Download Somatosensory Cortical Activity in Relation to Arm Posture

JOURNALOFNEUROPHYSIOLOGY
Vol. 76, No. 4, October
1996. Printed
in U.S.A.
Somatosensory Cortical Activity
Nonuniform Spatial Tuning
S. I. HELMS TILLERY,
J. F. SOECHTING,
AND
Graduate Program in Neuroscience, and Departments
Minneapolis, Minnesota 55455
SUMMARY
AND
in Relation to Arm Posture:
T. J. EBNER
of Physiology and Neurosurgery,
CONCLUSIONS
1. Single unitary activity in primate somatosensory cortex (SI)
was recorded while monkeys maintained a range of static arm
postures. Unit discharge was related to parameters defining the
posture of the arm by multiple linear regression techniques.
2. Two monkeys were trained to grasp a manipulandum
presented at locations distributed throughout their workspace. The
discharge of single units in SI was recorded for 3 s while the
monkeys maintained contact with the manipulandum and the mean
discharge rate over this hold time was related to the location of
the hand and to the shoulder and elbow joint angles of the arm.
3. Unitary activity of 17 1 neurons in the proximal arm region
of areas 3, 1, and 2 was recorded during the task. Of the total, 78
neurons had activity that varied with the location of the hand in
space. Neuronal discharge typically varied monotonically with the
target location, reaching a maximum at the borders of the workspace. The discharge rate in most of these neurons varied with
both shoulder and elbow angles.
4. Discharge rate was related to the hand’s location along three
axes by means of a polynomial fit. In approximately half of the
neurons, activity varied significantly only for displacements along
a single axis in space. However, many neurons exhibited nonlinear
relations between hand location along this preferred axis and discharge rate. Discharge rate did not vary for displacements of the
hand in the plane perpendicular to this preferred axis (null plane).
5. In other neurons, discharge rate varied for hand displacements in a plane, i.e., along two perpendicular axes. Displacements
of the hand along the axis perpendicular
to this plane (null axis)
did not affect the discharge rate. In only a small minority of neurons
did discharge rate vary for hand displacements along all three axes
in space.
6. The distribution of the sensitivity of the neural population to
hand displacements along arbitrary directions in space was not
uniform. On average, hand displacement along a vertical axis led
to the smallest modulation of neural discharge, and displacement
of the hand along the anteroposterior direction led to the largest
modulation of activity.
INTRODUCTION
The aim of movement is to act on or interact with the
environment. Controlling such an interaction, however, is
extremely difficult in the absence of information about the
relations between the movement and the environment.
Whether tracing the edges of an object or simply trying to
stand upright, one is heavily reliant on sensory inputs to
monitor the ongoing status of action.
This is certainly true for the control of arm movements
by the CNS. When the actions to be performed are either
rudimentary or well rehearsed, then performance in the absence of sensory information (either somatosensory or vi0022-3077/96
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Copyright
University
of. Minnesota,
sual) can be quite adequate. If the task is changed or the
starting arrangement is altered, however, performance degradesquickly. For example, monkeys with bilateral deafferentation of the arms can perform conditioned movements
about a single joint (Knapp et al. 1963) , planar pointing
movements (Polit and Bizzi 1979)) and even three-dimensional pointing movements (Taub et al. 1975). If the starting
position of the arm is changed without the animal’s knowledge, however, the animal is no longer able to reach to a
visual target (Polit and Bizzi 1979). Even in well-rehearsed
movements, careful analysis reveals performance flaws after
deafferentation. Humans with large-fiber sensory neuropathy
produce characteristic errors during both horizontal and vertical planar movements, even when the movements are well
practiced (Ghez et al. 1990; Sainburg et al. 1993) .
These observations highlight the importance of information arising from somatosensorsin the control of movement.
Despite the intimate involvement of this information in the
control of movement and the impact of somatosensoryinput
on movement-related signals in the CNS, the question of
how the information is processed and used remains unresolved (Hasan 1992; Hasan and Stuart 1988). One use of
somatosensoryinputs may be to determine the static posture
of the limb. For example, a representation of current arm
posture is necessaryto determine the direction and amplitude
of an intended movement (Flanders et al. 1992; Helms Tillery et al. 1991; Soechting and Flanders 1989). This representation could be provided by the somatosensory system.
Consistent with this viewpoint, physiological studies
throughout the somatosensory system have revealed discharge related to static limb postures. Peripheral afferents
(Edin 1992; Edin and Abbs 1991; Hulliger et al. 1979;
Knibestol and Vallbo 1970)) dorsal column nuclear neurons
(Millar 1972; Surmeier and Towe 1987), thalamic relay
neurons (Mountcastle et al. 1963; Poggio and Mountcastle
1963)) and somatosensory cortical (SI) neurons (Cohen et
al. 1994; Gardner 1988; Gardner and Costanzo 1981; Jennings et al. 1983; Mountcastle and Powell 1959) all show
regular changes in activity related to the posture of the arm.
Most of these studies involved changes in the posture of a
single joint. Unit activity at all levels is monotonically related to changes in joint angle, reaching a maximum at the
anatomic limits of joint rotation.
Costanzo and 6ardner ( 1981) showed that the discharge
of some SI neurons is related to the position of more than
one joint. Many muscles cross more than one joint, and
muscle spindle afferents, are thought to contribute substantially to encoding limb posture (Goodwin et al. 1972; Mc-
0 1996 The American
Physiological
Society
2423
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S. I. HELMS
TILLERY,
J. F. SOECHTING,
Closkey 1978). Because skin can also be expected to be
deformed locally by rotation about more than one joint (Edin
1992; Edin and Abbs 1991; Edin and Johansson 1995)) one
might expect that cutaneous afferents would also be sensitive
to changes in the posture of the limb as a whole.
Neurons in cortical areas other than SI are also tuned to the
position of the hand in space. Georgopoulos and colleagues
(Georgopoulos and Massey 1985; Georgopoulos et al. 1984;
Kettner et al. 1988) found that most cells in motor cortex
and area 5 had a discharge that was linearly related to static
hand position in space. Each cell’s activity was modulated
maximally by a translation of the hand in one direction in
space (the “best direction” ) . Thus static activity exhibited
a tuning that was similar to that found in the same areas
for active movements. Interestingly, although the static best
directions in areas 4 and 5 appeared to be distributed uniformly, Kalaska et al. ( 1983) found a nonuniform distribution of best directions during active movements, with a preference for movements directed anteriorly or medially. The
interpretation of the cosine tuning of neural discharge has
been controversial,
because a variety of kinematic and kinetic parameters may also exhibit cosine tuning under these
experimental conditions (Georgopoulos
et al. 1984; Helms
Tillery et al. 1995; Kalaska 199 1; Lacquaniti et al. 1995).
The question thus arises: how does the spatial tuning of
neurons in SI compare with the tuning that has been ascribed
to motor cortex and area 5? This question has been addressed
by Kalaska and colleagues (Cohen et al. 1994; Prud’homme
and Kalaska 1994; Prud’homme et al. 1994) with the use
of the same center-out task as the studies cited above. Those
researchers found that discharge rate in SI also exhibits a
cosine tuning to the location of the hand in a plane. However,
the distribution of the cells’ preferred directions was nonuniform, with a preponderance of the cells being tuned in the
anteroposterior direction.
In this paper we extend the analysis of the spatial tuning
of neurons in SI to three-dimensional
displacements of the
hand in space. We also used a larger range of hand locations
than previous studies, because such an experimental design
has the potential to provide a fuller description of a given
cell’s discharge (Fu et al. 1993). In agreement with previous
results, we found that SI discharge is related in a monotonic
fashion to hand location, but that this relationship can be
nonlinear. In particular, the discharge of each neuron only
encodes passive hand displacement along a specific direction
in space, or in a specific plane. In agreement with and extending previous findings, we found that cell discharge was
modulated maximally with hand displacement in the anteroposterior direction, and to lesser extent by displacement in
the vertical or mediolateral directions.
Abstracts of a portion of these results have been presented
previously (Helms Tillery et al. 1992, 1994; Soechting et
al. 1992).
METHODS
Overview
Two young female rhesus monkeys (Macaca mulatta) were
trained to grasp a manipulandum
placed in arbitrary locations in a
three-dimensional
workspace by a robot arm (UMI RT-100 or
Microbot
Minimover) . Because the manipulandum
provided
postural support, this task requires little tonic activation of arm
AND
T. J. EBNER
muscles (Helms Tillery 1994). Once the monkey had been trained,
the discharge of neurons in SI was recorded while the monkey
maintained static postures. The recorded discharge was then related
to the manipulandum
location as well as the posture of the arm
(shoulder and elbow angles) by means of linear regression techniques. Data were recorded from both left and right hemispheres,
contralateral to the arm performing the task. For convenience of
presentation, all data from the right hemisphere/left
arm are reflected across the midline.
Task and equipment
The task required monkeys to grasp a manipulandum consisting
of either a small sphere or a 1-cm-diam bar that was oriented
vertically. The locations, contained within a spherical wedge in
front of the monkey’s shoulder, were presented in a random block
design from two lists. The first list contained six targets: two located 5 cm in front of the monkey’s shoulder and 5 cm above or
below the shoulder, and four at the corners of a square lying in
the frontal plane at the limits of the animal’s reach. The second list
contained targets at 3-cm intervals throughout a volume contained
within 45’ to the left and right of the shoulder and 45’ above and
below the shoulder. The nearest targets were located 5 cm from
the monkey’s shoulder, and the farthest were at the limit of the
monkey’s reach (25-30 cm). This volume is referred to as the
monkey’s “workspace.”
Three colored light-emitting
diodes (LEDs) were mounted on
the manipulandum.
A trial was initiated by lighting a red LED,
cuing the monkey to press a 4-cm-diam button located near its lap.
While the monkey pressed the “hold”
button, the robot moved
the manipulandum
into position. Then a second cue lamp was lit.
A yellow lamp cued the monkey to grasp the stationary manipulandum and maintain contact for 3 s ( “grasp”
task). A green lamp
cued the monkey to press a button on the manipulandum
(Helms
Tillery et al. 1995). If the task was performed correctly, the monkey was rewarded with a drop of juice.
In this paper we describe the discharge observed in the grasp
task, where the monkey was able to receive support for the weight
of the arm by grasping the manipulandum.
Interspersed among the
trials in which the monkeys grasped the manipulandum were other
trials in which they were required to press a button on the face of
the manipulandum
(press) or grasp the handle after it had been
rotated into an oblique orientation (grasp oblique). Results from
these paradigms are described elsewhere (Helms Tillery 1994).
Behavioral analysis
The posture of the arm during these tasks was analyzed by a
video based system (Motion Analysis), with markers placed at the
shoulder, elbow, wrist, and the manipulandum.
Arm posture was
defined in terms of segment orientation angles (Soechting and Ross
1984), namely the yaw and elevation of the upper and forearm
segments. One angle (7 for the upper arm, a for the lower arm)
defined the yaw angle, measured in the horizontal plane, and the
other (0 for the upper arm, 0 for the forearm) gave the elevation,
measured in the vertical plane. The video derived data were used
to analyze the relations between target location, task, and arm
posture as described by the four orientation angles. The results of
this analysis are described in detail elsewhere (Helms Tillery et
al. 1995 ) . In the task described in this paper, the monkeys tended
to hold the manipulandum with the arm held within 10’ of a vertical
plane (cf. the natural posture of Scott and Malaska 1995 ) .
Surgical preparation
Once trained, the monkeys were outfitted with chambers for
chronic recording with the use of techniques previously described
(Fu et al. 1993). The monkeys were deeply anesthetized with
POSTURE-RELATED
ketamine (20 mg/kg ) and xylazine ( 1 mg/kg ) and placed in a
stereotactic frame. The skull was exposed by midline incision and
an 18-mm craniotomy was made at 7 mm anterior, 16 mm lateral
(Snider and Lee 196 1) . A metal cylinder was then affixed to the
skull with cranial screws and sealed with cranioplasty. During
postoperative recovery, the animals received analgesics (Nubane
or Buprenex) as needed and were kept on oral Amoxycillin
for a
period of 2-4 wk.
The monkeys were maintained
on a water-restricted
diet
throughout the training and recording periods. The animals typically received between 150 and 300 ml of water while working,
and supplements of fruit. The animals did not receive water while
in their home cages on any day that they worked, although food
was available ad libitum. The animals were cared for in strict
accordance with National Institutes of Health guidelines for the
care and use of laboratory animals.
Physiological methods
During recording sessions, the animals’ heads were fixed and
the discharge of cortical neurons was recorded with the use of
tungsten electrodes (impedance 5- 10 MO). Once a candidate neuron was isolated, several samples of the action potential were overlain on a computer screen. If the waveform was stable, and the
neuron showed no evidence of injury discharge, its chamber coordinates and the waveform of a single action potential were recorded,
along with the cell’s receptive field characteristics.
An experiment began with repeated presentation of the small
block of six target locations (see Fig. 2). The neural activity was
recorded during the 3-s period in which the animal held the manipulandum. As the experiment progressed, a cumulative average of
the cell’s discharge rate within fixed portions of the workspace
was displayed as a color-coded map on a computer monitor. This
permitted us to determine 1) whether the cell’s discharge was
modulated in relation to changes in arm position and 2) whether
these changes were consistent with repeated trials. If the cell’s
discharge showed both of these characteristics after two or three
repetitions of each location, the larger block of target positions
was presented. These targets were presented in a randomized block
design, and each cell’s discharge was recorded for 200-300 trials
or until isolation was lost. Activity was recorded as spike times at
3-kHz resolution throughout the 3-s hold period, and stored on a
trial-by-trial basis for later analysis. In each of the hemispheres,
recording was continued 5 or 6 days/wk for 3-6 mo.
Receptive Jield characterization
As each neuron was isolated, its receptive field properties were
determined and noted. Response type was classified either as “cutaneous” (brisk response to touch or stroke of a light brush or the
handle of the brush) or “deep” (no cutaneous responsiveness, but
response to rotation of a joint or palpation of a joint or muscle).
Note that the cutaneous classification was not exclusive: a neuron
classified as cutaneous could also have had a deep responsiveness.
All neurons described here had receptive fields on the arm.
Anatomic methods
Two methods were used to identify the relationship between the
coordinates of the recording chamber and locations within the cortex. In two hemispheres, several small lesions were produced by
passing current through the recording electrodes. In the remaining
hemisphere, five pins were placed into the cortex in the shape of
a cross centered in the chamber. In each case, the locations of the
marks provided both the X and Y directions of the chamber on the
cortex, and the vertical direction of travel for electrodes in the
chamber. After recording was completed, each monkey was deeply
anesthetized with an intramuscular injection of 0.5 ml xylazine and
ACTIVITY
IN
SI
2425
0.5 ml ketamine. The animal was perfused via the heart with normal
Ringer solution followed by Zamboni’s fixative. The brain was
removed, photographed, blocked, and placed in Zamboni’s fixative
for several days. The blocks were then infiltrated with sucrose and
cut into 40-pm frontal sections on a freezing microtome. Each 10th
section was mounted and stained for Nissl substance with the use
of either cresyl violet or thionin. The slides were carefully checked
for lesion sites, identifying as many as possible. The identified
lesions were localized by cross-checking the chamber map with
the photographs of the cortical surface.
Data analysis
The first step in the analysis of each cell’s discharge was to
class the cell as either modulated or nonmodulated
with changes
in arm posture. This determination
was made by an analysis of
variance. Because most of the recording sessions did not involve
repeated measures, we used multiple linear regression techniques
to determine whether each cell was modulated with hand location.
To this end the firing rate for each trial was averaged over the 3s sampling period and fitted to cubic equations in Cartesian coordinates of the hand
f = w, y, 2)
(0
where fis the discharge rate of the neuron, P is a cubic polynomial
having 20 terms, x is the anterior direction, y is medial, and z is
up. The 95% confidence intervals for each term were computed
and the terms for which this interval bracketed zero were discarded.
The remaining terms comprised the “best-fit”
equation for that
neuron. The regressions were performed with the use of a singular
value decomposition-based
method (Press et al. 1988). For each
neuron, an r2 value was computed as
r 2 = 1 - [X(X
-jg”/C(f;
-$>2]
(2)
where 5 was the firing rate on the ith trial, 7 was the mean firing
rate over all the trials, and j was the firing rate predicted for the
ith trial by the regression polynomial. Next, an F value was computed as
F = (r2/k)l[(l
where k was
was y2 - (k
which the F
as modulated
- r2)lm]
0
the number of coefficients with nonzero values, m
+ l), and y2 was the number of trials. Cells for
value was significant to the 0.05 level were defined
in the task.
Monotonicity
To provide a measure of the extent to which firing rate was
monotonically
related to hand location, the target location giving
the highest discharge rate was identified. Target locations were
first binned according to their height. The trials were then divided
into three subsets, one for each bin, and the polygon that defined
the perimeter of the sampled workspace in each bin was computed.
The perpendicular
distance from the point of maximal discharge
to each of the edges of the polygon was then computed. The smallest such value gave the distance from the point of highest discharge
to the edge of the sampled workspace.
Fitting to multiple axes
In some cases, a cell’s spatial tuning was restricted to a modulation along one axis in space. Displacements in a plane perpendicular to this axis did not affect neural discharge (null plane). In other
cases, neural activity was modulated by displacement of the hand
along two perpendicular axes (i.e., in a plane), and displacements
2426
S. I. HELMS
of the hand in a direction
tion) did not affect neural
The analytical procedure
with a linear model of the
TILLER-Y,
perpendicular to this plane (null direcactivity.
to define the best-fit axis or axes began
discharge rate
f = CC) + Cl x + c,y + c3z
This fit provided
charge
J. F. SOECHTING,
(4)
the axis x’, which best described the cell’s disx’ = (c,, c2,c&c:.
+ c; + cg)
(5)
Thus x’ is a unit vector pointing in a direction given by the X, y,
z coefficients from Eq. 4. Discharge rates were then fitted to a
cubic polynomial along this axis
f = ho + b,x’
+ b2xt2
-I- b3xr3
M-C y’>
f=
i(x’, y’, z’)
(7)
This procedure resulted in three equations (Eq. 6 and 7) relating
the discharge rate of the neuron to the location of the hand. In
each instance, terms whose coefficients did not differ significantly
from zero were discarded. In order to classify each cell as requiring
one, two, or three axes to account for the observed changes in
discharge rate, the r* values for the three fits were compared.
We used the criterion that for cells described as being tuned
along a best direction or in a modulation plane, the reduced fits
should account for 290% of the variance accounted for by the
complete fit.
Joint angles
We can also express the discharge directly in terms of joint
angles, because there is a unique relation between the location of
targets in space and the angles defining the posture of the arm
(Helms Tillery et al. 1995). One likely starting point is to classify
each neuron as being related to either the shoulder or the elbow.
To make this assessment, the data were fitted to a cubic polynomial
in elbow flexion (4)
f
= a0 + a,+
i- a242 + a3+3
(8)
The Y* obtained from this fit was then divided by the y2 that resulted
from the fit to the full cubic equation (Eq. 7). To fit the discharge
rate to single axes in joint space, we regressed the discharge rates
of the neurons to linear functions of the joint angles. This analysis
provided a single axis a’ that captured the greatest component of
variance in the same way as the first axis in Cartesian coordinates
(Eq. 5), except this axis had four terms, one for each of the joint
angles
a’ = (c,, ~0, c,, “~V~(C;; + c; -I- c: -I- c$)
1.
and receptive
T. J. EBNER
Enumeration of cells according
Jield characterization
Area
No. of
Cells
No. of Cells
Modulated
3a
3b
1
2
215
Total
8
42
55
38
28
171
5
19
24
13
17
78
Numbers
in parentheses
during the task.
refer
to cortical
Cutaneous
Receptive
Field
2
16
24
20
25
87
(0)
(8)
(10)
(10)
(17)
(45)
to cells whose
location
Deep Receptive
Field
5
25
27
12
2
71
activity
(4)
(11)
(14)
(2)
(0)
(31)
was modulated
(6)
To determine whether the discharge rate was modulated in directions perpendicular to this best axis, we used the following procedure. We first defined 18 planes containing the vector x’, each
separated by 10’ increments, and fitted the data with the use of a
cubic polynomial on two variables, X’ as defined above, and y I)
which was always perpendicular to x ‘. For each of these 18 planes
an r* value was computed. The y ’ giving the highest value for
Y* was chosen to be the second axis defining the modulation
plane. Finally, the data were fitted to a cubic polynomial along all
three axes
f=
TABLE
AND
(9)
Distribution of tuning directions
As we will show, different neurons are tuned along different
numbers of spatial axes. Given this result, displaying the distribution of the cells’ best axes does not fully capture the spatial properties of the population. The difficulty is to combine the results for
those cells modulated along single axes with those modulated in
two spatial directions. We have addressed this issue by reframing
the question: for a hand displacement along a given direction, what
proportion of the neurons is significantly modulated? We made
the assumption that, for cells modulated along a single axis, the
responsiveness to displacements along any other axis is proportional to the cosine of the angle between the two. For neurons with
null axes, we assumed that the cells’ responsiveness would be
proportional
to the projection of the displacement onto the cells’
modulation planes. This is equal to the absolute value of the sin
of the angle between the displacement and the null axis.
To present these data, we plotted the results on an equal-area
projection
of an upper hemisphere
(Watson 1983; see also
Schwartz et al. 1988). In this type of projection, equal areas on
the hemisphere are accorded equal areas on the projection. The
pole is represented as the center of the circle, and the equator is at
the circumference. This provides for a representation with minimal
distortion near the pole and maximum distortion near the equator.
RESULTS
Data base
Table 1 gives a breakdown of the cells recorded from the
arm area of SI into cortical area, receptive field type, and
whether they were modulated with the location of the hand.
Of the 171 cells, 78 (45%) were significantly modulated
by hand position in space. The cells that were significantly
modulated came from pools with both cutaneous and deep
sensory modalities and were distributed primarily across
areas 3b, 1, and 2 and the transition zone between areas 2
and 5. As noted in METHODS,
the criteria used to determine
a cell’s response modality favored a designation of cutaneous: a determination of cutaneous modality did not exclude the possibility that a cell also had deep responsiveness,
whereas a determination of deep response was exclusive.
Therefore the slight preponderance of cutaneous responsivenessin the sample does not necessarily imply a preponderance of recording sites in areas of cortex that are largely
cutaneous.
Figure 1 shows a coronal section through the central sulcus in the region of the recordings. Each cortical area is
indicated by a line at the gray-white border, and the lines
perpendicular to the cortical surface denote the demarcations
between areas. These were determined with the use of standard criteria (Jones et al. 1978; Powell and Mountcastle
1959; Prud’homme et al. 1994). The arrowheads in Fig. 1
denote two marker lesions that are indicated as asterisks in
the camera lucida drawing of this section (46) showing a
reconstruction of cell locations for this hemisphere.The cells
POSTURE-RELATED
ACTIVITY
IN
SI
2427
IOmm
FIG. 1. Section through
recording
sites showing location of neurons and lesions in 1 hemisphere.
A single 40-pm coronal
section through
the central sulcus is shown in the area of the recording
sites. Arrowheads:
electrolytic
lesions used for
localization.
Lines perpendicular
to the cortical surface: boundaries
between cortical areas. Closed circles: locations of neurons
whose activity was modulated in relation to arm posture. Open circles: locations of neurons whose activity was not modulated.
Asterisks:
marker lesions.
(indicated by circles) were all located in a medial-lateral
zone containing the forelimb representation in SI. Closed
circles denote cells whose activity was modulated in this
task. We did not record from neurons in the more medial
strip that has been described as representing the entire forelimb sequentially from the shoulder to the hand (Pons et al.
1985a,b).
Spatial modulation
A representative example of the discharge recorded from
a single cell is shown in Fig. 2. Located in area 3b, this
neuron had a deep receptive field and discharged briskly to
shoulder extension as well as to elbow flexion. The figure
was constructed by grouping all the trials for which the target
location fell within 2 cm of one of six points in space. Rasters
were then constructed showing the discharge recorded at
each location, and plotted with a schematic representing the
arm configuration for each target, as viewed in perspective
from behind the monkey’s shoulder. The tonic discharge in
this neuron was modulated with target location, the firing
rate being highest when the arm was located at low and
lateral positions. As the elevation and azimuth angles to the
target increased, the firing rate of the neuron decreased. The
coefficient of variation (ai/ ti, where ti is the mean spike
interval on the ith trial and cri is the SD of the spike intervals
on the ith trial) for this neuron averaged over all the trials
was 0.71. The average value for 52 significantly modulated
neurons so analyzed was 0.89.
To quantify the relation between the neuron’s discharge
rate and the location of the hand, the mean discharge rate
over the 3-s sampling period was fitted to a cubic polynomial
(i.e., Eq. 1) in the Cartesian parameters defining hand location, and the resulting best-fit equation was used to construct
a plot of the firing rate as a function of hand location. The
outcome of this method is illustrated in Fig. 3 with the use
of all the available data from the same cell as in Fig. 2 (ceZZ
frOlO0). In Fig. 3, left, are three perspective plots giving
the firing rate of the cell as a function of hand position, the
data having been binned according to the height of the target.
As with the rasters shown in Fig. 2, the discharge rate of
cell fro1 00, represented by the height of the perspective
grid, was greatest for targets located laterally and below the
shoulder. The plots in Fig. 3, right, are the predictions of
the polynomial fit. These plots are smoother than those in
Fig. 3, left, for two reasons. First, the points represented all
lie on the indicated horizontal plane, so there is no effect of
combining data from different heights. Second, computing
the linear regression filters out the random noise in the data.
Thus the perspective plots in Fig. 3, right, represent the
trends observed over the entire set of trials while omitting
the variations in discharge rate that were unrelated to the
changes in arm posture. The same general trends in the
relation between discharge rate and hand location can be
observed in Fig. 3, right and left.
Figure 3, bottom, is a plot of the residuals (the component
of the observed discharge not accounted for by the model)
as a function of the discharge rate of the neuron. This plot
shows that the distribution of the residuals was unbiased:
residuals were evenly distributed about the predicted dis-
2428
S. I. HELMS
TILLERY,
J. F. SOECHTING,
AND
T. J. EBNER
Time (sets)
FIG. 2. Activity
of a representative
neuron at different
arm postures. Rasters denote the activity
of 1 neuron in the
somatosensory
cortex (ST) at 6 different
arm postures. Postures of the right arm and limb segments (S, shoulder; E, elbow;
H, hand) corresponding
to each hand location are indicated
schematically
as they would appear viewed from behind the
monkey’s
shoulder. In this neuron, the tonic discharge was highest for hand locations
below and lateral to the shoulder
(bottom
right).
Each raster was constructed
by combining
all trials for which the hand location
fell within 2 cm of the
indicated location.
charge rate, and did not vary as a function of the firing rate.
This measure of the goodness of the fit indicates that the
cubic polynomial provided a reasonable description of the
discharge rate. In addition, this fit gave r2 = 0.84 [P( 6,94) =
83.2, P < O.OOl] ; thus the fit accounted for >80% of the
variance present in the data.
Two features of these plots are representative of results
from all of the modulated cells. First, the discharge rate
changed monotonically as the hand was displaced along any
given line. For example, as the target locations changed from
left to right at a given height, the firing rate of the neuron
increased continuously. Second, the major component of the
change in discharge rate occurred with displacements along
a horizontal axis. The magnitude of the change in discharge
rate for vertical displacements was less than that occurring
along the horizontal axis. In addition, the fact that the surfaces of the plots were nearly planar is consistent with this
neuron’s discharge rate being linearly related to hand position. In terms of the multiaxis analysis discussedlater, this
neuron’s discharge could be specified by knowing the location of the hand along the single axis <0.06, -0.91, -0.41>
(Es* 5).
Figure 4 shows representative plots of the spatial tuning
for four neurons. As in Fig. 3, the firing rate is plotted
on three horizontal planes located above, at, and below the
monkey’s shoulder. In Fig. 4 the firing rate is represented
by shading and the three planes are drawn in the correct
spatial relation to each other. Each plane is bounded by
target elevation and azimuth angles of 545” and distances
of 6 and 20 cm from the shoulder. The monkey’s shoulder
is located at the intersection of the center plane with the
vertical grid line.
The features observed on these plots capture the spectrum
of spatial modulation observed in SI during the grasp task.
For most of the neurons, discharge changed monotonically
with hand location. Examples include the plots for the cells
shown at the top Zeft, bottom Left, and top right. In the plot
shown at the top left (same cell as in Figs. 2 and 3)) the
contour lines are parallel to each other along the anteriorposterior direction. The maximal changes in discharge rate
occurred as the target location was displaced in a direction
perpendicular to these lines, along a left-right axis. The
evenly spaced contour lines imply that this neuron’s discharge is related linearly to hand location in space.
The two plots shown at the top right (labeled fro 111) and
bottom left (labeled liOO52) have the characteristics most
representative of this pool of neurons. Cell $41 II, also located in area 3b, had a small receptive field with deep responsivenesslocated on the posterior aspectof the axilla, whereas
ceZ2ZiOO52was located in area 3a and also had a receptive
POSTURE-RELATED
Data Representation
Data
Cubic Fit
ACTIVITY
IN
SI
2429
discharge rate and hand location was more complex.
The
discharge of this neuron was not monotonically related to
hand location. Instead, there was a saddle point near the
center of the workspace, the rate of discharge increasing
with hand locations both posterior as well as anterior to this
saddle point. Of the 78 neurons with significant modulation
in the grasp task, -20% had a complex relation to hand
location like that shown at the bottom right. The remainder
appeared monotonic.
Monotonicity
One measure of whether a neuron’s tiring rate varies
monotonically with the location of the hand in space is the
distance from the edge of the sampled workspace to the
target location giving the maximum tiring rate (see METHODS).
For cells satisfying this criterion, this distance will be
zero: the target location giving maximal discharge rate
should be located directly on the edge of the sampled workspace. Figure 5 shows that in >50% of the cells maximum
firing rate occurred at the borders of the workspace. and in
all casesmaximum firing was within 3 cm of the boundary.
However, as seenfor cell,fr-0075 in Fig. 4, there is a limitation to this measure.It is possible for a cell to have maximal
Residuals
s
? .:”
2
0
vs. Predicted
Discharge
_ ‘,.,I ;;;.?.‘“. i,.., ,.
.,..: .._.
I. ...,..,.. _I:.. .:
.,‘.,
10
20
..
,”
30
PK. 3. Modulatton
of neural acttvtty
wrth hand locatton.
Left. data
from the same neuron as shown m Fig 2; height of perpecttve
plot denotes
mean tirmg rate. To construct thts figure. the workspace
war spin into 3
horrzontal
bms, and all the trrals for whrch the target fell wrthm a particular
bm were pooled. The mean herght of each bin relative to the shoulder 1s
mdtcated to the right of the plots. The monkey’s
shoulder is approxrmately
centered m the medial-lateral
dnection on the near-rrght
edge of the middle
<lab Each slab extends 25 cm antertorly
and 1-15 cm to each side of the
caglttal plane. As m Frg. 2, thus neuron’s dtscharge rate Increased as the
hand was located more laterally
or downward.
Riglrt: perspective
plot\
constructed from the tit to a cubic polynomral
\how the same general trends.
Bol~m.
resrduali fat thts fit. Residuals are normally
drstrrbutcd
about the
predicted dtscharge rate, and do not show any regular change as a functton
of the discharge rate. Both measures rndrcate that the model order was
approprtate.
field on the arm that was classified as deep. In the plots for
both cells fro111 and liO0.52,firing rate increasesmonotonically as the workspace is traversed, but the contour lines are
neither straight nor evenly spaced, indicating that firing rate
cannot be characterized by a planar model, as was the case
for cell,frOlOO. The data plotted at the bottom left are closer
to linear than those shown at the top right, but neither is
adequately described by a linear function. As described below, the data at the top right require two axes to capture all
the variance, whereas the data at the bottom left were well
described with the use of only one spatial axis.
The final plot, shown at the bottom right (cell frOO75,
located in area 1 with a large cutaneous receptive field on
the ulnar side of the arm, extending over the upper arm
and forearm), illustrates a neuron whose relation between
0
‘0
Spikes/sex
0
10
0
50
FIG. 4. Spatial discharge profiles for 4 representative
neurons. The discharge rate as a function of hand position is shown for 4 neurons. These
figures were constructed
with the use of a cubic polynomial
fit to the
experimental
data. Each panel shows activity in 3 horizontal
planes: at the
shoulder and 8 cm above and below the shoulder. Dashed grid lines: azimuth
angles of +45”, 0”. and -45”
and a distance of 20 cm. Except for the
example shown at the hottom right, neural activity changed monotonically
as a function of hand location. The spatial tuning of nelrron ,JrOrOO (the
same neuron depicted in Figs. 2 and 3) is planar, indicating
a linear relation
between hand position and discharge rate. The profiles for IIWTOIZS ,fiOI I I
and liOO52 are monotonic,
but not linear. Ne~on ,JrOO75, shown at the
bottom
right, has a nonmonotonic
spatial profile, but such neurons were
encountered
less frequently.
S. I. HELMS
Distribution
of Peak-Edge
TILLERY,
J. F. SOECHTING,
Distances
0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Distance (cm)
FIG. 5.
Distribution
of distances from the hand location at which neural
activity is maximal to the edge of the workspace.
For each neuron, the trial
for which the neuron fired maximally
was identified.
The distance between
that location and the edge of the sampled workspace
was then computed,
and the distribution
of all such distances is shown in the figure. The mode
of this distribution
is at 0 cm, as would be expected if most of the neurons
had monotonic
spatial discharge profiles.
discharge for targets at the edge of the workspace without
having a monotonic discharge profile. As mentioned before,
-20% of the cells had a spatial tuning that exhibited a saddle
point.
Spatial tuning along a single axis or in a plane
Many of the cells could be characterized by their responses to a displacement of the hand along a single axis.
In these instances, neural discharge could be predicted by
taking the projection of the hand location onto that axis. In
other instances, neural discharge depended on the projection
of hand location onto a plane. The analytical procedures
used to characterize response along a single axis or in a
plane have been described in METHODS,
the first step in the
procedure being to define the best direction. Figure 6 shows
the firing rates of the four cells illustrated in Fig. 4 as a
function of hand location along the best direction. The two
cells at the left (labeled fro100 and liO052) show a simple
relation between discharge rate and hand location along this
axis. The small amount of scatter of the data points indicates
that one axis could account for the modulation of neural
activity for these two cells. For cell frOIO0 (top left), the
fit along a single axis accounted for 98% of the variance
that was accounted for by the full three-dimensional fit. Furthermore, except for a threshold nonlinearity, the fit was
AND
T. J. EBNER
close to linear. The larger amount of scatter of the data
points at the bottom left (cell liOO52) indicates that a second
axis contributed to the cell’s modulation: the first axis accounted for 87% of the variance, the second axis accounted
for 11%. For the two cells shown at the right, the plots
exhibit wide scatter for any given point along the best direction. This indicates that the single axis has not captured the
variance present in the data. For cell fro1 11 (top right), the
first axis accounted for 67% of the variance, with the second
axis accounting for most of the remainder (32%). A similar
result holds true for cellfrOO7.5, the first axis accounting for
60% of the variance and the second axis for 39%.
The spatial tuning of the four cells we have examined in
detail (Figs. 4 and 6) can be better appreciated in Fig. 7,
in which we show the predicted firing rate plotted in the
modulation plane. The axes extending to the right are the
best direction (cf. Fig. 6) and the axis extending back and
to the left is the axis, perpendicular to the best direction,
that provided the greatest reduction in the variance unaccounted for by the single-axis model ( see METHODS).
In
agreement with the qualitative observations obtained from
Fig. 6, there is little modulation in firing rate along the
second axis for the two cells shown at the left. The reason
why the activity of the two cells at the right cannot be
described by modulation along a single axis also becomes
apparent. Along a direction close to the best direction, the
activity of these cells appears to be increase monotonically,
but the tuning along the second axis appears closer to bell
shaped.
Figure 8 shows the distribution of the fraction of the total
variance accounted for due to the cells’ modulation along
each of the three axes. For -40% of the cells, the first axis
accounted for X30% of the variance accounted for by the
full three-dimensional
fit. When cell discharge was fitted to
the projection of hand location onto a plane, the model could
account for >80% of the variance in 88% of the cells, and
for >90% of the variance in 75% of the cells. Therefore
the third axis contributed little to the modulation of neural
discharge. It is also clear that some cells can be adequately
characterized by their tuning along a single direction in
space, but that others cannot. In fact, for 22% of the cells
the first axis accounted for <50% of the variance.
The extent to which tuning along a best direction in space
could account for the modulation in the activity of SI neurons
did not appear to be uniform across areas. In areas 3b and
1, a single axis accounted for 80% of the total variance in
44 and 45% of the cells, respectively, whereas this proportion was much less in area 2 (25%) and in the transition
zone between areas 2 and 5 (27%). (For area 3a, in 2 of 5
cells, a single axis accounted for >80% of the variance.)
The analytical procedure that we used to fit the data has
an implicit rank ordering. One would expect the first axis to
contribute most of the variance, the second axis a lesser amount,
and the third axis even less. (Under ideal conditions, the proportion of the variance accounted for by the 1st axis should be
> l/3, and that for the 2nd axis <0.5. As can be seen in Fig.
8, this condition was not always satisfied; sometimes a single
axis did not give an adequate fit to the data.) The question
arises: to what extent can the distributions shown in Fig. 8
arise from a random process of tuning in three dimensions,
assuming a strict rank ordering? In other words, is the finding
that the tuning of many cells can be characterized by a best
POSTURE-RELATED
-15
-10
-5
-25
.
l
liO052
. .. .
.
.
l
.
.
.
.
.
.
.
.
l.
.
0.
a.
l
8
.*
. ..* .
.
-15
-10
-5
0
25
-
20
-
15
-
l
.
l
-20
-.72x - .05y - .7Oz
.06x -.91y -.41z
.
243 1
IN SI
10
5
0
ACTIVITY
l
10 -
.
.
.
.
.
.
-.33x -.94y -.08z
5
-
0
-
-.64x + .76y - .09z
FIG. 6.
Discharge rate as plotted against hand location along the neuron’s “best direction.”
The same data shown in Fig.
4 are plotted as a function of hand location along the best-fit axis for that neuron. The extent of scatter of data points indicates
the extent to which each neuron’s activity was modulated by hand displacement
perpendicular
to this axis. Activity
in neurons
frO100 and ZiOO.52 depended principally
on hand displacement
along a single axis, whereas activity in the other 2 neurons
(fro1 11 and frO07.5)
exhibited
considerable
variation
unrelated
to hand location along the best-fit axis. Discharge
rate is
stated in spikes/s.
direction and that of virtually all cells by a “modulation plane’’
a result of a chanceprocess,or doesit reflect neural processing
to organize information about limb posture in a particular
manner?
To answer this question, we performed a simple simulation. Fifteen hundred random numbers uniformly distributed
over the interval (0,l ) were generated in groups of three.
Each triplet of numbers was then normalized so that its sum
of squareswas equal to 1, and the three resulting numbers
were ordered from largest to smallest. This gave three distributions of 500 numberseach. The distributions of the squares
of these numbers are plotted as lines in Fig. 8. These are
the distributions one would expect for the variance accounted
for by each of the three axes if those distributions were
generated by a completely random process. According to
this simulation, one would expect a single axis to account
for 90% of the variance in only 9% of the cells, and 80%
of the variance in 20% of the cells. Two axes would suffice
in 65% of the casesto account for 90% of the variance, and
in 86% of the cases they would account for 80% of the
variance. Thus the finding that the third axis generally contributed <20% of the variance is to be expected by chance.
However, this simulation indicates that many more cells
were tuned to hand displacement along a single axis than
expected by chance. Compared against the Monte Carlo re-
sults, the first axis in the experimental data accounted for
more variance than predicted by a random process (Kolmogorov-Smirnov test for the difference between the distributions shown in Fig. 8, top, D = 0.228, IV = 68, P < 0.05).
According to the same criterion, the second and third axes
contributed less to the overall variance of the data.
Spatial distribution of best directions and modulation
planes
The tuning of the population of cells we recorded from
was not uniformly distributed in space.More cells had activity that was modulated according to the location of the hand
along the anteroposterior axis than along either the vertical
or mediolateral axes. Figure 9 shows the distribution of the
best directions for the cells, projected onto the vertical plane
at the top and onto the horizontal plane at the bottom. There
is a preponderance of cells whose preferred direction was
close to horizontal (top) and in the anteroposterior direction
(bottom). The number of cells that had maximal discharge
rates at distal hand locations and at proximal hand locations
was about equal. No clear distinctions in the tuning of cells
in different cortical areas are obvious from inspection of
Fig. 9.
Because the modulation of only a fraction of the cells
2432
S. I. HELMS
TILLERY,
J. F. SOECHTING,
AND
. ..l.
-.
z
-
_s--
T. J. EBNER
_*_.--
-. -. l . -.
l . -.
-.
si
s
__--
l . -.
. .
-....
__--
liOO52
-.-_a--- _.--
_--- _---
_.-_.--
:
:’
(
-..
--.’
--._ --.-
--._
FIG. 7.
Modulation
planes. Data from the same 4 neurons are now plotted along the best-fit plane. The best-fit axis is
plotted going to the right, and the 2nd axis extends back and to the left. Neurons frOlO0 and ZiOO.52 (left) show very little
dependence
on location along the 2nd axis, whereas neurons fro111 and fvO07.5 (right)
exhibit significant
modulation
along
the 2nd axis. Discharge rate is stated in spikes/s.
could be characterized by their tuning along a single spatial
axis ( see Fig. 8 ) , we also took into account the fact that
some cells could best be described as being modulated in a
plane. The equal-area projection in Fig. 10 takes this fact
into account. In this plot we illustrate the percentage of
cells whose discharge would be modulated as the hand is
’ laced (quasistatically)
in a particular direction in space.
rice again, it is clear that there is a maximum for forwardly
for backwardly
directed displacements of the hand
(79%)) and minima for displacements of the hand in the
mediolateral (56% ) and vertical (57% ) directions. To deterrnine the statistical significance of this difference, we performed a bootstrap simulation, sampling at random from our
data set with substitution. For the anterior direction, the 95%
confidence limits were 86% and 72% and did not overlap
those for the other two directions (65% and 64% at the upper
limit).
So far, we have shown that the firing rate of SI neurons
can be rel ated to the locati on of the hand in space. This does
not imply, however, that hand location in space is actually
encoded by these neurons. It is equally possible, and perhaps
more plausible, that these neurons actually encode parameters more closely related to the rotation of the elbow and
shoulder joints, i.e., parameters that are more closely related
to the sensitivity of the mechanoreceptors.
Joint rotation
angles are uniquely and monotonically related to the location
of the hand in space (Flanders et al. 1992; Helms Tillery et
al. 1995; Lacquaniti et al. 1995; Soechting et al. 1995) at
least for a particular task. It is possible that the neuronal
discharge could be described in a simpler manner by relating
it directly to joint angles rather than hand location. This
would constitute evidence that these neurons encode arm
posture rather than hand location.
Therefore we also computed the relation between neural
discharge and joint angles for each of the modulated neurons,
but we did not find that this description was any simpler than
the description we have provided above. In some neurons, a
simple relation can be seen between one or another joint
angle and static discharge rate, but this observation did not
generalize to all the neurons. As a first step in the analysis,
we tested whether some neurons were preferentially tuned
to shoulder rotation and others to rotation at the elbow joint.
To this end, we fitted the data to a cubic polynomial in
elbow flexion (Es. 8) and computed the variance accounted
for by this model as a fraction of the variance accounted for
POSTURE-RELATED
ACTIVITY
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.8
1.0
IN SI
2433
the shoulder and elbow joints. More generally, the orientation angles of the upper arm and forearm have proven useful
in describing limb kinematics under a variety of experimental conditions (Helms Tillery et al. 1995; Lacquaniti et al.
1995; Soechting and Flanders 1989). To determine whether
neuronal activity encodes orientation angles, we first plotted
the firing rates against each of these angles. A representative
example is illustrated in Fig. 12, with the use of the data for
cell frOl00 (see Fig. 4). The discharge of this neuron was
related to the yaw angles of both the upper (q) and lower
((Y) arms-increasing
with q and decreasing with (Y. This
finding is consistent with the results in Fig. 4, showing that
the discharge rate of the neuron increased with increasing
target azimuth (Helms Tillery et al. 1995; Lacquaniti et al.
1995). However, there is considerable scatter in each of the
plots, indicating that the neuron’s discharge depended on
more than one of the orientation angles of the arm.
As a next step in the analysis, we fitted the data to a linear
combination of orientation angles, determining the best direction in joint space (Eq. 9). Finally we fitted the data to
cubic polynomials in the four angles. The results of this
analysis for the same neurons as in Fig. 4 are illustrated in
Fig. 13. The data for the two cells shown at the left are
reasonably well correlated to a linear combination of oricnta-
80
Third Axis
40
0
0.0
0.2
0.4
0.6
Variance Accounted for
FIG. 8. Percent of total variance accounted for by each of 3 spatial axes.
Each histogram
shows the proportion
of the total variance that could be
accounted for by incorporating
the component
of hand location along that
axis in the linear regression.
The distribution
for the 1st axis is highly
skewed to the right, indicating
that the fit along the 1st axis generally
accounted for a substantial
portion of the variance. The 3rd axis provided
little or no improvement
to the fit, indicating
that the cells’ spatial tuning
could be adequately
described by the projection
of hand location onto a
plane. Lines: theoretical
distributions
expected if the 3 T* values are ordered
random deviates constrained
only to sum to 1.
by fitting neural discharge to hand location. The results of
this analysis are shown in Fig. 11. If most of the neurons’
modulation were related to motion at a single joint, one
would expect a bimodal distribution, with values clustered
about values of 0 and 1. For -35% of the cells elbow rotation
accounted for <lo% of the variance. However, for the remaining cells, the contribution of elbow rotation to the total
variance was widely distributed with no evidence of clustering. In general, rotation about both shoulder and elbow joints
contributed to the cells’ modulation.
One possibility that could account for neuronal activity
being related to both shoulder and elbow rotation is that it
encodes arm posture according to a different set of parameters. The angles defining the orientation of the lower arm
(Soechling and Ross 1984) depend on the rotation of both
Area
Backward
FIG. 9.
Polar distribution
of the best directions for the neural population.
Top: projection
of each of the units’ best direction onto the vertical plane.
Bottom: projection
onto the horizontal
plane. Units are segrated according
to cortical area. Note the preponderance
of neurons tuned to displacement
of the hand in the anteroposterior
direction.
2434
S. I. HELMS
TILLERY,
J. F. SOECHTING,
Modulation Distribution
Forw arcl
AND
T. J. EBNER
angles and only three parameters describing hand location,
the description of neural activity in terms of hand location
appears to be simpler. However, this should not be taken
to imply that the activity of these neurons encodes hand
location.
DISCUSSION
Backward
56%.60%
61% 65%
71% - 75%
H 76%-X0%
q
Activity of neurons in SI is related monotonically to the
static location of the hand in space, generally reaching a
maximum at the extremes of the workspace. In some neurons, activity was defined by the projection of the hand’s
location on a single axis in space (best direction), akin to
the code of movement direction that has been described for
neurons in other cortical areas (cf. Caminiti et al. 1991;
Georgopoulos 1995; Georgopoulos et al. 1988; Kalaska et
al. 1990). However, the activity of the majority of the cells
in SI was defined by the projection of the hand’s location
on a plane (modulation plane). Considered as a population,
the sensitivity of these neurons to a quasistatic displacement
of the hand in space is not uniform: more neurons are tuned
to a displacement of the hand in the anteroposterior direction
than to hand displacementsin the other two directions. This
last finding confirms and extends the findings of Cohen et
al. (1994) and of Prud’homme and Kalaska (1994). In the
following, we take up the implications of these findings and
the similarities and differences in the spatial tuning of neurons in SI compared with those in areas 4 and 5.
FIG. 10.
Spatial tuning of neural responses. Sensitivity
of the population
of neurons to displacements
of the hand along different
directions is shown
on an equal-area
projection
of the upper hemisphere.
This plot takes into
account that the discharge of some neurons was modulated
by hand displacement in a plane. The relative sensitivity
of the population
to displacements along the anteroposterior
direction (79%) was greater than the sensitivity to displacements
along the other 2 cardinal axes (56%).
tion angles. The activity of cell liOO.52 was most strongly
related to the yaw angles of the upper arm (coefficient for
17= 0.41) and forearm (coefficient for (Y = -0.89). This
is consistent with the observation that this cell’s activity
increased as the hand was displaced laterally (cf. Fig. 4),
corresponding to an increase in n and a decreasein c~. The
activity for cell fro100 (top Zeft) was mostly related to the
yaw angle of the upper arm (q), and to a lesser extent to
the elevation angles of both the upper arm (0) and forearm
(/3). Note that for both the cells at the left, there is considerable scatter of the data points, indicating that there was also
modulation of neuronal activity about joint axes perpendicular to the ones indicated in the plots. The data points for the
two cells at the right show a much greater amount of scatter.
In fact, a comparison of Fig. 13 with Fig. 6 (illustrating
results for the same 4 cells) shows that there is much less
scatter of the data points when activity is plotted along a
best direction in the Cartesian space of hand displacement
than when it is plotted along a best direction in the joint
space of the orientation angles.
The activity of our sample of neurons can clearly be described in terms of joint angles. However, this description
is no simpler than when the activity is described in terms
of hand location in Cartesian coordinates. Only a small portion of the neurons (6 of 78) was related to only one of the
orientation angles. In fact, becausethere are four orientation
0.2
0.4
0.6
0.8
1.0
Elbow Fit r2/ Complete Fit r2
FIG. 11.
Dependence
of neural discharge rate on changes in elbow joint
angle. The data for each neuron were fitted to a cubic polynomial
in elbow
angle, and the Y* for that fit was normalized
against the Y’ for a cubic fit
to the hand location. Those neurons that were related only to rotations about
the elbow would give a value of 1.0 for this measure. Those neurons that
were independent
of the elbow and responsive
only to rotations about the
shoulder would give a value of 0.0. The distribution
is skewed toward
neurons related to the shoulder (see text), but there are a substantial number
of neurons giving values (>0.2
and <0.8)
consistent
with sensitivity
to
rotation about both joints.
POSTURE-RELATED
ACTIVITY
IN
SI
2435
Yaw
Elevation
.
.
.
l
l
.
.
:
.
l *
l
.
:*
.
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l
.
.*
. .
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. . .
l
l
.
l
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0”
50”
:
.A
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l
.
100”
25 1
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0.
l
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.
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.
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8 10 &50 ’ ..
.
.
.
.
.
l
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.
.
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0.
.
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e
-20°
.
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O0
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:
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:
.
.
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.
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20"
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.
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4o"
20'
40"
60"
80'
loo0
12. Discharge
rate as a function of segment orientation
angles. The discharge rate of neuron frM00
is plotted as a
function of the segment orientation
angles. This neuron exhibited
a monotonic
relation between discharge rate and the yaw
angles of both the upper and lower arm segments, but the scatter in the plots indicates this neuron’s discharge depended on
the elevation angles as well. Discharge
rate is stated in spikes/s.
FIG.
Spatial tuning
larger range of target locations (Caminiti et al. 1991; Fu et
Our observation that the relations between discharge rate al. 1993, 1995).
Activity in a number of other cortical areassuch asprecenand target location tended to be monotonic agreeswith previtral
area 4 (Georgopoulos et al. 1986; Schwartz et al. 1988)
ous findings in both somatosensory thalamus (Mountcastle
et al. 1963) and SI (Cohen et al. 1994; Gardner and Costanzo and postcentral area 5 (Kalaska et al. 1990) as well as the
1981; Mountcastle and Powell 1959) as well as peripheral cerebellum (Fortier et al. 1989) has been described as being
afferents (Edin and Abbs 1991; Hulliger et al. 1979). In each cosine tuned. Before and during movement, neurons in these
of these reports, the discharge of tonically active neurons areas discharge at a rate that is related to the angle between
modulated monotonically with increasing joint angles. This the direction in which the hand is about to move and the
held true whether the neurons had receptive fields on the neuron’s best direction. A similar result has been shown to
finger, wrist, elbow, or shoulder joints. The joint angles of hold in these same areas for discharge occurring while the
the shoulder and elbow are monotonically related to hand hand is held at various locations in a two-dimensional worklocation (Helms Tillery et al. 1995; Lacquaniti et al. 1995; space (Fortier et al. 1989; Georgopoulos and Massey 1985;
Soechting and Flanders 1989). Therefore one could expect Kettner et al. 1988).
The question of such a vector code of movement direction
the discharge of SI neurons to vary monotonically with hand
in
area 5 has recently been reexamined by Lacquaniti et al.
position.
For the majority of cells, activity could not be character- ( 1995). Those researchers examined neural activity during
ized in terms of a best direction, either in the Cartesian space movements from a central starting position to eight targets
located at the vertices of a cube and found that a vector
of hand location or in the four-dimensional space of joint
angles. In this respect our conclusions differ from those of code could adequately characterize neural activity. However,
Cohen et al. (1994) and of Prud’homme and Kalaska when the starting location was shifted medially or laterally,
( 1994), who found that a cosine tuning of cells in SI pro- the best direction of most neurons was found to depend on
vided an adequate description of their results both during the starting position. Testing a variety of other coding
active movements and during the maintenance of a static schemes for these neurons, these investigators found that
posture. However, the difference between their results and one basedon distance and direction (elevation and azimuth)
ours is more apparent than real. They examined movements from the shoulder (Flanders et al. 1992) provided the most
to only eight targets in a plane, with the use of repeated parsimonious description. (There is a unique relation bemeasures,whereas we examined posture at locations distrib- tween elbow angle and distance from the shoulder, and Lacuted randomly throughout the workspace. Furthermore, they quaniti et al. used the elbow angle rather than distance in
restricted their analysis only to that portion of cells (-60their model.) Their study reveals similarities and differences
70%) that was fitted adequately by a sinusoid. One might between the spatial tuning of neurons in SI and in area 5.
expect the characterization of a cell’s spatial tuning to un- They found a tendency for clustering of neural tuning along
cover a more complex pattern if one were to examine a one of the three coordinate axes (elbow angle, elevation,
2436
S. I. HELMS
TILLERY,
J. F. SOECHTING,
. . .-t
30 - fro100
20 -
T. J. EBNER
.
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AND
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60
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13. Best-fit
20
40
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axes in joint space. Data for the same neurons
joint space. Scatter of the data points indicates that neural activity
were perpendicular
to the one indicated in each panel.
FIG.
60
-20
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10 5 0 *
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15 -
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as shown in Fig. 4 are plotted along the best-fit axis in
was modulated
by hand displacements
along axes that
and azimuth), whereas we were unable to find evidence for
such clustering in SI. More neurons were tuned along the
elbow angle axis than the other two axes, both under static
and dynamic conditions. This finding reflects another difference between the two neural populations: we found few if
any neurons that were tuned predominately to the elbow
angle (Fig. 11). However, there is one fundamental similarity between the conclusions of Lacquaniti et al. and the
results we have presentedhere: neurons in both cortical areas
appear to encode the location of the hand in space, although
it is undetermined whether that is in terms of the spherangular coordinates used by Lacquaniti et al. ( 1995), the
Cartesian coordinates used here, or shoulder and elbow joint
angles. (There is a unique relationship between each of the
three setsof parameters, at least for any one particular task.)
Because SI provides information to both areas4 and 5, it
is relevant to consider the differences and similarities in the
kinematic spatial tuning of cells in these areas compared
with those in SI. Most of the studies that have ascribed
cosine tuning to motor cortical neurons have also used only
a limited number of movement directions, and it is possible
that if one were to use a greater number of target locations,
a single best direction would inadequately characterize the
activity of motor cortical neurons. However, the studies of
Schwartz ( 1992, 1994) suggest that a vector code of movement direction may in fact characterize motor cortical neuronal activity, because he was able to predict the hand’s
trajectory during sinusoidal drawing movements (i.e., a task
comprising an infinite number of movement directions) from
neuronal activity on the basis of a population vector code
(Georgopoulos et al. 1983). Nevertheless, it may be useful
to reexamine this issueby using the sameexperimental paradigms and analysis techniques in the study of pre- and postcentral cortical areas.
Nonuniform spatial tuning
We found that neurons in SI were preferentially sensitive
to changesin the location of the hand in the anteroposterior
direction. This asymmetry in tuning was statistically significant. It is also consonant with the observations of Cohen et
al. (1994) and Prud’homme and Kalaska (1994). Here we
take up two issues that are pertinent to this topic: I) the
possible origin of this asymmetry and 2) a comparison of
the distribution of spatial tuning of neurons in SI and in
areas 4 and 5.
Sampling bias is one possible source of the spatial asymmetries in tuning that we found. We recorded from neurons
in a large portion of the proximal arm region of areas 3b,
1, and 2 (Fig. 1). Nevertheless, it is possible that areas
contiguous to those we recorded from could contain neurons
that are preferentially tuned to displacements of the hand in
the mediolateral and vertical directions. This would imply
that neurons tuned to different movement directions are distributed inhomogeneously in SI. At present there is no evidence in support of this possibility, either in SI or in other
precentral or postcentral areas. As noted in RESULTS, we did
not record from the more medial strip described by Pons et
al. ( 1985a,b), and it is possible that neurons in that region
would exhibit spatial tuning different from what we described. We also recorded from only a few cells in area 3a,
and it is possible that our conclusions do not hold true for
this region of SI.
Another explanation is anatomic: peripheral somatosensors may be more sensitive to displacements of the hand in
the fore-aft direction than to displacements in other directions. Thus, if a displacement of the hand by 1 cm in the
anteroposterior direction led to a larger change in shoulder
and elbow angles than did a l-cm displacement of the hand
POSTURE-RELATED
in other directions, then one might expect preferential tuning,
expressed in Cartesian coordinates, even if the population
of neurons were equally sensitive to joint excursions in all
directions. This explanation appears unlikely, because there
is no bias in the sensitivity of the joint angles toward the
anterior direction. In fact, the two yaw angles (7 and a) are
biased toward the mediolateral direction, changing maximally for movements directed -45’ and 65” away from
the anterior direction ( see also Helms Tillery et al. 1995;
Lacquaniti et al. 1995 ) . In the horizontal plane, the two
elevation angles (7 and p) are tuned within a few degrees
of the anterior direction, but their maximum sensitivity in
the vertical plane is to hand displacements 40-45” from
horizontal.
We have already mentioned that our data are in agreement
with observations of Cohen et al. ( 1994) and of Prud’homme
and Kalaska ( 1994), who also found that the preferred directions of SI neurons were biased in the anterolateral direction
both during active movements and the maintenance of static
postures. There appears to be a similar bias for the spatial
tuning in area 5. Kalaska et al. ( 1983) found a preference
for movements that were directed either anteriorly or toward
the left, although, interestingly, the same group did not find
such an asymmetry under static conditions (Georgopoulos
et al. 1984). By contrast, the distribution of best directions
in area 4 appears to be uniform (Schwartz et al. 1988).
In summary, one can suggest that neuronal activity in
areas 3, 1, and 2 resembles more closely that found in area
5 than in area 4 in two ways: 1) there is a positional code
of hand location rather than a vector code of movement
direction and 2) the sensitivity of the neural activity to
changes in hand location does not appear to be uniform for
all directions in space. There is one more important distinction between neurons in area 5 and area 4 that this study
has not addressed: neurons in area 5 appear to be related
primarily to kinematic parameters, whereas neurons in motor
cortical areas appear to be influenced as well by parameters
related to movement kinetics (Georgopoulos
et al. 1992;
Kalaska et al. 1990). In this respect as well, it appears the
properties of SI neurons more closely resemble those in area
5 (Helms Tillery 1994; see also Fromm and Evarts 1982;
Prud’homme and Kalaska 1994; Soso and Fetz 1980).
This work was supported by National Institute of Neurological
Disorders
and Stroke Grants NS-15018
and NS-31530,
a Human Frontiers in Science
Program grant, and a National Science Foundation
Graduate Fellowship
to
S. I. Helms Tillery.
Present address of S. I. Helms Tillery:
Research Services ( 15 1) , VA
Medical Ctr., Syracuse, NY 13210.
Address for reprint requests: J. F. Soechting, Dept. of Physiology,
6-255
Millard
Hall, University
of Minnesota,
Minneapolis,
MN 55455.
Received
9 January
1995; accepted
in final form
24 May
1996.
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