Synchronization of Lower Limb Motor Unit Activity During Walking in Human Subjects

N. L. Hansen, S. Hansen, L.O.D. Christensen, N. T. Petersen, and J. B. Nielsen

Division of Neurophysiology, Department of Medical Physiology, The Panum Institute, Copenhagen University, 2200 Copenhagen N, Denmark


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Hansen, N. L., S. Hansen, L.O.D. Christensen, N. T. Petersen, and J. B. Nielsen. Synchronization of Lower Limb Motor Unit Activity During Walking in Human Subjects. J. Neurophysiol. 86: 1266-1276, 2001. Synchronization of motor unit activity was investigated during treadmill walking (speed: 3-4 km/h) in 25 healthy human subjects. Recordings were made by pairs of wire electrodes inserted into the tibialis anterior (TA) muscle and by pairs of surface electrodes placed over this muscle and a number of other lower limb muscles (soleus, gastrocnemius lateralis, gastrocnemius medialis, biceps femoris, vastus lateralis, and vastus medialis). Short-lasting synchronization (average duration: 9.6 ± 1.1 ms) was observed between spike trains generated from multiunit electromyographic (EMG) signals recorded by the wire electrodes in TA in eight of nine subjects. Synchronization with a slightly longer duration (12.8 ± 1.2 ms) was also found in 13 of 14 subjects for paired TA surface EMG recordings. The duration and size of this synchronization was within the same range as that observed during tonic dorsiflexion in sitting subjects. There was no relationship between the amount of synchronization and the speed of walking. Synchronization was also observed for pairs of surface EMG recordings from different ankle plantarflexors (soleus, medial gastrocnemius, and lateral gastrocnemius) and knee extensors (vastus lateralis and medialis of quadriceps), but not or rarely for paired recordings from ankle and knee muscles. The data demonstrate that human motor units within a muscle as well as synergistic muscles acting on the same joint receive a common synaptic drive during human gait. It is speculated that the common drive responsible for the motor unit synchronization during gait may be similar to that responsible for short-term synchronization during tonic voluntary contraction.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Cross-correlation analysis of electromyographic (EMG) activity has been widely used to obtain information about the organization of synaptic input to motoneurons during voluntary motor tasks.

Synchronized motor unit discharges have been demonstrated for a number of muscles in man using single motor unit spike trains (Bremner et al. 1991a,b; Datta and Stephens 1990; Datta et al. 1991; Davey et al. 1993; Farmer et al. 1993a; Nordstrom et al. 1992) or multiunit EMG recordings (Carr et al. 1994; Gibbs et al. 1995). The synchronization is most pronounced within the same muscle but may also be seen between synergistic muscles (Bremner et al. 1991a,b). It is possible to distinguish two different types of synchronization. One is of short duration (around 12 ms on average) and high amplitude (short-term synchronization), whereas the other is much broader and of relatively low amplitude. There are good arguments to suggest that a common input to the motoneurons from branches of last-order neurons plays a significant role in the generation of short-term synchronization (Bremner et al. 1991a,b), but, as pointed out by Vaughan and Kirkwood (1997), it cannot be excluded that synchronization of separate last-order inputs contributes to synchronization peaks with a width of more than 6 ms. Irrespective of this, evidence has been presented to suggest that the short-term synchronization depends on activity in the pyramidal tract. Short-term synchronization is thus lower or absent in patients with cortical, internal capsule, or spinal lesions (Datta et al. 1991; Davey et al. 1990; Farmer et al. 1993a).

Generally, synchronization has been studied during weak tonic muscle contraction. Whether a similar synchronization may be observed during more natural dynamic motor tasks is far less investigated. M. extensor carpi radialis motor units have been shown to exhibit similar cross-correlogram peaks during slow wrist movements (Kakuda et al. 1999), and synchrony of leg and trunk muscles has been demonstrated during balancing (Gibbs et al. 1995). In the cat, synchronization was originally reported for intercostal muscles during respiration (Kirkwood and Sears 1978).

In the present study we have used cross-correlation techniques to investigate the pattern of motor unit synchronization during human gait and thereby provide information about the organization of the central pathways, which are responsible for activation of the spinal motoneurons during gait. Part of these data has been published previously in abstract form (Christensen et al. 1998).


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

The experiments were performed on 25 healthy subjects, 21-66 yr old, 11 women and 14 men. All subjects gave informed written consent to the experiments, which were approved by the local ethics committee.

Experimental protocol

All subjects walked on a treadmill at their preferred walking speed (3-4 km/h). Six of the subjects walked at different speeds ranging from 0.8 to 6.0 km/h within the same experimental session. All experiments started with a short period of walking without EMG recording to familiarize the subject to treadmill walking. All recordings lasted 250-300 s.

In 15 subjects, recordings were also made during tonic dorsiflexion to compare synchronization in this situation with the synchronization observed during walking. While seated in a chair, the subjects maintained a steady contraction against a resistance. The torque level corresponded to around 30-40% of the maximal voluntary dorsiflexion effort. The subjects received visual feedback from an oscilloscope showing the torque and the rectified and integrated EMG activity recorded from the tibialis anterior muscle (TA). These recordings lasted 100-300 s.

EMG recordings

WIRE EMG RECORDINGS. In nine of the subjects, TA motor unit activity was recorded by two teflon-coated platinium-iridium wire electrodes (20 µm diam). The wires were closely twisted to minimize the distance between the tips and were inserted into the muscle using a hypodermic needle (0.7 × 40 mm), which was immediately withdrawn leaving the electrodes inside the muscle. Two sets of wire electrodes were inserted into the muscle with an interelectrode distance of at least 10 cm. The signals were amplified (1,000-5,000), filtered (99-1,000 Hz), and stored on a computer for later analysis. Raw data without filtering were also stored for coherence analysis (see Coherence analysis).

SURFACE EMG RECORDINGS. Surface EMG was recorded from two locations over the TA in 14 subjects. Paired recordings from two different muscles (1 electrode on each of the muscles) were made from the following muscle pairs: gastrocnemius lateralis:soleus (GL:Sol), gastrocnemius medialis:soleus (GM:Sol), GL:GM, TA:vastus lateralis (VL), TA:biceps femoris (BF), soleus:VL, GL:VL, GM:VL, soleus:BF, GM:BF, VL:BF, VL:vastus medialis (VM), and BF:semitendinosus (ST). The number of subjects in whom recordings from the different muscle combinations were obtained is mentioned in Table 2.

Bipolar surface Ag-AgCl electrodes (1 cm2 recording area, 2 cm between poles) were placed over the respective muscles. For the TA recordings, the electrode pairs were placed over the muscle at a distance of at least 10 cm. The signals were amplified (5,000-10,000), filtered (25-1,000 Hz), and stored on a computer for later analysis (sampling rate: 2,000 Hz). Raw data without filtering were also stored for coherence analysis (see below).

Data analysis

The largest spikes in the multiunit EMG were selected by way of a manually set voltage discriminator window (Fig. 1B). In the case of wire recordings, further identification of single unit activity was done by template matching based on the duration, area, and rising time of the initial spike (software package developed by Gilles Detillieux, Department of Physiology, University of Manitoba). The two trains of motor unit activity selected in this way were then visually inspected, and potentials, which deviated in shape from the most commonly observed motor unit shape, were manually deleted. The times of spike occurrence were stored and used for the analysis.



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Fig. 1. Cross-correlation method. A: electromyographic activity (EMG) recorded by 2 wires inserted into the tibialis anterior (TA) muscle during the swing phase of walking. B: the same EMG traces as in A but with an expanded time scale to show the discharges of the individual motor units that were identified by spike recognition software. The spike trains generated by the spike recognition software were used to construct the cross-correlation histograms using one of the spike trains as trigger and the other as samples (C). Counts in the histogram were averaged with respect to the trigger spike. D: EMG recordings from 2 surface electrodes placed over the TA muscle. E: same recordings as in D but with expanded time scale. Trigger levels were used to trigger only on the largest EMG bursts (motor unit activity). The spike trains generated in this way were used as in C for construction of the cross-correlograms (F). The vertical line indicates time 0 in both histograms (C and F).

CROSS-CORRELATION. With the use of the spike times from one recording as trigger events and the spikes of the other recording as sample events, a cross-correlogram was constructed of the probability of occurrence of a sample event with respect to the time of occurrence of the trigger events (Fig. 1). Cross-correlograms were constructed from at least 1,800 spikes (3,500 on average) in each spike train using a binwidth of 1 ms and a pre- and posttrigger period of either 100 or 300 ms. In the cross-correlograms constructed from tonic contractions, the limits of a central peak are usually determined from inflections in the cumulative sum as described by Davey et al. (1986). In data obtained during walking, the cumulative sum mainly reflects the EMG modulation during the gait cycle, which makes determination of the duration of the central peaks difficult. The presence and width of the central peak in these data were therefore primarily determined by visual examination of the cross-correlogram. It was checked that the estimation of the peak duration during tonic contraction was similar when determined from the cumulative sum as when determined from visual inspection of the cross-correlogram.

Several indexes have been used to quantify the strength of motor unit synchronization (Nordstrom et al. 1992). In this study we used two different indexes. 1) We calculated the absolute peak size (APS), which is given by the number of sample spikes per trigger spike above the baseline level during the period in which the peak is present. The background probability of a sample spike in a cross-correlogram constructed from TA EMGs recorded in a walking subject is much larger around 0 ms because of the EMG modulation during the gait cycle (Fig. 1, C and F). For all cross-correlograms obtained from gait data the baseline level was therefore defined as the mean bin height determined from the 10 bins before start of the peak and the 10 bins following the end of the peak. A similar estimation was also used for recordings during tonic contraction. For the recordings during tonic contraction, we also estimated the baseline level as the mean height of all bins. This resulted in essentially similar values as those obtained when calculating the mean bin height from the 10 bins before and after the peak. 2) We calculated the height of the largest bin in the central peak and divided it by the mean bin height. This index is referred to as the K value (Sears and Stagg 1976). In these calculations the mean bin height was determined in the same way as for the calculation of APS. The statistical limit for the peaks was set at 3radical m, where m is the mean bin height.

Differences in the size and duration of synchronization during gait and tonic contraction, between wire recordings and surface EMG recordings, as well as for different gait velocities were tested by two-way ANOVA.

Coherence analysis

Coherence of EMG signals has been described in detail in previous publications (Farmer et al. 1993a; Halliday et al. 1995).

Coherence is an estimate of the correlation between the frequency components of two spike trains, N1 and N2, and may be written as
‖<IT>R</IT><SUB><IT>12</IT></SUB>(<IT>&lgr;</IT>)<IT>‖<SUP>2</SUP>=‖</IT><IT>f</IT><SUB><IT>12</IT></SUB>(<IT>&lgr;</IT>)<IT>‖<SUP>2</SUP>/</IT><IT>f</IT><SUB><IT>11</IT></SUB>(<IT>&lgr;</IT>)<IT>f</IT><SUB><IT>22</IT></SUB>(<IT>&lgr;</IT>)
where lambda  is the frequency in Herz, f12(lambda ) represents the cross-spectrum, and f11(lambda ) and f22(lambda ) represent the auto-spectra of the two-component processes. In the present paper, coherence analysis was used only to check for possible cross-talk between the recording electrodes. The rationale for this was that if significant cross-talk existed, then coherence close to 1.0 would be seen over a broad range of frequencies.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Cross-correlation of TA wire recordings

Recording of multiunit EMG activity from two wire electrodes inserted into the TA muscle was performed in nine subjects. Examples of the EMG signals that were recorded are shown in Figs. 1-3. The TA muscle was generally active throughout the swing phase with two distinct bursts of activity in the beginning and end of the phase. It was not possible to obtain recording conditions where a single motor unit could reliably be discerned in both wire electrodes throughout the recording session. However, with the use of spike identification software (see METHODS), the number of individual units contributing to the correlograms was greatly reduced, and in the most optimal conditions only three or four units contributed to the triggered signal generated from each of the two wire recordings. One such example is illustrated in Fig. 2. The similarity of the shape of the individual motor units in each of the two wire recordings (Fig. 2A) and the averaged spikes generated from all the motor unit activity that was used to construct the correlogram (Fig. 2B) suggests that these two individual units were the main contributors to the triggered spike activity. This was also confirmed by visual inspection of all the motor unit activity from which the spike trains were generated.



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Fig. 2. Cross-correlation of multiunit TA EMG recorded from wire electrodes. Multiunit EMG activity was recorded in the swing phase of walking by 2 wire electrodes inserted into the TA muscle at a distance of 10 cm (A). The arrows indicate the unit that was identified using spike-recognition software. The traces in B show an average of the 2 wire recordings (top and bottom traces, respectively) triggered on the spike trains generated from each of the recordings. In the left traces the average was triggered on the spike trains in the top wire electrode, whereas the average was triggered on the bottom wire electrode in the 2 right traces. In C is shown the coherence of the 2 wire recordings. The horizontal dashed line gives the statistical significance. D: the cross-correlogram generated from the 2 spike trains from the recordings. The spike recognition program generated a total of 1,974 trigger spikes and 2,745 sample spikes. The subject walked with a speed of 4 km/h. The total walking session lasted 5 min, and the subject made 300 steps during this time.

The possibility that both wires picked up activity from the same motor units could be excluded from two observations. First, an averaged spike potential was not observed when the spike train of one wire recording was used for the average of the motor unit activity recorded from the other wire (Fig. 2B). Second, if significant cross-talk between the two recordings had occurred, coherence of activity in the two recordings would have been seen over a wide frequency range, but, as shown in Fig. 2C, this was not the case.

The central peak in the histogram correlating the spike trains generated from the two wire recordings (Fig. 2D) must therefore reflect a physiological process, which synchronizes the activity of the motor unit activity. The duration of the peak was 12 ms. This is similar to values reported for short-term synchronization of pairs of TA motor units recorded during tonic dorsiflexion in sitting subjects (Nielsen and Kagamihara 1994). In addition to this short-lasting and high-amplitude peak, a weaker and broader peak of synchronization is also observed in the histogram. We suspect this long-lasting weak synchronization to be caused by the modulation of the EMG activity during the gait cycle.

Most of the other wire recordings were less optimal than that shown in Fig. 2, and in general 5-10 motor units (judged from visual inspection) contributed to each of the generated spike trains (Fig. 1D). In eight of the nine subjects, central peaks were observed in the cross-correlogram constructed from these spike trains. The average duration of the peaks was 9.6 ± 1.1 (SE) ms. The average size when using the APS index was 0.101 ± 0.024. The average K value was 1.56 ± 0.34.

Cross-correlation of surface recordings

For the wire recordings it is possible to conclude that the central peak in the cross-correlogram is not caused by cross-talk, as the electrodes were placed far apart and visual inspection confirmed that the motor unit activity was isolated to a single electrode. This is not the case when using surface EMG electrodes, but due to ease of recording, surface EMG recording can be more universally applied than wire recordings. Accordingly, it is important to determine whether central cross-correlogram peaks can be determined from the EMG activity recorded from surface electrodes.

In seven subjects EMG was recorded simultaneously by surface and wire electrodes. In Fig. 3, A-D, examples of EMG recordings from surface and wire electrodes are shown together with the resulting cross-correlograms. The figure illustrates that, although a lower number of units contribute to the wire EMG than to the surface EMG, the corresponding cross-correlograms look very similar. A central cross-correlogram peak was found in all seven subjects. Coherence analysis based on the spike trains generated from the EMG recordings showed coherence only at low frequencies, suggesting that cross-talk could not explain any of the cross-correlation peaks. There was no difference in the size of the cross-correlogram peaks derived from the two recording tecniques (Fig. 3F; 2-way ANOVA, P > 0.19), but the duration of the central peak was significantly shorter for the wire recordings (9.8 ± 0.8 ms) than for the surface recordings (Fig. 3E; 12.8 ± 1.2 ms; 2-way ANOVA, P = 0.036). It should be noticed that small troughs were frequently observed on either side of the central peaks (most clearly seen in Fig. 3D). Such troughs were associated with large synchronization peaks and likely reflect the inability of the motor units to discharge at short intervals.



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Fig. 3. Comparison of correlograms obtained from TA wire and surface EMG recordings. A-D: examples of cross-correlograms constructed from TA recordings obtained during walking at 4.0 km/h. The data are from 2 different subjects (A and B, and C and D, respectively). To illustrate the difference in selectivity of wire and surface EMG recordings, a part of the 2 corresponding EMG traces is shown above each correlogram. A and C are constructed from wire recordings, B and D from surface recordings. E and F: duration and relative size of cross-correlogram peaks obtained from TA wire and surface EMG recordings in 7 subjects.

Effect of changing the walking speed on the synchronization of motor unit activity

Seven healthy subjects walked at four different speeds ranging from 1.0 to 4.0 km/h. In three subjects both surface and wire recordings from TA were obtained at different speeds. In all subjects a central peak was present in the correlograms derived from the surface recordings at all walking speeds. Two of the three subjects, in whom wire electrodes were also used, showed peaks in the correlogram derived from the wire recordings at all speeds. In the last subject a peak was seen in the cross-correlogram obtained from the wire recordings in three of four trials.

Examples of cross-correlograms derived from TA surface recordings in one subject walking at different speeds are shown in Fig. 4, A-D. It is seen that there was no clear relation between the speed of walking and the size of the central peak in this subject. This was also the general finding in the population of subjects regardless of whether the APS or K value were used as a quantitative measure of the amount of synchronization (Fig. 4, E and F, respectively). Regression analysis revealed a slight, but nonsignificant negative tendency with increasing walking speed for both indexes (dashed curve in Fig. 4, E and F).



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Fig. 4. The effect of changing the walking speed on the amount of TA motor unit synchronization. A-D: cross-correlograms from the same subject at different walking speeds (0.8, 2, 4, and 6 km/h, respectively). All correlograms were constructed from surface EMG recordings. Around 5,000 trigger events were used to construct the histograms. E and F: the absolute peak size (E) and the K value (F) for the central cross-correlogram peak obtained from the 7 subjects in whom the effect of changing the walking speed on the amount of motor unit synchronization was systematically investigated. Each symbol and full line represent 1 subject. The dashed lines are the regression lines calculated for the data. For the data in E, the slope of the curve was -0.016 and the y-intercept 0.159. The correlation coefficient was 0.16. For the data in F the slope was -0.12 and the y-intercept was 2.35. The correlation coefficient was 0.06.

Comparison of motor unit synchronization during gait and tonic dorsiflexion

Surface EMG recordings were obtained in 14 subjects during both gait and tonic dorsiflexion. A central cross-correlogram peak was found in 13 of 14 subjects during walking at either 3.1 km/h (3 subjects), 3.7 km/h (1 subject), or 4.0 km/h (10 subjects). During tonic dorsiflexion, central peaks were found in all 14 subjects. There were no significant differences in duration and size of the central peak during tonic dorsiflexion as compared with walking (Table 1; paired t-test, P > 0.1).


                              
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Table 1. Cross-correlation between EMG recordings from the tibialis anterior muscle

Cross-correlation of recordings from different leg muscles

In a total of nine subjects, surface EMG was recorded from two different leg muscles during the gait cycle. Results are summarized in Table 2. A central peak in the cross-correlogram was found in four of seven subjects for the LG:soleus correlation (1 example is shown in Fig. 5A), in five of eight subjects for MG:soleus (Fig. 5B), and in five of nine subjects for LG:MG (Fig. 5C). There was no significant difference between duration or size of the cross-correlogram peaks found in each muscle pair (Mann-Whitney, P > 0.4). Notice that the gait cycle modulation of EMG activity in the LG and soleus muscles (Fig. 5A) and the MG and soleus muscles (Fig. 5B) were different, resulting in a nonzero maximum of the broad peak (MG-LG muscle activity leads that of the Sol). Despite this, a short-lasting peak was apparent around time 0 in both cross-correlograms.


                              
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Table 2. Cross-correlation between EMG recordings from lower limb muscles during walking



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Fig. 5. Synchronization of EMG activity recorded from different muscles. Surface EMG recordings were made from different leg muscles during walking. In A recordings were made from lateral gastrocnemius (LG) and soleus muscles, in B the 2 recordings were made from medial gastrocnemius (MG) and soleus, in C the recordings were made from MG and LG, and in D the recordings were made from the biceps femoris (BF) and tibialis anterior (TA) muscles. In all cases a trigger level was set by spike recognition software. Approximately 7,000 trigger events were used to construct the correlograms.

In all investigated subjects there was overlap in the activity of the TA muscle and the VL and BF muscles toward the end of the swing phase and immediately after heel strike. No central peaks were found in any of the resulting cross-correlograms. One example of a correlogram obtained from paired TA and BF recording is shown in Fig. 5D. Of the subjects, in whom soleus (8 subjects), GL (5 subjects), or GM (5 subjects) EMG was recorded together with VL EMG, two showed simultaneous EMG activity in all three muscle pairs. One of them had a central peak in the cross-correlograms derived from the soleus:VL and GM:VL EMG pairs, but not in the one derived from the GL:VL pair (Table 2). The other subject did not show any cross-correlogram peaks. In the subjects, in whom BF and lower leg extensor EMG was recorded, one showed simultaneously activity of the soleus:BF EMG pair, whereas another subject showed simultaneous activity of GL and BF. In none of these cases could a peak in the cross-correlogram be found. Similarly, in the five subjects, where VL and BF EMG were correlated, no peaks were found.

In four subjects paired recording of VM and VL was performed. In all subjects a clear short-lasting central peak was observed. In two subjects recordings were also made from the BF and the semitendinosus-semimembranosus muscles. In both cases central peaks were observed in the cross-correlograms.

Finally, in five subjects recordings were made from the TA and soleus muscles from both legs. Although there was simultanous EMG activity in the right TA and left soleus as well as the left TA and right soleus, no central cross-correlation peaks were observed for any of these combinations.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Short-term synchronization of synergistic motor units is a well-known phenomenon and is thought to indicate the presence of a common input to the motoneurons, which contributes to the recorded motor unit discharges. In man, this has primarily been investigated during tonic muscle contractions. The main finding of the present study was that a similar synchronization of multiunit firings as observed in tonic contractions could be demonstrated during treadmill walking.

Methodological considerations

The cross-correlation method is based on the assumption that the two signals reflect activity in different motor units or populations of motor units. In the case of spike-triggered data from the intra-muscular recordings, it is possible to rule out that cross-talk contributed to the observed correlations on the following basis. First, the selectivity of the recordings ensured that a sufficiently small number of motor units were recorded allowing visual inspection to ensure that the same motor units were not recorded by both electrodes. Second, averaging of the signal recorded from one wire electrode, when triggered from the spike trains of the other, did not reveal any averaged motor unit potential. This indicated that the triggered event is not represented in the EMG record from the distant wire electrodes. Third, coherence analysis did not show any broad frequency range coupling as would be expected if the data were contaminated by cross-talk (cf. Fig. 2C). Fourth, if cross-talk had contributed to the cross-correlogram peaks, much larger and narrower peaks than observed would have been expected.

We may be less certain that cross-talk did not contaminate the surface EMG recordings. However, the likelihood of such contamination was reduced by triggering only on EMG potentials with the largest amplitude (cf. Fig. 1) and by ensuring that the electrodes were placed at a distance of more than 10 cm. Anatomical data suggest that the length of individual TA muscle fibers do not exceed 3 cm (Yamaguchi et al. 1990). As a further precaution we excluded all data in which coherence analysis revealed a broadband coupling (3 recordings). The similarity of the cross-correlation peaks obtained from the remaining surface EMG recordings and the peaks obtained from the wire recordings and the lack of very large and narrow peaks suggests to us that these procedures were indeed effective in suppressing a contribution from cross-talk. It is true that the duration of the peaks were significantly shorter in the wire recordings than in the surface recordings. However, this is not surprising, since the effect of variation in pre- and postsynaptic conduction velocities for the various motoneurons contributing will increase when more motoneurons are included in each EMG trace (see Farmer et al. 1991 for a discussion). Furthermore, the motor unit potentials were sharper and shorter lasting in the wire recordings than in the surface EMG recordings, and there was therefore also less "jitter" in the time of triggering on the individual potentials.

Is the synchronization caused by the gait cycle per se?

One inherent problem in analyzing motor unit activity during a rhythmic movement as gait is that the motor units within a muscle will be activated simultaneously with each step. In the case of tibialis anterior, the motor units will thus be recruited more or less simultaneously at the onset of swing. However, it is not likely that this should explain the very short lasting peaks of synchronization that we have observed here. When recording from different muscles it was often observed that the EMG activity in the two muscles was slightly out of phase. The activity in the MG thus usually started earlier than in soleus, and the main MG EMG activity was seen earlier than the main soleus EMG activity; but we nevertheless observed a narrow peak of synchronization around time 0 in the cross-correlograms, indicating a common drive separate from the signals shaping the EMG burst pattern.

What is the mechanism of the motor unit synchronization during gait?

The observed central peaks are likely to reflect some central process that synchronizes the discharges of motor units during gait. Two separate processes could be discerned. One was rather broad and of low amplitude and another was a distinct, short-lasting peak. The broad synchronization was caused by the modulation of the EMG during the gait cycle and reflects the general drive to the motoneuronal pool(s) during gait corresponding to the burst envelope (cf. Sears and Stagg 1976 for a similar observation in relation to cat respiration). It thus reflects synchronized activity of several separate inputs to the motoneurons. The second short-lasting process seems to be similar to the short-term synchronization of pairs of single motor unit activity, which has been described during tonic contractions (Bremner et al. 1991a,b). The short duration and high amplitude of the synchronization makes it likely that a common input from collaterals of last-order neurons projecting to the motoneurons, from which the recordings are made, plays a role in the generation of the synchronization. This is based on models developed from intracellular motoneuronal recordings in the respiratory system of the cat (Kirkwood and Sears 1978) and adapted for human motor unit recordings by Bremner et al. (1991a,b). Vaughan and Kirkwood (1997) demonstrated that disynaptic linkages may also produce synchronization of short duration and high amplitude in the correlogram of electroneurogram recordings in the cat and argued that only peaks with a duration of <6 ms may with certainty be attributed to common drive from branches of last-order neurons. None of the cross-correlogram peaks that we obtained in the present study fulfill this strict criterion. It seems likely to us that the longer duration and more complicated shape of motor unit potentials recorded by wire, needle, or surface EMG as compared with neurogram potentials introduces a considerable scatter in the time of triggering on the individual discharges and thereby may explain the (relatively) long duration of the cross-correlation peaks. In addition, cross-correlogram peaks constructed from multiunit EMG recordings will be of a longer duration than those constructed from single unit discharges due to variation in pre- and postsynaptic conduction velocities for the various motoneurons (Farmer et al. 1991). Although we thus cannot fully exclude that some other process (i.e., synchronization of separate inputs) contribute to the peaks, we nevertheless find it likely that common drive from branches of last-order neurons play a significant role. This applies both for recordings during gait as well as recordings during tonic contraction, but to which extent it is the exact same mechanism and the same central neurons, which are responsible in both cases, remains open.

For the short-term synchronization seen during tonic contraction, a strong case in support of a role of the pyramidal tract has been made. Datta et al. (1991) demonstrated that short-term synchronization was decreased in patients with lesions of the pyramidal tract, but not in patients with other CNS lesions. This was confirmed by Farmer et al. (1993b), who found that the decrease of short-term synchronization in hand muscles was correlated to the functional disability of patients with hemiplegia. Furthermore, Conway et al. (1995) demonstrated that coherence in the 15- to 32-Hz band between muscle recordings, which is closely related to short-term synchronization (Farmer et al. 1993a), may also be seen between magnetoencephalography (MEG) and EMG recordings. In later studies coherence has also been observed between EMG and electroencephalograph (EEG) recordings (Halliday et al. 1998), and it is now generally assumed that coherence in the 15- to 32-Hz frequency bands and short-term synchronization in man depend on activity in the pyramidal tract. If this is the case, the presence of short-term synchronization during gait may signify a contribution of pyramidal tract activity to the locomotor EMG. It has indeed been demonstrated using transcranial magnetic stimulation (TMS) that the excitability of the corticomotoneuronal pathway to leg motoneurons is high during gait (Capaday et al. 1999; Petersen et al. 1998; Schubert et al. 1997). It has also been demonstrated that block of the output from the motor cortex by TMS results in a decrease of the EMG activity during gait (Petersen et al. 2000). The idea that the short-term synchronization observed here during gait is caused by activity in the pyramidal tract would be consistent with these previous findings.

However, it is important to recognize that several other inputs to the motoneurons may also be involved in the generation of short-term synchronization during both gait and tonic contraction. In principle any system with last-order neurons that branch to supply several motoneurons and that have a sufficient synaptic strength could participate. Although the pyramidal tract certainly plays a significant role in the control of human motor behavior, many other descending and segmental pathways (such as Ia monosynaptic input and rubrospinal and vestibulospinal tracts) appear to branch sufficiently and to have a sufficiently strong drive to the motoneurons, at least in the cat and monkey. Future experiments involving patients with lesions of CNS pathways, comparison of data from man and different animal species, and coherence studies of EMG and EEG data may help to clarify this question further.

Quantitative evaluation of the amount of synchronization

It is difficult to draw any firm conclusions regarding the amount of synchronization during walking as compared with tonic contraction or during the different speeds of walking from several reasons. First, we cannot be sure that the same motor units were active in these different situations. Second, the discharge frequency of the individual motor units probably differed substantially in the different situations, which makes a quantitative comparison of the amount of synchronization problematic (Nordstrom et al. 1992). Finally, there are problems in relation to the way that we calculated the amount of synchronization and especially the mean height of the bins in the histograms (mean of the 10 bins before and after the central peaks). Often a broad synchronization was seen during walking in addition to the short lasting central synchronization peak (cf. Figs. 1-3). This would lead to an underestimation of the size of the central peak. On the other hand, troughs were often observed on either side of the central peak, which would lead to an overestimation of the central peak size (cf. Fig. 3D). The quantitative data can therefore only be used to conclude that there were no major differences in the amount of synchronization in the different situations and that the amount of common drive was in the same range. More subtle differences are likely to have been masked by differences in discharge frequency and sampling of different motor units in the different situations.

Cross-correlation of other leg muscles during gait

The most clear and strongest short-term synchronization was found for paired recordings from the TA muscle, but short-term synchronization was also found for EMG recordings from different ankle plantar flexor muscles as well as for knee extensors and flexors. Synchronization of activity recorded from these muscles was only observed in around 60% of cases, and the strength of synchronization was only around one-half of that observed for TA recordings. We were not able to make paired recordings within individual ankle plantar flexor muscles, and we therefore cannot tell whether this difference is due to a difference in synchronization of motor units recorded from different muscles as compared with motor units recorded from the same muscle or whether it reflects a difference in synchronization of ankle dorsiflexor motor units as compared with plantar flexor motor units.

Gibbs et al. (1995) investigated synchronization of multiunit EMG recorded from different leg muscles during standing and balancing and suggested, based on their findings, that a common drive is present only to muscle pairs that share the same action around a common joint during standing and balancing. Our present data suggest that this principle is also true in relation to walking, although we did observe some differences in relation to the findings by Gibbs et al. (1995). As in their study we found no synchrony between TA EMG and EMG recorded from either VL or BF, but in contrast to them we also only very rarely observed synchronization for paired recordings from any of the ankle plantar flexors and the knee extensors and flexors. One possible explanation of this is that ankle plantar flexors and either knee extensors or knee flexors are only rarely active at the same time during gait. We also observed less synchronization between the different heads of the triceps surae muscle as compared with the study by Gibbs et al. (1995), which suggests that these muscles may be used more independently during walking than during balancing. The function of the gastrocnemii muscles as knee flexors in addition to ankle plantar flexors during walking may be of importance in this relation.

Conclusions

It is concluded that cross-correlation of the multiunit surface EMG is a useful method to investigate functional inputs to the spinal motoneurons during human walking. This may open the possibility of obtaining insights to the central pathways responsible for the activation of the spinal motoneurons during walking as well as the mechanisms responsible for decreased gait ability in patients with diseases of the CNS.


    ACKNOWLEDGMENTS

We are grateful to J. Brønd and D. Halliday for programming the analysis software that were used in the experiments. We also thank J. Brønd for valuable help in the analysis of the data and B. Conway and D. Halliday for valuable discussions and comments on an early version of the manuscript.

This work was supported by grants from the Danish Health Research Council, the Danish Sports Research Council, the Novo Nordisk Foundation, and the Danish Society of Multiple Sclerosis. J. B. Nielsen is a research professor sponsored by the Desiree and Niels Yde Foundation.


    FOOTNOTES

Address for reprint requests: J. B. Nielsen, Div. of Neurophysiology, Dept. of Medical Physiology, The Panum Institute, Copenhagen University, Blegdamsvej 3, 2200 Copenhagen N, Denmark (E-mail: J.B.Nielsen{at}mfi.ku.dk).

Received 20 February 2001; accepted in final form 5 June 2001.


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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
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0022-3077/01 $5.00 Copyright © 2001 The American Physiological Society