Department of Cognitive and Neural Systems and Center for Adaptive Systems, Boston University, Boston, Massachusetts 02215
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ABSTRACT |
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Ajemian, Robert, Daniel Bullock, and Stephen Grossberg. Kinematic Coordinates In Which Motor Cortical Cells Encode Movement Direction. J. Neurophysiol. 84: 2191-2203, 2000. During goal-directed reaching in primates, a sensorimotor transformation generates a dynamical pattern of muscle activation. Within the context of this sensorimotor transformation, a fundamental question concerns the coordinate systems in which individual cells in the primary motor cortex (MI) encode movement direction. This article develops a mathematical framework that computes, as a function of the coordinate system in which an individual cell is hypothesized to operate, the spatial preferred direction (pd) of that cell as the arm configuration and hand location vary. Three coordinate systems are explicitly modeled: Cartesian spatial, shoulder-centered, and joint angle. The computed patterns of spatial pds are distinct for each of these three coordinate systems, and experimental approaches are described that can capitalize on these differences to compare the empirical adequacy of each coordinate hypothesis. One particular experiment involving curved motion was analyzed from this perspective. Out of the three coordinate systems tested, the assumption of joint angle coordinates best explained the observed cellular response properties. The mathematical framework developed in this paper can also be used to design new experiments that are capable of disambiguating between a given set of specified coordinate hypotheses.
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INTRODUCTION |
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Activity in primary motor cortex
(MI) has been implicated in a variety of aspects of movement behavior
from control of movement execution to participation in movement
planning. Specific examples of MI involvement in the control of
kinematic or kinetic attributes of multi-joint movements include
established correlations between cell firing rates and the following
movement variables: movement direction (Georgopoulos et al.
1982; Schwartz et al. 1988
), hand position
(Georgopoulos and Massey 1985
; Georgopoulos et
al. 1984
; Kettner et al. 1988
), force
(Georgopoulos et al. 1992
; Kalaska et al.
1989
), hand speed (Ashe and Georgopoulos 1994
;
Moran and Schwartz 1999a
; Schwartz 1992
),
movement amplitude (Fu et al. 1993
,
1995
), and target direction (Alexander and
Crutcher 1990b
; Shen and Alexander 1997
).
Further studies have shown that cell firing rates correlate with
aspects of movement planning such as movement preparation
(Alexander and Crutcher 1990a
; Kettner et al.
1996
), target sequence information (Carpenter et al.
1999
), and rapid motor adaptation (Wise et al.
1998
). Cell activity therefore shows relations to a multitude
of movement variables that span the sensorimotor spectrum.
Since not all MI cells are equally responsive to each of these
variables, it makes sense to separately investigate distinct components
of firing rate modulation. Although force or other movement variables
could be analyzed with the methods employed herein, the present
analysis focuses on cell response components related to a kinematic
variablemovement direction
because studies have demonstrated the
prevalence and strength of directional coding in MI (Ashe and
Georgopoulos 1994
) and because a large literature exists on
center-out tasks in which movement direction is the explicitly
controlled variable. Still, knowing that cell activity strongly
reflects a kinematic movement variable like direction does not specify
the nature of the cellular representation: Cartesian spatial
coordinates, joint angle coordinates, or muscle length coordinates all
might be used to represent movement direction at one neural stage or another.
For the entirety of MI, the supposition of a unique coordinate system
in which movement direction is encoded may be inappropriate since a
heterogeneity of coordinate systems may exist within a single brain
region (Crutcher and Alexander 1990). Indeed it is well-documented that the representations that mediate motor behavior are distributed, often in a graded manner, across extensive,
overlapping cortical regions (Fetz 1992
; Kalaska
and Crammond 1992
; Mushiake et al. 1991
).
Therefore we restrict our analysis to the single-cell level and ask:
how can one analyze the coordinate system in which an individual cell
encodes movement direction? Beyond outlining a general framework for
testing alternative coordinate hypotheses, we test three specific
coordinate systems, Cartesian spatial, shoulder-centered, and joint
angle, with regard to the data of Hocherman and Wise
(1991)
.
Model and approach
PREFERRED DIRECTIONS IN AN INTERNAL SPACE.
Georgopoulos et al. (1982) showed that the
movement-related activity of many MI cells in the standard center-out
task can be represented as
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(1) |
SPATIAL PD. A spatial pd is that hand motion direction, as represented in extrapersonal space, to which a cell will respond maximally during small movements made from a common starting posture. What is meant here by the term "space" is the coordinate system utilized by the experimentalist in making measurements, typically a Cartesian coordinate system whose axes are aligned with the task space: e.g., the planar surface on which the monkey performs a center-out task. This coordinate system will henceforth be referred to as Cartesian spatial coordinates.
INTERNAL PD. An internal pd is that movement direction that elicits maximal cell response when represented in whatever coordinates best characterize the cellular-level encoding of movement direction. This "internal" coordinate system of a cell may be Cartesian spatial coordinates, or it could be some other coordinate system, such as a joint angle or muscle length coordinate system, which is more closely coupled to the biomechanical variables directly affected by the cell through its output connections. Thus, although the spatial pd reflects the internal pd, it is the internal pd that describes a cell's distinctive role in the sensorimotor transformation.
For a well-defined internal coordinate system, mathematical transformations can be used to convert back and forth between a representation of direction in external space and its corresponding representation in the internal space. These transformations are in general posture dependent: the relationship between directions in the internal space and directions in external space changes as a function of posture. By using the distinctions between a spatial pd and an internal pd as well as the posture-dependent properties of the directional transformations between the spaces, a vector field method is developed that generates, for a given cell, spatial pd predictions that differ across the workspace as a function of coordinate hypothesis. ![]() |
METHODS |
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Model arm
The analysis in this paper assumes a 2-joint or 2-degree-of-freedom (2-DOF) arm moving on a 2-dimensional (2-D) planar workspace situated within the horizontal plane passing through the shoulder. This model arm, illustrated in Fig. 1A, will be referred to as the 2-DOF planar arm. The kinematic equations describing this arm are detailed in the APPENDIX. A critical feature of the 2-DOF planar arm that simplifies our analysis is that positions map one-to-one to postures.
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Modeling internal pds
One complication in adopting Eq. 1 as a general model
for cell firing rates in center-out type tasks is that spatial pds have been observed to vary with hand position (Caminiti et al.
1990) and, more generally, arm posture (Scott and
Kalaska 1997
). To account for more of the variance in cell
discharge as the center-out task base expands, additional predictor
variables (such as hand position) might be added to the regression
equation (Ashe and Georgopoulos 1994
). Alternatively, a
change in the coordinate representation of the variable of interest
(Lacquaniti et al. 1995
), which in this case is the
preferred movement direction, might allow an equation as compact as
Eq. 1 to account for a larger proportion of the variance. As
part of the search for a more generally applicable tuning equation, the
2-DOF planar arm model can be used to construct alternative coordinate
systems for the purpose of testing whether Eq. 1 (in which a
cell's pd is specified once and without regard to the arm's posture)
can provide a better data fit if the spatial pd is
interpreted as a specific instantiation of an underlying and invariant
internal pd. A constant internal pd, together with the relevant
coordinate transformation, can in principle fully explain the
observation of a posture-dependent spatial pd by generating a
systematic prediction of the manner in which the spatial pd changes
with posture.
To illustrate, suppose that the spatial pd of a cell at some reference
posture is direction and that the internal space of
a cell is coordinate system Z. Movement direction
in space maps to movement direction
in coordinate system Z. Now suppose that
movements are initiated from a new arm posture. How can we predict the
new spatial pd,
'? Assuming that
remains the cell's pd in internal coordinate system Z at
the new arm posture,
' can be calculated using the
reverse mapping: between directions in coordinate system Z
to directions in external space. In general, for all cases where the
internal coordinate system is not identical to external space,
' will not be the same as
, because
the transformation between directions in coordinate system Z
and directions in external space depends on posture (i.e., as the
posture changes, so does the local relationship between movement
directions in the 2 coordinate systems). This type of coordinate
analysis belongs to the branch of mathematics known as differential geometry.
Vector fields of spatial pds
Given a 2-DOF planar arm, hand position maps uniquely to arm
posture (which is not the case when the arm possesses redundant degrees
of freedom). Thus determining the spatial pd at every posture is
equivalent to uniquely determining the spatial pd at every hand
position in the workspace. Specifying a spatial direction and a
corresponding magnitude over a field of points in space defines a
vector field [in this case, a vector field of spatial pds as in
Zhang and Sejnowski (1999)]. Thus an internal pd in a
particular coordinate system implies a vector field of spatial pds. To
illustrate, plots of vector fields of spatial pds were constructed
under the assumption of each of three internal coordinate systems for a
sample cell whose spatial pd is 60° at a reference posture, as
indicated in Fig. 1A.
Cartesian spatial coordinates
The simplest vector field arises when the internal coordinate
system in which a cell encodes movement direction is the same Cartesian
spatial coordinate system in which spatial pds are measured. Psychophysical evidence (Morasso 1981) suggests that
movement planning may occur in this coordinate system. Spatial pds for this case will not vary with posture because the spatial pd at the
reference posture is also the cell's internal pd; in other words, the
identity transformation converts between the two representations of
direction. Figure 1C shows this constant-direction vector
field of spatial pds. The magnitude of each vector is unity; the
APPENDIX describes how magnitudes are determined. For this
and subsequent vector field plots, information regarding the direction
but not magnitude of the vectors is provided.
A vector at a given point in these vector field plots represents the cell's expected spatial pd if the center-out task were performed with that point as the movement origin. Because it is impractical to map out a cell's vector field of spatial pds by performing the center-out task as many times as there are arrows on the simulation plots, alternative testing methods are described later.
Shoulder-centered coordinates
One axis important for many mammals is the line between the
proximal and distal end of a limb; e.g., between the shoulder and the
hand (Maioli and Lacquaniti 1988). Psychophysical
studies (Flanders et al. 1992
; Soechting and
Flanders 1989
) have suggested the existence of a
shoulder-referenced spatial coordinate system, and cell data have been
interpreted in terms of a shoulder-referenced intrinsic coordinate
system (Caminiti et al. 1990
, 1991
;
Tanaka 1994
). In consideration of these observations,
suppose as shown in Fig. 1B that a cell's spatial pd is
computed in a mobile Cartesian spatial reference frame, one axis of
which is aligned with the axis connecting the shoulder to the hand. As
the shoulder-hand axis rotates (due to rotations at the shoulder and/or
elbow joints), the cell's spatial pd rotates by an equivalent amount.
Thus the rotational transformation converts between representations of direction in the two spaces. Figure 1D plots the
variable-direction vector field of spatial pds generated for the sample
cell with a constant pd in shoulder-centered coordinates.
Specifications for generating this vector field are contained in the
APPENDIX.
Joint angle coordinates
An MI cell may encode movement in a joint angle coordinate
system that represents a later stage in the sensorimotor transformation from spatial coordinates to muscle activations. Psychophysical studies
on motor adaptation (Gandolfo et al. 1996;
Shadmehr and Mussa-Ivaldi 1994
) have implicated
joint-based representations. Mussa-Ivaldi (1988)
suggested that MI cell activity could be a linear function of the rate
of multiple muscle length changes. More recently, Scott and
Kalaska (1997)
introduced a joint angle interpretation of MI
cell activity, and our interpretation is similar to theirs.
Suppose that at the reference posture, (R,
R), a cell possesses a spatial pd,
pd. Using the inverse of the Jacobian of the
kinematic transformation from joint angle coordinates to spatial coordinates, this spatial direction can be converted to a direction in
joint angle space. On assumption of a new arm posture, the Jacobian can
be used to convert the joint angle direction back to a spatial pd.
Since the Jacobian is posture dependent, application of the inverse
Jacobian followed by application of the forward Jacobian evaluated at a
new posture is not equivalent to operating with the identity
transformation; the composite transformation will result in a new
spatial pd. The mathematical details of constructing this vector field
are contained in the APPENDIX.
An intuitive explanation of what it means for a cell to possess an
internal pd in a joint angle coordinate system is as follows. Suppose
the internal pd for a cell is
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(2) |
Global description of vector fields
These three vector fields simulated for a sample cell clearly differ from one another. Is there any simple way to classify the differences in their structure without comparing vectors in the alternative vector fields one by one for each cell? The curl of a vector field is a local measure of the rotational tendency of vector field flow; that is, a measure at a point of how much the vectors rotate in the neighborhood of that point. Observing how the curl changes across the workspace helps to explicate the global structure of a vector field. Below we present the distinct curls for each of the three classes of vector fields described above. The mathematical details of the derivations are reported in the APPENDIX.
CARTESIAN SPATIAL COORDINATES. Cartesian spatial internal pds imply that the spatial pds do not change. Hence, there is no oriented flow to the vector fields, and their curls are everywhere zero.
SHOULDER-CENTERED COORDINATES.
Vector fields generated under the assumption of this coordinate system
yield
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(3) |
JOINT ANGLE COORDINATES.
For a cell tuned to an invariant direction in joint angle space, this
internal pd can be written as a normalized joint angle velocity vector
(* denotes normalization)
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(4) |
Utility of vector fields
Measuring the curl experimentally is problematic since it is a local measure whose accurate estimation at multiple points would require a high resolution sampling of the workspace that may be difficult to accomplish in practice. Nonetheless, for any pair of candidate coordinate systems, computation of the curl indicates whether the two coordinate systems give rise to vector fields of similar or disparate structure, and thus whether they are empirically distinguishable. Based on this fact, two distinct methods for experimentally disambiguating between distinguishable vector field structures are now described: "direct field sampling" and "indirect field sampling."
Direct field sampling
This method determines spatial pds at several different
workspace locations and then, using a least mean square analysis, compares the results with those predicted by the different coordinate hypotheses. For example, spatial pd predictions at the locations indicated by the thin-lined boxes in Fig. 1, C-E, can be
compared with the measured spatial pds at those locations to determine which coordinate system provides the best fit. Knowledge of the vector
field structures can optimize the discriminatory efficacy of the direct
field sampling paradigm by enabling workspace sampling that focuses on
those locations that engender very different predictions for the
coordinate systems being evaluated. For 2-D planar arm movements, no
experiment has been performed that directly sampled the workspace in
the manner suggested above, although Caminiti et al.
(1990) and Scott and Kalaska (1997)
have
performed experiments based on this concept (see
DISCUSSION).
Indirect field sampling
Another method relies on investigating cortical activity during long, curved movements that sweep broadly across the workspace, thereby visiting many postures and implicitly sampling a cell's vector field of spatial pds over a single trajectory. The pattern of movement-related activity registered by a cell along multiple such paths determines the cell's trajectory-selectivity or its tendency to respond preferentially to certain types of trajectories. A cell's trajectory-selectivity, if any, can serve as the signature for a specific coordinate system.
Equation 1 was initially applied only locally and only to
movements of constant spatial direction. For long, curved trajectories the spatial movement direction of the hand varies continuously, and the
hand position or arm posture can change significantly as well.
Schwartz (1992) demonstrated that, for the traversal of
sinusoidal trajectories, the activity of many MI cells varied continuously as a function of the continuously changing movement direction in accord with Eq. 1. That is, Eq. 1
held even when the neural recordings were taken during movements in
which the movement direction markedly varied, provided that there was
an appropriate temporal lead between the cell firing rate and the corresponding hand movement direction. A similar finding was made regarding a spiral tracing task in Moran and Schwartz
(1999b)
.
On the basis of these and other findings that suggest that directional control is an important aspect of movement control, we hypothesize that cells will respond in continuous accord with the principles of broad directional tuning (as embodied by tuning curves such as the cosine model) in arbitrary movement tasks. Thus the movement-related temporal discharge pattern of a cell during an arbitrary movement trajectory can be modeled by 1) breaking the trajectory into a large number of small, essentially linear, path segments; 2) determining the movement direction within a given bin; and 3) applying Eq. 1 to each of these path segments. These steps will determine the direction-dependent component of cell activity over the course of a movement path.
To complete the determination of the temporal response profile, we note that MI cell response for trained movements with unimodal speed profiles often takes the form of a phasic pulse or burstlike response. Many generative hypotheses are consistent with this shape. As the focus of this article is not on explicating the specific shape of the response, but on understanding how variations in cell response arise as a function of the directional characteristics of the movement path taken by the hand, we simply assume a generic burstlike shape for cell response in our simulations. Therefore to determine the temporal response profile, the directional component of cell activity (as determined in steps 1-3 above) is modulated by a generic Gaussian that embodies the phasic response properties of many MI cells. The Gaussian modulation is a fixed component of cell response used identically for all paths and coordinate assumptions, and it introduces no bias. Simulations showed that the precise form of the response envelope (which included different pulse shapes as well as the constant function) does not alter the results on trajectory-selectivity. A determination of trajectory-selectivity indicates that a cell responds preferentially to certain movement paths, and this path-dependent response depends on the variable directional component of cell response and not on the fixed modulatory component.
Averaging the activity over all the bins of a movement path determines
the mean firing frequency over the course of the entire movement. Thus
the average firing rate, , of a cell over the course of an
arbitrary trajectory can be expressed as
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(5) |
The data of Hocherman and Wise (1991) are now
analyzed within the framework of indirect field sampling for the
purpose of evaluating the adequacy of the three internal coordinate
hypotheses. That study investigated the correlation between individual
motor cortical cell activity and the curvature type of end-effector motion. Briefly, a monkey was trained (by use of intermediate via
points between the movement origin and target locations) to make
movements of different curvature types from an origin point to each of
three equidistant targets spaced at intervals of 30°. Both the arm
and the targets were constrained to lie on a 2-D planar surface. The
three movement types consisted of clockwise arcs, straight lines, and
counterclockwise arcs; a movement of each curvature type was made to
each of the three targets for a total of nine distinct trajectories,
which are numerically labeled in Fig. 2.
Unconstrained return movements were also part of the protocol, so even
though the targets were concentrated in a 60° wedge, movement
directions did span the entire 360° of the angular continuum.
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Cell activities were recorded in the arm area of MI both before and
during the movements. (In the actual experiment, cell recordings
occurred in several different epochs, but we only simulate activity for
a single movement-related epoch, which corresponds closely to their
"late movement epoch.") In the study, a neuronal modulation index,
Mj, was used as a normalized measure of a
cell's average movement-related activity for path j and was
computed with the equation
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(6) |
Using the method of indirect field sampling, we simulated the
experiment of Hocherman and Wise (1991) by 1)
computing each model cell's modulation index for all of the nine
movement paths using their normalization procedures, 2)
classifying model cells using their classification criteria, and
3) generating cellular temporal response profiles. A model
cell was identified by its spatial pd at the reference posture; the
population of model cells consisted of 360 cells, 1 for each degree of
the angular continuum. Simulation details are found in the
APPENDIX.
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RESULTS |
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Simulations of trajectory-selectivity
A key discovery of Hocherman and Wise (1991) was a
strong tendency for cells to respond preferentially to movements of the curved trajectory types. Illustrations of the results of the original experiment are given in Fig. 3,
A and C, which show the percentages of
trajectory-selective cells for each trajectory type using the strict (A) and relaxed (C)
criteria to classify cells.
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For the simulations run under the assumptions of Cartesian spatial, shoulder-centered, and joint angle coordinates, there were, respectively, 181, 156, and 135 task-related model cells of a total of 360 model cells. The spatial pds of these cells at the reference posture were almost entirely contained in the 0-180° range since the movement directions required to reach the targets also exist in that range. Plots in Fig. 3, B and D, depict the percentages of cells that were trajectory selective for each trajectory type using each classification criterion. Under the assumptions of both Cartesian spatial coordinates and shoulder-centered coordinates, the vast majority of the trajectory-selective model cells, 100 and 68%, respectively, were trajectory selective for the straight trajectory type when the strict classification criterion was used; using the relaxed criterion, the percentages were 98 and 69%. These simulation results are not consistent with the data where the vast majority of trajectory-selective cells are of the two curved movement types. Under the assumption of joint angle coordinates, however, the majority of model cells were (like MI cells) trajectory selective for the curved trajectories. Furthermore, the percentages of all three types of trajectory-selective cells using the joint angle model correspond well with the data for both classification schemes, as can be seen by comparing the graphs.
To understand the simulation results, recall Eq. 5. It implies that, over the course of a trajectory, a cell registers significant activity while the movement direction is parallel to the spatial pd; the greater the deviation from colinearity, the less the activity generated. The average firing rate of a cell for an entire movement, then, depends on the interaction between the vector field structure of spatial pds and the sequence of movement directions taken by the hand. Previously, it was shown that the hypothesis of a particular coordinate system imparts a signature structure to the vector field of spatial pds. Similarly, each type of movement curvature (clockwise, straight, counterclockwise) engenders its own characteristic pattern of movement directions. The movement direction for clockwise movements rotates continuously and in a clockwise manner from the beginning of the movement to its end for a net rotation of about 90°. The reverse is true for the counterclockwise movements. During straight movements, the movement direction never changes. The observed ratios of trajectory-selectivity for a given coordinate system can be understood by considering, within the context of the task, how these characteristic movement patterns interact with each vector field structure.
For example, the spatial pds of vector fields generated by the assumption of joint angle coordinates tend to rotate in a uniform direction over the entire course of each trajectory. Figure 4 shows plots of a model cell's spatial pd values over the course of a clockwise trajectory and a straight trajectory to a particular target under the assumption of each coordinate system. It can be seen in the plot of the joint angle coordinate simulation that the spatial pd is not initially aligned with the movement direction at the beginning of the clockwise movement, but the two gradually fall into alignment over the course of the trajectory. The reverse is true for the straight trajectory.
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A rotating movement direction can engender considerable activity when paired with a rotating spatial pd if they rotate in the same direction and if the movement direction rotates more sharply, over the same spatial extent, than the spatial pd (a situation that does arise in the case of joint angle coordinates for the curved movements in this experiment). The dual rotation facilitates the occurrence of an interval of overlap during which the two directions are nearly aligned. At some point, the movement direction "overtakes" the spatial pd, although these directions may not be initially aligned, and this tendency toward alignment occurs for multiple movements of the same curvature type even when the final targets of these movements are different. Thus the assumption of joint angle coordinates gives rise to relatively large proportions of cells that are trajectory selective for the curved trajectory types. In contrast, under the assumption of either Cartesian spatial or shoulder-centered coordinates, there is either no tendency or a much weaker tendency for the spatial pds to rotate over the course of the trajectories, and what rotation does occur is often not unidirectional over an entire trajectory. This produces model cells that respond preferentially to straight trajectories.
Simulations of cell response profiles
In addition to simulating the average activity over the course of an entire trajectory, the model can also simulate, as shown in Fig. 5, the temporal response profiles of a cell for each of the nine different movement trajectories under the assumption of joint angle coordinates. Figure 5 shows that cell response properties vary as a function of movement curvature. For example, the relative timing of peak activity depends critically on the time-evolving relationship between the hand's movement direction and the cell's spatial pd for the movement path under consideration. The peak activity for the model cell occurs 150-200 ms after the onset of movement-related activity for the counterclockwise movement paths and 275-325 after onset for the clockwise movement paths. This predicted time lag between the peak activities can be tested experimentally. Such temporal differences in activity profiles exist for all the response envelopes we tried since these differences stem from the variable directional component of cellular response, which is highly differentiated in this paradigm as a function of curvature type. For other model cells (depending on the spatial pd at the reference posture), the relative timing of peak activity as a function of movement curvature will be reversed: the peak activity will occur sooner for the clockwise paths than for the counterclockwise paths.
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This cell is typical of all model cells in two important respects:
1) its response characteristics, such as its peak firing rate, mean firing rate, and the timing of its peak firing rate, change
relatively gradually from one trajectory type to the next; and
2) the mean activity levels across trajectory types are
ordered in a characteristic manner, i.e., a clockwise
trajectory-selective cell will be most active for the clockwise paths,
least active for the counterclockwise paths, and intermediately active
for the straight movement paths (the inequality is reversed for
counterclockwise trajectory-selective cells). In what follows, we
analyze Hocherman and Wise (1991) data with respect to
the above two model cell response properties.
Comparison of model cell response properties with data
Do real MI cells exhibit graded responses such as those
illustrated in Fig. 5? Instead, an MI cell might be highly modulated for clockwise trajectories but relatively silent for straight and
counterclockwise movements. If curvature were explicitly encoded as a
movement primitive by MI cells, then one might expect such a
discretization of response characteristics. Some of the plotted response profiles in Hocherman and Wise (1991) seem to
support the all-or-none view, although this type of analysis was not
performed in that study. To assess whether MI cell activity more
closely conforms to the graded or categorical response characteristics, we obtained the original data files for 59 of the 76 task-related MI
neurons (which included 19 of the 24 trajectory-selective cells using
the strict criterion) from Hocherman and Wise (1991)
and analyzed the spread in activity for movements of different curvature types. Specifically, for each trajectory-selective cell, let
cw,
xcw, and
str denote cell activity
averaged over each set, respectively, of clockwise movements,
counterclockwise movements, and straight movements. For example,
cw denotes cell activity
averaged over the clockwise movement paths 1, 4, and 7 in Fig. 2.
Consequently, a separation index, analogous to the modulation index,
was defined for each trajectory-selective cell as
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(7) |
Figure 6A plots the
distribution of separation indices for the population of
trajectory-selective cells in Hocherman and Wise (1991).
The mean and median separation indices are 0.48 and 0.43, suggesting
that (outside of the small percentage of outliers present in the plot)
cell response varies relatively gradually as a function of movement
curvature. Figure 6B plots the corresponding distribution of
simulated separation indices for the population of model
trajectory-selective cells under the assumption of joint angle
coordinates. The mean and median separation indices are 0.35 and 0.34. Note that for both distributions the vast majority of separation
indices lie in the interval between 0.3 and 0.5. Therefore the gradual
variation exemplified by the Fig. 5 model cell is a characteristic
feature of both the model and the data.
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A second distinctive feature of model cell response properties is
the very specific ordering of mean activity as a function of
curvature type. In particular, for every clockwise trajectory-selective model cell, the following condition holds:
cw >
str >
xcw. This condition (with the
inequality accordingly reversed) also holds for every counterclockwise
trajectory-selective model cell. For the population of curved
trajectory-selective cells in Hocherman and Wise (1991)
,
89% of the cells (17/19) showed the same ordering in their activity.
Thus the model reproduces not only the observed graded responses, but
also the observed ordering of those responses.
Varying simulation parameters
There are no free parameters in the model, since the only model
variable is a cell's vector field of spatial pds, which is completely
determined as a function of the working coordinate hypothesis. However,
the simulations did require values for the location of the movement
origin, the speed profiles of the hand, the lengths of the arm
segments, and b0 and
b1 of a cell's tuning curve.
Regarding the kinematic movement parameters, Hocherman and Wise
(1991) did not have precise measurements for these quantities. Therefore while the values used in the simulations were in accord with
the specifications communicated to us by Dr. Wise, we systematically varied these parameters to probe the robustness of the results regarding trajectory-selectivity. The APPENDIX provides the
details of these sensitivity analyses, the findings of which
demonstrate the robustness of the simulation results for all three
coordinate systems. Varying the cellular parameters
b0 and
b1 [which must be assumed since no
center-out task is performed to determine them in Hocherman and
Wise (1991)
] did not alter a cell's trajectory-selectivity as shown
in the APPENDIX. The use of response envelopes with
different pulse functions or with the constant function made no
significant difference in the simulation results. Finally, although the
simulations employed a uniform distribution of spatial pds at the
reference posture (as revealed in Lurito et al. 1991
),
distributional skewing away from nonuniformity, such as that reported
in the literature (Georgopoulos et al. 1982
;
Scott and Kalaska 1997
) did not change the character of
the results under the assumption of any of the three internal
coordinate systems.
Amplitude effects
In the experiment of Hocherman and Wise (1991), the
curved paths were longer than the straight paths (23 cm as opposed to 20 cm), so the prevalence of curved trajectory-selective cells could
conceivably result from an amplitude dependence of the cell firing
function (Fu et al. 1993
, 1995
). However,
this hypothesis conflicts with the observed ordering of cell activity
by trajectory type, which indicates that, for the actual clockwise
trajectory cells,
cw >
str >
xcw (the order of inequalities
is reversed for counterclockwise trajectory selectivity). Even if
modulation indices are scaled outright by path length, the simulation
results for Cartesian spatial and shoulder-centered coordinates grossly
contradict this observed ordering.
Effect of other movement variables
We conducted analyses (see APPENDIX) to assess whether
a simple dependence of cell firing rates on either hand speed or hand position could alter the relative goodness-of-fit of the three coordinate hypotheses. The inclusion of these correlations did not
change the nature of the results. The joint angle coordinate hypothesis
continued to fit the data well while the other two coordinate systems
failed. Although we believe that amplitude and hand speed were the most
pertinent task variables (aside from direction) to consider in
explaining the data, these variables comprise only a subset of the
known correlates of MI cell activity (see INTRODUCTION).
Additional studies would be needed to assess whether correlations with
other movement-related variables could provide an alternative or
supplementary explanation of the Hocherman and Wise
(1991) results.
Prediction: internal pd controls spatial pd and trajectory-selectivity
The simulations of the Hocherman and Wise (1991)
experiment not only determine the percentages of cells selective for
the different trajectory types but also imply a relationship between a
cell's internal pd and its trajectory selectivity. Specifically, a
cell's spatial pd at a reference posture maps to a cell's internal pd; from the cell's internal pd, a vector field of spatial pds is
generated; from the cell's vector field of spatial pds, the cell's
trajectory selectivity is determined. Thus a mapping is constructed
from the spatial pd of a cell at a reference posture to the type of
trajectory selectivity which that cell is predicted to possess. For
example, a model cell with a spatial pd of 45° at the reference
posture is clockwise trajectory selective under the assumption of joint
angle coordinates. Table 1 depicts the complete predicted mapping from a cell's spatial pd at the reference posture to its trajectory selectivity using the example of joint angle
coordinates. This prediction can be tested in an experiment that
determines both spatial pds through the center-out task and cellular
trajectory selectivity through the curved motion task of
Hocherman and Wise (1991)
. The end result of this
composite protocol would be an empirical determination of the mapping
between spatial pds at a reference posture and type of trajectory
selectivity. Model mappings constructed for each internal coordinate
system could then be compared with the actual mapping to assess the
goodness-of-fit of alternative coordinate hypotheses.
|
Target selectivity
In addition to classifying cells as trajectory selective,
Hocherman and Wise (1991) classified cells as target
selective if the cells responded preferentially to movements to a
specific target as compared with the responses to movements to the
other targets. For the purpose of representing the findings of
Hocherman and Wise (1991)
on target selectivity and of
showing corresponding simulation results (using the strict criterion of
classification), let x/y/z indicate
that x% of task-related cells are target selective for
target 1, y% of cells are target selective for
target 2, and z% are target selective for
target 3. On the basis of Table 3 and Fig. 12b in
Hocherman and Wise (1991)
, the percentages of excitatory
target-selective cells found in that study were 42/29/29. For the
Cartesian spatial simulations, the percentages were 33/31/36; for the
shoulder-centered simulations, the percentages were 38/16/46; for the
joint angle simulations, the percentages were 46/16/38. Thus all three
coordinate hypotheses roughly reproduce the results on target
selectivity, and these target selectivity data cannot distinguish
between the coordinate systems.
Compatibility of joint angle coordinates with prior population vector analyses
The population vector algorithm (PVA) has been used to predict
movement direction over the course of a trajectory on a bin-by-bin basis with good results (for review, see Georgopoulos
1995). In standard use of the algorithm, the assumed spatial
pds do not change as the hand location changes from one bin to the
next. If spatial pds in actuality do vary across the workspace, the population vector should rotate away from the movement direction as the
movement progresses away from the point at which the cell's pd was
assessed. Such a mismatch between the population vector direction and
the movement direction would arise because the algorithm's invariant
representation of a cell's vectorial contribution comes to lie in a
direction slightly askew from the cell's actual preferred direction.
Nonetheless, the very robustness of the PVA, as an aggregate estimator
of movement direction, renders it insensitive to alternative coordinate
assumptions (Georgopoulos 1996
; Mussa-Ivaldi 1988
; Sanger 1994
). To assess sensitivity in the
current case, we performed a bin-by-bin population vector simulation
for all eight movements in the standard center-out task. In this PVA, computed cell activity was based on the bin(posture)-dependent spatial
pds determined by the joint angle coordinate model. The trajectories
were divided into 25 bins, each of length 20 ms. Under these
conditions, the population vector did rotate away from the actual
movement direction (due to the structure of the joint angle coordinate
system), but the rotation was modest and the resultant prediction error
was within the range of prior reports that used the PVA. The average
amount of rotation from the beginning of a trajectory to the end of the
trajectory was <10°. Further, the mean absolute differences between
the population vector direction and the movement vector direction over
all of the bins was <5°. That the mean signed difference between the
two vector directions over all of the bins was 0° indicates the
difficulty of using a population vector analysis to distinguish between
coordinate systems. Thus the joint angle coordinate hypothesis is
consistent with prior PVA results.
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DISCUSSION |
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This paper presents a framework for analyzing the coordinate
system in which an individual cell encodes movement direction. A
cell's preferred direction can be predicted to vary across the workspace in a distinct manner depending on the assumed internal coordinate system, and direct sampling experiments can be designed to
probe these variations. Indirect sampling experiments examining cell
activity over long, curved movements implicitly sample vector fields of
spatial pds and can be used to choose between alternative coordinate
hypotheses from the pattern of path-dependent activity. We simulated
one such experiment (Hocherman and Wise 1991) under the
assumption of three kinematic coordinate systems (Cartesian spatial,
shoulder-centered, and joint angle) and found that joint angle
coordinates robustly fit the MI data better than either of the other
two coordinate systems.
These results do not imply that all MI cells encode movement direction
in joint angle coordinates. First, only three coordinate systems were
tested, and there may exist another coordinate system that fits the
data better than joint angle coordinates. Second, even if a majority of
cells within a given brain region represent movement direction in one
particular coordinate system, evidence (Crutcher and Alexander
1990) suggests that there will often exist other cells in the
same brain region that utilize different coordinate representations.
Third, a recent investigation (Wise et al. 1998
) demonstrates the capacity of motor cortex to rapidly reorganize its
response properties during adaptation to a series of differentiated visuomotor tasks, perhaps implying that the CNS solves motor control problems by implementing task-specific solutions that utilize task-dependent coordinate decompositions of the sensorimotor
transformation. Finally, the current analysis focuses on the
representation of movement direction but, as reiterated above, cell
activity likely reflects information about other movement variables as
well. A more detailed exploration of the functional dependence of cell activity on multiple movement variables is warranted for clarifying these and other data.
By looking at the coordinate system in which an individual cell encodes
movement direction, it becomes possible to assess how populations of
cells with similar coordinate representations are distributed across a
cortical area. Hocherman and Wise (1991) recorded in the
supplementary motor area (SMA), dorsal premotor cortex (PMd), and
ventral premotor cortex (PMv) as well as MI. In these other cortical
regions, a smaller percentage of cells responded preferentially to
curved movements. From the present analysis we infer that MI represents
movement commands in a coordinate system possessing a stronger joint
angle character than do the SMA, PMd, or PMv. This conclusion is
consistent with the findings of Scott et al. (1997)
.
Our study uses curved movements as a means to probe the structure of a
cell's vector field of spatial pds by indirectly sampling the
workspace. Another way to investigate vector field structure is by
directly sampling the workspace, and two prior studies
involving proximal arm movements fall into that category:
Caminiti et al. (1990) and Scott and Kalaska
(1997)
. In Caminiti et al. (1990)
, a 3-D
center-out task was performed from three distinct movement origins that
were colinear (normal to the saggital plane), spaced 10 cm apart, and
situated in a transverse plane cutting through the shoulders. Spatial
pds were found to change across the workplace by a statistically
significant amount. These changes were fit reasonably well by assuming
that the change in a cell's spatial pd matched the rotation of the
shoulder joint from one workspace location to the next. Since the
rotation of the shoulder joint from one workspace location to the next,
proceeding from left to right, is virtually equivalent in this task to
the rotation of the shoulder-hand axis (18 and 20° for the former as
opposed to 21.8 and 21.8° for the latter), the shoulder-centered
coordinates defined in this paper would fit the data about as well.
Lacking information regarding the movement trajectories in joint angle space (which is here necessary since an unconstrained arm operating in
3-D space is motor redundant), we were unable to simulate this paradigm
under the assumption of joint angle coordinates.
In Scott and Kalaska (1997), a monkey performed the
center-out task in two different postures (natural and abducted) that corresponded to the same end-effector location in space. They noted a
significant posture by direction interaction effect present in the
response properties of a majority of cells and demonstrated statistically that changes in a cell's directional preference were a
major contributing factor. The difference between the mean spatial pds
across the two arm orientations was significant for 48% of the 422 cells examined. Scott and Kalaska (1997)
modeled these
data using Cartesian spatial, joint angle, and joint torque coordinate
systems. They found that joint angle coordinates best fit the data. The
results were incompatible with the assumption of either Cartesian
spatial or shoulder-centered coordinates.
On the basis of our analyses as well as the analyses in Caminiti
et al. (1990) and Scott and Kalaska (1997)
,
Table 2 provides an evaluation of the
adequacy of the three different coordinate systems modeled in this
paper with regard to three experiments, each of which investigated
proximal arm cell activity during unloaded reaching movements:
Caminiti et al. (1990)
, Hocherman and Wise (1991)
, and Scott and Kalaska (1997)
.
|
The observations of Caminiti et al. (1990) and
Scott and Kalaska (1997)
may appear to contradict the
findings of Schwartz (1992)
, which investigated the
temporal discharge patterns of individual cells in motor cortex during
the tracing of sinusoids. It was found that cell discharge patterns
correspond closely (high correlation coefficient) to what would be
predicted under the assumption of a fixed spatial pd (i.e., Cartesian
spatial coordinates), once the time lag between the cortical signal and
its controlling effect at the periphery is taken into account. One
could conceivably interpret these findings as support for Cartesian
spatial coordinates, although Schwartz (1992)
does not
address the issue of coordinate systems and makes no claims in this
regard. The framework for coordinate analysis established in this paper
suggests that the results of Schwartz (1992)
do not
support or refute any coordinate system hypothesis. Differentiating
between coordinate systems requires 1) probing the
organization of spatial pds across a broad postural range that includes
both the central and peripheral portions of the workspace, and
2) comparing the results with those predicted by alternative
coordinate systems. The height of the sinusoids in Schwartz
(1992)
ranged from 3 to 12 cm, and their horizontal extent was
roughly 15.5 cm. Although detailed postural information was not given,
the dimensions, location, and orientation of the 2-D workspace indicate
that the monkeys were able to trace the sinusoids without moving the
contributing joints through more than a relatively small fraction of
their full range of motion. Such was not the case in Caminiti et
al. (1990)
or Hocherman and Wise (1991)
, where
the larger dimensions of the workspace (30 × 10 × 10 cm and
20 × 20 cm, respectively) required a broader range of joint
angles that would make changes in a cell's spatial pd more easily discernible.
Spatial pds will not vary significantly over small postural changes
under any of the three coordinate systems considered, so it
is not surprising that Cartesian spatial coordinates engendered good
correlations in Schwartz (1992). Further, a definitive
analysis must compare correlations under the assumption of Cartesian
spatial coordinates versus correlations under the assumption of
alternative coordinate systems. Such comparisons are as important as
broad workspace sampling, and without them, one cannot make strong
inferences about coordinate systems.
Although our analysis has focused on proximal arm movements, the
approach can also be applied to the investigation of distal movements.
Kakei et al. (1999) performed a direct sampling
experiment on movements restricted to the wrist and hand. Preferred
directions of MI cells during a latency interval (final 100 ms before
movement onset) were determined in three different wrist postures:
pronated, supinated, and midway between pronated and supinated. On the
basis of the relative size of posture-dependent shifts in cellular pds, Kakei et al. (1999)
divided the population into a class
of "muscle-like" (sizeable pd shift) cells (32%) and a larger
class of "extrinsic-like" (limited pd shift) cells (50%). At least
two considerations argue for being cautious in treating these
extrinsic-like cells as truly extrinsic. First, roughly 60% of
extrinsic-like cells exhibited large posture-dependent gain changes, a
response feature analogously found in muscle activations but not
expected of a true extrinsic coding scheme. Second, as shown by
Scott and Kalaska (1997)
and by our simulations, not all
cells that encode direction in a purely intrinsic coordinate system
will exhibit significant shifts in their pds; it depends on the
specific internal pd. More research is needed to clarify the
implications of the important results in Kakei et al.
(1999)
.
The generality of the vector field framework makes it applicable to all
well-defined coordinate frames, including kinematic, kinetic, and
hybrid kinematic-kinetic frames. For example, a plausible hypothesis is
that motor cortical cell activity reflects combinations of muscle
shortening rates (Mussa-Ivaldi 1988). Support for the idea of bound muscle synergies comes from post-spike facilitation studies (Fetz and Cheney 1978
, 1980
; Fetz et al.
1976
), which suggest that motor cortical cells typically
project to motor neurons associated with more than one muscle.
Generating predictions for muscle-length coordinates requires a
detailed biomechanical model of the arm-muscle system and knowledge of
the recruitment patterns by which multiple muscles are synergistically
innervated by individual cortical cells. Extending the framework to
consider a kinetic, muscle-force based coordinate system remains
desirable, particularly for MI, because muscle forces ultimately drive
movements and because anatomical and physiological considerations have
long shown MI to provide prominent cortical input to the spinal cord
and motoneurons. Further, studies have established correlations between
MI cell activity and force for multijoint movements (Bullock et
al. 1998
; Kalaska et al. 1989
; Sergio and
Kalaska 1998
). Unfortunately, an analysis of a muscle force
coordinate system would require, in addition to a detailed
biomechanical model and knowledge of cortical recruitment patterns, an
understanding of all relevant elastic, inertial, and viscous forces
involved in center-out hand movements. Given the difficulty in gauging
these forces (which are intricately composed, highly complex, and
posture-dependent), reliably constructing an explicit muscle-force
based coordinate system is an exceedingly difficult task. While we did
not attempt to model such a coordinate system, skeletomuscular
considerations suggest that a vector field of spatial pds based on
muscle forces would possess a highly curved structure. A more
efficacious analysis of kinetic coordinate systems, however, can be
achieved by applying the vector field framework to an analysis of
postural variations of a cell's preferred direction of force in
isometric tasks (Sergio and Kalaska 1997
). Finally, the
framework can be extended to the analysis of a system of noncanonical
coordinates defined by a set of motor primitives like those proposed by
Bizzi et al. (1991)
to explain the results of
stimulating intermediate gray matter in the spinal frog. If a cell
controls a fixed linear combination of these force fields, a vector
field of spatial pds that represents either movement directions or
force directions can, in theory, be constructed and subsequently
evaluated in either movement or isometric tasks.
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APPENDIX |
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Joint angle coordinate vector fields of spatial pds
The forward and inverse kinematic equations of a 2-DOF planar
arm are
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(A1) |
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(A2) |
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(A3) |
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(A4) |
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(A5) |
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(A6) |
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(A7) |
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(A8) |
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(A9) |
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(A10) |
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(A11) |
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(A12) |
The rest of the APPENDIX, including simulation details and additional derivations, can be found at http://www.cns.bu.edu/pub/ajemian.
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ACKNOWLEDGMENTS |
---|
The authors thank R. Amos for valuable assistance in the
preparation of the manuscript and P. Cisek, M. Cohen, R. Raizada, M. Giamporcaro, and S. Wise for helpful comments about the data and model.
We also thank S. Wise for help with original analyses on data from the
study of Hocherman and Wise (1991).
This work was supported by the Defense Advanced Research Projects Agency (ONR N00014-92-J-4015), the Defense Advanced Research Projects Agency and the Office of Naval Research (ONR N00014-95-1-0409), the National Science Foundation (NSF IRI-90-00530 and NSF IRI-97-20333), and the Office of Naval Research (ONR N00014-91-J-4100, ONR N00014-92-J-1309, ONR N00014-94-1-0940, and ONR N00014-95-1-0657).
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FOOTNOTES |
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Address for reprint requests: D. Bullock or S. Grossberg, Dept. of Cognitive and Neural Systems, Boston University, 677 Beacon St., Boston, MA 02215 (E-mail: danb{at}cns.bu.edu or steve{at}cns.bu.edu).
Received 21 December 1999; accepted in final form 19 July 2000.
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REFERENCES |
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