Département de Physiologie, Centre de Recherche en Sciences Neurologiques, Université de Montréal, Montreal, Quebec H3C 3J7, Canada
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Scott, Stephen H. and John F. Kalaska. Reaching movements with similar hand paths but different arm orientations. I. Activity of individual cells in motor cortex. J. Neurophysiol. 77: 826-852, 1997. This study shows that the discharge of many motor cortical cells is strongly influenced by attributes of movement related to the geometry and mechanics of the arm and not only by spatial attributes of the hand trajectory. The activity of 619 directionally tuned cells was recorded from the motor cortex of two monkeys during reaching movements with the use of similar hand paths but two different arm orientations, in the natural parasagittal plane and abducted into the horizontal plane. Nearly all cells (588 of 619, 95%) showed statistically significant changes in activity between the two arm orientations [analysis of variance (ANOVA), P < 0.01]. A majority of cells showed a significant change in their overall level of activity (ANOVA, main effect of task, P < 0.01) between arm orientations before, during, and after movement. Many cells (433 of 619, 70%) also showed a significant change in the relation of their discharge with movement direction(ANOVA, task × direction interaction term, P < 0.01) during movement, including changes in the dynamic range of discharge with movement and changes in the directional preference of cells that were directionally tuned in both arm orientations. Similar effects were seen for the discharge of cells while the monkey maintained constant arm postures over the different peripheral targets with the use of different arm orientations. Repeated data files from the same cell with the use of the same arm orientation showed only small changes in the level of discharge or in directional tuning, suggesting that changes in cell discharge between arm orientations cannot be explained by random temporal variations in cell activity. The distribution of movement-related preferred directions of the whole sample differed between arm orientations, and also differed strongly between cells receiving passive input predominantly from the shoulder or elbow. The electromyographic activity of most prime mover muscles at the shoulder and elbow was also strongly affected by arm orientation, resulting in changes in overall level of activity and/or directional tuning that often resembled those of the proximal arm-related motor cortical cells. A mathematical model that represented movements in terms of movement direction centered on the hand could not account for any of the arm-orientation-related response changes seen in this task, whereas models in intrinsic parameter spaces of joint kinematics and joint torques predicted many of the effects.
There is considerable psychophysical evidence that the control of volitional movements such as reaching movements to a target involves a series of sensorimotor transformations proceeding from higher-level representations of the spatiotemporal form of movement to those specifying the causal details of its execution, culminating in signals that generate the appropriate muscle activity patterns (Georgopoulos 1991 Task apparatus and design
Two juvenile male rhesus monkeys (Macaca mulatta, 4-6 kg) were trained to make visually guided reaching movements from a central position to eight peripheral light-emitting diode targets. The basic apparatus and task have been described elsewhere (Kalaska et al. 1989
Data collection
Standard recording methods were used to study the activity of individual cells in MI on the side contralateral to the arm used to make reaching movements (Kalaska et al. 1989 Data analysis
Each trial was divided into three behavioral epochs: 1) center hold time (CHT), when the monkey remained at the central target before the illumination of the target light-emitting diode; 2) a combined reaction and movement time (RT+MT), from the illumination of the target light to the end of the arm movement; and 3) target hold time (THT), from the end of the movement to the end of the trial (Kalaska et al. 1989 Muscle activity
Muscle activity patterns during the motor tasks were analyzed with the use of techniques similar to those used to analyze single-cell activity. For comparison of the tonic level of EMG of single muscles between two different arm orientations, the EMG for a given muscle was normalized to its largest value recorded for either arm orientation during any behavioral epoch.
Limb kinematics
The average trajectory of the hand to each target was calculated for each orientation. Each movement was divided into 20 equidistant points along its trajectory. The mean and SD of the spatial locations of each of the 20 points along the trajectory to each particular target were calculated across all trials in which cell activity was recorded for each orientation (n = 3,095). Movements made with the left arm were mirror transposed about the midsagittal axis (90-270°).
Hand and joint kinematics
The trajectory of the hand to each of the eight targets was similar when the monkey made reaching movements in the natural and abducted orientations (Fig. 1B). Hand paths were slightly curved and the variability in hand path trajectories for the abducted and natural orientations overlapped extensively. A detailed analysis of the trajectories along with the population signal of cell activity will be considered in a subsequent publication.
Cell data base
The activity of 619 cells was recorded in MI in four hemispheres of two rhesus monkeys (M. mulatta, 4.0 and 5.5 kg). For the first monkey, 144 and 215 cells were recorded in the left and right hemispheres, respectively; for the second monkey, 165 and 95 cells were recorded in the left and right hemispheres, respectively. Penetrations were concentrated in or near the anterior bank of the central sulcus and were confined to the shoulder and elbow representations located medial to the distal arm representation (Kwan et al. 1978
Variation in overall level of discharge with arm orientation
A common effect of arm orientation was a change in the overall level of activity of cells (Figs. 2 and 3A; ANOVA, main effect of task, P < 0.01). A significant task effect in different task epochs indicated a significant change in the level of tonic discharge of cells between the two arm orientations when the monkey maintained its hand at the central start position (CHT), or in the grand mean of the movement-related activity averaged across all eight directions during RT+MT, or in the posture-related activity during holding of the hand at the eight outer targets during THT (Table 1). Tonic discharge during CHT changed between control and abducted conditions in 356 of 619 cells (58%; F test, P < 0.01). Average absolute change in tonic activity during CHT between natural and abducted arm orientations for all cells was 7.2 spikes/s. However, the change in tonic activity of the sample was distributed randomly about zero (Fig. 3A), and the mean discharge rate of cells in the two arm orientations was similar (13.7 and 14.2 spikes/s for natural and abducted orientations; paired t-test, P > 0.10). Similar percentages of cells showed a significant main task effect of arm orientation on the grand mean of activity measured across all eight directions during RT+MT (53%) and THT (51%) between the two orientations (Table 1).
Variation in directional tuning with arm orientation
A majority of cells (433 of 619, 70%) showed a significant task × direction interaction effect during RT+MT (F test, P < 0.01, Table 1), indicating a change in the nature of the relationship between cell activity and movement direction between arm orientations that is independent of the shifts in overall level of activity (main task effect) described in the previous section. Similarly, 489 of 619 cells (79%) showed a significant task × direction interaction effect during THT (Table 1).
Distribution of preferred directions for different arm orientations
The distribution of preferred directions of the total sample of cells was very broad but not statistically random for either RT+MT or THT epochs in either arm orientation (Fig. 6). According to the Rayleigh test, the distributions for the natural and abducted orientations during the RT+MT epoch were best described as bimodal (P < 0.001) with major axes oriented at 98-278° and 104-284°, respectively. During THT, the distribution for the abducted orientation was also bimodal (P < 0.001) with its major axis oriented at 129-309°. In contrast, the distribution of preferred directions for the natural orientation during THT was best described as unimodal (P < 0.01) and oriented at 79.7°.
Response properties of cells with passive input from the periphery
A total of 534 cells responded to passive movement of the shoulder and/or elbow joints. Although some cells (43%) responded only to passive movement of one of the two joints (classes 1 and 5, Fig. 7A), the majority of cells (57%) responded to varying degrees of passive movement at both joints. A larger proportion of cells was related to passive movement of the shoulder compared with the elbow. This partially reflects a sampling bias: among other factors, many elbow-related cells were also strongly responsive to forearm pronation/supination or wrist movements and so were rejected from our sample.
Variation in cell activity between repeated data files
Because data in the two orientations were collected in separate sequential files, any systematic temporal variability in the activity of a cell will be confounded with any arm orientation effect on the cell's discharge. To determine the stability of the cell responses for movements in a given arm orientation over an extended period of time, a second set of data files was collected for some cells and the responses in the repeated data files in the same arm orientation were compared. To control for possible carryover effects between arm orientations, the repeated data files were collected in no fixed order and not necessarily in the same order as in the original data set. A total of 55 repeated files (24 natural and 21 abducted) was recorded from 30 different cells. There was a significant change in the level of discharge during CHT for only 8 of 55 (14%) repeated files (F test, main effect, P < 0.01, Table 1, Fig. 9A). Moreover, the average absolute change in cell discharge was only 2.3 spikes/s and only two (4%) pairs of repeated files showed a change in cell discharge >10.0 spikes/s (Fig. 9B). The magnitude of change in discharge for these cells between replicated files was statistically smaller than their observed change in discharge between arm orientations (8.5 spikes/s; P < 0.001; paired t-test), and also statistically smaller than the change in discharge between arm orientations for the entire cell sample (7.2 spikes/s, P < 0.001, Wilcoxon-Mann-Whitney test). Main effects were correspondingly rare and weak between replicated files during RT+MT and THT (Table 1).
EMG activity during reaching movements in different arm orientations
The EMG activity from the major muscles spanning the elbow and shoulder was recorded in both monkeys. Sixteen to 19 muscles were recorded in each arm of each monkey, resulting in a total sample of 70 muscle data sets. Most muscles were unimodally tuned during the motor task (Fig. 10) (Kalaska et al. 1989
Mathematical models
A number of models were developed to aid in the interpretation of the response properties of cells during the motor task (see APPENDIX). Three different populations of units are presented here: 1) units that encode the direction of hand movement in space (H units); 2) units that encode the direction of angular movement at the shoulder and elbow joints (K units); and 3) units that encode the torque at the shoulder and elbow joints (T units).
HAND-CENTERED COORDINATES.
A key feature of this model was that unit activity reflected the intertrial variability in the path of the monkey's hand both within and between different arm orientations, on the basis of the observed mean and SD of the hand path at the midpoint of the movement to each peripheral target (see Fig. 1B and APPENDIX). The directional shift of these H units between arm orientations was usually small (Fig. 13), unlike the response of motor cortical cells. Most H units (56%) showed a change of <5° in preferred direction between the natural and abducted orientations, and the average absolute change between orientations was only 5.2°. The directional tuning of motor cortical cells was more sensitive to changes in arm orientation than was the directional tuning of this population of H units (Fig. 14; nonparametric test for dispersion, P < 0.001) (Batschelet 1981
JOINT KINEMATIC COORDINATES.
This model was designed to predict how cells specifying motor commands about shoulder and elbow joint angle changes would behave during whole arm reaching movements in different directions with the use of different arm orientations. The effect of arm orientation on the activity of K units during reaching movements (Fig. 15) showed many similarities to that of motor cortical cells. The directional tuning of many units showed a pronounced change between arm orientations, whereas other units did not show any change in directional tuning. Average absolute change in their preferred directions was 33.3° (Fig. 15,) which was less than observed for the cell population in motor cortex during the RT+MT epoch (Fig. 14; nonparametric test for dispersion, P < 0.01), and the arithmetic mean change in the preferred direction was only 1.2° clockwise. This suggests that changes in arm orientation had a greater effect on the directional tuning of individual motor cortical cells than on a simulated population of units that explicitly encode joint angular movement at the shoulder and elbow.
JOINT TORQUE COORDINATES.
The behavior of T units also showed some similarities to that of motor cortical cells and EMG activity. For instance, variations in arm orientation had little effect on the preferred direction of movement of some T units, whereas others showed large changes in directional tuning (Fig. 16). The average absolute directional shift was 63.5° between orientations and the arithmetic mean change in preferred direction for the entire population between abducted and natural arm orientations was 16° clockwise. This population of units encoding torque at the shoulder and elbow was more sensitive to changes in arm orientation than motor cortical cells (Fig. 14; nonparametric test for dispersion, P < 0.01). There was a relatively systematic relationship between the change in the preferred direction of these T units and their preferred direction of hand movement in the natural arm orientation (Fig. 16). Changes in the preferred direction were limited to a 180° band that shifted systematically with the preferred direction of movement for the natural arm orientation. There was some evidence for a similar but less sharply defined cyclical relation between cell preferred direction in the natural orientation and the change in directional tuning in the abducted orientation in both motor cortical cells (Fig. 3) and in the K units of the joint kinematics model (Fig. 15).
It is a truism that to reach to a visual target, the CNS must transform the image of the target on the retina into contractions of motor units in arm muscles. Understanding how the CNS generates a reaching movement therefore requires knowledge of the nature and number of intervening sensorimotor transformations and how they might be realized explicitly or implicitly by neuronal circuits (Feldman and Levin 1995 Do single motor cortical cells explicitly encode intrinsic variables of movement?
The present findings reveal that the discharge of many single MI neurons located in the bank of the central sulcus covaried with arm orientation during planar reaching movements. Mathematical models of units encoding intrinsic joint-centered kinematic or kinetic parameters predicted a complex relationship between changes in arm orientation and changes in directional tuning that had many similarities to those observed for motor cortical cells, whereas the extrinsic hand space model predicted very little change of directional tuning of single cells with arm orientation. Therefore this study shows that motor cortical discharge during reaching movements is influenced by arm geometry, and any coordinate system proposed for the movement representation in motor cortex that does not take into account the geometry of the arm provides an inadequate description of the activity of most single MI cells. However, the results are not proof that MI neurons encode movement either explicitly or exclusively in an intrinsic parameter space related directly to kinesiological features of movement.
Do single motor cortical cells explicitly encode extrinsic variables of movement?
The summed activity of MI cell populations has been shown to covary with the direction and path of the hand in space (Georgopoulos et al. 1982 Nature of the parameter space for motor cortical discharge
What parameter space best describes the activity of MI cells remains a formidable technical and conceptual problem. The present observations emphasize that the well-established broad directional tuning of motor cortical neurons (Fu et al. 1993
Motor cortex and the selection of coordinated multimuscle recruitment patterns
Whatever the nature of the parameter space(s) in which MI cells are functioning, it is informative to consider the implications of their response properties in terms of motor output. Ultimately, the desired movement is produced by generating the appropriate coordinated pattern of activity of a large number of muscles in the arm. The powerful effect of direction on the activity of muscles is well established and produces directional tuning functions that typically bear considerable similarity to those of MI cells (Buchanan et al. 1986 Movement representations at the single-cell and population levels
Although the present data argue against explicit coding of the direction of hand movement at the single-cell level for all MI cells, it is possible that a representation of hand trajectory is coded at the population level (Georgopoulos et al. 1982
INTRODUCTION
Abstract
Introduction
Methods
Results
Discussion
References
; Kalaska 1991
, 1995
; Kalaska and Crammond 1992
; Soechting and Flanders 1989
, 1992
). These processes are often described for heuristic purposes in terms of the convenient but arbitrary parameter spaces (hand path, joint angles, joint torques, etc.) and transformations (inverse kinematics, inverse dynamics) of newtonian mechanics. However, it is highly unlikely that the brain controls movement by explicitly solving the newtonian laws of motion. Instead, psychophysical studies are revealing the physiological parameters, reference frames, and transformations by which the motor system plans and implements movements (Flanders et al. 1992
; Gordon et al. 1994
; Hogan 1984
; Karst and Hasan 1991a
,b
; Lacquaniti 1989
; Lacquaniti et al. 1995
; Shadmehr and Mussa-Ivaldi 1994
; Soechting and Flanders 1989
, 1992
). For instance, a key step in this presumed sequence is the transformation from a representation related to the motion of the hand or the target location in space to a representation related to the mechanical details of its implementation by the arm (Karst and Hasan 1991a
,b
; Soechting and Flanders 1992
). We refer to the latter class of representations as intrinsic and the former class as extrinsic to dissociate those representations that explicitly specify the geometry or mechanics of the limb from those that do not. For instance, hand path is an extrinsic representation because it does not provide explicit information about limb geometry, because a given hand path can be produced by a wide range of arm geometries and joint rotations. Psychophysical studies have also suggested that this transformation may not be directly from extrinsic coordinates to a representation of pure intrinsic coordinates (joint angles, muscle lengths, or joint torques), but rather to a hybrid reference frame reflecting the spatial orientation of limb segments relative to a body-centered origin (Soechting and Flanders 1989
, 1992
). We also refer to these hybrid coordinate systems as intrinsic frames, to signify that they specify the geometry of the limb.
; Fetz 1993
; Georgopoulos 1991
, 1995
; Hogan 1984
; Humphrey and Tanji 1991
; Kalaska 1991
, 1995
; Kalaska and Crammond 1992
; Kalaska and Drew 1993
; Mountcastle 1995
). For instance, whether primary motor cortex (MI) functions predominantly before or after the putative transition from extrinsic to intrinsic coordinates is still controversial.
; Evarts 1968
; Fromm 1983
; Humphrey 1972
; Smith et al. 1975
). The consensus of those single-joint studies implicated MI in the generation of signals that covaried with muscle activity in an intrinsic reference frame, that is, a late stage in the putative sequence of transformations.
, 1988
). Those studies demonstrated that shoulder-related cells were broadly tuned with the direction of movement of the hand, centered on a preferred movement direction that varied from cell to cell. The pattern of activity of the total population covaried with the trajectory of hand movement. More recent studies of motor cortical discharge during continuous tracing motions of sinusoidal and spiral trajectories concluded more specifically that the MI population signaled the instantaneous movement direction and velocity along the hand path in a reference frame centered on the hand (Schwartz 1992
, 1993
, 1995
). This suggested that MI generates a representation of movement in an extrinsic reference frame of hand motion in space, a higher level of representation than indicated by single-joint studies. However, Mussa-Ivaldi (1988)
noted that cells signaling arbitrary intrinsic movement parameters, such as muscle length, would also show broad directional tuning during reaching movements, because intrinsic and extrinsic movement parameters were linked by simple trigonometric relations. Therefore the true nature of neuronal discharge can only be revealed by systematic experimental dissociation of different movement parameters.
, 1990
). The discharge of many single cells was modulated by the direction of external loads (Kalaska et al. 1989
), and the directional signal generated by the sample population under different load conditions often deviated from the actual direction of movement (Kalaska and Crammond 1992
). This implicated MI in the transformation from a representation of the spatiotemporal form of the movements to one that covaried with kinetic parameters of movement, but did not imply that the cells were explicitly signaling newtonian mechanical parameters such as joint torques or output forces (Kalaska et al. 1989
, 1990
). The design of the task in that study could not distinguish between an extrinsic or intrinsic representation.
, 1991
). Many cells showed large and idiosyncratic changes in directional tuning during movements in the three cubes, even though the relative direction of movement from the central start position to the targets in each was identical. This challenged the conclusion that the representation of movement in MI at the single-cell level was centered on the hand. The overall change in cell tuning across the cell sample was mainly a rotation about the vertical axis, corresponding reasonably well to the change in angle of the shoulder to place the hand at the central starting position in each cube. This implicated MI cells in the transformation from a representation of movement in extrinsic coordinates to one in intrinsic shoulder-centered coordinates.
, 1991)
is that each of the eight sets of parallel movement directions was performed in three different parts of space. Therefore it is still possible that the changes in directional tuning of single cells reflected the extrinsic spatial location of the trajectories, and not the changes in arm geometry. In the present study we attempt to clarify this issue. Monkeys were trained to make reaching movements of the arm along similar trajectories with the hand at shoulder level, while holding the arm in one of two different orientations. In the natural orientation, the upper arm and forearm formed a near-vertical plane with the elbow located below the line between the shoulder and hand. In the second orientation, the elbow was abducted nearly to shoulder level, so that the upper arm and forearm were oriented predominantly in the horizontal plane. If single MI cells represent movement exclusively in an extrinsic hand-centered reference frame, their activity should be insensitive to the change in arm orientation. In contrast, if their activity reflects to some degree attributes of movement that covary with arm geometry, their discharge should change in different arm orientations. A preliminary report of this work has been published (Scott and Kalaska 1995
).
METHODS
Abstract
Introduction
Methods
Results
Discussion
References
). However, for this experiment, the position of the handle held by the monkey was 1 cm below shoulder height and ~12 cm from the free end of the pendulum (Fig. 1A). The monkey positioned a pointer at the free end of the manipulandum over the central light-emitting diode (target radius 1.0 cm) for 1-3 s, then moved the pointer to one of eight peripheral targets (target radius 1.5 cm) equally spaced on an 8-cm radius circle when the target was illuminated, and then held the pointer over the target for 2 s. The X-Y position of the manipulandum was measured to 0.1-mm resolution at 100 Hz (Science Accessories, model G/P-3). The eight target lights were presented five times in a randomized block design.
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FIG. 1.
A: task apparatus used in this study. In the natural orientation (left), the monkey grasped a handle on a pendulum-like manipulandum with the use of its preferred arm orientation with the elbow suspended below the line between the hand and shoulder. In the abducted orientation (right), a transparent plate was positioned just below the handle on the manipulandum, requiring the monkey to abduct its arm to shoulder level to grasp and move the handle. B: average hand trajectory to each target for the natural (left) and abducted (right) orientations for all trials recorded for this study. Each trajectory was divided into 20 equal-length segments and the mean (X-Y) position of each of the 20 segments was calculated. Crosses: X-Y position of the hand (mean ± SD) for each segment. C: angles of the shoulder and elbow joints when the hand was at the central start position (C) and at each of the 8 peripheral targets for the 2 different arm orientations.
).
). During each recording session, a microelectrode was advanced through MI while the monkey alternately used the natural or abducted arm orientation. Cells active during the motor task were isolated and examined for their response to passive movement of limb joints. Cells responding predominantly to shoulder or elbow movements were studied further, whereas cells responsive to trunk, wrist (including forearm supination/pronation), or hand movements were not included in the data sample. A scale from 1 to 5 was used to identify subjectively the relative response of each cell to passive movement of the shoulder and elbow joints. A score of 1 signified only elbow input, 5 only shoulder input. Cells without identified inputs were only recorded in the task if adjacent cells within that electrode penetration were related to shoulder and elbow movements. These latter cells were scored as 0, meaning no obvious passive input. Cells that were recorded in the two tasks, but for which a complete passive sensory exam was not completed successfully, were not scored. The activity of each cell was recorded while the monkey performed five complete replications of eight movements first in one orientation and then in the other.
). The analyses in the present study were based on the average neuronal activity in each behavioral epoch. The temporal aspects of cell discharge will be studied in a future report (Kalaska 1996
; Scott and Kalaska 1996
).
; Georgopoulos et al. 1988
). The directional bias of a cell during movement (RT+MT) and posture (THT) can be characterized by a mean vector whose orientation defines the cell's preferred movement direction (Batschelet 1981
; Georgopoulos et al. 1982
). The length of the mean vector (0-1) serves as a measure of the increasing sharpness of a cell's directional tuning (Batschelet 1981
). The length of the mean vector was determined from a given cell's discharge across all movement directions, as recorded in the task. Then, a shuffling procedure randomly reassigned single-trial data to different "movement directions" and the length of the resulting mean vector of the shuffled data was determined. The cell was considered directionally tuned if the length of no more than 40 of 4,000 shuffled mean vectors exceeded the task-related mean vector length of the cell (P < 0.01).
). This unbalanced ANOVA identifies those cells that show a main effect between the task conditions (i.e., a change in overall level of discharge between the two arm orientations), a direction effect (a variation in cell activity with movement direction across task conditions), and a task × direction interaction effect. A significant interaction effect is particularly important because it indicates that a cell shows a significant change in the nature of the relationship of its discharge with the movement direction of the hand. There are two principal ways that a cell could show a task × direction interaction. In one case, the cell's tuning curve could retain the same preferred direction in both orientations but show a gain change, that is, a difference in amplitude (dynamic range) of the cell's directional tuning curve from its maximum to its minimum. At the extreme, a cell could be directionally tuned in one orientation but nondirectional (i.e., dynamic range of 0) in the other arm orientation. Alternatively, a cell's tuning curve could retain the same dynamic range in the two arm orientations but show a change in directional preference. Of course, these effects are not mutually exclusive and cells could potentially show combinations of both effects. Because the nature of the interaction effect, whether mainly a gain change or a directional shift, is of interest for understanding the nature of the influence of arm orientation on cell discharge, the following analyses were performed.
). For this test, repeated estimates of a cell's preferred direction are required for each arm orientation. Therefore a preferred direction was calculated separately for each of the five replication tuning curves of cell activity to provide five measures of the cell's preferred direction in each arm orientation. The Watson-Williams test determines whether or not there is a significant difference (P < 0.01) between the mean angles of the two distributions of five replication preferred directions for each arm orientation. It is important to emphasize that this is a robust test of only a shift in the distribution of replication preferred directions. The source of variability in this procedure is the temporal variability of the directional tuning curve of the cell measured in each replication of the eight movement directions and not the full directional variability of neural activity expressed in the underlying directional tuning curves from which the replication preferred directions were derived.
).
RESULTS
Abstract
Introduction
Methods
Results
Discussion
References
).
). Cells included in this study were unimodally tuned (Georgopoulos et al. 1982
; Kalaska et al. 1989
) in at least one of the behavioral epochs (RT+MT or THT) in one of the two arm orientations, and frequently for both epochs and orientations. Several hundred other cells were tested in the task but were not included in the data base because they were not active in the task or not related to movements of the proximal arm.
View this table:
TABLE 1.
Variation of motor cortical cell and proximal arm muscle activity during reaching tasks
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FIG. 2.
Comparison of the activity of a motor cortical cell during reaching movements with the use of the natural and abducted arm orientations. A: path of the hand at 10-ms intervals during movements to the 8 peripheral targets in the 2 arm orientations. B: response of the cell during reaching movements in the 2 arm orientations. Each raster illustrates the discharge pattern of the cell during 5 repeated trials to each target. Arrowheads: start of movement. Thicker bars before and after the arrowhead on each raster line: time of appearance of the target light and the end of movement, respectively. C: velocity profiles of the hand movement to the lower target (270°) with the use of the natural and abducted orientations. D: preferred direction of the cell discharge calculated for each replication of 8 movements to each target (short vectors) and the mean preferred direction for the 5 replications (long vectors). RT+MT, combined reaction time and movement time; THT, target hold time.
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FIG. 3.
Changes in the activity of motor cortical cells during reaching movements in different arm orientations. A: change in the level of tonic discharge of cells during center hold time (CHT). Frequency histogram (left) of difference in cell discharge between abducted and natural orientations. Bin size: 5 spikes/s. Middle bin represents activity change of ±2.5 spikes/s. Scatter plot (right) shows the relationship between the level of discharge of each cell during CHT in the natural arm orientation vs. the magnitude of change in discharge between orientations. Crosses: cells with a significant difference in discharge (P < 0.01, F test). Circles: cells with no significant difference (P > 0.01). B and C: diagrams at left show the proportion of cells directionally tuned in the natural orientation only (horizontal lines), abducted orientation only (vertical lines), and both (hatched), as well as those not directionally tuned in either arm orientation (unfilled). The number of cells in each group is indicated beside each pie slice. The frequency histograms (middle) illustrate the change in directional tuning of cells between natural and abducted orientations (bin size: 10°, central bin: ±5°, positive value denotes a counterclockwise rotation in preferred direction from natural to abducted orientations). Scatter plots (right) show the relationship between the preferred direction of movement of each cell in the natural orientation vs. the magnitude of change in the preferred direction between orientations. Symbols denote statistical significance (F-test, task × direction interaction, P < 0.01) as in scatter plot in A.
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FIG. 4.
Comparison of the change in the overall level of discharge between arm orientations across the 3 behavioral epochs. Changes in arm orientation resulted in correlated changes in cell discharge before, during, and after movement. The 2 intersecting diagonal lines are the regression lines for ordinate on abscissa and abscissa on ordinate.
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FIG. 5.
Cumulative frequency histograms of the distribution of changes in directional tuning of cells that were directional in both arm orientations, but showed a significant (S) or nonsignificant (NS) task × direction interaction effect during the RT+MT (A) or THT (B) epochs.
). In other words, a change in directional tuning of a given cell during movement between the two arm orientations tended to be sustained after movement, while constant arm postures were maintained at the peripheral targets.
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FIG. 6.
Frequency distribution of the preferred directions of cells that were directionally tuned during movement (RT+MT) or posture (THT), in the natural and abducted orientations. The length of each segment is proportional to the number of cells with a preferred direction within the corresponding 20° segment.
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FIG. 7.
A: frequency distribution of motor cortical cells with differing degrees of response to passive movement of the shoulder and elbow. B: distribution of preferred directions of elbow-related (scored 1 or 2) and shoulder-related (scored 4 or 5) cells for RT+MT in natural and abducted orientations.
). In contrast, no significant difference was found when the right arm distribution was compared with the left arm distribution when the latter was mirror transposed (P > 0.10), so that the two distributions could be pooled without distorting the data. Similar trends were evident for the shoulder-related cells during RT+MT (Fig. 8). For instance, the principal axes for the left (non-mirror reversed) and right arms were 58.4 and 110.3°, respectively.
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FIG. 8.
Frequency distribution of the preferred directions of shoulder-related cells in the abducted arm orientation for the left and right arms (cells recorded in contralateral motor cortex). Note that major axes for the nontransposed distributions related to the left arm are mirror images of the distributions related to the right arm (see text).
). Correspondingly, the average absolute shift in directional tuning for this epoch was slightly larger for shoulder-related cells (37.6°) than for elbow-related cells (33.8°). This trend of shoulder-related cells being more sensitive to changes in arm orientation than elbow-related cells was also seen during the RT+MT epoch.
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FIG. 9.
Change in activity of motor cortical cells between repeated files with the use of the same arm orientation. A: change in tonic discharge during CHT. B and C: change in directional tuning of cells between repeated files during RT+MT and THT, respectively. Format same as Fig. 3. Note the relatively consistent activity between repeated files, compared with the changes in cell activity for reaching movements with the use of different arm orientations (Fig. 3).
). Similar stability of directional preferences was seen during the THT epoch of repeated files (Fig. 9C).
, 1990
), and most elbow muscles were more active in the present paradigm than previously observed (Georgopoulos et al. 1982
; Kalaska et al. 1989
).
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FIG. 10.
Activity of posterior deltoid (A) and long head of the triceps (B) during reaching movements in the natural and abducted arm orientations. Each histogram is the sum of rectified electromyographic (EMG) activity recorded for 5 repetitions of movements to each target. Vertical dotted lines: start of movement.
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FIG. 11.
Changes in the EMG activity of proximal arm muscles during reaching movements in different arm orientations. A: change in the tonic level of EMG during CHT between orientations. Level of EMG normalized to maximal value recorded in any behavioral epoch. B and C: changes in the directional tuning of EMG during RT+MT and THT, respectively, between orientations. Format of diagram same as in Fig. 3.
45 (Fig. 11B). The average absolute change in directional tuning was 29.8°, which was less than that observed for the motor cortex population (Fig. 14, P < 0.05, nonparametric test for dispersion), and the distribution of changes was centered near 0°. Similar to the motor cortex cell sample, slightly less than half of the EMG records (21 of 52, 40%) showed a significant shift in the distribution of replication preferred directions between arm orientations during RT+MT (Watson-Williams test). Corresponding effects in the dynamic range and directionality of EMG activity were seen during the THT epoch (Fig. 11C, Table 1).
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FIG. 14.
Cumulative frequency distribution of the change in directional tuning between arm orientations observed during RT+MT for motor cortical cells and muscles, and predicted for populations of units encoding different movement parameters. H, units encoding direction of hand movement in space; K, units encoding direction of angular movement at shoulder and elbow joints; T, units encoding torque at shoulder and elbow joints.
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FIG. 12.
Frequency distribution of preferred directions for EMG of proximal arm muscles in natural and abducted orientations during RT+MT and THT epochs.
). Therefore observed variations in hand path between arm orientations result in only minor changes in the directional tuning of units that encode the extrinsic kinematics of hand movement.
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FIG. 13.
Changes in directional tuning for a simulated population of units encoding the direction of hand movement. Variability in directional tuning reflected the observed trial-to-trial variability and systematic differences in the hand path between arm orientations (see Fig. 1B). Top: same format as in Fig. 3. Bottom: distributions of preferred directions, same format as in Fig. 6.
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FIG. 15.
Changes in directional tuning for a simulated population of units encoding joint angles at the shoulder and elbow. Top: same format as in Fig. 3. Bottom: distributions of preferred directions, same format as in Fig. 6.
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FIG. 16.
Changes in directional tuning for a simulated population of units encoding joint torque at the shoulder and elbow. Top: same format as in Fig. 3. Bottom: distributions of preferred directions, same format as in Fig. 6.
DISCUSSION
Abstract
Introduction
Methods
Results
Discussion
References
; Flanders et al. 1992
; Georgopoulos 1991
, 1995
; Kalaska 1991
, 1995
; Kalaska and Crammond 1992
; Karst and Hasan 1991a
,b
; Lacquaniti 1989
; Lacquaniti et al. 1995
; Soechting and Flanders 1992
). The present study was designed to test whether single cells in MI contributed to the control of reaching movements at a stage before or after a putative transformation from an extrinsic representation related to the path of the hand or target location in space to an intrinsic representation related to the properties of the proximal arm motor apparatus. If the activity of neurons explicitly signaled information in an extrinsic space, as defined here, their activity would not be altered when reaching movements were made with the use of similar hand trajectories but with different arm orientations. In contrast, changes in arm orientation during reaching will alter the activity of neurons signaling movement in an intrinsic parameter space.
; Thach 1978
), this would produce a broad distribution of tuning properties for shoulder-related cells in the natural arm orientation.
; Fromm 1983
; Humphrey 1972
; Humphrey and Tanji 1991
; Kalaska et al. 1989
; Smith et al. 1975
; Thach 1978
). One must also consider the simple nature of the mathematical models developed for this study. In particular, the joint torque model only considered the forces exerted at the hand to initiate movement of the manipulandum to each target and not the forces to move the limb itself, nor any forces exerted by the monkey on the manipulandum out of the plane of motion. It is difficult to predict how inclusion of these other joint torques would affect the behavior of the model units. Moreover, a model that expresses the kinetics of the task in a more physiological muscle-based space may yield different predictions than did the joint torque model. Nevertheless, the fact that there were some similarities between the behavior of T units and muscle activity suggests that even this very simple model has some utility.
, 1991)
, who also showed that the directional preference of cells was influenced by the starting posture of the arm and was not fixed to the absolute spatial direction of hand movement. Furthermore, they found a systematic rotation in tuning across the population of cells that followed the angular rotation at the shoulder about the vertical axis necessary to make the movements in each region of the work space. We observed no such systematic shift in the directional tuning of the sample population. This reflects a fundamental difference in task design. In the study by Caminiti et al. (1990)
, movements were made in three-dimensional space and the arm orientation rotated predominantly about one of the three orthogonal axes (vertical). As a result, any rotation of the tuning function of a cell about any of the three spatial axes with the change in arm geometry would be expressed and observed in the task of Caminiti et al. In contrast, in the present study, hand movements were confined to the horizontal plane, so that we could only observe that part of the tuning function of each cell expressed within that plane. However, the arm rotation in our task occurred about an axis in the plane of the task, not orthogonal to it. As a result, the directional tuning functions of most cells, if coupled to arm orientation in an arm- or body-centered coordinate system, will rotate mainly into or out of the plane of hand movements and not within it and would result in no net rotation of their distribution, as was observed. It is also noteworthy that the distribution of shifts in directionality of EMG activity, which is strongly coupled to arm geometry (Buchanan et al. 1986
; Buneo et al. 1995
; Flanders and Soechting 1990
; Karst and Hasan 1991a
,b
), likewise did not show any systematic bias.
described a regression analysis of area 5 activity in the same task apparatus used for the study by Caminiti et al. (1990)
. Consistent with the present results, Lacquaniti et al. concluded that a body-centered coordinate frame accounted better for the discharge across all three cubic work spaces than did a hand-centered directional reference frame in each cube separately. However, Lacquaniti et al. could not distinguish between coordinate systems that specified only the location of the hand relative to the body and those that partly or completely specified intrinsic parameters (joint or limb segment angles). This resulted because movement variables in different parameter spaces were all highly correlated (Lacquaniti et al. 1995
; Mussa-Ivaldi 1988
). Our results suggest that for many MI cells, any attempt to account for their activity in a parameter space that only specifies hand location and movement direction, without reflecting the intervening limb geometry, is untenable. For instance, the finding that the distributions of preferred directions of shoulder-related cells recorded with the left and right arms in the abducted orientations were bilobed with a directional bias that was mirror reflected about the sagittal plane presumably paralleled the mirror-reflected geometry and mechanics of the two arms. This mirror-image symmetry provides further evidence that an important component of the discharge of MI cells during reaching movements can be best described by a body-centered or even a limb-centered coordinate framework (Caminiti et al. 1990
, 1991
).
, 1983
, 1988
; Schwartz 1993
). Subsequent studies have extended this finding by relating the discharge of single motor cortical cells simultaneously with several different extrinsic kinematic parameters, such as the direction, velocity, amplitude, and target location of straight-line movements (Ashe and Georgopoulos 1994
; Fu et al. 1993
, 1995
), and the instantaneous direction and speed of continuously curved trajectories (Schwartz 1992
, 1993
, 1995
).
, 1993
), so that there would have to be large changes in velocity for movements away from the cell's preferred direction to significantly alter its directional tuning. Velocity changes of that magnitude were not seen (Fig. 2). Moreover, the regression studies reported that movement direction was typically the single most important factor determining cell activity and that velocity and other extrinsic parameters were less important, suggesting that the addition of other factors will have a minor impact on the predictions of the hand space model.
; Soechting and Flanders 1992
). However, the changes in discharge level and directional tuning observed during the dynamic (RT+MT) and static (CHT, THT) epochs of the present task were statistically correlated. This is readily explained if the orientation-related response changes in the static and dynamic epochs had a common causal origin related to a stable relation between cell activity and motor performance during dynamic and static epochs of the task (Crammond and Kalaska 1996
).
; Buneo et al. 1995
; Flanders and Herrmann 1992
; Flanders and Soechting 1990
; Karst and Hasan 1991a
,b
; Kuo 1994
; Zajac and Gordon 1989
). In a number of cases, the change in arm orientation did not produce a sufficient change in those factors to cause an alteration in the covariation of muscle activity with hand movement direction in the horizontal plane of the task.
, 1995
; Georgopoulos 1995
; Georgopoulos et al. 1983
; Kalaska et al. 1989
; Schwartz 1992
, 1993
, 1995
; Schwartz et al. 1988
) is not of itself sufficient to favor one alternative over others. Mathematical models here (Fig. 17) and elsewhere (Lacquaniti et al. 1995
; Mussa-Ivaldi 1988
; Sanger 1994
; Tanaka 1994
) have demonstrated that units encoding either extrinsic or intrinsic features of movement can be broadly tuned to the direction of hand movement in each arm orientation. The directional tuning of model units signaled the desired change in state in their particular parameter space as a function of movement direction. Moreover, broad directional tuning is a property of cells in every structure studied with reaching movements, and the discharge of these cell populations covaries to different degrees with a wide range of movement parameters (Kalaska and Crammond 1992
). The significance of this finding is subject to two very different interpretations.
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FIG. 17.
Directional tuning curves of 10 randomly chosen units encoding movement in 3 different parameter spaces, illustrating the variation of their activity level as a function of the direction of planar hand movements in the natural arm orientation. The tuning curve of each unit is centered on its "preferred direction," identified during the planar hand movements.
, 1995
; Kalaska and Crammond 1992
; Kalaska et al. 1989
, 1990
). Furthermore, the discharge of a single cell may not express movement in a single distinct reference frame. Instead, cell activity usually shows partial correlations to a number of different movement attributes from different parameter spaces, as if expressing weighted combinations of signals in different reference frames (Alexander et al. 1992
; Fetz 1992
, 1993
; Kalaska 1991
, 1995
; Kalaska and Crammond 1992
; Thach 1978
). Rather than representing the movement explicitly in a distinct parameter space, single cells may be signaling the covariation of movement attributes in different reference frames, and so implicitly affect a sensorimotor transformation. The representation of a given parameter space would be found in the partial correlations distributed across a heterogeneous population of cells.
, 1995
). Most studies have failed to dissociate them adequately, and the problem is exacerbated by the highly stereotyped motor behavior of overtrained monkeys. As a result, should cells encode movement in a particular reference frame, they will inevitably also show strong partial correlations with many different movement parameters in other reference frames. According to this point of view, demonstration of multiple partial correlations in cell activity reveals less about underlying central mechanisms than about the fact that extrinsic and intrinsic kinematic and kinetic parameters are all inextricably coupled through the laws of motion and skeletomuscular mechanics.
; Flanders and Herrmann 1992
; Flanders and Soechting 1990
; Kalaska et al. 1989
, 1990
; Karst and Hasan 1991a
,b
; Turner et al. 1995
; Wadman et al. 1980
). EMG patterns are also strongly dependent on mechanical factors that vary with limb posture (Buchanan et al. 1986
; Flanders and Soechting 1990
; Karst and Hasan 1991a
,b
). For instance, Karst and Hasan (1991a
,b
) found that the patterns of recruitment of muscle activity at the shoulder and elbow during planar pointing movements were less related to the absolute direction of hand movement than to the direction of the target relative to the angle of the forearm, i.e., its initial posture. Furthermore, the specific multimuscle coordination pattern appears to be highly dependent on specific task conditions, implying that fixed multimuscle "synergies" applicable over a broad range of task conditions cannot be a major mechanism to facilitate multimuscle coordination (Buchanan et al. 1986
; Karst and Hasan 1991a
,b
; MacPherson 1991
; Soechting and Lacquaniti 1989
). The CNS must have other means to specify the requisite coordinated multimuscle pattern for each task condition.
, 1995
; Kalaska and Drew 1993
). Consistent with this hypothesis, the discharge of corticomotoneurons is often highly specific to the nature of the fractionated muscle activity patterns within which their target muscles are being recruited (Bennett and Lemon 1996
; Muir and Lemon 1983
).
, 1988)
and of external loads (Kalaska et al. 1989
). The modulations of single-cell activity reported here and by Caminiti et al. (1990
, 1991)
reveal that this process does not reflect only extrinsic directional requirements, but is also influenced by the arm geometry by which the movement is accomplished. This places at least part of the multimuscle specification process at the cortical level, rather than relegating it entirely to the spinal level (Kalaska and Drew 1993
). Single MI cells can only alter the level of activation of their particular muscle field as a unit. The overall muscle recruitment pattern is shaped by the global pattern of activity of the MI population, by the subsequent pattern of termination of corticospinal and other descending axons on spinal interneurons and motoneurons, and by the distribution of activity within those spinal circuits. The possible contribution of MI to the selection of muscle recruitment patterns is further supported by the similarities in the patterns of variation of onset times and initial response magnitudes as a function of movement direction for activity of both MI neurons and proximal arm muscles before the onset of reaching movements (Scott 1996). This does not mean that MI specifies the precise level and temporal pattern of activity of each muscle. This is produced by the interplay between many convergent descending signals and local spinal processes (Kalaska and Drew 1993
). Furthermore, we do not suggest that the changes in directional tuning of MI cells explicitly signal the changes in the directionality of EMG activity as a function of arm orientation (Buneo et al. 1995
). However, the present results suggest that the motor cortex might contribute to the mechanisms required to specify muscle recruitment patterns as a function of arm orientation. To what degree MI activity parallels arm-posture-related changes in muscle recruitment patterns and how it might contribute to the specification of coordinated muscle patterns during reaching movements requires testing of cell responses over a broader range of arm postures.
; Phillips 1969
). However, the reafferent input converging onto a single MI cell will alter its activity, and thus the level of activation of its muscle field, as a function of joint angles, muscle lengths, external perturbations, and loads. Repeated across all the cells composing the motor output map in MI, reafferent input will continually modulate activity across MI, thereby changing the output of MI to other input signals such as the desired direction of movement or target location as a function of the current status of the peripheral skeletomuscular system.
).
, 1995
). Neuronal correlates of movement attributes and higher-order planning processes completely independent of causal muscle activity have been documented in MI in many studies (Alexander et al. 1992
; Georgopoulos 1991
, 1995
; Humphrey and Tanji 1991
; Kalaska and Crammond 1992
; Thach 1978
). Their presence provides further circumstantial evidence that MI is implicated in the sensorimotor transformations required to control movement, and is not just responsible for generating a homogenous representation of movement in a single well-defined parameter space.
, 1983
, 1988
; Schwartz 1993
, 1995
). Caminiti et al. (1990
, 1991)
reported that despite the changes in directional tuning of single cells in different parts of the work space, the summed population vector signals continued to covary with movement direction, suggesting a dissociation of the nature of the representation of reaching movements at single-cell and population levels. We will address this important issue in a subsequent paper.
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ACKNOWLEDGEMENTS |
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We acknowledge the expert technical assistance of L. Girard, and thank T. Drew, A. Georgopoulos, and A. Smith for comments on a draft of the paper. We also thank A. Georgopoulos and C. Bingham for extensive consultation on statistical analyses used in this study. Figure 1 was prepared by G. Filosi.
This work was supported by the Medical Research Council Group Grant in Neurological Sciences. S. Scott was supported by an MRC Postdoctoral Fellowship and is presently a Chercheur Boursier of the Fonds de la Recherche en Santé du Québec (FRSQ).
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APPENDIX: MATHEMATICAL MODELS OF POPULATIONS OF UNITS ENCODING MOTOR COMMANDS IN DIFFERENT PARAMETER SPACES |
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A number of models were developed to aid in the interpretation of the response properties of cells during the two motor tasks. As in many previous studies, our analysis of cell activity in MI during whole arm reaching movements described the relationship between cell activity and the direction of hand movement. To interpret the significance of these findings, it is important to understand how the activity of cells specifying motor commands in different parameter spaces would covary with the direction of hand movement during reaching movements in two different arm orientations. Two types of models were developed: one in which populations of single units specified the extrinsic kinematics of hand movement in three-dimensional space, and another in which units specified the intrinsic joint kinematics (change in joint angles) or kinetics of movement (change in joint torques) at the shoulder and elbow joints.
Hand-centered coordinates
This model used a population of 400 units that each encoded hand trajectory (H units) along a preferred direction in three-dimensional space, in a Cartesian coordinate frame (X, Y, Z). A preferred direction of hand movement was chosen randomly for each unit on the basis of direction cosines l1, m1, and n1 relative to the positive X-, Y-, and Z-axes, respectively. The direction of hand movement was defined by direction cosines l2, m2, and n2, where n2 = 0 because hand movement was limited to the horizontal plane. Unit activity H(A) during movement was proportional to the cosine of the angle between its preferred and the actual directions of movement
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(A1) |
Joint kinematic coordinates
This model used a population of 400 units that specified joint kinematics (K units), with each unit encoding a preferred direction of angular movement at the shoulder and elbow. The monkey's arm was modeled with three DOF at the shoulder [a, abduction(+)/adduction; b, flexion(+)/extension; c, internal(+)/external rotation] and one DOF at the elbow [d, flexion(+)/extension: see Limb kinematics). For each unit, a preferred direction of angular movement at the joints was chosen randomly on the basis of direction cosines a1, b1, c1, and d1 relative to the a, b, c, and d axes, respectively. This preferred direction defined a specific combination, or proportion, of angular movement at the shoulder and elbow, and thus each unit encoded movement in four-dimensional joint-angle space. For a given direction of movement, the angular excursions at the shoulder and elbow joints necessary to the move the hand from the start to the target position were defined with the use of direction cosines a2, b2, c2, and d2. Note that while the direction cosines defining the direction of joint movement during reaching vary with arm orientation, the preferred direction of each unit remains constant in joint-angle space. Unit activity K(A) was proportional to the cosine of the angle between its preferred direction of joint movement and the actual direction of joint movement
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(A2) |
Joint torque coordinates
A second joint-centered model was developed to consider how units involved in controlling joint dynamics (torques) would behave during the reaching tasks. The actual muscular torques at the elbow and shoulder cannot be calculated with the present experimental paradigm because the direction and the magnitude of the force applied by the monkey to the manipulandum was not monitored. However, the relative magnitude of the joint torques necessary to initiate movement of the manipulandum toward the targets can be estimated simply by transforming a spatial vector (oriented from the center start position to the peripheral target) into joint space. This model used a population of 400 units that each encoded a preferred direction of joint torque (T units). The shoulder was again modeled with three DOF [p, abduction(+)/adduction; q, flexion(+)/extension; r, internal(+)/external rotation] and one DOF at the elbow [s, flexion(+)/extension]. For each T unit, a preferred direction of joint torque was chosen randomly on the basis of direction cosines p1, q1, r1, and s1 relative to the p, q, r, and s axes, respectively. This preferred direction defined a specific combination, or proportion, of joint torque at the shoulder and elbow, and thus encoded movements in four-dimensional joint-torque space. For a given direction of movement, the torques necessary to move the manipulandum from the central start to the peripheral target were defined with the use of direction cosines p2, q2, r2, and s2. Note that although the direction cosines for the joint torques required to move the handle vary with arm orientation, the preferred direction of each T unit remains constant in joint-torque space. Unit activity T(A) was proportional to the cosine of the angle between its preferred combination of joint torques and the actual combination of joint torques
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(A3) |
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FOOTNOTES |
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Address for reprint requests: J. F. Kalaska, Dept. de Physiologie, Centre de Recherche en Sciences Neurologiques, Université de Montréal, C.P. 6128, Succursale Centre-ville, Montreal, Quebec H3C 3J7, Canada.
Received 1 March 1996; accepted in final form 22 October 1996.
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REFERENCES |
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