. The purpose of this study was to evaluate four groups of current and commonly used techniques for the removal of ECG contamination from EMG signals. ECG recordings at two intensity levels (rest and 50% maximum predicted heart rate) were superimposed on 11 uncontaminated biceps brachii EMG signals (rest, 7 isometric and 3 isoinertial levels). The 23 removal methods used were high pass digital filtering (finite impulse response (FIR) using a Hamming window, and fourth-order Butterworth (BW) filter) at five cutoff frequencies (20, 30, 40, 50, and 60 Hz), template techniques (template subtraction and an amplitude gating template), combinations of the subtraction template and high pass digital filtering, and a frequency subtraction/signal reconstruction method. For muscle activation levels between 10% and 25% of maximum voluntary contraction, the template subtraction and BW filter with a 30 Hz cutoff were the two best methods for maximal ECG removal with minimal EMG distortion. The BW filter with a 30 Hz cutoff provided the optimal balance between ease of implementation, time investment, and performance across all contractions and heart rate levels for the EMG levels evaluated in this study.     

Mechanisms of firing patterns in fast-spiking cortical interneurons

Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. Their spike responses to step currents occur almost immediately following the step onset or after a substantial delay, during which subthreshold oscillations are frequently observed. Their firing patterns include high-frequency tonic firing and rhythmic or irregular bursting (stuttering). What is the origin of this variability? In the present paper, we hypothesize that it emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. To test this hypothesis, we construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na(+), delayed-rectifier K(+), and slowly inactivating d-type K(+) conductances. The model is analyzed using nonlinear dynamical system theory. For small Na(+) window current, the neuron exhibits high-frequency tonic firing. At current threshold, the spike response is almost instantaneous for small d-current conductance, gd, and it is delayed for larger gd. As gd further increases, the neuron stutters. Noise substantially reduces the delay duration and induces subthreshold oscillations. In contrast, when the Na(+) window current is large, the neuron always fires tonically. Near threshold, the firing rates are low, and the delay to firing is only weakly sensitive to noise; subthreshold oscillations are not observed. We propose that the variability in the response of cortical FS neurons is a consequence of heterogeneities in their gd and in the strength of their Na(+) window current. We predict the existence of two types of firing patterns in FS neurons, differing in the sensitivity of the delay duration to noise, in the minimal firing rate of the tonic discharge, and in the existence of subthreshold oscillations. We report experimental results from intracellular recordings supporting this prediction.     

Sex differences in rabbit masseter motoneuron firing behavior

To evaluate whether sex differences in the proportions of fibers of different phenotypes in the masseter muscle might be the result of differences in the behavior of their motoneurons, we studied the firing patterns of masseter motoneurons in adult male and female rabbits. Activity in individual motoneurons was determined from high spatial resolution EMG recordings made during cortically evoked rhythmic activation of the masticatory muscles. Although some motoneurons could be said to fire according to slow-tonic or fast-phasic patterns, most did not. In both sexes a substantial range of median firing rates and median firing durations was found. In adult males, masseter motoneurons fired more rapidly than those recorded from adult females. No significant sex differences in motoneuron firing duration were found. These results are consistent with the hypothesis that androgen-induced differences in rabbit masseter muscle fiber phenotype are a reflection of differences in motoneuron firing rate. Whether this effect of androgen is directly upon the motoneurons or is the result of a response of muscle fibers to androgen remains to be investigated.     

Influence of motor units recruitment and firing rate on the soundmyogram and EMG characteristics in cat gastrocnemius

The separate contributions of the recruitment level and of the firing rate of the motor units on the soundmyogram and electromyogram time domain parameters were investigated during stimulation of the motor nerve of the cat gastrocnemius muscle. Upon orderly increase in the number of active motor units at a fixed firing rate, both the peak to peak amplitude (P-P_max) and the root mean square (RMS) of the sound myogram increased. At full recruitment the increase in firing rate from 2.5 to 50 Hz induced an exponential decline in the P-P_max. The RMS, however, followed this trend only from 15 to 50 Hz while showing an increase from 2.5 to 10 Hz. During simultaneous changes of recruitment and firing rate, the effect of increasing the number of motor units on the P-P_max and RMS is dampened by the increasing firing rate. The peak to peak amplitude of the EMG compound action potential increased with the number of active motor units. Moreover, its amplitude was not influenced by the firing rate. The EMG RMS, however, increases as a function of the firing rate. The results indicate that both the number and the firing rate of the active motor units contribute to the determination of the soundmyogram characteristics. Moreover, the peculiar changes of the soundmyogram time domain properties, compared to the ones of the EMG, allow one to differentiate the influence of the motor units number and firing rate on the electrical and mechanical performance of the muscle when stimulated.     

Influence of motor unit firing statistics on the median frequency of the EMG power spectrum

Changes in the EMG power spectrum during static fatiguing contractions are often attributed to changes in muscle fibre action potential conduction velocity. Mathematical models of the EMG power spectrum, which have been empirically confirmed, predict that under certain conditions a distinct maximum occurs in the low-frequency part of the spectrum, indicating the dominant firing rate of the motor units. The present study investigated the influence of this firing rate peak on the spectral changes during a static fatiguing contraction at 50% of maximum EMG amplitude in the frontalis and corrugator supercilii muscles. An exponential decrease of the median frequency (MF) of the EMG power spectrum was observed when the firing rate peak was absent. When the firing rate peak was present, an exaggerated decrease of MF in the beginning of the contraction was found, which was associated with an increase in firing rate peak magnitude. In later stages of the contraction, a partial recovery of MF occurred, concomitant with a decrease in firing rate peak magnitude. The influence of the firing rate peak on MF was also investigated during nonfatiguing contractions of the frontalis muscle at 20, 40, 60, and 80% of maximum EMG amplitude. A curvilinear relationship between MF and contraction strength was found, whether firing rate peaks were present or absent. The presence of firing rate peaks, however, was associated with a decrease in MF which was inversely related to contraction strength, due to the inverse relationship between firing rate peak magnitude and contraction strength.     

Statistical analysis of temporal evolution in single-neuron firing rates

A fundamental methodology in neurophysiology involves recording the electrical signals associated with individual neurons within brains of awake behaving animals. Traditional statistical analyses have relied mainly on mean firing rates over some epoch (often several hundred milliseconds) that are compared across experimental conditions by analysis of variance. Often, however, the time course of the neuronal firing patterns is of interest, and a more refined procedure can produce substantial additional information. In this paper we compare neuronal firing in the supplementary eye field of a macaque monkey across two experimental conditions. We take the electrical discharges, or 'spikes', to be arrivals in a inhomogeneous Poisson process and then model the firing intensity function using both a simple parametric form and more flexible splines. Our main interest is in making inferences about certain characteristics of the intensity, including the timing of the maximal firing rate. We examine data from 84 neurons individually and also combine results into a hierarchical model. We use Bayesian estimation methods and frequentist significance tests based on a nonparametric bootstrap procedure. We are thereby able to conclude that a substantial fraction of the neurons exhibit important temporal differences in firing intensity across the two conditions, and we quantify the effect across the population of neurons.     

Equilibrating errors: reliable estimation of information transmission rates in biological systems with spectral analysis-based methods

Shannon’s seminal approach to estimating information capacity is widely used to quantify information processing by biological systems. However, the Shannon information theory, which is based on power spectrum estimation, necessarily contains two sources of error: time delay bias error and random error. These errors are particularly important for systems with relatively large time delay values and for responses of limited duration, as is often the case in experimental work. The window function type and size chosen, as well as the values of inherent delays cause changes in both the delay bias and random errors, with possibly strong effect on the estimates of system properties. Here, we investigated the properties of these errors using white-noise simulations and analysis of experimental photoreceptor responses to naturalistic and white-noise light contrasts. Photoreceptors were used from several insect species, each characterized by different visual performance, behavior, and ecology. We show that the effect of random error on the spectral estimates of photoreceptor performance (gain, coherence, signal-to-noise ratio, Shannon information rate) is opposite to that of the time delay bias error: the former overestimates information rate, while the latter underestimates it. We propose a new algorithm for reducing the impact of time delay bias error and random error, based on discovering, and then using that size of window, at which the absolute values of these errors are equal and opposite, thus cancelling each other, allowing minimally biased measurement of neural coding.     

Estimating absolute rates of molecular evolution and divergence times: a penalized likelihood approach.

Rates of molecular evolution vary widely between lineages, but quantification of how rates change has proven difficult. Recently proposed estimation procedures have mainly adopted highly parametric approaches that model rate evolution explicitly. In this study, a semiparametric smoothing method is developed using penalized likelihood. A saturated model in which every lineage has a separate rate is combined with a roughness penalty that discourages rates from varying too much across a phylogeny. A data-driven cross-validation criterion is then used to determine an optimal level of smoothing. This criterion is based on an estimate of the average prediction error associated with pruning lineages from the tree. The methods are applied to three data sets of six genes across a sample of land plants. Optimally smoothed estimates of absolute rates entailed 2- to 10-fold variation across lineages.     

Motor unit firing rates during isometric voluntary contractions performed at different muscle lengths

Firing rates of motor units and surface EMG were measured from the triceps brachii muscles of able-bodied subjects during brief submaximal and maximal isometric voluntary contractions made at 5 elbow joint angles that covered the entire physiological range of muscle lengths. Muscle activation at the longest, midlength, and shortest muscle lengths, measured by twitch occlusion, averaged 98%, 97%, and 93% respectively, with each subject able to achieve complete activation during some contractions. As expected, the strongest contractions were recorded at 90 degrees of elbow flexion. Mean motor unit firing rates and surface EMG increased with contraction intensity at each muscle length. For any given absolute contraction intensity, motor unit firing rates varied when muscle length was changed. However, mean motor unit firing rates were independent of muscle length when contractions were compared with the intensity of the maximal voluntary contraction (MVC) achieved at each joint angle.     

Vector reconstruction from firing rates

In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons.     

Spatio-temporal processing of surface EMG signals from the sternocleidomastoideus muscle to assess effects of radiotherapy on motor unit conduction velocity and firing rate—A pilot study

Radiation therapy causes both muscle and nerve tissue damage. However, the evolution and mechanisms of these damages are not fully understood. Information on the state of active muscle fibres and motoneurons can be obtained by measuring sEMG signals and calculating the conduction velocity (CV) and firing rate of individual motor units, respectively. The aim of this pilot study was to evaluate if the multi-channel surface EMG (sEMG) technique could be applied to the sternocleidomastoideus muscle (SCM) of radiotherapy patients, and to assess if the CV and firing rate are altered as a consequence of the radiation.

Surface EMG signals were recorded from the radiated and healthy SCM muscles of 10 subjects, while subjects performed isometric rotation of the head. CV and firing rate were calculated using two recently proposed methods based on spatio-temporal processing of the sEMG signals. The multi-channel sEMG technique was successfully applied to the SCM muscle and CV and firing rates were obtained. The measurements were fast and simple and comfortable for the patients. Sufficient data quality was obtained from both sides of seven and four subjects for the CV and firing rate analysis, respectively. No differences in CV or firing rate were found between the radiated and non-radiated sides (p = 0.13 and p = 0.20, respectively). Firing rate and CV were also obtained from a myokymic discharge pattern. It was found that the CV decreased significantly (p = 0.01) during the bursts.     

Quantifying genotyping errors in noninvasive population genetics

The use of noninvasively collected samples greatly expands the range of ecological issues that may be investigated through population genetics. Furthermore, the difficulty of obtaining reliable genotypes with samples containing low quantities of amplifiable DNA may be overcome by designing optimal genotyping schemes. Such protocols are mainly determined by the rates of genotyping errors caused by false alleles and allelic dropouts. These errors may not be avoided through laboratory procedure and hence must be quantified. However, the definition of genotyping error rates remains elusive and various estimation methods have been reported in the literature. In this paper we proposed accurate codification for the frequencies of false alleles and allelic dropouts. We then reviewed other estimation methods employed in hair- or faeces-based population genetics studies and modelled the bias associated with erroneous methods. It is emphasized that error rates may be substantially underestimated when using an erroneous approach. Genotyping error rates may be important determinants of the outcome of noninvasive studies and hence should be carefully computed and reported.     

Optimal configurations of spatial scale for grid cell firing under noise and uncertainty

We examined the accuracy with which the location of an agent moving within an environment could be decoded from the simulated firing of systems of grid cells. Grid cells were modelled with Poisson spiking dynamics and organized into multiple ‘modules’ of cells, with firing patterns of similar spatial scale within modules and a wide range of spatial scales across modules. The number of grid cells per module, the spatial scaling factor between modules and the size of the environment were varied. Errors in decoded location can take two forms: small errors of precision and larger errors resulting from ambiguity in decoding periodic firing patterns. With enough cells per module (e.g. eight modules of 100 cells each) grid systems are highly robust to ambiguity errors, even over ranges much larger than the largest grid scale (e.g. over a 500 m range when the maximum grid scale is 264 cm). Results did not depend strongly on the precise organization of scales across modules (geometric, co-prime or random). However, independent spatial noise across modules, which would occur if modules receive independent spatial inputs and might increase with spatial uncertainty, dramatically degrades the performance of the grid system. This effect of spatial uncertainty can be mitigated by uniform expansion of grid scales. Thus, in the realistic regimes simulated here, the optimal overall scale for a grid system represents a trade-off between minimizing spatial uncertainty (requiring large scales) and maximizing precision (requiring small scales). Within this view, the temporary expansion of grid scales observed in novel environments may be an optimal response to increased spatial uncertainty induced by the unfamiliarity of the available spatial cues.     

Digital filters for firing rate estimation

When a rate histogram is used to represent the firing pattern of a neuron there is the potential for serious error due to aliasing, and because of this the rate histogram is a very poor way to represent neural activity. It is theoretically possible to encode a signal in a spike train and decode it without error by filtering and sampling. There is no natural optimal filter design for this problem, but it is possible to specify the characteristics of a good rate estimating filter heuristically and design a filter with these characteristics. Two rate estimating filters are described here. Their performance has been tested, and compared to the rate histogram and the French-Holden rate estimating algorithm, by measuring their ability to recover signals encoded as impulse sequences by Integral Pulse Frequency Modulation (IPFM). These filters are simple to implement and perform well. They should be used in preference to the rate histogram.     

Estimation of Respiratory Nasal Pressure and Flow Rate Signals Using Different Respiratory Sound Features

BackgroundRespiratory sounds are associated with the flow rate, nasal flow pressure, and physical characteristics of airways. In this study, we aimed to develop the flow rate and nasal flow pressure estimation models for the clinical application, and find out the optimal feature set for estimation to achieve the optimal model performance.MethodsRespiratory sounds and flow rate were acquired from nine healthy volunteers. Respiratory sounds and nasal flow pressure were acquired from twenty-three healthy volunteers. Four types of respiratory sound features were extracted for flow rate and nasal flow pressure estimation using different estimation models. Effects of estimations using these features were evaluated using Bland-Altman analysis, estimation error, and respiratory sound feature calculation time. Besides, expiratory and inspiratory phases divided estimation errors were compared with united estimation errors.ResultsThe personalized logarithm model was selected as the optimal flow rate estimation model. Respiratory nasal flow pressure estimation based on this model was also performed. For the four different respiratory sound features, there is no statistically significant difference in flow rate and pressure estimation errors. LogEnvelope was, therefore, chosen as the optimal feature because of the lowest computational cost. In addition, for any type of respiratory sound feature, no statistically significant difference was observed between divided and united estimation errors (flow rate and pressure).ConclusionRespiratory flow rate and nasal flow pressure can be estimated accurately using respiratory sound features. Expiratory and inspiratory phases united estimation using respiratory sounds is a more reasonable estimation method than divided estimation. LogEnvelope can be used for this united respiratory flow rate and nasal flow pressure estimation with minimum computational cost and acceptable estimation error.     

Neural networks for self-adjusting mutation rate estimation when the recombination rate is unknown

Estimating the mutation rate, or equivalently effective population size, is a common task in population genetics. If recombination is low or high, optimal linear estimation methods are known and well understood. For intermediate recombination rates, the calculation of optimal estimators is more challenging. As an alternative to model-based estimation, neural networks and other machine learning tools could help to develop good estimators in these involved scenarios. However, if no benchmark is available it is difficult to assess how well suited these tools are for different applications in population genetics.Here we investigate feedforward neural networks for the estimation of the mutation rate based on the site frequency spectrum and compare their performance with model-based estimators. For this we use the model-based estimators introduced by Fu, Futschik et al., and Watterson that minimize the variance or mean squared error for no and free recombination. We find that neural networks reproduce these estimators if provided with the appropriate features and training sets. Remarkably, using the model-based estimators to adjust the weights of the training data, only one hidden layer is necessary to obtain a single estimator that performs almost as well as model-based estimators for low and high recombination rates, and at the same time provides a superior estimation method for intermediate recombination rates. We apply the method to simulated data based on the human chromosome 2 recombination map, highlighting its robustness in a realistic setting where local recombination rates vary and/or are unknown.     

Neuron's firing time

The interspike interval distribution of neuronal firing is analyzed by a model that assumes unit effect EPSP's lasting an exponential length of time. The model allows a general interarrival distribution; this contrasts with the numerous models requiring Poisson arrivals. The Laplace transform of the time to firing, modelled as the first passage time to a fixed arbitrary threshold level, is found. Comparisons are made for exponential and regular interarrivals using the first two moments of the time to firing. Surprisingly, the mean and variance of the time to reach any threshold level greater than one is greater for regular arrivals for any ratio of mean interarrival intervals to mean EPSP duration greater than 0.6.