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Neuron, Vol. 38, 115–125, April 10, 2003, Copyright 2003 by Cell Press Synchrony between Neurons with Similar Muscle Fields in Monkey Motor Cortex Andrew Jackson,1 Veronica J. Gee, Stuart N. Baker,2 and Roger N. Lemon* Sobell Department of Motor Neuroscience and Movement Disorders Institute of Neurology UCL, London WC1N 3BG United Kingdom Summary Synchronous firing of motor cortex cells exhibiting postspike facilitation (PSF) or suppression (PSS) of hand muscle EMG was examined to investigate the relationship between synchrony and output connectivity. Recordings were made in macaque monkeys performing a precision grip task. Synchronization was assessed with cross-correlation histograms of the activity from 144 pairs of simultaneously recorded neurons, while spike-triggered averages of EMG defined the muscle field for each cell. Cell pairs with similar muscle fields showed greater synchronization than pairs with nonoverlapping fields. Furthermore, cells with opposing effects in the same muscles exhibited negative synchronization. We conclude that synchrony in motor cortex engages networks of neurons directly controlling the same muscle set, while inhibitory connections exist between neuronal populations with opposing output effects. Introduction Synchronous neural activity in the cerebral cortex has been the focus of much recent interest. For instance, synchronous spike discharge in the visual cortex has been implicated in the mechanism of perceptual binding (von der Malsburg, 1981; Eckhorn et al., 1988; Singer and Gray, 1995). The “binding hypothesis” proposes that V1 neurons become synchronized when they encode information pertaining to the same object in a visual scene. This synchrony increases the salience of related features at a higher processing level. It has also been suggested that assemblies of synchronously active neurons play a more widespread role in the processing of information by the brain. If synchronous EPSPs exert a greater influence over a postsynaptic neuron than asynchronously arriving inputs, then synchrony could provide a mechanism by which information can be reliably transmitted through a network of weak synaptic connections (Abeles, 1982, 1991; Vaadia et al., 1995; Alonso et al., 1996; Singer et al., 1997). These issues can now be addressed due to developments in multielectrode *Correspondence: [email protected] 1 Present address: Department of Physiology and Biophysics, University of Washington, Seattle, Washington 98195. 2 Present address: Department of Anatomy, University of Cambridge, Cambridge CB2 3DY, United Kingdom. recording techniques (Eckhorn and Thomas, 1993; Nicolelis et al., 1997; Baker et al., 1999a). Synchrony has also been observed in the discharge of neurons in primary motor cortex (M1) (Murphy et al., 1985; Smith and Fetz, 1989; Hatsopoulos et al., 1998; Baker et al., 2001), and it is possible that synchronous cortical activity may be more effective at driving motoneurons (Baker et al., 1999b; Kilner et al., 2002). The observation of cortico-muscular coherence at the same frequency as cortical oscillations (Conway et al., 1995; Baker et al., 1997; Kilner et al., 1999) suggests that the temporal structure of synchronous cortical output can be transmitted to the spinal level. If synchrony in M1 arises from the organization of cells into functional assemblies, then one possibility is that the activity of cells influencing common muscles could be synchronized together. Indeed, an early study by Smith (1989) (reviewed by Fetz et al., 1990) provided evidence to support such an organization. We have conducted a systematic test of this hypothesis for pairs of M1 neurons exhibiting postspike facilitation (PSF) or suppression (PSS) of hand muscles in monkeys performing a precision grip task. The postspike effects of a single cortical neuron on muscle activity can be revealed by spike-triggered averaging of rectified EMG (Fetz and Cheney, 1980; Lemon et al., 1986). PSF typically occurs at a latency consistent with a monosynaptic excitatory pathway from the cortex to motoneurons (Fetz and Cheney, 1980; Lemon et al., 1986; Baker and Lemon, 1998). Corticospinal projections originate from layer V of M1 and other motor areas, and descend via the pyramidal tract to contralateral spinal segments (Porter and Lemon, 1993). Corticomotoneuronal (CM) cells produce monosynaptic excitation of motoneurons, while corticospinal projections to inhibitory interneurons cause postspike suppression of muscles. Many CM cells produce postspike effects in more than one muscle; the extent of these effects defines the target “muscle field” (Fetz and Cheney, 1980; Buys et al., 1986). Using multiple electrodes we were able to record simultaneously motor cortex neurons with both overlapping and nonoverlapping muscle fields. Synchrony between cells was assessed by cross-correlation analysis (Perkel et al., 1967; Kirkwood, 1979; Baker et al., 2001). Particular care was taken to ensure that the underlying firing pattern of each neuron did not bias the measure of synchronization. We found that cell pairs with similar muscle fields exhibited significantly greater synchronization than pairs with nonoverlapping muscle fields. Furthermore, cells with opposing effects in the same muscles exhibited fewer synchronous spikes than would be expected by chance, referred to as “negative synchronization.” These results could not be explained by a disproportionate contribution of synchronous spikes to STA effects. We conclude that synchrony arises within networks of CM cells with common target muscles and that inhibitory mechanisms act between neurons with opposing effects in the same muscles. Neuron 116 Figure 1. Cross-Correlation Histograms of Cell Activity (A) Cross-correlation histogram (CCH) for a synchronous pair of PTNs (cell L236p5: 35,000 spikes; cell L236p6: 38,400 spikes). Dashed line shows predicted correlation calculated from instantaneous firing rate estimates. (B) Percentage excess of observed counts above predicted. Dashed lines indicate 95% significance; dark shading indicates ⫾5 ms of zero lag. (C and D) Equivalent analysis for a PTN pair exhibiting negative synchronization (cell L205p2: 67,100 spikes; cell L205p5: 45,000 spikes). (E) Distribution of synchronization values for all cell pairs. Dark shading indicates crosscorrelations exhibiting a peak or trough greater than 95% significance. (F) Distribution of peak width at half-maximum (PWHM) of significant peaks (upward bars) and troughs (downward bars). (G) Distribution of time lags of significant peaks/troughs. Results Two monkeys (macaca mulatta) performed a precision grip task (Baker et al., 2001). These results are based on 31 recording sessions (monkey [M] 33: 6; M36: 25) in which at least two neurons with significant postspike effects were simultaneously recorded from primary motor cortex. A total of 101 neurons (M33: 21; M36: 80, 84% positively identified as pyramidal tract neurons [PTNs]) yielded 144 cell pairs. Analysis was performed on continuous sections of data typically lasting 30 min and incorporating around 300 trials. Between 9,700 and 136,500 spikes in total were recorded from each neuron (mean 44,200). Cross-Correlation of Spike Trains Figure 1A shows a cross-correlation histogram (CCH) for a pair of simultaneously recorded PTNs. Because the activity of both cells may covary in relation with the task, we also calculated a cross-correlation predictor using estimates of the instantaneous firing rate of each cell (IFR) (Pauluis and Baker, 2000; Baker et al., 2001). This is shown by the dashed line in Figure 1A and takes into account correlated firing rate modulations but not the precise timing of spikes. Figure 1B shows that the percentage excess of observed CCH counts above this predictor exhibits a significant central peak. An excess of 18% in the zero-lag bin fell to below half this value within one bin width (2 ms). Synchronization was defined as the mean excess within ⫾5 ms of zero lag; the value in this case of 6% was typical for the cell pairs we analyzed. In addition to positive synchrony, we also observed CCH troughs indicative of negative synchrony. Figures 1C and 1D shows one such cell pair. In this case the synchronization value was ⫺7%, reflecting fewer synchronous spikes between ⫾5 ms than would be expected by chance. Figure 1E shows the distribution of synchronization values obtained from all cell pairs used in this study (mean, 2.4%; SD, 5.6%; n ⫽ 144). 80% of CCHs exhibited a significant central peak or trough. The shading in Figure 1E indicates the synchronization values for these cell pairs. Note that the CCH peaks for some cell pairs with low synchronization values nevertheless reached significance. This is partly because significance testing was applied to individual bins, while the synchronization value represents the average of the five central bins. In addition, the significance level will depend upon the number of spikes used to compile the CCH, and if this is large, then small effects can reach significance. The peak width at half-maximum (PWHM) and time lag of significant CCH peaks and troughs are shown in Figures 1F and 1G, respectively. Note that many of these effects were quite narrow, although the width at the base of the peak was typically two to four times the PWHM (c.f. Figure 1B). Most maxima occurred in the central bin (⫾1 ms), whereas the distribution of minima was offset by 2–4 ms (Figure 1G). We also observed symmetrical CCH troughs with minima either side of zero lag (c.f. Figure 1C). These differences in timing might suggest that positive synchronization arises mainly from common inputs to each cell, whereas negative synchronization involves serial inhibitory connections (Perkel et al., 1967). In this study, no attempt was made to differentiate between types of cross-correlation effect. In the subsequent analysis, only the synchronization value as defined above was used, irrespective of the significance, width, or timing of peaks and troughs. The range of ⫾5 ms of zero lag was chosen since it covers the large majority of effects. The advantages of this method are that no assumptions are made about the Synchrony and Muscle Field in Motor Cortex 117 Figure 2. Spike-Triggered Averages of EMG (A) Muscle fields for two cells exhibiting positive synchrony (same pair as Figure 1A). Dashed line shows time of trigger spike. Vertical scale represents percentage excess above a prediction based on IFR estimates. Asterisks indicate significant effects at 95% level calculated from the prespike period. “S” indicates effects which reached significance but were discarded according to the PWHM criterion. (B) Muscle fields for two cells exhibiting negative synchronization (Figure 1C). (C) Distribution of the latencies of peak PSF (upward bars) and PSS (downward bars). Shading indicates effects in intrinsic and extrinsic hand muscles. (D) Distribution of the PWHM of PSF and PSS effects. shape of effects, and that the measure is unbiased by total spike count. In addition, 5 ms is close to the membrane time constant of motoneurons (Zengel et al., 1985), so this measure should have some relevance to the physiological consequences of synchrony at the spinal level. Spike-Triggered Averaging of EMG Postspike effects were identified from spike-triggered averages (STAs) of rectified EMG. As with the cell-cell cross-correlations, these cell-EMG averages were normalized and expressed as percentage excess above a predictor calculated from the IFR estimate. The size of the muscle field, as determined by the number of significant effects at the 95% level, was between one and six muscles per CM cell (average M33: 1.4; M36: 3.2), although since a restricted set of muscles were sampled, this is likely to underestimate the true extent. Figure 2A shows all STAs for two cells exhibiting positive synchronization (same pair as Figure 1A). Both cells showed similar facilitation of EDC and ECR. Figure 2B shows muscle fields for two cells exhibiting negative synchronization (Figure 1C). Note that these cells produce opposing postspike effects in AbPL, EDC, and ECR. The second cell also facilitated FDP, FDS, and AbPB. Two quantities were used to characterize each effect: the latency of peak facilitation (or suppression) and the PWHM. Figure 2C shows the latencies of all PSF and PSS effects. Effects in intrinsic hand muscles had a slightly longer latency (mean, 11.0 ms) compared with extrinsic hand muscles (mean, 10.0 ms; two-sample t test, p ⫽ 0.0004), consistent with a longer peripheral conduction distance. In addition, PSF had a shorter latency (mean, 10.1 ms) than PSS (mean, 10.9 ms; two- sample t test, p ⫽ 0.03) probably reflecting transmission via an additional inhibitory synapse (Jankowska et al., 1976; Kasser and Cheney, 1985; Maier et al., 1998). The PWHM of postspike effects is relevant since it provides one means of separating genuine facilitation from effects which can arise if a non-CM cell fires synchronously with CM cells facilitating the same muscle (Hamm et al., 1985; Smith and Fetz, 1989; Kirkwood, 1994). Baker and Lemon (1998) used a computer simulation to conclude that the duration of PWHM provided a good criterion for accepting PSF effects as genuine. Accordingly, 12/208 PSF effects (6%) with a PWHM ⬎ 7 ms were discarded as potentially arising from synchrony. Examples of excluded PSFs can be seen for muscle AbPL in Figure 2A. Three cells were removed from the analysis as a result of all their postspike effects being judged as caused by synchrony. Baker and Lemon (1998) discussed two potential PWHM criteria (7 or 9 ms). In choosing the more stringent of these, it is possible that some genuine effects may have been excluded. However, for the purposes of the present study, this was considered preferable to the inclusion of synchrony effects which could potentially bias the results. Figure 2D shows the PWHM distribution for accepted PSF and PSS effects. Note that no criterion was applied to the PWHM of PSS effects; these would be expected to be wider due to the additional inhibitory synapse. Therefore the 10% of PSS effects with a PWHM greater than 7 ms were included in the analysis. Relationship between Synchronization and Postspike Effects To determine whether synchrony between a pair of CM cells was related to the similarity between the two mus- Neuron 118 Figure 3. Example of a Synchronized Cell Pair with Overlapping Muscle Field (A) Spike-triggered averages of rectified EMG from 1DI and FDP for two PTNs (cell L03p7: 47,000 spikes; cell L03p8: 52,000 spikes). No significant effects were observed with other EMGs. (B) Vector representation of the significant postspike effects of each cell. Muscle field divergence is defined as the angle between vectors. (C) CCH for the same cell pair indicates synchronous discharge. (D) Typical finger and thumb lever position traces for a single trial and average firing rate profile for these cells (average of 430 trials aligned to the start of the hold period). cle fields, the postspike effects of each cell were represented as a vector (see Experimental Procedures). This allowed us to quantify the similarity between two muscle fields as the angle between two vectors. This muscle field divergence can have values between 0⬚ and 180⬚, where 0⬚ indicates identical effects for the two cells, 90⬚ indicates mainly nonoverlapping (orthogonal) effects, and 180⬚ indicates opposing effects (PSF versus PSS) in the same muscles. Figure 3 describes the calculation for one cell pair. Both neurons exhibited PSF effects in 1DI; cell L03p7 also facilitated FDP (Figure 3A). No significant effects were observed in the remaining five muscles for either cell. Therefore, the muscle field divergence for these cells was 45⬚ (Figure 3B). A positive CCH peak (Figure 3C) indicated that the discharges of this cell pair were synchronized. Both cells’ discharges showed marked modulation during the precision grip task (Figure 3D). Figures 4A and 4B plots the relationship between the muscle field divergence and the synchronization (as quantified from the CCHs) for all cell pairs. A significant negative correlation was observed with both animals (M33: n ⫽ 29, Pearson’s r ⫽ ⫺0.57, p ⫽ 0.001; M36: n ⫽ 115, r ⫽ ⫺0.55, p ⬍ 0.0001). Cell pairs with positive synchronization tended to have similar postspike effects (divergence ⬍ 90⬚), while pairs exhibiting negative synchronization values had opposing effects (divergence ⬎ 90⬚). This result was confirmed by dividing the cell pairs into three groups according to the muscle field divergence: similar, orthogonal, and opposing. Figure 4C shows the mean synchronization for each group. Similar cell pairs had a mean synchronization of 5.3%, whereas for opposing cell pairs it was ⫺2.0%. Both groups were significantly different from the orthogonal group (mean synchronization, 1.7%; two-sample t test: similar-orthogonal, p ⫽ 0.0003; opposing-orthogonal, p ⫽ 0.0001). The total number of postspike effects produced by both cells was calculated for all pairs. This combined muscle field size was not significantly correlated with either synchronization or the absolute magnitude of synchronization (n ⫽ 144, Pearson’s r ⫽ ⫺0.11 and ⫺0.03, respectively). Therefore, synchrony between CM cells did not influence the size of the muscle field for each cell. Task Relationship of Synchronous Neurons CM neurons tend to exhibit either phasic activity during movement, tonic activity during the hold period, or a combination of both (Cheney and Fetz, 1980; Maier et al., 1993). Figure 3D shows the average firing rate profiles for two cells with overlapping muscle fields. The profiles are aligned to the start of the hold period as defined by both finger and thumb traces entering the target displacement window. Despite clear synchrony between these cells (Figure 3C), inspection of Figure 3D reveals different task modulation. Cell L03p7 exhibited a phasic burst during movement with declining activity during the hold period; cell L03p8 showed no phasic component but was active tonically throughout the hold period. This suggests that synchrony can be observed between neurons with similar postspike effects but different task dependence. The similarity between the task modulation of each cell pair was quantified by calculating the correlation coefficient between firing rate profiles (compiled from 1 s before to 2 s after the hold period start with a bin width of 0.1 s). Figure 4D shows this firing profile correlation plotted against synchronization. As has been reported by previous studies (Smith, 1989; Fetz et al., 1990; Pinches, 1999), there is a wide scatter of points, with a small but significant positive correlation between firing rate similarity and synchronization (data from both monkeys pooled: n ⫽ 144, r ⫽ 0.20, p ⫽ 0.02). Since Synchrony and Muscle Field in Motor Cortex 119 Figure 4. Relationship between Synchronization and Muscle Field Divergence (A) Scatter plot of muscle field divergence (defined as the angle between two muscle field vectors, see Experimental Procedures) against synchronization for cell pairs recorded in M33. (B) Equivalent plot for cell pairs recorded in M35. (C) Mean synchronization for all cell pairs grouped by muscle field divergence: similar (⬍90⬚, n ⫽ 67), orthogonal (⫽90⬚, n ⫽ 39), and opposite (⬎90⬚, n ⫽ 38). Data from both animals pooled. (D) Similarity of task modulation (defined as the correlation coefficient between firing rate profiles) plotted against synchronization for all cell pairs. other studies have reported an association between the firing pattern during movement and the muscle field of some CM cells (Cheney et al., 1991; Bennett and Lemon, 1996), a pair of cells with similar task modulation might be expected also to have similar muscle fields. However, this association cannot explain the relationship between synchronization and muscle field divergence. Having accounted for the similarity of task modulation using partial correlation, the relationship between synchronization and muscle field divergence remains statistically significant (n ⫽ 144, rpartial ⫽ ⫺0.54, p ⬍ 0.0001). However, the partial correlation coefficient between similarity of task modulation and synchronization, accounting for the effect of muscle field divergence, is not significant (n ⫽ 144, rpartial ⫽ 0.13, p ⫽ 0.12). Therefore, this weak correlation between synchronization and similarity of task modulation may be explained by a stronger interaction with muscle field similarity, combined with an association between firing patterns and muscle fields. Effect of Electrode Separation One possible explanation for the correlation between synchronization and muscle field similarity could be covariation of both factors with the distance between neurons. Neighboring neurons might be expected to exhibit greater synchrony (Fetz et al., 1990; Abeles, 1991; Matsumura et al., 1996; Hatsopoulos et al., 1998) and project to similar muscles (Cheney and Fetz, 1985; Lemon et al., 1987). Estimating the neuronal separation as the distance between the tips of the recording electrodes, we found that synchronization was significantly correlated with electrode separation (r ⫽ ⫺0.35, p ⫽ 0.001). Figure 5A shows the mean and standard deviation of synchronization for cell pairs grouped according to electrode separation. The range of observed synchronization values is greatest for the closest pairs, and the mean value declines with increasing separation. However, there was no correlation between muscle field divergence and electrode separation (r ⫽ ⫺0.02, p ⬎ 0.2) as shown in Figure 5B. Therefore, over the range of interneuronal separations sampled here, distance does not seem to influence the probability that the two neurons will have overlapping muscle fields. Double Spike-Triggered Averaging of EMG Another possible explanation for the correlations in Figures 4A and 4B is that the synchronous spikes from a Figure 5. Relationship between Synchronization and Electrode Separation (A) Mean and standard deviation of synchronization for cell pairs grouped according to electrode separation (300–500 m, 500–700 m, etc.). Both the mean and the range of observed synchronization values decreases with increasing separation. (B) Mean and standard deviation of muscle field divergence of cell pairs, again grouped according to separation. There is no effect of separation on the probability of muscle field overlap. Neuron 120 Figure 6. Double Spike-Triggered Averages of EMG (A) Method of double spike-triggered averaging. Time relative to each spike train runs along separate axes (t1, t2). Each spike pair contributes to the average along a diagonal, offset from the main diagonal according to the interspike interval. (B) Typical double spike-triggered average for two cells which exhibited PSF effects on muscle EDC. The CCH for this pair is shown in Figure 1A. The facilitation caused by each cell is separated and averaged along the different axes. (C) Linear sum of average postspike effects closely matched the observed interaction. (D) Double spike-triggered average for a cell pair with opposing effects on muscle ECR. The CCH for this pair is shown in Figure 1C. (E) Linear sum of average postspike effects. pair of CM cells could contribute disproportionately to the PSF observed in STAs. This might arise either from nonlinearities in the response of motoneurons to synchronous EPSPs, or as a result of higher order synchrony between M1 neurons (Martignon et al., 1995, 2000). The latter would be characterized by correlated firing among a group of cells such that some of the synchronous spikes from any cell pair would coincide with spikes from the wider population of CM cells. In either case, we would expect that if a pair of cells exhibits a CCH synchrony peak, then the STAs for each cell would be similar since the same synchronous events would be dominating both averages. We assessed this possibility by compiling EMG averages triggered by a pair of CM cell spikes. Figure 1A shows the CCH for a synchronized cell pair. Both these cells exhibited PSF effects in EDC (Figure 2B). Figure 6B shows the double spike-triggered average of this muscle for the same cell pair. In this average, time relative to each spike train runs along different axes (t1, t2; see Figure 6A). Thus the PSF caused by each cell independently can be separated from the effect of synchronous spike pairs. If it is only synchronous spikes which contribute to PSF effects, then facilitation should mainly be observed along the diagonal t1 ⫽ t2. Figure 6B indicates that this is not the case. The vertical band of facilitation around time t1 ⫽ 10 ms shows the PSF caused by cell 1 (L236p5). The horizontal band at t2 ⫽ 10 ms is the PSF by cell 2 (L236p6). Plotted below and to the left of Figure 6B are these responses, averaged over the vertical (t2) and horizontal (t1) dimensions, respectively. Cell 1 individually produces a maximum facilitation of 8%, for cell 2 the peak is 7%. Where the two bands intersect shows the response to a synchronous spike pair (t1 ⫽ t2 ⫽ 10 ms). At this point there is a 15% modulation above baseline, suggesting that the facilitation due to the synchronous discharge of these cells is similar to the linear sum of the individual effects. Figure 6C shows the prediction of the linear model for the double STA of this cell pair, calculated by recombining the one-dimensional averages in Figure 6B (see Experimental Procedures). Splitting the STA into two dimensions in this way is computationally time consuming and severely reduces the signal-to-noise ratio. Therefore, double STAs were compiled only for a subset of 39 cell-cell-muscle combinations with large spike numbers and clear facilitation of the muscle by both cells individually. For this sample, the value of the double STA was compared with the prediction of linear summation at the time lags appropriate for maximum facilitation by each cell. The result is shown in Figure 7. Most points fall around the dotted line representing an observed facilitation by synchronous spikes equal to that predicted by a linear summation of individual effects. This was true for cell pairs evoking both small and quite large effects, with the mean (⫾ standard error) ratio of observed to predicted facilitation equal to 1.04 ⫾ 0.05. Therefore, on the basis of Synchrony and Muscle Field in Motor Cortex 121 ferred directions (Georgopoulos et al., 1993; Lee et al., 1998). Figure 7. Facilitation by Synchronous Spikes versus the Linear Sum of Individual Effects Plot of double-spike triggered average versus the prediction of the linear summation model at the time lags appropriate for maximum facilitation by each cell individually. Dotted line represents facilitation due to a synchronous pair of spikes that equals the linear sum of the facilitation caused be each cell separately. Points above this line represent supralinear effects. Data from 39 cell-cell-EMG combinations. these data, there was no statistically significant difference between the facilitation by synchronous pairs of spikes from cells which share a target muscle and an equivalent number of unsynchronized spikes. However, the 95% confidence range for this ratio encompasses values between 0.94 and 1.14; thus, small nonlinearities would not be resolved by this method. Figure 6D shows a double spike-triggered average for two cells with opposing effects in muscle ECR (Figure 2B). The cross-correlation for this pair is shown in Figure 1C. Once again, the double STA reveals the effect of each cell independently and that the combined effect is well described by a linear sum (Figure 6E). Discussion Synchronous Networks within the Motor Cortex This study has investigated synchrony among motor cortex neurons causing postspike facilitation or suppression of muscle activity. CM cells active during precision grip have restricted muscle fields (Buys et al., 1986; Maier et al., 1993; Bennett and Lemon, 1996). We found that the greatest synchronization was observed between pairs of cells with overlapping muscle fields. Cells with nonoverlapping muscle fields exhibited less synchrony. Furthermore, cells with opposing effects in the same muscles had negative synchronization, implying fewer synchronous spikes than would be expected by chance. Thus, synaptic connections mediating synchrony exist predominantly between CM cells sharing the same output connectivity. This system of organization could underlie the synchronization of cells with similar task modulation (Fetz et al., 1990; Pinches, 1999), including during reaching movements for which correlations are strongest for pairs of neurons with similar pre- Influence of Synchrony on the Magnitude of Postspike Effects One issue already raised is the effect of synchrony on the magnitude of postspike facilitation and suppression as revealed by STAs (Fetz and Cheney, 1980; Smith and Fetz, 1989; Kirkwood, 1994). In the most extreme case, a cell with no physiological connection to motoneurons could exhibit PSF if it is active in synchrony with genuine CM cells. More significantly, synchrony effects could have contributed to the main finding of this study, the correlation between synchrony and muscle field overlap (Figure 4). Several approaches to resolving this problem can be considered. First, the size of muscle field was not correlated with synchronization, suggesting that synchrony between the recorded neurons is not responsible for a significant number of spurious postspike effects. Second, by using the PWHM criterion set out in Baker and Lemon (1998), we discarded some postspike effects that may have arisen from synchrony alone. This criterion is based on the logic that synchrony PSF will result from genuine effects convolved with the shape of the cross-correlation peak between cells, resulting in a wider profile. It should be pointed out however that the PWHM criterion is based on a computer simulation depending critically on several factors, particularly the width of the CCH peaks between cells. Classification of genuine and synchrony effects was found to be adequate for cross-correlation widths (at half-maximum) down to at least 2.8 ms (Baker and Lemon, 1998), but for some of the narrower CCH peaks summarized in Figure 1F, separation of postspike effects based on PWHM may be unreliable. A third approach was developed by Smith and Fetz (1989) who selectively removed spikes to eliminate cross-correlation peaks before compiling spike-triggered averages. For a cell pair exhibiting 21% synchronization (around the upper limit of the values found in this study), the authors concluded that this accounted for only around 10% of the PSF magnitude. Furthermore, the effect of the CCH peak was assessed by convolution with the PSF produced by the synchronous cell. Such approaches assume linear summation of the facilitation by each cell individually, and the absence of higher order synchrony. The validity of these assumptions can now be qualified using the double STA results described here. First, in all cases the double STA revealed that it was not only the synchronous spikes producing postspike facilitation (Figure 6). Despite this, the contribution of synchrony to PSF magnitudes could affect the proportion of effects reaching significance, producing a spurious correlation between synchronization and muscle field overlap. This situation will be exacerbated if synchronous spikes contribute disproportionately to the PSF. However the double STA revealed that at worst (within the 95% confidence range of the data) the effect of synchronous spikes was 1.14 times the effect of asynchronous spikes. Incorporating this nonlinearity into the convolution method will influence the result by only 14%. Simulations using typical spike trains recorded from this study showed that for a synchronization of Neuron 122 6%, only 1.5% of the PSF from one cell appears in the STA for the second cell, and such effects cannot account for the strength of correlation observed in Figure 4 (see Supplementary Material at http://www.neuron. org/cgi/content/full/38/1/115/DC1). Nevertheless, pairwise synchrony with other cells in the population may exaggerate the observed facilitation by each cell independently. The extent of this cannot be assessed from this study, since it depends on the total number of CM cells within each assembly, which cannot be determined from the present data. Functional Significance of Synchrony In sensory systems it has been proposed that synchrony may provide a solution to the problem of “binding” lowlevel features encoded by individual neurons into more complex high-level representations of objects. A similar combinatorial problem can be envisaged in the motor system. For example, if features of a movement are represented by the distributed activity of a population of neurons, a synchrony code could help to distinguish and organize the processing of information pertaining to individual muscles. Although corticospinal projections to target motoneurons are anatomically constrained, it is possible that the synchrony observed between CM cells reflects “motor binding” at a level higher than the CM cell output, or has functions at the other targets influenced by CM cells through their collaterals at spinal and supraspinal levels (Ugolini and Kuypers, 1986). Some theories propose that synchronous presynaptic activity should exert a greater influence over postsynaptic neuronal discharge than asynchronous activity (Abeles 1982; Singer et al., 1997). We attempted to test this for CM projections by using double STAs to compare the facilitation caused by synchronous and asynchronous pairs of spikes. Because of extensive branching of CM cells within the motor nucleus of the target muscle (Porter and Lemon, 1993), we would expect considerable convergence in the projections to motoneurons from CM cell pairs which share postspike effects. We observed no significant difference between the facilitation caused by synchronous spikes and the linear sum of the effect of each cell individually, suggesting that synchronous activity may be no more effective at driving motoneurons than the same number of spikes occurring asynchronously. Unitary EPSPs from CM cells are probably of small amplitude and derived from a small number of synaptic contacts (Lawrence et al., 1985). It has been shown that pairs of small EPSPs arriving at separate dendrites on the same motoneuron sum approximately linearly (Prather et al., 2001), although this may not be true for larger or more numerous potentials arriving synchronously (Porter and Hore, 1969; Poliakov et al., 1997; Matthews, 1999). Our result is consistent with the finding of no interaction between the responses to ICMS pulses delivered simultaneously to separate M1 sites (Baker et al., 1998). Nonlinearities have been observed in the muscle response evoked by two spikes from the same CM cell separated by a short interval (Fetz and Cheney, 1980; Lemon and Mantel, 1989), but this involves sequential activation of the same synapses on target motoneurons and probably invokes different biophysi- cal mechanisms than pairs of inputs spatially separated on the motoneuron’s dendrites. Even without nonlinear summation of motoneuron EPSPs, widespread higher order synchrony among large populations of CM cells would be expected to produce an apparent facilitation which is greater than the sum of individual effects for any cell pair. However, the large background of synchronous spikes occurring by chance would dilute any effect of genuine higher order synchrony events in the average. To illustrate this for a CM cell pair with a typical synchronization value, we could suppose that any increased facilitation revealed by the double STA arises from just 6% of the total number of synchronous spike pairs. A ratio of synchronous to asynchronous facilitation of 1.14 would be consistent with these synchronous spike pairs being accompanied by on average an extra 5.7 spikes from the entire population of CM cells facilitating that muscle. Although it should be emphasized that this represents the upper bound consistent with these results, and that the observed mean ratio of 1.04 leads to a more modest 1.3 additional spikes, it therefore remains a possibility that small assemblies of CM cells could exhibit higher order synchrony. In relation to this, it is interesting to note that Martignon et al. (2000) concluded that higher order interactions in frontal areas did not seem to be frequent. In addition, Baker and Lemon (2000) found that repeating temporal patterns of spikes from three or more neurons (of which higher order synchrony is a special case) occurred at chance levels within the motor cortex. Synchronization between PTNs within the cortex might be due to shared inputs, or arise from recurrent networks of intracortical axon collaterals (Landry et al., 1980; Ghosh and Porter, 1988; Jackson et al., 2002). In either case, it is clear that synchronization can occur between cells whose activity relates to the same muscles but different features of a movement (Figure 3). Synchrony between such cells could result from the necessity to coordinate activity during different phases of a movement. Furthermore, modulation of synchrony in the motor cortex during motor tasks (Vaadia et al., 1995; Riehle et al., 1997; Baker et al., 2001) suggests systematic variations in the degree of synchronization within or between network assemblies required for specific motor activities. This may encode movementrelated information (Hatsopoulos et al., 1998) and possibly account for changes in the size and form of postspike effects which can occur across and within tasks (Buys et al., 1986; Bennett and Lemon, 1996). Finally, the negative synchronization observed between cells with opposing effects in the same muscles could be explained if some of the cells we recorded were interneurons, inhibiting CM cells. This would lead to a cross-correlation trough and, potentially, postspike suppression of muscles facilitated by the inhibited cells. However, since the majority of cells were identified as PTNs, the most likely explanation is that suppression of activity in networks with opposing postspike effects occurred via axon collaterals of the recorded cells projecting to inhibitory interneurons (Renaud and Kelly, 1974; Thomson et al., 1995). The connectivity underlying positive and negative synchronization may serve to form discrete dynamic assemblies of motor cortex output neurons with com- Synchrony and Muscle Field in Motor Cortex 123 mon target muscles. Different assemblies might reflect the enormous repertoire of skilled hand movements in primates as a task-related pattern of muscle combinations (Hughlings Jackson, 1874; Turton et al., 1993). Experimental Procedures Behavioral Task Two purpose-bred adult female monkeys (m. mulatta) weighing 6.1 kg (M33) and 5.0 kg (M36) were used for this study. The precision grip task (see Baker et al., 2001) involved squeezing two springloaded levers between thumb and index finger. A successful trial required a movement into target followed by a hold period of 1 s before release. Auditory cues were given when the levers were within target and at the end of the hold period. Recording Details of surgical procedures and the Eckhorn multiple-electrode recording system (Thomas Recording Ltd., Marburg, Germany) have been described by Baker et al. (1999a, 2001). EMG was recorded from muscles flexor digitorum profundus (FDP), flexor digitorum sublimis (FDS), extensor digitorum communis (EDC), extensor carpi radialis (ECR), abductor pollicis longus (AbPL), abductor pollicis brevis (AbPB), and first dorsal interosseous (1DI) in both animals. In M33, we additionally recorded from adductor pollicis (AdP) and abductor digiti minimi (AdDM). In M36, all EMGs were recorded using implanted patch electrodes (Miller et al., 1993); in M33, a combination of implanted, surface, and needle electrodes was used. Cross-talk between EMG recordings was not more than 20%; for most pairs it was negligible. Cortical recordings were made in the hand area of M1, contralateral to the performing hand; both hemispheres were investigated in both animals. Chamber center coordinates were A13 L18 (M33) and A10 L17 (M36). Up to 14 glassinsulated platinum electrodes (impedance 1–3 M⍀, 4 ⫻ 4 grid with interelectrode spacing of 300 m) were independently lowered into the cortex to search for cells. PTNs were identified by their antidromic response to PT stimulation (latencies, 0.9–4 ms; thresholds, 20–200 A) and collision testing (Lemon, 1984). Off-line, single units were discriminated using principal component analysis on the spike waveform and cluster cutting (Eggermont, 1990). All procedures were performed in accordance with appropriate UK Home Office regulations. Analysis Synchronization Synchrony between pairs of simultaneously recorded neurons was assessed by compiling cross-correlation histograms with a bin width of 2 ms, which were compared with a predictor calculated from instantaneous firing rate (IFR) estimates for each cell (Pauluis and Baker, 2000; Baker et al., 2001). Synchronization was quantified as the percentage excess of observed counts above expected counts averaged across the five central bins (⫾5 ms of zero lag). In addition, we tested the significance of peaks and troughs, assuming Poisson distributed bin counts. Peaks and troughs which exceeded 95% significance were characterized according to timing and peak width at half-maximum (PWHM). As a precaution, cross-correlations for which the PWHM equalled 1 bin width (2 ms) were recompiled with a bin width of 0.5 ms to ensure that no peaks were precise at the shorter time scale; this would have suggested an artifact contaminating both channels. Muscle Field Divergence Postspike facilitation (PSF) or suppression (PSS) of muscles was identified from spike-triggered averages of rectified EMG sampled at 5 kHz. These were compiled between 50 ms either side of the trigger spike and normalized by the cross-correlation between IFR and rectified EMG. Effects were considered significant if they exceeded twice the standard deviation of the prespike period and satisfied the PWHM criterion discussed elsewhere. The muscle field for each cell with at least one significant effect was defined as a vector m where mi ⫽ ⫹1, ⫺1, or 0 for respectively a PSF, PSS, or no effect in muscle i. For a pair of cells, the similarity between muscle fields was assessed by calculating the divergence angle, , between the two muscle field vectors: ⫽ cos⫺1 m ·m . 冢|m ||m |冣 1 1 2 (1) 2 Double Spike-Triggered Averaging The contribution of synchrony to postspike effects was investigated by averaging rectified EMG triggered by a pair of spikes from two cells. A double spike-triggered average is a function of two dimensions X(t1,t2), with time relative to each spike train running along separate axes t1, t2. Let the times of spikes from the two cells be represented T i1, T j2. E(t), the level of rectified EMG at time t is then averaged separately at every point for which a spike pair i, j satisfies: T i1 ⫹ t1 ⫽ T j2 ⫹ t2 ⫽ t. (2) As a result, each spike pair contributes one sweep of EMG to the average along the diagonal defined by Equation (2). The effect of synchronous spikes is represented along the main diagonal (t1 ⫽ t2), while asynchronous pairs are offset from this (Figure 6A). Averages were expressed as the percentage excess above a predicted average XIFR(t1,t2) calculated from IFR estimates for each cell F1(t ), F2(t ): 冮E(t)F1(t ⫺ t1)F2(t ⫺ t2)dt , 冮F1(t ⫺ t1)F2(t ⫺ t2)dt (3) Xobserved (t1,t2) ⫺ XIFR(t1,t2) ⫻ 100. XIFR(t1,t2) (4) XIFR (t1,t2) ⫽ Xnorm(t1,t2) ⫽ If there is no interaction between the postspike effects of the individual cells, then this excess Xnorm should approximate to a linear sum of the contribution from each cell independently: Xlinear (t1,t2) ⫽ X1(t1) ⫹ X2 (t2) ⫹ c, (5) where X1(t1), X2 (t2) are obtained by averaging Xnorm(t1,t2) over the independent dimension and c is a constant required to conserve the overall mean. To reduce computational time and improve signal-to-noise, double spike-triggered averaging was performed on rectified EMG which had been smoothed and downsampled to 500 Hz. Acknowledgments The authors thank Daniel Wolpert, Peter Kirkwood, Rachel Spinks, Thomas Brochier, Helen Lewis, and Lucy Anderson for their assistance. This work was funded by the Medical Research Council, The Wellcome Trust, and the Human Frontiers Science Program. Received: July 23, 2002 Revised: February 20, 2003 Accepted: March 6, 2003 Published: April 9, 2003 References Abeles, M. (1982). 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