The Journal of Neurophysiology Vol. 80 No. 4 October 1998, pp. 1961-1980
Copyright ©1998 by the American Physiological Society

Corticomotoneuronal Postspike Effects in Shoulder, Elbow, Wrist, Digit, and Intrinsic Hand Muscles During a Reach and Prehension Task

Brian J. McKiernan, Joanne K. Marcario, Jennifer Hill Karrer, and Paul D. Cheney

Department of Molecular and Integrative Physiology, and Smith Mental Retardation and Human Development Research Center, University of Kansas Medical Center, Kansas City, Kansas 66160

	ABSTRACT

Abstract Introduction Methods Results Discussion References

McKiernan, Brian J., Joanne K. Marcario, Jennifer Hill Karrer, and Paul D. Cheney. Corticomotoneuronal postspike effects in shoulder, elbow, wrist, digit, and intrinsic hand muscles during a reach and prehension task. J. Neurophysiol. 80: 1961-1980, 1998. We used spike-triggered averaging of rectified electromyographic activity to determine whether corticomotoneuronal (CM) cells produce postspike effects in muscles of both proximal and distal forelimb joints in monkeys performing a reach and prehension task. Two monkeys were trained to perform a self-paced task in which they reached forward from a starting position to retrieve a food reward from a small cylindrical well. We compiled spike-triggered averages from 22 to 24 separate forelimb muscles at both proximal (shoulder, elbow) and distal (wrist, digits, intrinsic hand) joints. Of 174 cells examined, 112 produced postspike effects in at least one of the target muscles. Of those cells, 45.5% produced postspike effects in both proximal and distal forelimb muscles. A nearly equal number (44.7%) produced postspike effects in distal muscles only, whereas a clear minority (9.8%) produced postspike effects in only proximal muscles. The majority of CM cells (71.4%) produced effects in two or more muscles, with an average muscle field of 3.1 ± 2.1 (mean ± SD) for facilitation plus suppression. Of 345 postspike effects identified, 70.7% were facilitation effects and 29.3% were suppression effects. The large majority of effects (72.2%) were in distal muscles. When averaged by joint, the latency and peak magnitude of postspike facilitation showed a stepwise increase from proximal to distal joints. The results of this study show that the majority of CM cells engaged in coordinated forelimb reaching movements facilitate and/or suppress muscles at multiple joints, including muscles at both proximal and distal joints. The results also show that CM cells make more frequent and more potent terminations in motoneuron pools of distal compared with proximal muscles.

	INTRODUCTION

Abstract Introduction Methods Results Discussion References

Anatomic and electrophysiological studies have shown that most corticospinal axons branch extensively within the spinal cord and terminate monosynaptically within multiple motoneuron pools located at one spinal level or distributed across several adjacent spinal levels (Asanuma et al. 1979; Fetz and Cheney 1980; Fetz et al. 1976; Shinoda et al. 1979, 1981). By employing spike-triggered averaging (SpTA) of rectified electromyographic (EMG) activity, Fetz and Cheney (1980) demonstrated that the majority of corticomotoneuronal (CM) cells produce postspike facilitation (PSpF) in multiple muscles of the distal forelimb in monkeys performing a wrist movement task. In addition to facilitation, Kasser and Cheney (1985) showed that single CM cells also suppress antagonist muscles during simple wrist movements. The existence of divergent outputs from CM cells to muscles of the distal forelimb raises the possibility that even more complex output patterns may be represented in single CM cells and that those more complex patterns might include muscles of the elbow, shoulder, and trunk in addition to those of the wrist and digits.

Weak stimulating currents applied directly to the surface of motor cortex produce monosynaptic excitatory postsynaptic potentials (EPSPs) in motoneurons of both distal and proximal muscles in the forelimb and the hindlimb of the monkey, although the projection appears consistently stronger to motoneurons of distal muscles (Clough et al. 1968; Jankowska et al. 1975; Phillips and Porter 1964; Preston et al. 1967). Lemon et al. (reported in Porter and Lemon 1993) recorded EPSPs from 358 motoneurons of the cervical spinal cord of the monkey and reported stronger and more frequent EPSPs in motoneurons of distal muscles than proximal after stimulating corticospinal axons in the medullary pyramid.

Several recent studies have employed transcranial magnetic stimulation (TMS) to study corticospinal projections in humans (Colebatch et al. 1990; Lemon et al. 1995; Palmer and Ashby 1992). All studies suggest projections from motor cortex to motoneurons of both proximal and distal arm muscles. Palmer and Ashby found that TMS produced strong net facilitation of motoneurons of the first dorsal interosseous muscle, weaker net facilitation of the motoneurons of forearm muscles and biceps brachii, and no net effect on triceps brachii or the deltoid. These findings are in contrast to those of Colebatch et al. (1990), who stimulated subjects as they performed active, voluntary reaching movements and concluded that in some cases the strength of the CM projection to proximal muscles is as strong as that to distal muscles. Functional magnetic resonance imaging studies in humans also have demonstrated overlap between areas representing different distal joints (Sanes et al. 1995) as well as areas representing proximal and distal joints (Rao et al. 1995).

In an attempt to focus stimulating currents on smaller numbers of cortical cells, others have employed intracortical microstimulation (ICMS) of cells in the precentral gyrus (Asanuma and Rosen 1972; Donoghue et al. 1992; Humphrey 1986; Kwan et al. 1978a,b; Lemon et al. 1987). Early ICMS work suggested adjacent but not overlapping areas for activation of proximal and distal muscles (Asanuma and Rosen 1972). More recent work has suggested that there are areas where both proximal and distal muscles can be activated by ICMS (at least those muscles at contiguous joints) although the activation of proximal muscles is less frequent and typically requires larger amounts of current.

A limitation to the use of any type of electrical stimulation of the motor cortex is that the applied current does not activate single cells. Instead it depolarizes a potentially large group of cells that are either in the vicinity of the stimulating electrode or lie in the current path (Asanuma et al. 1976). This makes it difficult to determine if the simultaneous effects generated in the motoneurons of distal and proximal muscles result from the activation of single CM cells that branch to terminate in both proximal and distal motor nuclei or if they result from the combined activation of many cells that are intrinsically linked by axonal networks in the motor cortex and individually terminate in either proximal or distal motoneuron pools (Huntley and Jones 1991). Although the ICMS technique limits the activating current to a smaller physical area, it probably activates many cells indirectly, including some that may lie a distance away from the stimulating electrode (Asanuma et al. 1976; Jankowska et al. 1975; Lemon et al. 1987; Porter and Lemon 1993).

In addition to electrophysiologic studies, anatomic studies of the monkey motor cortex also have revealed zones of overlap in the representations of distal and proximal muscles (He et al. 1993, 1995). Although the findings of these electrophysiologic and anatomic studies have demonstrated zones of overlap in the motor cortical representation of distal and proximal muscles in monkeys, no study to date has tested systematically the hypothesis that single CM cells make synaptic connections with motoneurons of both distal and proximal muscles of the forelimb.

The spike-triggered averaging technique employed by Fetz and Cheney (1980) makes it possible to investigate the synaptic output linkages of single cells in the motor cortex. The purpose of this study was to use spike-triggered averaging of EMG activity to determine if CM cells influence muscles at multiple joints of the forelimb in monkeys performing a reach and prehension task. This task required coactivation of proximal and distal muscles in coordinated, functional synergies consistent with execution of different phases of the task. We compiled SpTAs of EMG activity from 22 to 24 muscles of the shoulder, elbow, wrist, digits, and hand during performance of the task. Almost half of the CM cells we identified facilitated motoneurons of both proximal and distal forelimb muscles. Onset latencies for postspike facilitation showed a stepwise increase by joint from the shoulder (shortest latency) to the intrinsic muscles of the hand (longest latency). The magnitudes of postspike facilitation also showed a general increase from proximal to distal muscles, suggesting that CM cells make more frequent and more potent terminations in the motoneuron pools of distal muscles compared with proximal muscles. These results have been reported previously in preliminary form (McKiernan et al. 1994).

	METHODS

Abstract Introduction Methods Results Discussion References

Training procedures

The data for this study were collected from two male rhesus monkeys (Macaca mulatta). The monkeys were obtained when they were 3 yr of age and were trained to perform a reach and prehension task. Each monkey weighed ~6 kg when data collection began. During each data collection session, the monkey was seated in a standard primate chair, which was placed in a sound-attenuating chamber. The monkey's left (untested) arm was restrained during task performance in a foam padded tube that had been fitted to the forearm and elbow. The right arm was unrestrained. The monkey was guided in performance of the task by audio and video cues provided by an IBM-compatible microcomputer.

Behavioral task

The task chosen for this project involved activation of multiple proximal and distal forelimb muscles in natural, functional synergies as the monkey actively reached for a food reward and returned it to his mouth (Fig. 1A). It was based on the principle of the Klüver board (Glees 1961). However, instead of an array of cylinders with different diameters and depths, our food-retrieval apparatus consisted of a 15.24 × 15.24 × 5.08 cm phenolic block with a single cylindrical food retrieval well milled into it (Fig. 1B). The well measured 35 mm deep by 53 mm in diameter. A series of six concentric lexan cylinders with wall thicknesses of 0.32 mm allowed us to adjust the diameter of the retrieval cylinder from 5.4 to 1.6 cm in 6.4-mm steps. With no lexan cylinders inserted into the base, the monkey could place his entire hand into the food-retrieval well. With all of the lexan cylinders inserted into the base, the monkey could only insert a single digit into the food-retrieval well. We typically inserted five of the six cylinders into the base; this allowed the monkey to use no more than two digits to dig the food reward from the well.

View larger version (43K): [in this window] [in a new window]   FIG. 1. A: monkey began the task with his free arm relaxed at his side and his hand resting on a pad containing a microswitch (1). After the computer gave the GO signal and delivered a food pellet into the retrieval well, the monkey was free at any time to reach to the well (2), retrieve the pellet, and return it to his mouth (3). B: schematic of the modified Klüver device used as a food-retrieval well.

The monkey began the task with his free arm relaxed at his side and his hand resting on a plexiglass plate containing a microswitch. The monkey's shoulder was neutral with respect to flexion, extension, and abduction, the elbow was flexed to ~90°, the forearm was pronated, and the wrist was in a neutral position between flexion and extension with the palm down and digits extended (Fig. 1A). The computer detected closure of the microswitch and alerted the monkey to the fact that the system was in the "ready" condition by displaying a blue-colored screen and sounding a tone through a speaker in the sound-attenuating booth. After a short amount of time in this "ready" condition, the screen color changed to green, a higher pitched tone sounded, and the feeder was activated, dispensing a single 94-mg banana-flavored food pellet. The pellet fell through a clear plastic tube into the food-retrieval well (described in the preceding paragraph). The task was completely self-paced, and the monkey was free at any time to reach to the well, retrieve the pellet, and deliver it to his mouth. Infrared light-emitting diodes mounted on and within the food-retrieval apparatus allowed the computer to detect when the pellet fell into the well, when the monkey's hand entered the well, and when the monkey successfully removed the pellet from the well. Additionally, the outputs of the home plate microswitch and all infrared detectors were routed to a patch panel where they could be recorded on analog tape concurrently with unit and EMG data.

Surgical procedures

After training, a 22-mm-diam stainless steel chamber was centered over the hand area of the motor cortex of the left hemisphere of each monkey and anchored to the skull with 25-30 vitallium screws and dental acrylic. Threaded nylon nuts also were anchored in dental acrylic over the occipital aspect of the skull to allow for attachment of a flexible head restraint system during recording sessions. For all implant surgeries, the monkeys were tranquilized with ketamine (10 mg/kg) and anesthetized with isoflurane gas. Surgeries were performed in an Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC)-accredited facility using full sterile procedures. Postoperatively, monkeys received prophylactic antibiotic and analgesic medication. All work involving these monkeys conformed with the procedures outlined in the Guide for the Care and Use of Laboratory Animals published by the National Institutes of Health.

EMG data from 22 to 24 different forelimb muscles were recorded with pairs of insulated, multistranded stainless steel wires (Cooner AS632) inserted transcutaneously into each of the target muscles (Table 1). Approximately 2 mm of insulation was removed from the end of each wire before insertion. The bared end of each lead wire was inserted "backward" into the cannula of a 21-gauge needle for insertion. This procedure formed a hook at the end of each wire that tended to anchor the wire in the muscle after the needle was withdrawn. Once inserted, each wire could withstand mild tugging without dislodging. The insertion points for each muscle were identified based on palpation and dissection studies in which optimal insertion points were mapped with reference to external bony landmarks. The ends of each pair of wires were separated by ~5 mm (Loeb and Gans 1986). The placement of each electrode pair was tested for accuracy by electrical stimulation through the electrodes while observing the nature of the resulting movement. In some cases, this also was done midway through the life of the implant to confirm location. Once all electrodes were positioned, the wires were anchored to the monkey's arm with medical adhesive tape (J&J 5174). This tape is elasticized and highly adhesive. In general, the tape remained firmly anchored to the skin throughout the life of implant. The EMG implants were installed in three independent sections: one for the forearm that included muscles of the wrist, digits, and intrinsics of the hand; one for the upper arm that included muscles of the elbow; and one for the shoulder. With this modular approach, specific sections could be replaced, if necessary, without disturbing the entire implant. Each monkey wore a canvas jacket with a full sleeve on the right forelimb while in his home cage to protect the implanted wires. The implants generally remained functional for 5-8 wk.

For the purposes of analysis, we defined proximal muscles as the five muscles acting primarily about the shoulder (SHL) plus the seven acting primarily about the elbow (ELB). Distal muscles were divided into three groups: five acting around the wrist (WRS), five controlling the digits (DIG), and two intrinsic muscles of the hand (INT) (Table 1).

Recording procedures

The electrical activity from single motor cortex cells was recorded using glass- and mylar-insulated platinum-iridium electrodes with typical recording impedances between 0.7 and 1.5 M. The electrode was positioned over the recording area using an X-Y positioner and was advanced into the motor cortex with a manual hydraulic microdrive. Cortical cell and EMG activity were simultaneously recorded on analog-tape with position signals from the task.

Spike- and stimulus-triggered averaging procedures

During each recording session, cortical cell activity and EMG activity were monitored continuously on oscilloscopes. The action potentials of single cells in the motor cortex served as the triggers for computing SpTAs. Single-unit spikes were isolated from other cortical cell activity with a pair of time/amplitude window discriminators connected in series. Two PDP-11/73 computers rectified and digitized the analog EMG signals before compiling simultaneous SpTAs from all recorded muscles. Some data collection and analysis also were performed with CED (Cambridge Electronics Design) hardware and custom-written software for spike-trigger averaging (Neural Averager). The sampling rate for spike-triggered averaging was 4 kHz, and the analysis period was 60 ms: 20 ms preceding the unit spike and 40 ms following it.

Stimulus-triggered averaging of rectified EMG activity (StTA) was used in conjunction with spike-triggered averaging to evaluate the motor output of the group of neurons in the vicinity of the electrode tip (Cheney and Fetz 1985). Rather than triggering the computer from the spikes of single cells, biphasic square wave current pulses were used as trigger events with this method. The pulses were delivered at a low rate (10-20 Hz.) to avoid temporal summation. Typical current intensities ranged from 10-20 A.

Latency and magnitude of postspike effects

Before quantifying postspike effects, we routinely used our analysis software to subtract any nonstationary, ramping baselines (e.g., see Fig. 6 in Lemon et al. 1986). To do this, we visually identified and marked a range of bins that defined the ramp function. The software then calculated the ramp function by performing a linear least squares fit to the selected data range, offsetting it so that the baseline mean would be retained after the ramp subtraction.

View larger version (19K): [in this window] [in a new window]   FIG. 6. Histograms showing the number of cells with a given muscle field size. A: average muscle field for all effects was 3.1 ± 2.1. B: 9 of the cells with suppression did not produce any facilitation. Average muscle field for the facilitation effects produced by the remaining cells was 2.4 ± 1.4. C: 47 of the cells with facilitation did not produce any suppression. Average muscle field for the suppression effects produced by the remaining cells was 1.7 ± 1.0.

We then computed a number of measures to quantify the latency and magnitude of postspike facilitation and suppression in target muscles. First, the EMG values from a range of bins in the pretrigger period were averaged to arrive at a baseline mean and standard deviation (SD). The baseline typically was determined by averaging the first 10 ms of each record (20 to 10 ms pretrigger). Onset and offset latencies of postspike effects (PSEs) were defined as the points where the envelope of the SpTA crossed a level equivalent to 2 SD above or below the mean of the baseline EMG.

The peak of each effect was defined as the highest point in PSpF or the lowest point in postspike suppression (PSpS) between the onset and offset latencies. The magnitude of the PSpF was quantified in terms of its PPI) and mean percent mean increase (MPI) above baseline. Expressions for these two measures are as follows: PSpS effects were quantified similarly as the peak percent decrease (PPD) and mean percent decrease below the baseline.

Averages latencies and magnitudes were calculated for all facilitation effects and all suppression effects at each of the five joints. For each measure of latency and magnitude, we tested for statistical significance (P < 0.05) between joints by using a one-way analysis of variance for independent groups followed by a Neuman-Keuls post hoc test to examine pair-wise differences.

Measurement of EMG cross-talk

Before drawing conclusions about the distribution of PSEs in concurrently active forelimb muscles, it was important to eliminate potentially redundant effects that could be produced by the volume conduction of electrical activity from one facilitated muscle to electrodes in neighboring muscles (Buys et al. 1986; Fetz and Cheney 1980). Cross-talk between EMG electrodes was evaluated by constructing EMG-triggered averages. This procedure involved using the motor unit potentials from one muscle as triggers for compiling averages of rectified EMG activity of all other muscles. A criterion established by Buys et al. (1986) was used to eliminate effects with cross-talk. To be accepted as a valid postspike effect, the ratio of PSpF (or PSpS) between test and trigger muscle needed to exceed the ratio of their cross-talk peaks by a factor of two or more. One muscle of any muscle pair that did not meet this criterion was eliminated from the database. Twenty-five potential spike-triggered averages were eliminated from the database after testing for cross-talk.

View larger version (31K): [in this window] [in a new window]   FIG. 2. A: diagram illustrating the electromyographic (EMG) sweep-filtering protocol. Cell spikes 1-7 occurred when there is no appreciable EMG in the target muscle. Based on criteria adopted for this study, these spikes were not accepted as triggers for the spike-triggered averaging (SpTA) for this muscle, although they may have been used as triggers for another muscle. There was sufficient EMG surrounding the cell spikes 8-13 to allow them to be accepted as triggers for the SpTA. B: SpTA of palmaris longus (PL) EMG from cell 96K4 constructed with sweep-filtering protocol. C: SpTA of same cell-muscle pair constructed without sweep-filtering protocol.

View larger version (30K): [in this window] [in a new window]   FIG. 3. Single trial records illustrating temporal changes in rectified and filtered EMG for all 24 forelimb muscles during performance of the reach and prehension task. Labels (left) indicate which individual muscles were grouped to represent different joints of the forelimb. Position signals (bottom) indicate when the monkey's hand was resting on "home plate" and when it was in the food-retrieval cylinder. Numbers under the home plate signal refer to the phases of movement identified in Fig. 1A. EMG-triggered averages revealed no significant cross-talk between any 2 muscles in this implant. Visual examination of expanded records of the abductor pollicis brevis (APB) and 1st dorsal interosseus (FDI) EMGs in this figure confirmed the lack of temporal synchronization of peaks.

Response averaging procedures

Response averages were used to evaluate the activity of neurons and all muscles during each of the three behavioral tasks. Response averages were referenced to one of the movement signals used to monitor performance of the task (e.g., hand entering the food retrieval cylinder). The averages were typically based on 40-60 trials and contained EMG from all recorded muscles as well as unit activity and movement signals. Response averages were four seconds in duration with a binwidth of 10 ms.

EMG sweep filtering based on minimum activity level

Early in this project we noted the appearance of a small number of PSpF in SpTAs that had very low levels of mean, baseline EMG despite several thousand trigger spikes. This indicated that at least some of the trigger spikes occurred during periods when there was little or no EMG in the target muscle. Those spikes simply added to the trigger count without materially contributing to the SpTA. In our previous studies employing spike-triggered averaging, the nature of the task (simple wrist flexion-extension against a resistive load) all but ensured that there would be a significant overlap between the activity of a cortical cell and the EMG activity of target muscles. One of the goals in using this task, however, was to achieve a rich variety of different muscle synergies and patterns of coactivation. Because we sampled a much larger number of muscles and because the nature of the task afforded the monkey significantly greater degrees of freedom at all joints involved in the task, no cell that we sampled was strongly coactive with all of the muscles recorded. For any given cell, the cell-muscle coactivation patterns across the 22-24 sampled muscles typically ranged from strongly coactive to little or no overlap of cell and muscle activity. Response averages revealed that muscle activation patterns during the task remained grossly similar from day to day and cell to cell. Therefore, the variations in cell-muscle coactivation resulted from the unique firing patterns of each new cortical cell sampled during task performance.

View larger version (42K): [in this window] [in a new window]   FIG. 4. Examples of postspike effects (PSEs) based on presence or absence of synchrony facilitation. A: pure postspike facilitation (PSpF) with no synchrony facilitation. B: PSpF on synchrony with clear facilitation superimposed on synchrony facilitation. C: synchrony facilitation alone. Onset latency for B was established by visual examination of the record to identify the point where the average broke sharply upward from the gradually sloping baseline. Onset latencies are similar for A and B, whereas the onset of C occurs just after the acceptance pulse (indicated by the vertical line at time = 0 ms). Peak latencies for all 3 types of effects are very similar in this example.

To get a clearer indication of the amount of cell-muscle coactivation and to avoid adding sweeps containing only random, low-level noise to the SpTA, a feature was added to our averaging software that allowed us to evaluate the level of EMG activity in all muscles associated with each cortical cell spike before counting the spike as a valid trigger event for that muscle. For each potential sweep, the computer averaged the EMG in each target muscle over the full analysis period (20 to +40 ms around the trigger spike). Only sweeps that had an average level of EMG activity 5% of full-scale signal input were added to the SpTA for a given target muscle. We chose this relatively conservative threshold criterion to ensure that sweeps were only added if there was significant EMG activity present (Fig. 2A). After adopting this "sweep-filtering" protocol, we occasionally found clear PSEs in averages with as few as 200 trigger spikes. Two thousand trigger events previously had been recommended as the minimum number of trigger events for a valid test of the presence of a postspike effect (Fetz and Cheney 1980). Some authors, however, have reported the appearance of unequivocal PSEs with as few as 50 (Porter and Lemon 1993) or 95 trigger events (Bennett and Lemon 1994).

Figure 2, B and C, illustrates the results when SpTAs were constructed for the same cell-muscle pair from the same segment of data. The average in Fig. 2B shows clear PSpF with sweep-filtering on and only 287 trigger events, while the average in Fig. 2C was constructed without sweep-filtering and included 11,145 trigger events. Given the fact that SpTAs compiled from very small numbers of trigger events often closely resembled those constructed with thousands of trigger events, we analyzed the magnitude of all PSEs as a function of the number of trigger events. PSEs were divided into three groups: those with <1,000 trigger events, those with 1,000-2,000 trigger events, and those with >2,000 trigger events. The group with <1,000 trigger events showed significantly greater magnitudes of PSpF than the other two groups (P < 0.05). Further analysis revealed that this was due to the fact that the mean value of the baseline EMG does not plateau and stabilize until ~1,000 sweeps. Therefore, we have excluded from our quantitative analysis of PSE magnitude and latency all averages based on <1,000 sweeps (9 facilitation effects and 5 suppression effects, each with a minimum of 200 sweeps). However, these effects were included in the analysis of PSE distribution.

Secondary features either before or after the postspike effect (e.g., at approximately +25 and +35 ms in Fig. 2C) also were commonly observed. These secondary features appear to be related to properties of the triggering cell's autocorrelogram (Kasser and Cheney 1985; Lemon and Mantel 1989). In some instances, the peaks of these secondary features rose above the 2 SD level. However, such peaks were attributed to secondary effects and excluded from analysis.

	RESULTS

Abstract Introduction Methods Results Discussion References

EMG activity during reach and prehension task

Figure 3 illustrates representative EMG records (after full-wave rectification and low-pass filtering) (Cheney et al. 1998) from all 24 muscles during performance of the reach and prehension task. A broad coactivation of all muscles was evident each time the monkey reached for and retrieved the food reward, yet it is also easy to appreciate that there was a high degree of individual variability in activity patterns of different muscles throughout the task.

Synchrony facilitation

Flament et al. (1992) noted that, in some SpTAs, a clear PSpF occasionally appeared on top of a broader increase in EMG activity that had risen above the baseline at a latency too short for it to be attributed to the cortical unit being used as the trigger. Smith and Fetz (1989) concluded that the broad facilitation component observed in many PSEs may be the result of synchrony between a CM cell and other cortical cells receiving a common synaptic input. Flament et al. (1992) developed a classification scheme that separated PSpF into three categories based on the relative contribution of synchrony to the average. We adopted their method to categorize all postspike effects identified in this study (Fig. 4). Postspike facilitations were identified as Pure PSpF (PSpF, Fig. 4A) if the envelope of the effect crossed a level equivalent to 2 SD above the baseline mean at a latency that was reasonable for CM cell facilitation of the target muscle. Fetz and Cheney (1980) used 3.8 ms as the probable minimal acceptable latency for the onset of postspike facilitation of wrist and digit muscles (although they report that a few of the PSpFs in their study had latencies slightly less than this). However, Lemon, Mantel, and Muir (1986) later contended that 3.8 ms was an unreasonably short latency. Based on the results of stimulation in anesthetized monkeys, they suggested that 5.0 ms is the shortest acceptable latency for the onset of facilitation in forearm muscles. In this study, we chose 5.0 ms as the minimum acceptable latency for the onset of PSpF in forearm muscles. By measuring the relative distances to muscles of the shoulder, elbow, and hand in our monkeys, we then estimated minimum onset latencies for other joints as follows: shoulder muscles (3.4 ms), elbow muscles (4.2 ms), and intrinsic hand muscles (6.0 ms).

If the envelope of the EMG in the SpTA rose above the 2 SD threshold at a latency too short to be consistent with facilitation by the triggering cell, we attributed all or part of the increase to synchrony facilitation. PSEs that contained evidence of synchrony facilitation were characterized as either PSpF on synchrony (PSpF+S, Fig. 4B) or synchrony facilitation (SyncF, Fig. 4C). SpTAs classified as PSpF+S appeared to consist of both a synchrony facilitation, beginning at a latency too early to be attributed to CM connections, and a true PSpF riding on top of the synchrony facilitation. The true PSpF could be identified by the presence of a clear discontinuity in the rising phase of the synchrony facilitation. We believe this discontinuity marks the onset of a true PSpF (Fig. 4B). The onset and offset latencies of the true PSpF component of the postspike effect in the PSpF+S category were established by visual examination of each record. The same criteria also were applied to all SpTAs in which true suppression effects were identified "on top" of synchrony suppression.

Because it was possible to identify a true PSE in the PSpF+S and PSpS+S categories, we combined the PSpF and PSpF+S effects into one group and the PSpS and PSpS+S effects into another group for the purposes of quantifying a cell's muscle field and analyzing the distribution of PSEs across all joints and muscles sampled. However, because the presence of underlying synchrony facilitation could obscure the true onset and offset latencies of a PSE and could complicate magnitude measurements, we used only the pure PSpF and PSpS effects to quantify the latencies and magnitudes of facilitation and suppression for different joints and muscles.

Properties of cells and muscle fields

A total of 174 cells were tested during the reach and prehension task, and 3,692 SpTAs were examined for the presence of PSEs. Many additional cells were not tested for the presence of PSEs even though their activity modulated with one or more aspects of the task. We routinely compiled StTAs at 15 µA immediately after isolating a potential CM cell. If no effects were noted in the StTA, no SpTAs were constructed for that cell. Figure 5 illustrates the electrode penetration sites for each of the two monkeys and identifies the tracks that yielded at least one PSE.

View larger version (18K): [in this window] [in a new window]   FIG. 5. Maps illustrating the sites of electrode penetrations within the recording chambers of the 2 monkeys (N and K) used in this study. , track in which 1 PSE was recorded in a proximal muscle but no PSEs were recorded in distal muscles. , track in which 1 PSE was recorded in a distal muscle but no PSEs were recorded in proximal muscles. , track in which 1 PSE was found in proximal muscles and 1 in distal muscles somewhere in the track although not necessarily at the same depth. For monkey N, the recording chamber was centered 16.1 mm anterior to stereotaxic 0 and ~18 mm lateral to the midline. For monkey K, the coordinates were 10 and 18 mm, respectively. In monkey N, 48 recording sites were located <3.0 mm from the cortical surface (1st activity) and 23 were located deeper than 3.0 mm; in monkey K, the corresponding numbers were 15 and 12. CS, central sulcus.

Of the 174 cells tested, 112 cells (64.4%) showed PSEs in at least one of the tested muscles (facilitation or suppression, Table 2). Eighty of the 112 cells (71.4%) produced PSEs (facilitation or suppression) in more than one muscle. Muscle field size ranged from 1 to 10 [average 3.1 ± 2.1 (mean ± SD) for facilitation plus suppression]. When considering facilitation effects only, the average muscle field was 2.4 ± 1.4 for 101 cells. Conversely, the average muscle field when considering only suppression effects was 1.7 ± 1.0 for 59 cells. Figure 6 shows the distribution of muscle fields for all cells tested.

View larger version (46K): [in this window] [in a new window]   FIG. 7. SpTAs from 2 different cells that produced PSEs in both proximal and distal muscles. A, proximal: PSpF in anterior deltoid (ADE), with PSpF+S in pectoralis major (PEC), long head of the biceps (BIL) and brachialis (BRA), and postspike suppression (PSpS+S) in long head of the triceps (TLON). Distal: PSpF in FDI and PSpF+S in extensor carpi radialis (ECR) with PSpS in APB and PSpS+S in extensor digitorum 4, 5 (ED45). Broad synchrony facilitation also can be seen in latissimus dorsi (LAT, proximal) and extensor digitorum communis (distal). Palmaris longus (PL) and dorsal epitrochlearis (DE) were not recorded in this monkey, and the teres major (TMAJ) leads were not functioning at the time of this recording. B: PSpF in short head of the biceps (BIS, proximal) and APB (distal).

The 112 identified CM cells produced 345 total PSEs. Of those, 244 (70.7%) were facilitation effects, whereas 101 (29.3%) were suppression effects; 146 (59.8%) of the facilitation effects were categorized as PSpF and 98 (40.2%) were categorized as PSpF+S. There was almost no difference between the numbers of pure suppression effects and suppression effects on synchrony with 52 of 101 effects (51.5%) categorized as PSpS and 49 (48.5%) categorized as PSpS+S.

Pure synchrony facilitation and suppression

We also recorded a total of 128 effects that were categorized as either SyncF or SyncS. We found fewer SyncF effects (56) than either of the other two facilitation categories (PSpF and PSpF+S). However, we identified more SyncS effects (72) than either of the other two suppression categories.

Distribution of effects by joint

Of the 112 cells that produced at least one pure PSE or PSE on synchrony, only 11 (9.8%) produced effects exclusively in proximal muscles, whereas 51 cells (45.5%) produced effects exclusively in distal muscles (Table 2). Fifty cells (44.7%) produced effects in at least one proximal and one distal muscle. Ninety six (27.8%) of the 345 accepted PSEs were in proximal muscles, whereas 249 (72.2%) were in distal muscles (Table 2). Figure 7 shows two examples of cells that produced PSEs in proximal and distal muscles. In this figure, significant PSEs are surrounded by dotted lines. Cell 100N1 (Fig. 7A) had a relatively large muscle field with five effects in proximal muscles and five in distal muscles. The proximal muscles included PSpF in anterior deltoid (ADE), PSpF+S in pectoralis major (PEC), long head of the biceps, and brachialis, and PSpS+S in the TLON. In distal muscles, the cell produced PSpF in first dorsal interosseus (FDI), PSpS in abductor pollicis brevis (APB), PSpF + S in extensor carpi radialis, and PSpS+S in extensor carpi ulnaris and extensor digitorum 4, 5. For comparison, cell 83K2 (Fig. 7B) had a muscle field of only two but produced PSpF in both cases: short head of the biceps (proximal) and APB (distal).

The majority of PSEs occurred in muscles acting at the wrist and digits [211 of 345 (62.1%), Fig. 8A]. Fewer total effects were obtained for the muscles of the elbow and shoulder, and the intrinsic muscles of the hand yielded the fewest total number of effects. Digit muscles showed the largest number of facilitation effects, while the wrist muscles exhibited the largest number of suppression effects.

View larger version (28K): [in this window] [in a new window]   FIG. 8. Distribution of PSEs (facilitation and suppression) across joints (A) and muscles (B). Numbers include pure PSEs as well as those where a true PSE was superimposed on a synchrony effect.

The fact that the intrinsic hand muscles showed the fewest total PSEs could be related to the fact that we recorded only two intrinsic muscles compared with five muscles at the shoulder and up to seven at the elbow. The totals in Fig. 8B indicate that the number of PSEs in each individual intrinsic hand muscle is greater than the number of effects in most individual shoulder and elbow muscles. To get a clearer picture of the relative frequency of PSEs at each joint, we normalized the results for each joint by dividing the total number of PSEs by the total number of muscles recorded. Because dorsal epitrochlearis (DE) and palmaris longus (PL) were not recorded in monkey N, they were not included in the normalization. After normalization, the average number of PSEs in the intrinsic muscles was greater than either shoulder or elbow, though it was still slightly lower than the number for wrist and digit muscles. The normalized data also confirmed the results of Fig. 8A, in which digit muscles showed the largest number of facilitation effects and wrist muscles the largest number of suppression effects.

Comparison of effects in flexor and extensor muscles

We also compared the number of effects (facilitation and suppression) that occurred in all flexor muscles acting at a given joint against the number that occurred in extensor muscles acting at the same joint. Figure 9 illustrates the results when the total number of effects were normalized to the number of flexor or extensor muscles recorded at a given joint. Again, because DE and PL were not recorded for monkey N, they were not included in the calculations for the elbow and wrist, respectively. At the shoulder, PEC and ADE were classified as flexors, whereas posterior deltoid, teres major, and latissimus dorsi were classified as extensors. The intrinsic muscles APB and FDI were not included in this comparison because neither can be accurately classified as flexor or extensor.

View larger version (27K): [in this window] [in a new window]   FIG. 9. Comparison of PSEs on flexor and extensor muscles at each joint. At the shoulder, PEC and ADE were classified as flexors, whereas posterior deltoid, TMAJ, and LAT were classified as extensors. Intrinsic muscles APB and FDI were not included in this comparison because neither can be classified accurately as a flexor or extensor. A: number of facilitation effects normalized for number of muscles recorded at each joint. B: number of suppression effects normalized for number of muscles recorded at each joint. (Because DE and PL were not recorded for monkey N, they were not included in the calculations for the elbow and wrist, respectively).

At the shoulder and elbow there were more total facilitation effects in the flexor muscles, whereas at the wrist and digits there were more facilitation effects in the extensor muscles (Fig. 9A). Contrary to these findings, there were more suppression effects in the extensor muscles of the shoulder and elbow and the flexor muscles of the wrist (Fig. 9B). Digit extensors showed slightly more suppression effects than flexors, matching the facilitation pattern.

Another way to look at the distribution of effects in flexor and extensor muscles is to characterize the pattern of PSE produced by a given cell in all the flexor and extensor muscles it facilitates or suppresses at any given joint. Using the same criteria for flexor and extensor muscles as described above, we looked at the pattern for each cell at every joint where it had at least one PSE. We first identified the instances where a cell produced facilitation or suppression of one or more muscles on only one side of a joint (flexor or extensor, see Table 3). Effects on only one side of a joint (flexor or extensor) were by far the most common (164 of 213 possible cases). However, we found 49 instances where a cell produced PSEs in at least one flexor and one extensor muscle at the same joint (5 at the shoulder, 8 at the elbow, 20 at the wrist, and 16 in the digits). These patterns of facilitation and/or suppression were further divided into five categories: cofacilitation (facilitation in at least 1 flexor and 1 extensor at the same joint with no suppression); cosuppression (suppression in at least 1 flexor and 1 extensor at the same joint with no facilitation); reciprocal effects (facilitation in flexor muscles and suppression in extensor muscles or vice versa); and mixed effects (facilitation and suppression effects in flexors or extensors). The reciprocal pattern was most common when a cell produced PSEs in both flexor and extensor muscles (30 of 49 cases). At the shoulder and elbow, reciprocal effects more commonly involved facilitation in flexor muscles and suppression in extensor muscles. This pattern was reversed at the wrist and digits where there were more instances of reciprocal effects with facilitation in extensor muscles. Twelve of the 49 cases involved cofacilitation (1 at the shoulder, 3 at the elbow, and 4 at both the wrist and digits). The only case of cosuppression occurred at the shoulder. Mixed effects occurred only at the wrist (2 cases) and digits (4 cases).

Temporal characteristics of postspike effects

To compare the temporal characteristics of PSpF and PSpS across joints, we computed the average onset, peak and offset latencies of pure PSpF and PSpS (Fig. 10). Within each latency category, shoulder muscles had the shortest average latency of facilitation with gradual stepwise increases in latency from shoulder muscles through intrinsic hand muscles (Fig. 10A). The average onset latency of the intrinsic muscles (9.5 ms) was significantly greater than the average onset of the shoulder muscles (7.2 ms). A similar pattern existed for peak and offset latencies.

View larger version (57K): [in this window] [in a new window]   FIG. 10. Comparisons of onset, peak, and offset latencies across joints for pure PSpF and pure PSpS (mean + SD). A, onset: intrinsic mean is significantly greater than shoulder but neither the intrinsic nor shoulder mean is significantly different from any other joint. Peak: intrinsic mean is significantly greater than means of shoulder, elbow, and wrist. Digit mean is significantly greater than the shoulder mean. Offset: intrinsic mean is significantly greater than shoulder, elbow, and wrist means but is not significantly different from digit mean. Digit mean is significantly greater than shoulder and elbow means but is not significantly different from wrist or intrinsic means. B: no significant differences were found between the means of any joint for onset, peak, or offset latency.

In contrast to PSpF, the average latencies for the onset, peak, and offset of PSpS did not show a clear stepwise increase from proximal to distal muscles. Although none of the differences was statistically significant, the average onset, peak, and offset latencies for muscles of the shoulder, elbow, and intrinsic hand were greater than those for muscles of the wrist and digits for both onset and peak latencies (Fig. 10B).

We also compared the average onset, peak and offset latencies of PSpF to those of PSpF+S and SyncF at each joint. The average onset latency of PSpF was very similar at each joint to the average of the "true PSpF" component of PSpF+S that we identified by eye in the SpTA. This gave us further confidence that the averages classified as PSpF+S contained a true postspike facilitation that could reasonably by mediated by the trigger cell. The mean onset latency of SyncF was shorter than the other two types of facilitation at each joint, and the differences were statistically significant at the elbow, wrist, and digits. There were no significant differences in average peak or average offset latencies between any of the three types of facilitation effects.

Comparison of the magnitude of PSpF across joints

Figure 11 illustrates the differences in average magnitude of the PSpF and PSpS for muscles at different joints. A consistent increase in average PPI from proximal to distal joints was evident for PSpF, although this trend was not as consistent within just flexor or extensor muscle groups. The average PPI for the intrinsic muscles (9.2%) was significantly greater than the PPI for shoulder muscles (6.0%), and the average PPI for digit muscles (8.3%) just missed being significantly greater than shoulder PPI. There were no other significant differences in PPI between joints. Suppression effects showed a different pattern. The average PPD of wrist PSpS was greater than the average PPD of both shoulder and digit PSpS. There were no other significant differences between joints (Fig. 11B).

View larger version (14K): [in this window] [in a new window]   FIG. 11. Comparison of the magnitude of PSEs by joint. A: average peak percent increase (PPI) for the intrinsic muscles is significantly greater than the PPI for shoulder muscles. There were no other statistically significant differences between joints. B: average peak percent decrease (PPD) is significantly greater for the wrist than shoulder and digit PPD. There were no other significant differences in PPD between joints. Although the magnitudes were smaller, the relative distributions of mean percent mean increase and mean percent mean decrease were very similar to that pictured here for PPI and PPD.

Although the magnitudes were smaller, the pattern of MPI results across joints was very similar to PPI for both facilitation and suppression. Therefore, we are reporting primarily PPI data. However, a summary of MPI results can be found in Table 4. The magnitudes of PSpF and PSpS also were analyzed separately for flexor and extensor muscles. Neither PPI nor PPD in flexor muscles was significantly different from that in extensors at any joint.

Properties of CM cells with proximal/distal muscle fields

As a final analysis, we examined the characteristics of the PSEs produced by the 51 cells that had muscle fields that included both proximal and distal muscles. Although the average muscle field for the cells in this group grew to 4.2 (compared with 3.1 for the entire sample), the distribution, latency, and magnitude of the PSEs closely resembled those noted previously for all effects. These 50 cells produced 238 total PSEs. Eighty-three of the effects (38.4%) occurred in proximal muscles, whereas 133 (61.6%) were in distal muscles. Consistent with the entire sample, the clear majority of effects produced by these cells (74.1%) was facilitation.

The normalized distribution of PSEs across joints for these cells was almost identical to the whole sample with the greatest number of effects per muscle occurring in digit and wrist muscles, slightly fewer in intrinsic hand muscles, and the fewest in shoulder and elbow muscles.

Average onset, peak, and offset latencies of PSpF showed a very similar stepwise increase from the shoulder joint through the intrinsics. However, unlike the whole sample, there were no significant differences between joints for onset or peak latencies. The latencies for PSpS in this group were distributed in the same pattern as the entire sample, where the onset, peak, and offset latencies for muscles of the shoulder, elbow, and intrinsic hand were greater than those for muscles of the wrist and digits. The distribution of average PPI and MPI was likewise very similar to that seen in the entire sample, with the only noticeable difference being a slight reduction in the magnitude of the average PPD in wrist muscles compared with the average PPD at other joints.

	DISCUSSION

Abstract Introduction Methods Results Discussion References

The results of this study demonstrate for the first time that many CM cells facilitate motoneurons of both proximal and distal forelimb muscles in the monkey. The CM cells in this study were all activated during a task requiring broad coactivation of both proximal and distal muscles in functional synergies for multijoint coordination of the forelimb. Previous studies based largely on single-joint tasks have shown that CM cells commonly produce PSpF in two or more distal muscles (Buys et al. 1986; Fetz and Cheney 1980; Kasser and Cheney 1985). Evidence for PSEs in proximal muscles has been much more limited. Fourment et al. (1995) recently reported PSpF in elbow flexor muscles from cortical cells isolated during an elbow task. Although we found CM cells with muscle fields that were confined exclusively to distal or proximal joints, the most remarkable finding of this study was the relatively large number of cells (almost 45%) that simultaneously facilitated at least one distal and one proximal muscle. It should be noted, however, that this large percentage of proximal/distal cells may not be representative of all regions of motor cortex because tracks in monkey K were located primarily where previous StTAs revealed effects in both proximal and distal muscles.

Our finding of PSEs in both proximal and distal muscles is consistent with the results of many previous stimulation studies (Clough et al. 1968; Donoghue et al. 1992; Jankowska et al. 1975; Phillips and Porter 1964; Preston et al. 1967). Those studies all indicated that the output of the motor cortex is directed to proximal as well as distal muscles, but the nature of the stimulation used in each case made it impossible to determine if excitation of proximal and distal muscles resulted from a single branching CM cell or an interrelated group of cells, each with separate terminations on proximal or distal muscles. The results of this study indicate that, in at least some instances, single cells of the motor cortex terminate on motoneurons of both proximal and distal muscles. However, the fact that more than two-thirds of the PSEs occurred in distal muscles, and the fact that we found a stepwise increase in the peak magnitude of PSpF from the muscles of the shoulder joint through the intrinsic muscles of the hand supports the conclusions of earlier studies that CM terminations in motoneuron pools of distal muscles are more frequent and/or more potent than those in motoneuron pools of proximal muscles.

A system in which a portion of the CM cells terminates in the motoneuron pools of both proximal and distal muscles could enhance the coordination of multijoint movements by locking together the activation of synergist muscles. For example, a CM cell with divergent output could facilitate the coactivation of distal and proximal muscles that commonly is observed in the reach and prehension task. Such cells also could facilitate the activation of wrist and digit muscles while stabilizing proximal joints in positions suitable for manipulation of the environment.

Our findings are in contrast to those of Asanuma and Rosen (1972), Kwan et al. (1978a,b), and Gibbs et al. (1995). Asanuma found "adjacent but not overlapping" zones for activation of muscles at different joints. Kwan et al. found only a small number of locations where ICMS in the primate motor cortex elicited movements at two contiguous joints. Furthermore they did not find a single case where ICMS produced movements involving two or more noncontiguous joints. Gibbs et al. constructed cross-correlations of surface EMG from different muscles in human subjects and found no evidence for common synaptic input to motoneurons of cocontracting muscles that did not share a common joint. However, they only tested one proximal/distal muscle combination in the forelimb (radial wrist flexor and biceps). Moreover, they acknowledge that their findings do not rule out the possibility of common synaptic input to proximal and distal muscles. One possible explanation for the fact that Gibbs et al. found no evidence for synchronized output to both proximal and distal muscles is the fact that their subjects were performing an isometric task. We can speculate that even though their subjects produced EMG in both proximal and distal muscles, there was no need for activation of CM cells with divergent outputs because the need for fine coordination of distal and proximal muscles might be reduced in this case. In support of this argument, Schieppati et al. (1996) suggested that the motor responses of both proximal and distal muscles in humans can be task dependent and related to the degree of control required by a particular task. In fact, we could study several CM cells under two or three different task conditions and will report those data in a separate paper.

In support of our findings, Karrer et al. (1995) used stimulus-triggered averaging to examine output effects from primary motor cortex on 22 muscles of the forelimb during a reach and prehension task. They found poststimulus effects in both proximal and distal muscles at 114 of 464 stimulation sites where effects were produced. Cofacilitation of distal and proximal muscles was most common in a boundary region between the core of the distal muscle representation and the lateral arm of the proximal muscle representation.

Comparison of postspike effects with previous studies

Most previous studies that used SpTA to study the output effects of CM cells have concentrated on forearm muscles acting at the wrist and digits, (e.g., Fetz and Cheney 1980; Kasser and Cheney 1985; Lemon et al. 1986). A few have examined the effects on the intrinsic muscles of the hand (Lemon et al. 1986), or muscles of the elbow (Fourment et al. 1995).

A summary of the characteristics of our PSEs in comparison with those of previous studies is found in Table 4. In general, the latencies and magnitudes of the PSEs we obtained are consistent with those found in earlier studies. For example, although we used a different behavioral task, the mean onset latency for PSpF at the wrist and digits in our study was 8.6 ms, which is between the 6.7 ms reported by Fetz and Cheney (1980) and the 9.8 reported by Lemon et al. (1986). The latency reported in the earlier study by Fetz and Cheney might have been shorter because it could have included some effects the true onset of which was obscured by synchrony facilitation. The average magnitudes of our PSpF (PPI and MPI) were less than those reported by Fetz and Cheney (1980) and Lemon et al. (1986), but very similar to those reported by Kasser and Cheney (1985). The PPI results reported by Fetz and Cheney (1980) are significantly greater than ours, but they stated that their results were computed from the 53 PSpF in their sample with the largest magnitudes, whereas our results are based on all PSpF found in SpTAs of wrist and digit muscles.

At the elbow, our mean onset latency of 7.7 ms for PSpF is considerably shorter than the 9.4 ms reported by Fourment et al. (1995). However, they note that their overall mean latency may have been inflated by a small portion of their sample and report that the mean onset latency for two-thirds of their sample was 6.8 ms, which is less than our finding.

The onset latencies for PSpS of wrist and digit muscles in our study are not markedly different from those reported by Lemon et al. (1986) or Kasser and Cheney (1985). However, the magnitude of suppression in our study is significantly greater than that reported by Kasser and Cheney (1985). This could be related to task differences. Kasser and Cheney tested cells during a simple wrist-movement task, whereas in the present study the arm was unrestricted and coordination of multiple joints was required.

Muscle field size

Porter and Lemon (1993) noted that, "CM cells which facilitate single muscles are very much in the minority." They concluded that, "the fundamental organizing principle of the cortico-motoneuronal output is one of influence over activity in multiple muscles." Our results agree fully with this conclusion as 80 of 112 cells with postspike effects (71.4%) had a muscle field involving two or more muscles. However, while some of the cells we recorded did have muscle fields as large as 10, our average muscle field of 2.4 (facilitation only) is similar to those reported in earlier studies despite the fact that we sampled a much larger number of muscles. For example, Fetz and Cheney (1980) reported that cells related to wrist flexion had an average facilitation muscle field of 2.1 (of 5 muscles sampled), whereas extensor-related cells had an average muscle field of 2.5 (of 6 muscles sampled). Kasser and Cheney (1985) reported average facilitation muscle fields of 2.6 and 3.0 for cells primarily related to wrist flexion and extension, respectively. Buys et al. (1986) reported an average muscle field of 1.4 when recording from 5 muscles of the hand and forearm and 2.0 when recording from 10. Although the ICMS technique used by Donoghue et al. (1992) would have excited a cluster of cells in the motor cortex rather than a single CM cell, they reported that most stimulation sites activated only 3-5 of the 13 recorded hand, wrist, and elbow muscles they sampled simultaneously.

Could muscle field size have been influenced by the characteristics of muscle activity associated with the prehension task in our study? It is unlikely that small differences in the level of EMG activity in the prehension task compared with a simple wrist flexion/extension task or a precision grip task would have had any effect. There is also evidence that the magnitude of PSEs is not related in any consistent way to the level of EMG activity (Bennett and Lemon 1994; Fetz and Cheney 1980). The duration of periods of muscle activity in this study is generally more brief than in previous studies using simple wrist or precision grip tasks. However, the sweep-filtering method employed in this study should have overcome this difference between tasks by ensuring the presence of a minimal level of EMG activity for all accepted spikes.

Use of the sweep-filtering protocol did result in a wide variation in sweeps (triggers) for different muscles. The average number of sweeps in muscles that showed PSEs was 4,733 ± 3,733 (range 266-24,025) compared with 4,120 ± 3,785 (range 221-29,666) for muscles without PSEs. Muscles with <200 sweeps were excluded from analysis. The difference between these means was not statistically significant. Therefore, it is unlikely that our results were biased by differences in the number of sweeps. We also should point out that had we not instituted the sweep-filtering protocol, all muscles would have had equal numbers of sweeps. However, for most muscles this number would have been inflated and a meaningless measure of valid sweeps.

Differential output to flexor versus extensor muscles

In their study of PSEs in muscles of the wrist and digits, Fetz and Cheney (1980) reported that, "as a group, extensor muscles tended to be more strongly and more frequently facilitated than flexor muscles." Our results almost completely agree with theirs in that the extensor muscles of the wrist and digits were facilitated more frequently than the flexor muscles and the magnitude of PSpF was modestly greater for wrist extensors than wrist flexors during performance of this task. The only exception to the findings of Fetz and Cheney was that the average magnitude of PSpF in our digit flexor muscles was slightly greater than that for digit extensors, although this difference was not statistically significant.

Preston et al. (1967) reported that cortical inhibition was more prominent in forelimb flexor muscles. Kasser and Cheney (1985) also reported that PSpS from CM cells is more common in flexor muscles of the wrist and digits. In terms of frequency of effects, our results agree only at the wrist, where we found significantly more suppression effects in flexors than extensors. At all other joints we found more frequent suppression effects in extensor muscles than flexor muscles. Our results do agree with previous studies of wrist-related CM cells in that the magnitude of PSpS was greater for flexor muscles at all joints tested. Particularly striking in our data are the high frequency and large magnitude of suppression effects in wrist muscles.

It is possible that these results may reflect the relative levels of muscle activation for completion of the reach and prehension task. The flexors of the shoulder and elbow were generally more active than the extensors as they worked to move and stabilize the forelimb in space, while the extensors of the wrist and the flexors of the digits typically showed broad and/or strong activation during manipulation and removal of the food pellet from the retrieval well. However, it is not clear that the magnitude of PSpF or PSpS depends on the level of EMG in any consistent and systematic way (Bennett and Lemon 1994; Fourment et al. 1991). Moreover, it can be appreciated from Fig. 9 that, with the exception of wrist suppression, differences between effects in flexors and extensors are relatively small.

When we characterized the pattern of effects in muscles at each joint, facilitation of either flexors or extensors without effects in antagonist muscles was most common. Cofacilitation was relatively rare at all joints despite the fact that our task involved periods of broad coactivation of flexor and extensor muscles (Fig. 3). Moreover of the 12 cases of cofacilitation observed, only two cases remained after we eliminated weak PSEs and those two were both in digit muscles. The low incidence of cofacilitation is consistent with previous studies of CM cell effects on wrist and digit muscles (Fetz et al. 1989; Kasser and Cheney 1985), but in contrast to the relatively high incidence of cofacilitation from rubromotoneuronal (RM) cells (Mewes and Cheney 1991). In contrast to cofacilitation, reciprocal effects were relatively common, especially for wrist muscles. Within the group of reciprocal effects, there were more instances of facilitation of flexors at the shoulder and elbow (with suppression of extensors) and more instances of facilitation of extensors at the wrist and digits (with suppression of flexors). The frequency of reciprocal effects at wrist and digit muscles is consistent with previous studies of CM cells (Kasser and Cheney 1985) and RM cells (Mewes and Cheney 1991). Reciprocal effects were clearly less common at the shoulder and elbow joints, although this could be due in part to the fact that PSpS at these joints was also slightly weaker than at distal joints.

Comparison of postspike effects from cortical and red nucleus cells

The corticospinal and rubrospinal systems share many common features that we have noted in previous reports (Mewes and Cheney 1991, 1994). However, one marked difference between CM and RM cells has been the stronger preferential facilitation of wrist and digit extensor muscles and suppression of wrist and digit flexor muscles exhibited by RM cells. This extensor preference was clear in both spike and stimulus-triggered averages of EMG activity (Cheney et al. 1991b; Mewes and Cheney 1991; Sinkjaer et al. 1995). For example, of 62 cells identified as rubromotoneuronal by Mewes and Cheney (1991), 69% facilitated extensor muscles exclusively or preferentially. Others also have noted that cells of the magnocellular red nucleus are more frequently related to extension tasks than flexion tasks (Gibson et al. 1985; Miller and Houk 1995).

A recent stimulus-triggered averaging study from our laboratory has shown that this strong extensor preference of RM cells also may extend to proximal forelimb joints (Belhaj-Saïf et al. 1998). This supports the work of Sinkjaer et al. (1995) who found a strong extensor bias at the elbow with spike-triggered averaging during performance of a reach and prehension task. Although results from the current study do show evidence of some extensor preference in the output of CM cells on distal muscles, it was relatively small compared with that of RM cells. Furthermore our finding that CM cells produce more facilitation effects in flexors of the shoulder and elbow stands in contrast to the extensor preference found for proximal muscles by Belhaj-Saïf et al. (1998) and Sinkjaer et al. (1995).

Model to explain different types of postspike effects

Figure 12 proposes a model of possible mechanisms for the occurrence of facilitation effects in muscles at multiple joints, including muscles of proximal and distal joints. Shoulder, elbow, wrist, digit, and intrinsic hand muscles are represented along with their respective motoneuron pools. The simplest explanation for cofacilitation of muscles at proximal and distal joints is represented by CM cell A. This cell makes synaptic connections with motoneurons of both proximal and distal muscles as shown. If no other factors were involved, all the target muscles of this cell would show PSpF like that illustrated for the INT, DIG, and SHL muscles. The magnitude of PSpF for each facilitated muscle reflects the number of synaptic terminations from cell A within the different motoneuron pools, and the latency of PSpF reflects the conduction distance from the CM cell to each muscle, the CM cell and motoneuron conduction velocity, and the underlying synaptic linkage.

View larger version (28K): [in this window] [in a new window]   FIG. 12. Diagram illustrating how SpTAs from a single cell in the motor cortex might produce the different types of PSEs observed in this study. Cells A-C: 3 different CM cells located in close proximity to 1 another in primary motor cortex. Each has connectivity with 1 motoneuron pools (labeled 1-5). CM cell A terminates in the motoneuron pools of both distal and proximal muscles, although it makes more frequent terminations in the motoneuron pools of distal muscles. CM cell B terminates in the motoneuron pools of distal muscles only, although it makes connections with more than one distal muscle. CM cell C terminates in the motoneuron pool of proximal muscles only and demonstrates fewer connections in its termination than either cells A or B. Cell S is not a cortical output cell, but provides common synaptic input to all 3 CM cells.

Of course, it is not possible to know with certainty that all the PSEs we observed are from monosynaptic connections to motoneurons, particularly when it is clear that effects mediated by disynaptic pathways can be detected with SpTA (Cheney et al. 1991a). This fact raises the additional possibility (not reflected in this model) that some PSEs may have been from cortical interneurons with axonal branches to CM cells supplying both distal and proximal motoneuron pools. However, we feel this possibility is unlikely because our cells generally had large spikes characteristic of pyramidal cells.

Although the majority of our facilitation effects were PSpF in type, we also found a large number of effects where a clear facilitation was superimposed on a broader facilitation that could be attributed to synchrony among CM cells in motor cortex (Flament et al. 1992). In Fig. 12, this type of effect is represented by the facilitation in the WRS muscle. Common synaptic input (cell S) to the CM cell population produces a small degree of synchrony among CM cells A-C. The WRS motoneurons receive synaptic input from CM cells A and B. The presence of such synchrony and the existence of dispersion in the cross-correlogram of cells A and B yields a broad, weak facilitation in the WRS muscle that is termed synchrony facilitation. However, the presence of synaptic connections from the recorded CM cell (A) to WRS motoneurons results in a true PSpF that is superimposed on the synchrony facilitation. In contrast, ELB motoneurons are only coupled to the recorded cell (A) through common synaptic input from cell S. Cell A does not make synaptic connections with ELB motoneurons. Therefore, PSpF is not present in the ELB muscle, and synchrony facilitation is the only effect that develops.

Are there alternative explanations for the finding that many reach-related CM cells influence muscles at multiple joints? For example, many single-joint CM cells might be coactivated by common synaptic input during the reaching task, and this common input could synchronize the discharge of many single cells leading to a pattern of PSEs that only appears to involve muscles a multiple joints when, in fact, the individual CM cells involved have terminations that are confined to muscles at a single joint. Is it possible that the effects we have interpreted as cofacilitation and suppression in muscles at multiple joints actually include a primary PSpF at one joint plus synchrony effects at other joints that we are misinterpreting? Although this possibility cannot be completely ruled out, several arguments seem to make it highly unlikely. First, as described throughout the paper, we have been very careful to identify and account for synchrony effects. Moreover, if all effects with synchrony are excluded leaving only the pure facilitation or suppression effects, the basic findings of the study would be unchanged (e.g., Fig. 7). One also might expect that if synchrony effects were significantly contaminating the results, muscle field size would be significantly increased. We actually did expect to find larger muscle fields than reported in previous studies because we recorded a larger number of muscles and because so many of the muscles were coactive during some phase of the task. However, muscle field size in our study was not larger than that reported in previous studies based on much more restricted movement tasks and fewer recorded muscles. Finally, the PSEs from individual cells predicts the cell's pattern of activation in different tasks. We could record several CM cells not only during the reach and prehension task but also during two additional tasks: a wrist movement task that preferentially activated distal muscles and an elbow/shoulder task that predominantly activated proximal muscles. We have performed a preliminary analysis on five cells that were recorded in relation to all three tasks. Three of the five had PSEs in both proximal and distal muscles, whereas the other two had PSEs in distal muscles only. All five of the cells showed activity that was fully consistent with the pattern of their PSEs on proximal and/or distal muscles. For example, all three of the cells with PSEs in proximal and distal muscles were intensely active, as expected, during the reach and prehension task in which all of their target muscles were coactivated. However, all three cells were relatively inactive during the wrist task (distal muscles only) and the elbow/shoulder task (largely proximal muscle modulation) despite intense activation of part of the cell's muscle field during performance of these tasks. The lack of CM cell activation in these tasks may be due to the fact that only a part of the cell's muscle field was coactivated, whereas the reach and prehension task coactivated the cell's entire muscle field. The fact that the cell's involvement in different tasks is predictable from its muscle field strengthens the functional significance and authenticity of the PSEs we have identified.

Conclusions

Based on the data presented and arguments regarding synchrony effects, we conclude that single CM cells often facilitate and/or suppress muscles at multiple joints (including both proximal and distal joints) during a reach and prehension task. Such cells may facilitate the execution of coordinated multijoint movements that require the coactivation of proximal and distal muscles. However, our results also suggest that CM cells make more frequent and more potent terminations in the motoneuron pools of distal muscles than proximal muscles.

	ACKNOWLEDGEMENTS

  The authors thank R. Lininger, T. Gleason, J. Kenton, and R. Thompson for technical assistance and L. Shupe, University of Washington, Seattle, for programming assistance and for providing the Neural Averager program.

  This work was supported by National Institutes of Health Grants NS-25646 and HD-02528.

	FOOTNOTES

  Address for reprint requests: P. D. Cheney, Smith Mental Retardation and Human Development Research Center, University of Kansas Medical Center, Kansas City, KS 66160-7336.

  Received 27 June 1997; accepted in final form 2 July 1998.

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