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

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.     

Limitations of the surface electromyography technique for estimating motor unit synchronization

Motor unit synchronization was estimated from the surface electromyograms (EMG) of the first dorsal interosseus muscle of human volunteers by a simplified surface-EMG technique (Milner-Brown et al. 1973, 1975). Single motor units were identified from intramuscular recordings and were used to obtain a spike-triggered average of the surface-EMG. The discharge rate of a reference motor unit was controlled at two levels (high and low), and the effect of motor unit activity on the surface-EMG estimate of synchronization was studied in 56 motor units. The surface-EMG estimate of motor unit synchronization was significantly higher when the reference motor unit discharged at the high rate than when it discharged at the low rate. A regression analysis indicated that the synchronization ratio calculated from the surface EMG was significantly correlated with the level of EMG activity in the muscle. Motor unit synchronization was also estimated from surface-EMG measurements that were derived by computer simulation. The simulation permitted manipulation of motor unit activity (discharge rate and recruitment) with a complete absence of synchrony among the units in the pool. The stimulated surface-EMG index was influenced by an artifact associated with signal rectification, and this effect changed non-monotonically with motor unit activity. Furthermore, the increase in the motor unit activity reduced the signal-to-noise ratio of the spike-triggered surface EMG average, and consequently decreased the sensitivity of the surface-EMG index as an estimate of motor unit synchronization. We conclude that the simplified surface-EMG method (Milner-Brown et al. 1973, 1975) does not provide a useful index of motor unit synchronization due to its inability to accurately distinguish the synchronization from methodological effects related to a rectification artifact and variation in the signal-to-noise ratio.     

Optimization of Input Patterns and Neuronal Properties to Evoke Motor Neuron Synchronization

The study used a computational approach to identify combinations of synaptic input timing and strength superimposed on a variety of active dendritic conductances that could evoke similar levels of motor unit synchronization in model motor neurons. Two motor neurons with low recruitment thresholds but different passive properties were modeled using GENESIS software. The timing and strength of synaptic inputs and the density of dendritic ion channels were optimized with a genetic algorithm to produce a set of target discharge times. The target times were taken from experimental recordings made in a human subject and had the synchronization characteristics that are commonly observed in hand muscles. The main finding was that the two parameters with the highest association to output synchrony were the ratio of inward-to-outward ionic conductances (r = 0.344; P = 0.003) and the degree of correlation in inhibitory inputs (r = 0.306; P = 0.009). Variation in the amount of correlation in the excitatory input was not positively correlated with variation in output synchrony. Further, the variability in discharge rate of the model neurons was positively correlated with the density of N-type calcium channels in the dendritic compartments (r = 0.727; P < 0.001 and r = 0.533; P < 0.001 for the two cells). This result suggests that the experimentally observed correlation between discharge variability and synchronization is caused by an increase in fast inward ionic conductances in the dendrites. Given the moderate level of correlation between output synchrony and each of the model parameters, especially at moderate levels of synchrony (E < 0.09 and CIS < 1.0), the results suggest caution in ascribing mechanisms to observations of motor unit synchronization.     

Task-Dependent Intermuscular Motor Unit Synchronization between Medial and Lateral Vastii Muscles during Dynamic and Isometric Squats

Purpose

Motor unit activity is coordinated between many synergistic muscle pairs but the functional role of this coordination for the motor output is unclear. The purpose of this study was to investigate the short-term modality of coordinated motor unit activity–the synchronized discharge of individual motor units across muscles within time intervals of 5ms–for the Vastus Medialis (VM) and Lateralis (VL). Furthermore, we studied the task-dependency of intermuscular motor unit synchronization between VM and VL during static and dynamic squatting tasks to provide insight into its functional role.

Methods

Sixteen healthy male and female participants completed four tasks: Bipedal squats, single-leg squats, an isometric squat, and single-leg balance. Monopolar surface electromyography (EMG) was used to record motor unit activity of VM and VL. For each task, intermuscular motor unit synchronization was determined using a coherence analysis between the raw EMG signals of VM and VL and compared to a reference coherence calculated from two desynchronized EMG signals. The time shift between VM and VL EMG signals was estimated according to the slope of the coherence phase angle spectrum.

Results

For all tasks, except for singe-leg balance, coherence between 15–80Hz significantly exceeded the reference. The corresponding time shift between VM and VL was estimated as 4ms. Coherence between 30–60Hz was highest for the bipedal squat, followed by the single-leg squat and the isometric squat.

Conclusion

There is substantial short-term motor unit synchronization between VM and VL. Intermuscular motor unit synchronization is enhanced for contractions during dynamic activities, possibly to facilitate a more accurate control of the joint torque, and reduced during single-leg tasks that require balance control and thus, a more independent muscle function. It is proposed that the central nervous system scales the degree of intermuscular motor unit synchronization according to the requirements of the movement task at hand.     

Inhibition synchronizes sparsely connected cortical neurons within and between columns in realistic network models

Networks of compartmental model neurons were used to investigate the biophysical basis of the synchronization observed between sparsely-connected neurons in neocortex. A model of a single column in layer 5 consisted of 100 model neurons: 80 pyramidal and 20 inhibitory. The pyramidal cells had conductances that caused intrinsic repetitive bursting at different frequencies when driven with the same input. When connected randomly with a connection density of 10%, a single model column displayed synchronous oscillatory action potentials in response to stationary, uncorrelated Poisson spike-train inputs. Synchrony required a high ratio of inhibitory to excitatory synaptic strength; the optimal ratio was 41, within the range observed in cortex. The synchrony was insensitive to variation in amplitudes of postsynaptic potentials and synaptic delay times, even when the mean synaptic delay times were varied over the range 1 to 7 ms. Synchrony was found to be sensitive to the strength of reciprocal inhibition between the inhibitory neurons in one column: Too weak or too strong reciprocal inhibition degraded intra-columnar synchrony. The only parameter that affected the oscillation frequency of the network was the strength of the external driving input which could shift the frequency between 35 to 60 Hz. The same results were obtained using a model column of 1000 neurons with a connection density of 5%, except that the oscillation became more regular.Synchronization between cortical columns was studied in a model consisting of two columns with 100 model neurons each. When connections were made with a density of 3% between the pyramidal cells of each column there was no inter-columnar synchrony and in some cases the columns oscillated 180° out of phase with each other. Only when connections from the pyramidal cells in each column to the inhibitory cells in the other column were added was synchrony between the columns observed. This synchrony was established within one or two cycles of the oscillation and there was on average less than 1 ms phase difference between the two columns. Unlike the intra-columnar synchronization, the inter-columnar synchronization was found to be sensitive to the synaptic delay: A mean delay of greater than 5 ms virtually abolished synchronization between columns.     

Synchronous membrane potential fluctuations in neurons of the cat visual cortex

We have recorded intracellularly from pairs of neurons less than 500 microm distant from one another in V1 of anesthetized cats. Cross-correlation of spontaneous fluctuations in membrane potential revealed significant correlations between the cells in each pair. This synchronization was not dependent on the occurrence of action potentials, indicating that it was not caused by mutual interconnections. The cells were synchronized continuously rather than for brief epochs. Much weaker correlations were found between the EEG and intracellular potentials, suggesting local, rather than global, synchrony. The highest correlation occurred among cells with similar connectivity from the LGN and similar receptive fields. During visual stimulation, correlations increased when both cells responded to the stimulus and decreased when neither cell responded.     

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

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.     

The role of the cerebellum in modulating voluntary limb movement commands

We recorded the activity of cerebellar Purkinje cells (PCs), primary motor cortical (M1) neurons, and limb EMG signals while monkeys executed a sequential reaching and button pressing task. PC simple spike discharge generally correlated well with the activity of one or more forelimb muscles. Surprisingly, given the inhibitory projection of PCs, only about one quarter of the correlations were negative. The largest group of neurons burst during movement and were positively correlated with EMG signals, while another significant group burst and were negatively correlated. Among the PCs that paused during movement most were negatively correlated with EMG. The strength of these various correlations was somewhat weaker, on average, than equivalent correlations between M1 neurons and EMG signals. On the other hand, there were no significant differences in the timing of the onset of movement related discharge among these groups of PCs, or between the PCs and M1 neurons. PC discharge was modulated largely in phase, or directly out of phase, with muscle activity. The nearly synchronous activation of PCs and muscles yielded positive correlations, despite the fact that the synaptic effect of the PC discharge is inhibitory. The apparent function of this inhibition is to restrain activity in the limb premotor network, shaping it into a spatiotemporal pattern that is appropriate for controlling the many muscles that participate in this task. The observed timing suggests that the cerebellar cortex learns to modulate PC discharge predictively. Through the cerebellar nucleus, this PC signal is combined with an underlying cerebral cortical signal. In this manner the cerebellum refines the descending command as compared with the relatively crude version generated when the cerebellum is damaged.     

Unifying hypothesis for the motoneuronal code in neurological disorders

The elucidation of the motoneuronal code in disorders characterized by prominent motor deficits, such as stroke, Parkinson disease (PD), chronic cerebellar degeneration (CCD) and amyotrophic lateral sclerosis (ALS), would have major impacts for early diagnosis, understanding of disease progression, monitoring the effects of conventional or emerging therapies, and rehabilitation. We propose that the fundamental rules of motoneuronal coding are violated in these neurological diseases. Unraveling of the motoneuronal signatures will lead to a new classification of movement disorders. We suggest that: (1) paresia due to a stroke is associated with a reduction in the mean motor unit firing rates (MUFR), abnormal commonality between pairs of motor units (MU), and an abnormal MU/force relationship; (2) in PD, MU do not discharge at the appropriate MUFR at given recruitment thresholds due to underactivity of the motor cortex. Treatment restores the linear relationship between recruitment threshold and MUFR; (3) in CCD, the hierarchical organization of MU is impaired, the commonality between pairs of MU is deregulated, the derecruitment of MU is abnormal, and EMG discharges show periods of electrical silences in MU discharges (ESIMUD); (4) in the early stages of ALS, a premature recruitment of high-threshold MU at low force levels occurs. The hierarchical organization is lost. Fluctuations in timing intervals between successive discharges of given MU are increased. Impaired MU contractile properties resulting from upper motor neuron disease are associated with decreased maximal M wave following lower motor neuron disease. The decline in mechanical force output following fatigue is associated with frequency shifts in the myoelectric signal and reduced muscle fiber conduction velocity. We suggest that patterns of MU discharges are distinct in acute diseases and in chronic neurological conditions, as a result of plasticity, compensatory patterns and reorganization of motor cortex output involving changes in functional connectivity in the central nervous system (CNS).     

Motor "binding:" do functional assemblies in primary motor cortex have a role?

In this issue of Neuron, Jackson and colleagues describe a functional correlate of neural synchrony related to movement control. Synchrony strength in cortico-motoneuronal output neurons in primary motor cortex depended upon similarity of these neurons' connectivity pattern with the spinal cord. These results could form the foundation for subsequent investigations of motor binding.     

Nonlinear surface EMG analysis to detect changes of motor unit conduction velocity and synchronization.

Amplitude and frequency content of the surface electromyographic (EMG) signal reflect central and peripheral modifications of the neuromuscular system. Classic surface EMG spectral variables applied to assess muscle functions are the centroid and median power spectral frequencies. More recently, nonlinear tools have been introduced to analyze the surface EMG; among them, the recurrence quantification analysis (RQA) was shown to be particularly promising for the detection of muscle status changes. The purpose of this work was to analyze the effect of motor unit short-term synchronization and conduction velocity (CV) on EMG spectral variables and two variables extracted by RQA, the percentage of recurrence (%Rec) and determinism (%Det). The study was performed on the basis of a simulation model, which allowed changing the degree of synchronization and mean CV of a number of motor units, and of an experimental investigation of the surface EMG signal properties detected during high-force-level isometric fatiguing contractions of the biceps brachii muscle. Simulations and experimental results were largely in agreement and show that 1) spectral variables, %Rec, and %Det are influenced by CV and degree of synchronization; 2) spectral variables are highly correlated with %Det (R = -0.95 in the simulations and -0.78 and -0.75 for the initial values and normalized slopes, respectively, in the experimental signals), and thus the information they provide on muscle properties is basically the same; and 3) variations of %Det and %Rec in response to changes in muscle properties are significantly larger than the variations of spectral variables. This study validates RQA as a means for fatigue assessment with potential advantages (such as the higher sensitivity to changes of muscle status) with respect to the classic spectral analysis.     

Membrane potential synchrony in primary visual cortex during sensory stimulation

When the primary visual cortex (V1) is activated by sensory stimulation, what is the temporal correlation between the synaptic inputs to nearby neurons? This question underlies the origin of correlated activity, the mechanism of how visually evoked activity emerges and propagates in cortical circuits, and the relationship between spontaneous and evoked activity. Here, we have recorded membrane potential from pairs of V1 neurons in anesthetized cats and found that visual stimulation suppressed low-frequency membrane potential synchrony (0-10 Hz), and often increased synchrony at high frequencies (20-80 Hz). The increase in high-frequency synchrony occurred for neurons with similar orientation preferences and for neurons with different orientation preferences and occurred for a wide range of stimulus orientations. Thus, while only a subset of neurons spike in response to visual stimulation, a far larger proportion of the circuit is correlated with spiking activity through subthreshold, high-frequency synchronous activity that crosses functional domains.     

Impact of adaptation currents on synchronization of coupled exponential integrate-and-fire neurons

The ability of spiking neurons to synchronize their activity in a network depends on the response behavior of these neurons as quantified by the phase response curve (PRC) and on coupling properties. The PRC characterizes the effects of transient inputs on spike timing and can be measured experimentally. Here we use the adaptive exponential integrate-and-fire (aEIF) neuron model to determine how subthreshold and spike-triggered slow adaptation currents shape the PRC. Based on that, we predict how synchrony and phase locked states of coupled neurons change in presence of synaptic delays and unequal coupling strengths. We find that increased subthreshold adaptation currents cause a transition of the PRC from only phase advances to phase advances and delays in response to excitatory perturbations. Increased spike-triggered adaptation currents on the other hand predominantly skew the PRC to the right. Both adaptation induced changes of the PRC are modulated by spike frequency, being more prominent at lower frequencies. Applying phase reduction theory, we show that subthreshold adaptation stabilizes synchrony for pairs of coupled excitatory neurons, while spike-triggered adaptation causes locking with a small phase difference, as long as synaptic heterogeneities are negligible. For inhibitory pairs synchrony is stable and robust against conduction delays, and adaptation can mediate bistability of in-phase and anti-phase locking. We further demonstrate that stable synchrony and bistable in/anti-phase locking of pairs carry over to synchronization and clustering of larger networks. The effects of adaptation in aEIF neurons on PRCs and network dynamics qualitatively reflect those of biophysical adaptation currents in detailed Hodgkin-Huxley-based neurons, which underscores the utility of the aEIF model for investigating the dynamical behavior of networks. Our results suggest neuronal spike frequency adaptation as a mechanism synchronizing low frequency oscillations in local excitatory networks, but indicate that inhibition rather than excitation generates coherent rhythms at higher frequencies.     

Connexin36 mediates spike synchrony in olfactory bulb glomeruli

Neuronal synchrony is important to network behavior in many brain regions. In the olfactory bulb, principal neurons (mitral cells) project apical dendrites to a common glomerulus where they receive a common input. Synchronized activity within a glomerulus depends on chemical transmission but mitral cells are also electrically coupled. We examined the role of connexin-mediated gap junctions in mitral cell coordinated activity. Electrical coupling as well as correlated spiking between mitral cells projecting to the same glomerulus was entirely absent in connexin36 (Cx36) knockout mice. Ultrastructural analysis of glomeruli confirmed that mitral-mitral cell gap junctions on distal apical dendrites contain Cx36. Coupled AMPA responses between mitral cell pairs were absent in the knockout, demonstrating that electrical coupling, not transmitter spillover, is responsible for synchronization. Our results indicate that Cx36-mediated gap junctions between mitral cells orchestrate rapid coordinated signaling via a novel form of electrochemical transmission.     

Local synchronized activity of neurons of different classes in cat primary visual cortex

The goal of the present study was to investigate the local synchronized neuronal activity in the cat visual cortex and the role of different classes of neurons in neural synchrony. Four classes of neurons were identified on the basis of electrophysiological properties of extracellularly recorded cells: RS, FS, IB, and FRB. It was revealed that neurons with short spikes and FRB type of activity were first engaged in synchronization. The model study revealed that neurons with the short action potential had more stable synchronized activity.     

The extraction of neural strategies from the surface EMG.

This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.     

Crosstalk in surface electromyography of the proximal forearm during gripping tasks.

Electromyographic (EMG) crosstalk was systematically analyzed to evaluate the magnitude of common signal present between electrode pairs around the forearm. Surface EMG was recorded and analyzed from seven electrode pairs placed circumferentially around the proximal forearm in six healthy individuals. The cross-correlation function was used to determine the amount of common signal, which was found to decrease as the distance between electrode pairs increased, but was not significantly altered by forearm posture (pronation, neutral, supination). Overall, approximately 40% common signal was detected between adjacent electrode pairs (3 cm apart), dropping to about 10% at 6 cm spacing and 2.5% at 9 cm. The magnitude of common signal approached 50% between adjacent electrode pairs over the extensor muscles, while over 60% was observed between neighbouring sites on the flexor aspect of the forearm. Although flexor and extensor EMG amplitude was similar, less than 2% common signal was present between flexor and extensor electrode pairs during both pinch and grasp tasks. Maximum grip force production was not affected by forearm rotation for pinch, but reduced 18% from neutral (mid-prone) to pronation during grasp (p=0.01). In spite of differences in grip force, mean muscle activity did not vary between the three forearm postures during maximum pinch or grasp trials. While this study improved our knowledge of crosstalk and electrode spacing issues, further examination of forearm EMG is required to improve understanding of muscle loading, EMG properties and motor control during gripping tasks.     

Noise Suppression and Surplus Synchrony by Coincidence Detection

The functional significance of correlations between action potentials of neurons is still a matter of vivid debate. In particular, it is presently unclear how much synchrony is caused by afferent synchronized events and how much is intrinsic due to the connectivity structure of cortex. The available analytical approaches based on the diffusion approximation do not allow to model spike synchrony, preventing a thorough analysis. Here we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to correlated spiking on a fine temporal scale between pairs of neurons. We employ direct simulation and extend earlier analytical methods based on the diffusion approximation to pulse-coupling, allowing us to introduce precisely timed correlations in the spiking activity of the synaptic afferents. We investigate the transmission of correlated synaptic input currents by pairs of integrate-and-fire model neurons, so that the same input covariance can be realized by common inputs or by spiking synchrony. We identify two distinct regimes: In the limit of low correlation linear perturbation theory accurately determines the correlation transmission coefficient, which is typically smaller than unity, but increases sensitively even for weakly synchronous inputs. In the limit of high input correlation, in the presence of synchrony, a qualitatively new picture arises. As the non-linear neuronal response becomes dominant, the output correlation becomes higher than the total correlation in the input. This transmission coefficient larger unity is a direct consequence of non-linear neural processing in the presence of noise, elucidating how synchrony-coded signals benefit from these generic properties present in cortical networks.     

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

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.