1Canadian Institutes of Health Research Group in Sensory-Motor Systems, Department of Anatomy and Cell Biology, Queen's University, Kingston, Ontario K7L 3N6; and 2Départment de physiologie, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
![]() |
ABSTRACT |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Cabel, D. William, Paul Cisek, and Stephen H. Scott. Neural Activity in Primary Motor Cortex Related to Mechanical Loads Applied to the Shoulder and Elbow During a Postural Task. J. Neurophysiol. 86: 2102-2108, 2001. Whole-arm motor tasks performed by nonhuman primates have become a popular paradigm to examine neural activity during motor action, but such studies have traditionally related cell discharge to hand-based variables. We have developed a new robotic device that allows the mechanics of the shoulder and elbow joints to be manipulated independently. This device was used in the present study to examine neural activity in primary motor cortex (MI) in monkeys (macaca mulatta) actively maintaining their hand at a central target as they compensated for loads applied to the shoulder and/or elbow. Roughly equal numbers of neurons were sensitive to mechanical loads only at the shoulder, only at the elbow, or loads at both joints. Neurons possessed two important properties. First, cell activity during multi-joint loads could be predicted from its activity during single-joint loads as a vector sum in a space defined by orthogonal axes for the shoulder and elbow. Second, most neurons were related to flexor torque at one joint coupled with extensor torque at the other, a distribution that paralleled the observed activity of forelimb muscles. These results illustrate that while MI activity may be described by independent axes representing each mechanical degree-of-freedom, neural activity is also strongly influenced by the specific motor patterns used to perform a given task.
![]() |
INTRODUCTION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Primary motor cortex is known to
be intimately involved in volitional motor control, yet little is known
on how this region coordinates motor patterns at different joints
(Drew et al. 1996; Murphy et al. 1985
;
Scott 2000
). Although few would doubt that the activity
across primary motor cortex (MI) is involved in controlling the whole
limb, the crucial question is how individual neurons participate in
this process. Many studies have examined neural activity during
whole-arm motor tasks in monkeys (Georgopoulos 1995
;
Kalaska et al. 1997
), but for technical reasons, these
studies have correlated neural activity to variables related to the
hand. Although neural activity has been shown to be broadly tuned to the direction of hand movement, several studies illustrate that other
factors related to the motor periphery often influence cell discharge
(Kalaska et al. 1989
; Scott and Kalaska
1997
; see also Kakei et al. 1999
). Since these
motor tasks only consider hand-based variables, inferences on how the
neural activity may be related to the underlying mechanics at the
shoulder and elbow cannot be explored.
We have recently developed a new experimental device that can directly
manipulate the mechanics of the shoulder and elbow joints of monkeys
(Scott 1999). This device allows us to examine whole-limb motor tasks where the global goal is to move or maintain the
hand in space while systematically modifying the properties of each
joint independently. The present study illustrates the first
description of the response of motor cortical neurons using this
device. Monkeys were trained to maintain their hands at a central
target while flexor or extensor loads were applied to the shoulder
and/or elbow joints. The experimental data illustrate specific features
on how neural discharge reflects the mechanical loads at multiple joints.
![]() |
METHODS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Three juvenile male rhesus monkeys each weighing
approximately 7 kg were used in this study, two for the neural
recordings and all three for the electromyographic recordings. Each
monkey was trained to wear a mechanical exoskeleton on its right arm that permitted flexion and extension motion of the shoulder and elbow
joints in the horizontal plane (Scott 1999). Hinge
joints on the device were aligned with the monkey's shoulder and elbow joints, and custom-made arm troughs attached the linkage to its upper
arm and forearm. Motors attached to the linkage monitored joint angle
and allowed mechanical loads to be applied to the shoulder and/or elbow
joints. Each monkey was trained to maintain its right hand within an
8-mm radius target positioned in the center of the work-space.
Constant-magnitude torques (±0.11 Nm) were applied by the device to
the shoulder and/or elbow joints. Three loads were used at each joint
(flexor, null, and extensor), giving a total of nine load conditions
for the two joints. A load applied to one joint required the monkey to
change the net muscular torque only at that joint while maintaining the
muscular torque at the other joint unchanged. The magnitude of the load
was sufficiently large to require the monkey to actively control the
position of the hand and create measureable changes in forelimb muscle
and MI activity (see RESULTS) and was small enough that the
monkeys would perform the task throughout a 2- to 3-h recording
session. For each trial, the load was first applied, the target light
was then illuminated, and the monkey was required to move to and
maintain its hand at the central target for more than 3 seconds.
The monkey was trained to maintain its hand on the surface of the
forelimb/hand trough so that the mechanical load was applied directly
to the upper arm and forearm, and not through the hand. Five repeat
trials of each load condition were presented in a pseudo-random block design. Data analyses were based on the mean discharge of the cell for
the last 2 seconds of each trial.
We used conventional techniques for extracellular recording of single
neuron activity related to the proximal arm in primary motor cortex
(Scott and Kalaska 1997). Recording chambers were implanted surgically under inhalation anesthetic. Neurons recorded in
the task were located in the rostral bank and crown of the central
sulcus where trains of electrical stimulation (11 pulses, 333 Hz,
0.2-ms pulse width, range 5-50 µA) elicited movement of the shoulder
or elbow. The territory where cell activity was sampled in motor cortex
was similar to the region examined in previous studies on reaching
(Scott and Kalaska 1997
). Cells were examined in the
task if they responded only to passive movement of the shoulder or
elbow. Cells that did not respond to passive movement of any of the
forelimb joints were recorded in the task if neighboring neurons
responded to passive movement of the shoulder and elbow. Many of the
neurons recorded in the present postural task were also active for
other tasks, such as reaching. A full account of the response of these
neurons both within and between tasks will be presented in future
studies. All procedures were approved by the Queen's University Animal
Care Committee and followed university and national guidelines for
animal care.
Previous studies examining neural activity in motor cortex during
multi-joint motor tasks have traditionally used experimental paradigms
with movements or loads that are equally distributed in space and of
similar magnitude (Georgopoulos et al. 1982;
Kalaska et al. 1989
; Scott and Kalaska
1997
). The directional tuning of a cell can then be defined by
scaling unit vectors aligned with the direction of movement (or load)
by the discharge of the cell during each trial and then summing these
vectors for each direction to provide the cell's directional
preference. The purpose of the present article was to relate neural
activity to loads applied at each individual joint so the loads were
kept constant at each joint regardless of the load at the other joint.
Therefore the magnitude of the total torque at the two joints was 0.22 Nm when loads were applied at both joints and 0.11 Nm when loads were applied at only one joint. To remove any possible effect that load
magnitude may have on estimates of each cell's preferred direction
(PD) in joint torque space, trials were divided into two groups: those
where torques were applied at only one of the two joints and those
where torques were applied concurrently at both joints. For each block
of trials, a PD was calculated for each group by scaling unit vectors
aligned with the direction of load in joint-torque space by the
discharge of the cell during that trial and then summing these vectors
for each load condition in that group. This created two PDs, one for
each group, and these were averaged to identify a PD for each block of
trials. A cell's PD was defined as the average from the five repeat
blocks. Except for the division of trials into two groups, this
technique is identical to that used in previous studies (Scott
and Kalaska 1997
). We compared the PD generated using the
techniques described above as compared with the PD computed based on
single-joint loads only or multi-joint loads only. The average absolute
difference in the PD was only 8.8°, suggesting that the division of
trials into two groups and then averaging the results had a minimal
effect on the PD for each cell. The significance of the PD for each
cell was determined using a "bootstrapping" statistical method
(Scott and Kalaska 1997
). This method compares the
length of the PD vector "R" based on the cell's discharge during
the task as compared with values of R based on random reshuffling of
the discharge rates across all load conditions. The cell's discharge
pattern was statistically tuned if fewer than 40 of 4,000 reshuffled
samples (1%) had larger mean vector lengths than that observed in the task (unshuffled data).
A two-factor ANOVA was used to define whether a cell was modulated by loads at the shoulder and/or elbow joints. The ANOVA used elbow and shoulder muscular torque as factors with three levels within each factor: the requirement of an extensor, flexor, and unloaded torque. ANOVA results flagged significant changes in cell discharge across the three levels of each factor and were used to classify cells as related to the shoulder, the elbow, or to both joints. Note that the two main statistical tests used in this study (ANOVA and directional preference) are not identical, and therefore the number of cells flagged by each test will not be identical (see RESULTS). For example, a cell that increased its activity both for flexor and extensor torques at the elbow and did not respond to the shoulder loads would be flagged as an "elbow" cell by the ANOVA, but would not be found to be directionally tuned.
The present experimental paradigm allows us to examine how the discharge patterns of MI cells reflect mechanical loads at multiple joints simultaneously. The response of each neuron for shoulder, elbow, and combined loads can be defined as Vectors (S, E, and ES, respectively). The length of Vector S was calculated as the absolute difference in cell discharge rate between flexor and extensor loads applied to the shoulder. The length of Vector E was defined in a similar manner for flexor and extensor loads at the elbow. The length of Vector ES was defined as the absolute difference in cell discharge rate between the multi-joint load closest to the cell's joint-torque PD and the opposite load. We tested two models to identify whether the activity of neurons during multi-joint loads could be predicted from their responses to single-joint loads. In essence, we tested whether Vector ES could be predicted from Vectors E and S. One possibility is that Vectors E and S are aligned and oriented in the same direction (left panel of Fig. 2A). In this Linear Summation Model, the activity of a cell when loads are applied to both joints equals its activity when a load is applied only at the shoulder added to its activity when a load is applied only at the elbow. A second possibility is that Vectors E and S are orthogonal to each other (left panel of Fig. 2B). This Vector Summation Model is not related to the specific geometry of the limb during the task. Rather, this model supposes that brain activity can be represented by a coordinate frame of the motor periphery with each mechanical degree-of-freedom (DOF) representing an independent, orthogonal axis. When loads are applied at both the shoulder and elbow, the estimated activity of the cell equals the vector summation of the cell's response when loads are applied independently at the shoulder (Vector S) and elbow (Vector E). The predicted activity of each cell was computed for each model and compared with the actual discharge rate of the cell for the multi-joint loads (the length of Vector ES).
Electromyographic (EMG) activity of proximal arm muscles was recorded
using pairs of single-strand wires inserted percutaneously in three
monkeys, as well as through pairs of multi-strand wires implanted
chronically in the first monkey under aseptic conditions (see
Loeb and Gans 1986; Scott and Kalaska
1997
). Most muscles spanning the shoulder and elbow joints were
sampled in at least one of the three monkeys including mono-articular
(anterior, posterior, and middle deltoids, pectoralis major,
brachialis, brachioradialis, infraspinatus, supraspinatus, teres major,
subscapularis and latissimus dorsi, medial, lateral triceps) and
bi-articular muscles that span both joints (long head of triceps,
dorso-epitrochlearis, long and short head of biceps). Some muscles in
the first monkey were sampled twice, once with percutanous electrodes
and once with chronic electrodes. Although electrode placement was
usually obvious due to the minimal adipose tissue overlying the
forelimb muscles, the location of the wires was assessed using
microstimulation through the recording leads (<2 mA, 33 Hz for 333 ms). The techniques described above to interpret neural activity
(directional tuning and significant modulation with joint loads) were
also used to interpret the activity of the proximal limb muscles based
on the integrated electromyographic activity during the last 2 s
of each trial.
![]() |
RESULTS |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
We recorded the activity of 160 neurons in the proximal-arm region of MI. Of these neurons, 107 modulated their activity for mechanical loads at the shoulder or elbow (ANOVA, P < 0.01) and were analyzed further to examine how their discharge was related to torque at the two joints. The activity of 74 and 68 neurons showed a significant change in activity when loads were applied to the shoulder and elbow, respectively (ANOVA, P < 0.01). Most of these cells showed a reciprocal change in activity with torque where roughly equal numbers of neurons increased activity for flexor or extensor torque and decreased activity for the other. Across the population, cell activity was equally responsive to shoulder and elbow loads (average difference between flexor and extensor shoulder loads, 11.9 ± 7.9 spikes/s, mean ± SD; elbow loads, 10.8 ± 8.2 spikes/s).
We found that some cells modulated their activity when the monkey compensated for mechanical loads applied at only one joint, but were unresponsive to loads applied at the other joint. Figure 1B illustrates a neuron that modulated its activity with changes in muscular torque at the shoulder, but did not show any significant modulation with changes in torque at the elbow (ANOVA, P < 0.01). The diagram on the right illustrates activity for this cell as a contour plot in joint-torque space. A high discharge rate is colored red, and a low discharge rate is blue. The near vertical banding pattern of colors illustrates that this neuron modulated its activity with loads at the shoulder (horizontal axis) and was insensitive to loads at the elbow (vertical axis). The black arrow, the preferred direction of the cell in joint-torque space, is oriented to the right aligned with the shoulder flexor axis (PD = 2°, P < 0.01). A total of 39 cells modulated their activity only for loads applied to the shoulder, whereas 33 cells were sensitive to loads only at the elbow (ANOVA, P < 0.01).
|
We also found that 35 neurons were sensitive to mechanical loads applied at both joints (ANOVA, P < 0.01). Figure 1C illustrates the activity of a cell for the nine load conditions. Neural activity tended to increase for shoulder extensor and elbow flexor loads. Maximal activity was observed when the monkey generated a shoulder extensor torque combined with an elbow flexor torque. The preferred direction of this cell in joint-torque space was oriented diagonally in the top left quadrant (PD = 138°, black arrow in right panel of Fig. 1C).
The present observations illustrate that MI cells can covary their
discharge with mechanical loads at more than one joint, and raise the
important question of how the activity of MI cells reflects motor
patterns at multiple joints simultaneously. Across the population of
neurons, the Linear Summation Model tended to overestimate the
discharge pattern of cells (Fig.
2A; paired t-test, n = 107, P < 1010). To ensure that the overestimation of the
linear summation model was not simply due to clipping at low discharge
rates or due to saturation at high discharge rates during certain
loads, we reexamined the response of a subset of primary motor cortical
neurons that fulfilled two criteria. First, cell discharge rate had to
remain above 5 spikes/s for all load conditions. Second, cell activity also had to be recorded during a reaching task, and the maximal discharge during the postural task had to be at least 20% less than
peak discharge rate during movement. This latter criterion was used to
demonstrate that the discharge rate of the cell had not saturated
during the postural task. Even with these strict criteria, the Linear
Summation Model still over-predicted the discharge rate of this
subpopulation of cells (n = 36, paired t-test, P < 10
5).
The failure of the linear summation model suggests that motor cortical
cells reflect a more complex signal related to the two joints. In
general, estimates of neural activity when loads were applied to both
joints based on neural activity when loads were applied to each joint
independently were much better when using the Vector Summation Model.
Across the population, this model could predict the activity of motor
cortical cells for combined loads at the shoulder and elbow joints
(Fig. 2B; paired t-test, n = 107, P > 0.10).
|
One of the most interesting observations we found was that there was a
specialized organization for representing mechanical loads applied at
the two joints. Most multi-joint neurons (30/35, 86%) were maximally
active for mechanical loads that required flexor muscular torque at one
joint combined with extensor muscular torque at the other, or vice
versa. Significantly fewer neurons (5/35, 14%) were maximally active
when either flexor or extensor torques were generated at both joints
(P < 0.01, 2 test against
equal probabilities for each group).
This overrepresentation of flexor torque at one joint combined with
extensor at the other can be illustrated by plotting the distribution
of PDs of MI cells in joint-torque space (Fig.
3). Eighty-eight cells were directionally
tuned to the load according to the bootstrap test. The size of each pie
slice is proportional to the number of cells maximally active for a
given combination of loads at the two joints. The distribution is
statistically nonuniform with two clear clusters, one related to elbow
flexors and shoulder extensors and another cluster associated with
elbow extensors and shoulder flexors (P < 0.001, Bimodal Rayleigh test) (Batschelet 1981). The major axis
of the distribution was 143-323° (angle defined counterclockwise
from x-axis). This asymmetry cannot be explained by the
action of biarticular muscles since biarticular biceps and triceps
muscles provide combined flexor or combined extensor muscle torques,
respectively (top right and bottom left quadrants
in Fig. 3).
|
Electromyographic activity was recorded in a total of 48 muscles during the task, and 41 of these were found to be modulated by loads at the shoulder or elbow (ANOVA, P < 0.01). The activity of a majority of muscles (25/41) was sensitive to loads at both joints, with fewer responsive only to loads at the shoulder (7) or elbow (9). This is somewhat surprising since only seven of the muscles in our sample from the three monkeys spanned both joints (biarticular muscles). The activity of 19 of 34 monoarticular muscles were responsive to loads applied at both joints. Figure 4, A and B, illustrates variations in the activity of two monoarticular muscles for the nine load conditions. The activity of anterior deltoid, a shoulder flexor, was only dependent on the net muscular torque at the shoulder (ANOVA, P > 0.01; Fig. 4A). In contrast, the activity of brachioradialis, an elbow flexor, increased when the monkey generated an elbow flexor torque and was most active when the monkey also generated a shoulder extensor torque as compared with a shoulder flexor torque. The response of specific muscles was generally similar across the three monkeys.
|
A total of 34 muscles was defined as directionally tuned in
joint-torque space (P < 0.01). The directional
preference of 27 (79%) of these muscles was maximally active for loads
that required flexor torque at one joint combined with extensor torque
at the other, or vice versa (P < 0.01, 2 test against equal probabilities for each
group). Figure 4C illustrates the distribution of PDs for
the activity of forelimb muscles sampled in this study. The
distribution was not uniform when compared against a unimodal
distribution (115°, z = 5.51, P < 0.01, Unimodal Rayleigh test), but this nonuniformity was even more
pronounced when tested against a bimodal distribution (128-308° for
major axis, z = 9.95, P < 0.001, Bimodal Rayleigh test).
![]() |
DISCUSSION |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
It seems intuitive that the activity of a single-joint muscle
would simply reflect the mechanical load at the spanned joint. However,
we found that the activity of many single-joint muscles was dependent
on the load applied at both the spanned and nonspanned joints. Similar
couplings have been observed for muscles during elbow flexion/extension
and supination/pronation in humans (Buchanon et al.
1989; van Zuylen et al. 1988
), as well as for
individuated finger movements in nonhuman primates (Schieber
1995
). This coupling is not related to intersegmental dynamics
since there is no body motion in our postural task. Rather, this
coupling can be best understood as a consequence of the mechanical
action of biarticular muscles. Any activity in these muscles to oppose
a mechanical load at one joint creates muscular torque at the other
joint, which must then be opposed by corresponding antagonistic muscle activity at this other joint. For example, activity in biceps to
generate an elbow flexor torque to oppose a load also creates a flexor
torque at the shoulder that is compensated by recruitment of shoulder
extensor muscles. Such coupling complicates the mapping between joint
torque and muscle activity and obfuscates attempts to delineate and
dissociate motor patterns based on experimentally defined
degrees-of-freedom of the limb. More importantly, the action of
biarticular muscles provides many advantages to the motor system such
as improved coordination (Zajac 1993
), energy transfer
(Bobbert and van Ingen Schenau 1988
), and stabilization (McIntyre et al. 1996
; Osu and Gomi
1999
).
We found that roughly equal numbers of neurons were statistically
related to loads only at the shoulder, loads only at the elbow and
loads at both joints. This latter group of multi-joint cells would
appear to demonstrate that single neurons are involved in controlling
motor patterns at multiple joints, a physiological correlate of
corticomotoneurons that synapse on motoneurons of muscles at both
the shoulder and elbow (McKiernan et al. 1998). However, as stated above, since the activity of some
monoarticular muscles was modulated by loads applied at both the
shoulder and elbow, we cannot conclude at this time that
"multi-joint" neurons are necessarily involved in the control of
multiple muscles spanning different joints.
The present goal-directed motor task can be interpreted using the
coordinate transformation model where the CNS performs a series of
sensorimotor transformations from a signal of spatial location to a
pattern of muscle activity including intermediary representations
related to hand and joints (Kalaska et al. 1997; Soechting and Flanders 1992
). The present postural task
specifically addresses the later representations since the spatial
target, hand, and joint kinematics all remained constant while joint
torque and muscle activation patterns were varied. We directly
manipulated joint torque in this task so that all combinations of
torque at the two joints were equally tested. Yet, the distribution of
PDs of MI neurons was not uniform in joint-torque space, but was skewed toward one of two quadrants and suggests that the load-related activity
in MI is not a simple representation of joint torque. The distribution
of PDs of shoulder and elbow muscle activities showed a similar skew
suggesting that MI activity during this task at least partially
reflects the selection of muscle activities for the task. If MI does
not explicitly represent joint torque, then it is difficult to identify
where such a representation would exist since most other cortical
regions involved in these volitional limb motor tasks, such as dorsal
premotor cortex or parietal area 5, tend to be less sensitive to
variables related to the limb, such as arm geometry or load
(Kalaska et al. 1989
; Scott et al. 1997
). One remaining possibility is cerebellum since neural
activity in dentate and interpositus nuclei have been shown to be
sensitive to mechanical loads (Thach 1978
).
However, our observation that cell activities during multi-joint loads are best predicted by the Vector Summation Model does illustrate the heuristic value of interpreting neuronal activities in MI in joint-based frames, such as joint torque. One could conceive of a large variety of ways in which a cell could respond to single- and multi-joint loads. In general, suppose that you plot the response of a cell to a given pattern of loads as a vector in some arbitrary coordinate space, where the length of the vector is proportional to cell discharge and the direction represents the pattern of the loads. Thus a load at only the elbow might be represented by one vector while a load at only the shoulder might be represented by another, and a load at both joints simultaneously might be represented by still another vector. Is there a simple way in which these vectors might be related? Both the Linear and the Vector model are really special cases of a general model that assumes that responses to multi-joint loads are a sum of the vector responses to single-joint loads. The difference lies in how the single-joint vectors are oriented with respect to each other; in the Linear Model they are collinear, in the Vector Model they are orthogonal. In general, given an arbitrary relationship between cell activity and load pattern, one cannot expect any particular angle between single-load responses to yield a sum vector that predicts the multi-load response. The fact that orthogonal vectors make the best prediction implies that MI cells use a particular special case where MI cell activity related to the shoulder and elbow joints can be treated as independent axes. One might therefore suggest that in more complex movements, other DOFs that come into play would define still other orthogonal axes in the coordinate system used to control posture, an empirical prediction that could be tested in experiments with three-dimensional or redundant-DOF planar movements. It is important to note that the success of the Vector Summation Model was based on the neural responses across the sampled population of cells. Future work will examine whether the activity of individual neurons fit with the Vector or Linear Summation Models or other more arbitrary representations.
![]() |
ACKNOWLEDGMENTS |
---|
The authors are grateful for expert technical assistance from K. Moore and the constructive criticisms on previous versions of the manuscript from members of the Canadian Institutes of Health Research (CIHR) in Sensory-Motor Systems at Queen's University and members of the Centre de Recherche en Sciences Neurologiques at the University of Montreal. Computer illustration in Fig. 1 was created by J. Sieck.
This research was supported by CIHR Grant MT-13462 and a Medical Research Council Scholarship to S. H. Scott. P. Cisek is supported by a scholarship from the National Institute of Neurological Disorders and Stroke (F32 NS-10354).
![]() |
FOOTNOTES |
---|
Address for reprint requests: S. H. Scott, Dept. of Anatomy and Cell Biology, Queen's University, Kingston, Ontario K7L 3N6, Canada (E-mail: steve{at}biomed.queensu.ca).
Received 6 February 2001; accepted in final form 13 June 2001.
![]() |
REFERENCES |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|