Download Encoding of target direction and speed during visual instruction and

European Journal of Neuroscience, Vol. 11, pp. 4433±4445, 1999
ã European Neuroscience Association
Encoding of target direction and speed during visual
instruction and arm tracking in dorsal premotor and
primary motor cortical neurons
M. T. V. Johnson,1 J. D. Coltz3 and T. J. Ebner1,2,3
Departments of 1Neuroscience and 2Neurosurgery, and 3The Graduate Program in Neuroscience, University of Minnesota, Lions
Research Building, 2001 Sixth Street SE, Minneapolis, MN 55455, USA
Keywords: linear regression, Macaca mulatta, primate, pursuit tracking
Abstract
The encoding of direction and speed in the discharge of dorsal premotor (PMd) and primary motor (MI) neurons was studied during
two-dimensional visually-instructed pursuit arm movements in which eight directions and four constant speeds were independently
manipulated. Each trial consisted of equal durations of visual observation of target movement without hand movement (cue) and
visual pursuit-tracking of the target with the hand (track). A total of 240 neurons was recorded from PMd and MI in two Macaca
mulatta monkeys. Two classes of regression analyses were used to relate neuronal ®ring during the cue and track periods to direction
and speed. First, the average ®ring from each period was ®tted to target direction or speed. Period-averaged ®ring signi®cantly
correlated with direction more frequently in the track than in the cue period. Conversely, correlations with speed (with or without
direction) were more common in the cue than in the track period. Secondly, a binwise regression evaluated the temporal evolution of
®ring correlations with direction and speed. Supporting the period-based results, signi®cant binwise correlations of the discharge with
speed occurred preferentially during the cue period when there was no hand movement. Prior to movement, correlations of the ®ring
with direction became signi®cant and continued through the movement. Both analyses demonstrated a distinct tendency for neurons
to be modulated by speed information early and by direction information later. This temporal parcellation re¯ects both the sequential
demands of the task and constraints placed on the neural computations. The early representation of target speed is hypothesized to
re¯ect the need to calculate a `go signal' for the initiation of movement.
Introduction
Speed is a critical control parameter for volitional arm movements
involving single or multiple joints. Several motor control strategies
are based on the speci®cation and control of movement speed (i.e.
`speed control hypothesis', Freund & Bundingen, 1978; Enoka, 1983;
`speed-sensitive strategy', Corcos et al., 1989). Multijoint reaching
movements are characterized by a stereotypic, bell-shaped velocity
pro®le that appears to be an organizing feature of limb movement (for
review see Georgopoulos, 1986). The control of speed is also
interrelated with other movement parameters such as amplitude
(Freund & Bundingen, 1978; Viviani & Terzuolo, 1982), error (Fitts,
1954), and trajectory (Soechting & Lacquaniti, 1981; Viviani &
Terzuolo, 1982). Thus, several lines of behavioural evidence
underscore the necessity of the central nervous system to be informed
of, and control, speed of movement.
Electrophysiological studies have described speed or velocity
modulation of primary motor cortex (MI) neurons during limb
movements (Hamada, 1981; Hore & Flament, 1988; Schwartz, 1992,
1993; Ashe & Georgopoulos, 1994). Modulation of neuronal ®ring by
limb movement speed/velocity has also been found in the parietal
cortex (Motter et al., 1987; Ashe & Georgopoulos, 1994), cerebellum (Mano & Yamamoto, 1980; Marple-Horvat & Stein, 1987; Coltz
Correspondence: Professor Timothy J. Ebner/ Dr Michael T. V. Johnson, as
above. Email: [email protected]
Received 10 May 1999, revised 29 July 1999, accepted 13 August 1999
et al., 1999) and basal ganglia (Georgopoulos et al., 1983). Neuronal
®ring in the extrastriate visual cortices is also modulated by target
speed (Colby et al., 1993; Lagae et al., 1993). Thus, a variety of
cortical and subcortical structures play a role in the representation and
transformation of the sensorimotor aspects of limb movement speed.
However, no study of dorsal premotor (PMd) or MI neurons has
systematically varied movement speed during multijoint movements
and then quanti®ed the associated changes in neuronal discharge.
Therefore, the ®rst aim of this study was to determine how the
discharge of PMd and MI neurons is modulated in a pursuit-tracking
task that independently manipulated speed and direction (Coltz et al.,
1999; Johnson et al., 1999).
The discharge of motor cortical neurons is also modulated by
movement direction (Georgopoulos et al., 1982, 1984; Caminiti
et al., 1991; Schwartz, 1992; di Pellegrino & Wise, 1993; Fu et al.,
1993, 1995), distance and position (Fu et al., 1993, 1995; Kettner
et al., 1988), offset torque (Kalaska et al., 1989), arm posture
(Caminiti et al., 1991; Scott & Kalaska, 1997), gaze (Boussaoud,
1995; Boussaoud et al., 1998) and visuomotor aspects of reach (Shen
& Alexander, 1997a, b; Zhang et al., 1997). This raises the question
of how multiple parameters of movement are encoded without
degrading the different types of information. Two multiplexing
schemes, vectorial summation (Kalaska et al., 1989; Redish &
Touretzky, 1994) and temporal parcellation (Fu et al., 1995) have
been demonstrated to occur in the discharge of motor cortical
neurons. Therefore, the second aim of this study was to determine the
4434 M. T. V. Johnson et al.
timings of any speed, velocity and direction modulation in the
discharge of PMd and MI neurons. An abstract of some of these data
has been presented (Johnson et al., 1997).
Methods
Experimentation was conducted according to the National Institutes
of Health Guide for the Care and Use of Laboratory Animals and was
approved by the Institutional Animal Care and Use Committee of the
University of Minnesota.
Behavioural task
The behavioural paradigm and recording procedures are described
fully in a previous publication (Johnson et al., 1999). Brie¯y, two
female rhesus monkeys (Macaca mulatta, 4±6 kg) were trained using
juice rewards to use a two-joint manipulandum to make visuallyguided arm tracking movements over a horizontal workspace. To
begin a trial (Fig. 1A), the animal positioned a cursor (0.5-cm
diameter) in a square start box (1.2-cm diameter) located at the centre
of the workspace. The duration of the `hold period' was randomly
varied between 1 and 2 s. Following the hold period, the `cue period'
began with a square target box (1.2-cm diameter) appearing at one of
the eight target positions (0±315° in 45° increments), each 5 cm
distant from the start box. The target moved at one of four constant
speeds (2, 3, 4 or 5 cm/s) along a straight line to intersect the start
box. During the cue period, the animal was required to maintain the
cursor in the start box while the moving target was observed. When
the target reached the central start box, the start box was extinguished
and the target continued to move with the same direction and speed
for another 5 cm until it reached the workspace periphery. During this
`track period' the animal was required to keep the cursor within the
target box by making a visually-guided, pursuit-tracking movement.
The trial was aborted if at any time the cursor fell outside the start
box or the target during the track period. Using a randomized block
design, each direction±speed combination was presented 10 times.
Figure 1 shows that the animals were able to track at the required
speeds (C) using straight handpaths (B). Details of the behaviour
including the eye movements and EMG (electromyographic) activity
are described in the earlier report (Johnson et al., 1999).
Surgical, electrophysiological and histological procedures
After the task was learned, chronic recording chambers were placed
to straddle the dorsal premotor and primary motor cortices using
aseptic techniques under full anaesthesia (Johnson et al., 1999).
Once recovery was complete, conventional techniques were used to
record single units with paralyene-coated tungsten microelectrodes
(3±10 MW). A neuron was recorded only if its activity was audibly
modulated relative to the task or to active reaching movements.
Following discrimination, a spike train was digitized (1-ms intervals),
and binned to 20 ms. The x and y positions of the hand/cursor
(sampled at 1 kHz from the manipulandum potentiometers) were
smoothed using a moving average extending 6 20 points. The x, y
and tangential velocities were obtained by numerical differentiation.
Both position and velocity data were then compressed into 20 ms
bins. The spike trains and kinematic data were then aligned on target
onset at the start of the cue period and averaged over trials of like
direction and speed. Eye movements and EMG activity were also
acquired (see Johnson et al., 1999 for details).
Analysis
Linear regression analyses were used to relate the neuronal discharge
to direction and speed. Direction was treated as a unit vector with the
origin centred on the workspace. Speed was de®ned as a scalar
describing the rate of target movement irrespective of direction.
Velocity is a vector with both direction and magnitude (i.e. speed).
Operationally, therefore, velocity modulation was de®ned as the
simultaneous modulation of a cell's discharge by both direction and
speed over a speci®ed time interval (see below: Binwise regression
methodology).
Period regression methodology
FIG. 1. (A) Schema of the tracking paradigm and trial sequence. (B) Hand path
trajectories (average of 10 trials in each direction) for the 2 cm/s target speed.
(C) Pro®les of the tangential velocity of the hand over the trial sequence for
the four target speeds of 5, 4, 3 and 2 cm/s. The tangential velocity traces for
movements along 0, 90, 180 and 270° were superimposed with each trace
representing an average of 10 trials. The vertical dotted line at time zero
indicates the start of the cue period. Target speed is indicated by the
interrupted lines.
The initial analysis consisted of ®tting the average ®ring in the cue or
track periods to each of two predictors, direction and speed, using
separate regression models. These are referred to as `period
regressions'. For each of these models, the data from all 32
direction±speed combinations (eight directions and four speeds) were
used. Because each model (equations 1 and 2) involved 32 data points
and two predictors, the criterion for a signi®cant ®t was, in terms of
regression, an R2 > 0.2 (F-test, P < 0.05). Regression of the average
®ring to the predictors was determined separately for the cue and
track periods.
For the linear regression to direction, the mean ®ring, f, for each
direction, q, was ®tted to a cosine and sine function, i.e. a cosine
tuning model (Georgopoulos et al., 1982; Schwartz, 1992; Fu et al.,
1993).
f(q) = a0 + a1 cos(q) + a2 sin(q)
(equation 1)
This equation can be rearranged to f(q) = a0 + c1 cos(q±qpd) in which
qpd is the preferred direction and c1 is a measure of the depth of
modulation (Georgopoulos et al., 1982). The preferred direction was
Ó 1999 European Neuroscience Association, European Journal of Neuroscience, 11, 4433±4445
Motor cortical encoding of direction and speed 4435
referenced to the direction in which the target was moving (Johnson
et al., 1999).
To determine the relationship between ®ring and speed, the
regression model ®tted the mean ®ring, f(s), to target speed, s.
f(s) = b0 + b1 s + b2 s2
(equation 2)
2
The s term was included in the model to allow for nonlinearity in the
speed coding and to balance the number of terms with those used for
direction (equations 1 and 3).
Binwise regression methodology
To evaluate the temporal evolution of the correlations of the
discharge with direction and speed, a multivariate `binwise'
regression was performed as used previously (Fu et al., 1995). The
model used equation 3 to relate the ®ring rate, on a ®ner scale, to both
target direction and speed over time (t).
f(t) = d0(t) + d1(t) cos(q) + d2(t) sin(q) + d3(t) s + d4(t) s2
(equation 3)
Firing, f(t) and the regression coef®cients, dn(t), are now functions of
time. The ®t to the model is calculated bin by bin over the cue and
track periods. To calculate the binwise regression (equation 3), the
spike data was ®rst smoothed by ®tting a Gaussian function with a
standard deviation of 40 ms to each bin. The multiple regression
algorithm differed from that used previously (Fu et al., 1995) in that
type II sums of squares were used to evaluate the contributions of the
partial regression terms to the total model variance rather than a best
R2 model optimization procedure (Neter, 1996). The two directional
terms [d1(t) cos(q) + d2(t) sin(q)] were combined for the partial R2 for
direction and the two speed terms [d3(t) s + d4(t) s2] were combined
for the partial R2 for speed. Both partial R2s were directly comparable
in terms of the amount of ®ring variability explained because the
number of predictors was equal. A highly conservative criterion of 10
consecutive bins (200 ms) with an R2 > 0.2 (P < 0.05) for each bin was
required to accept a relationship between the ®ring and a parameter as
signi®cant. This strict criterion was used to avoid spurious, shortduration correlations and to take into account the fact that the binwise
regression is based on multiple statistical tests.
Two approaches were used to compare neuronal ®ring at different
speeds (Fig. 2). The ®rst approach was to `clip' the data and evaluate
only the 1 s prior to and after the cue-track transition. Although
keeping the absolute timing intact, this method ignores large
segments of data. The second approach was to `stretch' the temporal
pro®le of the neuronal ®ring from the faster speeds (3, 4 and 5 cm/s)
to the same number of 20-ms bins as the slowest target (2 cm/s). This
was accomplished by ®tting a cubic spline to the ®ring pro®le of the
faster three speeds and interpolating to 250 bins, the same number of
bins in a 2-cm/s trial. Thus, all target speeds resulted in the same
number of instantaneous ®ring frequency bins, such that speed
comparisons could be made across each sequential bin. The duration
of each bin ranged from 8 ms (5 cm/s) to 20 ms (2 cm/s). The
evolution of correlations between ®ring and the parameters was thus
evaluated as a function of target travel distance. As this method maps
the sequence of ®ring directly onto the workspace, the absolute
timing in each trial period is lost. However, by including the complete
temporal pro®le, this method preserves the entire task sequence.
Because the same period of time (500 ms) was analysed for the hold
period, this component of the discharge was not included in the
interpolation. Figure 2 compares the results of the temporal regression
analysis using the `stretched' vs. the `clipped' data for the ®ring of
one neuron. As shown, the stretching of the ®ring frequency
histograms faithfully reproduces the temporal pro®le of the discharge
pattern and the R2 pro®les. A comparison of the two approaches was
undertaken for the ®ring of the 240 neurons and yielded similar R2
pro®les. Also, interpolation to 100 bins, the number of bins in the 5cm/s trial, was also undertaken and resulted in almost identical
correlation pro®les (data not shown). The binwise regression results
are independent of whether the data was expanded or compressed.
Therefore, the stretching methodology was used in this report to
permit an analysis of the entire ®ring pro®le, including the hold
period.
Trade-off of speed and duration
FIG. 2. Comparison of the two approaches used to accommodate records of
differing durations for the temporal regression analysis. Firing frequency
histograms are shown (above) for one neuron during 0° movement trials. The
500-ms hold prior to the cue period was the same length for all target speeds.
The remainder of the records vary in length from 5 to 2 s for the 2 and 5 cm/s
target speeds, respectively. The shorter trials can be `stretched' (left) using
spline interpolation or `clipped' (right) to the shortest 2 s length common to all
target speeds marked by the vertical dotted lines. The binwise regressions
resulted in pro®les of the total R2, direction partial R2 and speed partial R2,
which were similar for both methods. The `stretching' method was used only
for the binwise regressions.
Speed and duration were inversely related because tracking distance
was constant. Consequently, the duration of each cue or track period
ranged from 1 to 2.5 s for the extremes of target speed from 5 to 2 cm/
s, respectively. This raises the question of whether the speed±duration
trade-off could lead to false positive or negative results. For ®ring
characterized by relatively constant increases or decreases with speed
this does not present a problem because the analyses were based on
average ®ring, which was normalized to time. However, for transients
of ®ring the speed±duration trade-off could lead to erroneous results.
Such transient responses to a visual target presentation are found in a
minority of PMd and MI neurons classi®ed as `signal cells' (di
Pellegrino & Wise, 1993; Johnson et al., 1996; Johnson et al.,
1999). Therefore, several additional analyses were undertaken to
insure that neither the period nor the binwise regressions were
corrupted.
Figure 3A illustrates the problem for a worst-case hypothetical
`signal' response in the cue period of 40 spikes/s for a duration of
Ó 1999 European Neuroscience Association, European Journal of Neuroscience, 11, 4433±4445
4436 M. T. V. Johnson et al.
from the speed regression for the partial and entire cue or track
periods (Fig. 3A) shows that two methods yielded almost identical
®ndings. To allow for the response latency and the fact that timelocked `signal' transients occurred most frequently just after the
reaction time, a comparison was made between ®rst 150±650 ms and
the full cue period. The two slopes were almost identical with an R2
of 0.82 and a slope of 1.0 (Fig. 3A). For the track period, comparison
of the slopes from the ®rst 500 ms vs. the entire period resulted in an
R2 of 0.71 and a slope of 0.7 (Fig. 3A). Therefore, the potential
problem of ®ring transients causing erroneous speed regressions did
not occur. For simplicity, only the results from the period regression
based on the ®ring in the entire cue and track periods are reported.
The speed±duration trade-off could also affect the ®ndings based
on the binwise regressions. For the same hypothetical neuronal
response, the stretching procedure alters the location of time-locked
transients (Fig. 3B). Staggering of this transient produces spurious
speed correlations, causing the temporal pro®le of the speed slope to
rapidly switch from negative to positive, which makes little
physiological sense. Therefore, the occurrence of biphasic slopes
was taken to indicate a time-locked component in the ®ring and these
neurons (n = 31) were removed from the binwise analysis. All 31
neurons had time-locked ®ring transients, usually in the initial 200±
300 ms of the cue period.
Results
Neuronal database
FIG. 3. Due to the use of equal 10-cm target travel distances for the four target
speeds, a speed±duration trade-off resulted. (A) (Left) The distortions to the
period-averaged ®ring speed correlation slopes are shown for a worst-case
`signal' neuron which ®res only for a ®xed duration (200 ms) transient of
40 spikes/s. The cue period averages span 2.5±1 s (striped boxes). However,
because the transient is of ®xed duration and frequency and no other ®ring
occurs, the resultant averages divide this ®xed instantaneous frequency by the
number of observations which ranges from 125 (2-cm/s trials) to 50 (5-cm/s
trials), resulting in an apparent speed slope of 1.6 spikes/cm. (Right)
Comparison of speed slopes (b1, equation 2) calculated using averages over
the total cue or track period to those calculated using a ®xed duration. For the
cue period, a ®xed subperiod from 150 to 650 ms was used to allow for
reaction time. For the track period, the ®rst 500 ms were used. For the cue
period, the comparison regression is ®tted by Full cue = ±0.3 + 1.0 3 150±650ms cue (R2 = 0.82). For the track period, the regression is ®tted by Full
track = ±0.03 + 0.7 3 0±500-ms track (R2 = 0.71). (B) (Left) The distortions to
the binwise speed correlation are shown for the same hypothetical neuron as in
A. The shorter durations are all spline-interpolated to the same number of bins
(250) as the longest duration (2 cm/s) trials. This results in a staggering of the
transient as a function of target distance. (Right) The binwise speed regression
slope is shown as a function of target distance.
200 ms at a latency of 200 ms. The average ®ring over the four speeds
is 8.0, 6.4, 4.8 and 3.2 spikes/s due to the unequal time periods (1.0,
1.25, 1.67 and 2.5 s), yielding a false-positive speed regression slope
of 1.6 spikes/cm/s. In contrast, the average ®ring calculated over a
®xed duration (in this case the ®rst second of the cue period) results in
8.0 spikes/s at each speed and no correlation to speed exists.
Therefore, the magnitude of the problem may be ascertained by
comparing the slopes of the regressions to speed from the entire cue
period with those based on a ®xed duration. As the shortest cue period
duration was 1 s, regressions to speed were performed for ®ring
averaged over the different 500-ms epochs of the cue and track
periods and the slopes were compared to the results from the entire
period.
As described in Results section, signi®cant speed modulation (with
or without direction modulation) was found in 79 cue periods and 46
track periods (out of a total of 240 cells). Comparison of the slopes
The discharge of 240 neurons from two animals and three
hemispheres was studied. This sample of neurons has been previously
analysed, addressing the question of how directional tuning changed
across the cue and track periods (Johnson et al., 1999). The neurons
studied were located in the PMd and MI (for the locations of
electrode penetrations, see ®g. 5 of Johnson et al., 1999). The
population of neurons recorded did not include cells in the frontal eye
®elds or the ventral premotor area.
Examples of direction, speed, and velocity modulation in PMd/
MI neurons
The ®ring of 226 neurons (94%) had signi®cant directional ®ts of
their ®ring in either the cue (n = 14), track (n = 41) or both periods
(n = 171) to the 32-point cosine tuning model (equation 1) with or
without speed modulation. The ®ring of 101 neurons (42%) had a
signi®cant ®t to speed (equation 2) with or without directional
modulation in the cue (55), track (22) or both (24) periods. Examples
of direction- and speed-modulated neuronal ®ring are illustrated in
Figs 4±6. The ®ring frequency histograms are depicted with a change
in time scale for each of the speeds as they were normalized to target
travel distance. However, the regressions of average ®ring in the cue
or track period were not dependent on this stretching (see Methods
section).
The spike frequency histograms shown in Fig. 4 are from a neuron
in which the ®ring was signi®cantly modulated by target speed in the
cue period and by movement direction in the track period. During the
cue period, while the monkey viewed the target, the ®ring was
inversely related to target speed. This inverse relation occurred along
all tracking directions. During the track period the neuronal ®ring was
not modulated by target speed but exhibited broad directional tuning
with the greatest ®ring for movements around 225° and decreased
®ring for tracking at 45°. The graphs in the centre are the directional
tuning curves derived from the regressions to the cosine tuning model
(upper) and the linear component of the regressions to target speed
(lower) for the cue (right) and track (left) periods. The average ®ring
Ó 1999 European Neuroscience Association, European Journal of Neuroscience, 11, 4433±4445
Motor cortical encoding of direction and speed 4437
FIG. 4. Firing frequency histograms from a neuron that was modulated by target speed during the cue period and directionally-tuned during the track period. Firing
frequency histograms for each target speed are arranged by direction (0° to 315°) around the workspace. The 500-ms hold period is shown to the left of each
histogram. The cue and track periods of the histograms are shown at different time scales for the different tracking speeds, as they were normalized to target travel
distance. The ticks on the x-axis represent 500 ms intervals. Time axis for each target speed is shown for the 0° direction. The upper inset graphs in the centre
display the average ®ring data points and the 32-point cosine tuning curves for the eight directions over the four target speeds. The lower inset graphs display the
regression ®t to the ®ring for the four target speeds (2, 3, 4 and 5 cm/s) over the eight directions. Calibration line (to the right of the 270°, 2 cm/s ®ring histogram)
indicates ®ring rate in spikes/s.
in the cue period was signi®cantly modulated by target speed
(R2 = 0.66, b1 = ±3.0 spikes/cm); however, the ®t to the cosine tuning
model was not signi®cant. During the track period the ®ring was
directionally tuned (R2 = 0.79, c1 = 2.3) with a preferred direction of
243° but the relationship between ®ring and target speed was not
signi®cant. This cell was, therefore, modulated by speed in the cue
period and direction in the track period.
Decreases in ®ring with increased target speed (a negative
regression slope) occurred in 42% of the 101 neurons in which the
period regressions to target speed yielded a signi®cant ®t. The
remaining 58% of the cells increased ®ring with increasing speed
(positive regression slope, b1). An example of such a cell is shown in
Fig. 5. The histograms and regression results reveal increased ®ring
for greater target speeds during the cue period (R2 = 0.45,
b1 = 2.4 spikes/cm). However, the cue period discharge was not tuned
to target direction. Conversely, during the track period the ®ring was
not modulated by target speed but was directionally tuned (R2 = 0.91,
c1 = 23.7) with a preferred direction of 81.2°. Thus, during the cue
period this neuron responded to increases in target speed with
increased ®ring, in contrast to the speed±discharge relationship
shown in Fig. 4. Two common features of the ®ring of both cells were
the presence of signi®cant speed modulation only in the cue period
and directional tuning only in the track period, thus the lack of
velocity modulation.
The ®ring of a large fraction of the cells (n = 79 out of 240, 33%)
and periods (n = 91 out of 480, 19%) was signi®cantly modulated by
both parameters within at least one of the task periods. An example of
such a neuron is shown in Fig. 6. Signi®cant speed modulation
occurred during the cue period (R2 = 0.34) and the discharge
increased with increasing target speed (b1 = 5.0). Signi®cant directional tuning was present for both the cue (R2 = 0.48, c1 = 11.7) and
the track (R2 = 0.47, c1 = 10.5) periods. The cell's preferred direction
Ó 1999 European Neuroscience Association, European Journal of Neuroscience, 11, 4433±4445
4438 M. T. V. Johnson et al.
FIG. 5. Firing frequency histograms and direction and speed regression results from the discharge of a neuron demonstrating increased ®ring with increasing speed
in the cue period and directional tuning in the track period. The ticks on the x-axis represent 500-ms intervals. Time axis for each target speed is shown for the 0°
direction. Conventions as in Fig. 4.
was nearly identical for the cue and track periods (112.9° and 117.1°,
respectively). The discharge was not signi®cantly modulated by
speed during the track period. Therefore, the overlap between
direction and speed modulation was con®ned to the cue period.
Summary of the period-based direction and speed modulation
The database included 240 neurons, so the ®ring in a total of 480 cue
and track periods was analysed using the period regressions to
direction (equation 1) or speed (equation 2). The ®ring in 125 of 480
periods (26%) demonstrated speed modulation and the ®ring in 399 of
480 (83%) periods demonstrated directional tuning. Directional
tuning alone was the most common ®nding (Fig. 7). The average
discharge in 64% (308 out of 480) of the periods demonstrated a
signi®cant ®t only to direction without speed modulation (131 cue
and 177 track periods). Therefore, directional tuning (without speed
modulation) was more common in the track than in the cue period.
Modulation of the discharge by direction and speed was the second
most common group. The ®ring in 19% of the periods (91 out of 480)
had signi®cant correlation with both direction and speed (53 cue and
38 track periods). A signi®cant ®t limited to target speed was found in
7% of the periods (26 cue and 8 track periods). Therefore, speed or
the combination of direction and speed modulation was more
common in the cue than in the track period. This difference in the
relative proportions of direction, direction plus speed, and speed
modulation between the cue and track periods was signi®cant
(P < 0.001, c2 3 3 2 table).
Spatial distribution of speed-modulated neurons
Of interest was whether there was any spatial distribution over the
cortical surface to the neurons whose discharge was modulated by
speed. This was evaluated by dividing the neurons with signi®cant
speed modulation into three groups: those with speed modulation (i)
only in the cue period; (ii) only in the track; and (iii) in both periods.
The relative percentages of these three groups in the PMd and MI was
evaluated ®rst. Of the 55 neurons modulated by speed in the cue
period, 39 were located in the PMd and 16 were in the MI. Of the 22
neurons modulated by speed in the track period, three were found in
the PMd, whilst 19 were found in the MI. The 24 neurons modulated
Ó 1999 European Neuroscience Association, European Journal of Neuroscience, 11, 4433±4445
Motor cortical encoding of direction and speed 4439
FIG. 6. Firing frequency histograms and direction and speed regression results from a neuron demonstrating increased ®ring with increasing target speed in the cue
period and directional tuning in both cue and track periods. Conventions as in Fig. 4.
FIG. 7. Summary of results from the period regressions of the average ®ring
from the cue and track periods to speed and direction. The numbers of periods
with signi®cant correlations to only direction, direction and speed, and to only
speed are speci®ed for the cue (black bars) and track (white bars) periods.
in both periods were evenly distributed (12 were in the MI and 12
were in the PMd). The change in relative proportions of these three
functional groups between the two cortical areas was signi®cant
(P < 0.001, c2 3 3 2 table). The percentages of cells falling into each
category were also assessed as a function of distance from the central
sulcus (Fig. 8A). The relative percentages of cells with signi®cant
speed modulation in the cue period increased from 36% at 0±4 mm
from the central sulcus to 60% at 4±8 mm and 67% at 8±12 mm. The
percentages of cells with speed modulation in the track period
decreased from 31% at 0±4 mm to 25% at 4±8 mm and 11% at 8±
12 mm. Figure 8A does not extend to 12±16 mm as only eight cells
fell in this range; however, all were modulated by speed in the cue
period. These trends also suggest that cells modulated by speed in the
cue period were located more anteriorly in PMd and cells modulated
by speed in the track were located more posteriorly in MI.
A spatial distribution also existed for neurons modulated by
direction. This gradient was shown for similar functional categories
using a different directional regression methodology (see ®g. 11 of
Johnson et al., 1999). Based on the 32-point regression methodology
used in this paper and the same three functional categories de®ned
above for speed modulation, the distribution of directional modulation as a function of trial period and distance from the central sulcus
was constructed (Fig. 8B). The gradient of increasing incidence of
directional modulation in the cue period paralleled that found for
speed, increasing from 1% at 0±4 mm to 4% at 4±8 and 17% at 8±
12 mm. The percentage of neurons with directional modulation in the
track period decreased as a function of distance from the central
Ó 1999 European Neuroscience Association, European Journal of Neuroscience, 11, 4433±4445
4440 M. T. V. Johnson et al.
FIG. 8. (A) Locations of neurons relative to the central sulcus with three
functional types of speed coding. The proportions of neurons with (i) speed
coding in only the cue period (black bars) (ii) speed coding in only the track
period (white bars), and (iii) speed coding in both cue and track periods (grey
bars). The proportions of the three groups add to 100% for each 4-mm distance
segment. (B) Proportions of neurons with (i) direction coding in only the cue
period (black bars) (ii) direction coding in only the track period (white bars),
and (iii) direction coding in both cue and track periods (grey bars).
FIG. 9. The temporal R2 pro®les from the multivariate binwise regressions
over the trial period are shown for the three neurons in Figs 4±6. The partial R2
for speed (d3(t) s + d4(t) s2) and direction (d1(t) cos(q) + d2(t) sin(q)) were
plotted as a function of the trial sequence. The total R2 pro®le over the trial is
also plotted. To the far right, the speed slope d3(t) was plotted over the trial.
The lengths of the cue and track periods were normalized as described in
Fig. 2. Dashed vertical lines represent the end of the hold period. Solid vertical
lines represent the cue±track transition. Vertical hatching of the R2 pro®les
indicate signi®cant correlations (P < 0.05 for 10 consecutive bins, see Methods
section).
sulcus (31 to 17 to 10% in these same intervals). A similar trend was
also observed for speed (Fig. 8A); however, the percentage of neurons
with directional modulation in both periods was greater than for
speed modulation.
Analysis of the temporal multiplexing of direction and speed
To obtain a temporal pro®le of the correlation of the discharge with
direction and speed, a binwise multivariate regression was performed
(equation 3, Methods section). More precise timing relations are
obtainable with the binwise regressions than with the period average
regressions; however, results from the two were in general agreement.
Of particular interest was the presence of velocity modulation in the
discharge of single neurons. Figure 9 shows the results from the
binwise regression of ®ring to speed and direction for the three
neurons shown in Figs 4±6. The partial R2 for speed reached
signi®cance and peaked in the cue period for each neuron. This is
in agreement with the period regression results in which the discharge
of each of these cells had a signi®cant ®t with speed in the cue period.
The partial R2 for speed did not reach signi®cance in the track period
for the ®rst two neurons (A and B), also in agreement with the results
from the period regressions. For the third cell shown in C the partial
R2 for speed was signi®cant only for 1 cm of the track period.
However, this brief period of speed modulation was not suf®cient to
yield a signi®cant relationship with speed when the average ®ring of
the track period was regressed to speed (Fig. 6). The temporal pro®les
of the partial R2 for direction differed from the partial R2 for speed in
that signi®cance was not reached until later in the cue period (C) or
until the track period (A and B). As predicted from the period
regressions, the cue period ®ring of the cell shown in C was
modulated by both direction and speed. The partial R2 for direction
was signi®cant throughout almost the entire track period (A±C). The
temporal pro®le of the speed slope (d3, equation 3) was monophasic,
negative for the cell in A and positive for the cells in B and C, in
agreement with the period regressions. A negative speed slope
indicated that the cell in A increased its ®ring for decreasing target
speeds. A positive speed slope indicated that the cells in B and C
increased ®ring for increasing target speeds. This coincides with the
speed slopes based on the period regressions shown in the insets of
Figs 4±6. The lack of rapid sign changes in the speed slope in A±C
demonstrated that spurious regressions were not present (see Methods
section).
Complete speci®cation of the velocity vector requires both
direction and speed modulation. From the binwise regression results,
the cell shown in A lacked simultaneous correlation to direction and
speed and therefore this cell was not modulated by velocity at any
time through the trial sequence. In contrast the cell shown in C had a
period of overlap of speed and direction modulation beginning in cue
and extending into early track. This cell had a temporal ordering of
the modulation by the three parameters, speed modulation ®rst, then
velocity, and direction last. For the cell shown in B, speed modulation
occurred ®rst and direction modulation later with a short epoch of
velocity modulation between. Therefore, signals encoded in the ®ring
of these three cells generally exhibited a serial ordering of speed, then
direction.
In 159 cells, the binwise regression results for speed yielded a
signi®cant ®t and in 223 the ®t to direction was signi®cant. As 31
cells produced biphasic slopes in the speed partial regression, only
128 cells with signi®cant speed correlations were used in subsequent
analyses (see Methods section). Partial R2 temporal pro®les for speed
and direction for these data were averaged and are shown in Fig. 10A.
The average partial R2 for speed reached signi®cance early in the cue
period and became nonsigni®cant before movement onset. This
®nding parallels the greater number of signi®cant ®ts of averaged
discharge to target speed in the cue period (Fig. 7). In contrast, the
average temporal pro®le of the partial R2 to direction reached
signi®cance approximately midway through the cue period, and
continued to rise throughout the cue period. The average partial R2 for
direction remained signi®cant throughout the entire track period. This
predominance of directional tuning in the track period was found as
well using the period regressions to direction. The points along the
trial sequence at which the partial R2 for speed and direction reached
signi®cance (B) as well as the points at which the maximum R2
occurred (C) are shown in histogram form. Each bar represents a
target travel distance of 0.2 cm. Signi®cant speed encoding occurred
Ó 1999 European Neuroscience Association, European Journal of Neuroscience, 11, 4433±4445
Motor cortical encoding of direction and speed 4441
FIG. 10. Population average pro®les of the trial sequence binwise regressions.
For each speed or direction partial R2 record to be included in the average, the
criterion of 10 sequential bins with signi®cant (P < 0.05) regressions had to be
met. (A) The averaged trial sequence binwise regressions are shown for the
speed partial R2 and direction partial R2. The mean (thick line) and the +1 SD
boundary (thin line) are shown. The solid horizontal line at R2 = 0.2 denotes
the P < 0.05 signi®cance level. The dashed vertical line marks the end of the
hold period. The solid vertical line marks the cue±track transition. (B) The
distance of target travel at which the speed and direction partial R2 sequences
became signi®cant is plotted in histogram form. (C) The distance at which the
R2 sequences for speed and direction reached a maximum is shown. The mean
6 SD for each distribution is plotted above each histogram.
as early as 0.4 cm in the cue period, with the average distance of onset
at 1.5 6 0.9 cm into the trial sequence. The average distance at which
the peak R2 for speed occurred was 2.3 6 1.2 cm into the trial
sequence. Based on the binwise regressions, speed modulation
occurred primarily in the cue period. Signi®cant direction modulation
had an average onset distance of 2.5 6 1.7 cm into the trial sequence
which was later in the cue period than for speed. However, the peak
R2 for directional tuning occurred during the track period, after the
target had travelled 7.4 6 1.6 cm, approximately halfway through the
track period. Therefore, speed modulation occurred preferentially in
the cue and directional tuning dominated the track period.
How are subpopulations of directional- and speed-modulated
neurons recruited through the trial sequence? Using indices based
on the binwise regression, the sampled population of the 223
directionally-modulated and 128 speed-modulated neurons was
analysed as a function of trial sequence. Onsets of initial signi®cance
for direction and speed correlations were plotted as the cumulative
percentage of modulated neurons in Fig. 11A. The subpopulation of
speed-modulated neurons reached signi®cance rapidly during the ®rst
2 cm of target travel in the cue period. The subpopulation of
directionally-modulated neurons reached signi®cance more gradually
over the entire cue period. However, both subpopulations were active
in the cue period. Profound differences were apparent at the positions
in the trial sequence at which each subpopulation reached maximal
signi®cance (Fig. 11B). Speed-modulated cells were recruited to
maximal signi®cance in the cue period while the direction-modulated
cells reached maximal signi®cance in the track period. From the
perspective of reaching maximal signi®cance, the two subpopulations
were almost completely segregated over the trial sequence.
FIG. 11. (A) Cumulative percentage of neurons signi®cantly modulated to
direction (squares) and speed (circles) as a function of the trial sequence. (B)
Cumulative percentage of direction- and speed-modulated neurons reaching
peak signi®cance (maximal R2) as a function of the trial sequence. The dashed
vertical line marks the end of the hold period. The solid vertical line marks the
cue±track transition. The horizontal axis shows the distance of target travel.
FIG. 12. Average binwise regression pro®les for neurons with both distance
and speed partial R2 pro®les that were signi®cant. First, the partial R2 pro®les
from individual neurons had the nonsigni®cant portion (R2 < 0.2) removed. An
estimate of the velocity partial R2 pro®le was constructed using the regions of
simultaneously signi®cant direction and speed partial R2 pro®les. The partial
R2 pro®les were averaged (thick line) and the +1 SD boundary (thin line)
calculated.
The temporal R2 pro®les for speed and direction (Figs 10 and 11)
show that, across the population of cells, direction and speed
modulation both occurred in the cue period. A similar result emerged
Ó 1999 European Neuroscience Association, European Journal of Neuroscience, 11, 4433±4445
4442 M. T. V. Johnson et al.
from the period regressions, that direction and speed modulation in
the cue period was prevalent (Fig. 7). However, neither of these
analyses answers the question to what extent direction and speed are
simultaneously modulated in the ®ring of single cells. To answer this,
neurons with signi®cant partial R2 pro®les for direction and speed
were ®rst chosen. Secondly, the nonsigni®cant portion (R2 < 0.2) of
the partial R2 pro®les from each individual neuron were removed.
Lastly, an estimate of the partial R2 pro®le for velocity was
constructed from the regions of simultaneously signi®cant direction
and speed R2 pro®les. Once processed for each neuron, the partial R2
pro®les were averaged. Figure 12 shows the averaged direction, speed
and velocity partial R2 pro®les as a function of target travel distance
for this group of neurons. The direction partial R2 pro®le rises
through the cue reaching a maximum in the track period,
characteristically similar to the direction pro®les shown in Figs 9
and 10. The speed partial R2 pro®le peaks during the middle of the
cue period, similar to the speed pro®les of Figs 9 and 10. The
estimated velocity partial R2 pro®le was half that of the speed and
rose slightly later than the speed partial R2. Using the increased
resolution of the binwise regressions, the presence of velocity
modulation in individual neurons proved to be limited.
Discussion
The discharge of PMd and MI motor cortical neurons was studied
during a pursuit-tracking task in which the direction and the speed of
the target were systematically varied. Several characteristics of the
paradigm dictated the analysis and interpretation of the results. First,
the paradigm explicitly varied direction and speed, and con®ned the
tracking to straight, radially-directed movements. Therefore, speed
was independent of tracking direction. In other tasks, direction and
speed continuously covaried (Schwartz, 1992, 1993) and precluded
an independent assessment of the effects of these two parameters on
the neuronal discharge. Secondly, two sources of information, target
direction and speed, were presented continuously through the trial
sequence. Thus, the temporal pro®le of the correlation of the ®ring
with direction and speed could be determined. Thirdly, the instructed
delay design of the paradigm allowed the neuronal ®ring to be
recorded during a visuomotor transform in which the animal was
required to map the direction and speed of the moving target in the
cue period onto the upcoming track period. This permitted an
evaluation of the evolution of the PMd and MI discharge during a cue
period consisting of a dynamic visual stimulus and during a track
period consisting of error-constrained pursuit tracking.
Relating neuronal ®ring comparisons to task parameters:
regression methodologies
In the literature, three classes of regression techniques have been used
to determine how movement parameters (i.e. direction, velocity and
acceleration) are encoded in neuronal ®ring. These classes will be
referred to as `period average', `time course' and `binwise'
regressions. Each approach has certain advantages and disadvantages.
Period average regressions were the ®rst utilized to relate the ®ring
during a behaviourally relevant epoch to peak velocity or acceleration
of step movements (Hamada & Kubota, 1979; Hamada, 1981;
Burbaud et al., 1991). More recently, multiple linear regression
models have been used for period average data (Fu et al., 1993; Coltz
et al., 1999). In the present study, period average regressions were
used extensively to determine if direction, speed or both (i.e.
velocity) modulated the average ®ring in the cue and track periods.
Time course regressions have been used to ®t the temporal pro®le of
the discharge to the temporal pro®le of the movement kinematics,
introducing time lags and leads to optimize the R2 of the model. This
method was used by Marple-Horvat & Stein (1987) to analyse the
®ring of cerebellar cells and by Ashe & Georgopoulos (1994) for
motor cortical and parietal neurons. The former used separate
regressions to position, velocity or acceleration while the latter used a
multivariate regression model, ®tting ®ring to target direction and
hand position, velocity and acceleration. However, great care must be
exercised when using time course regressions due to the high
prevalence of spurious signi®cant regressions resulting from
nonstationarity (i.e. nonconstant means and changing variances over
time) and auto-correlated errors in the residuals (Granger & Newbold,
1974). The problem of auto-correlated errors in the regression
analysis of time series has been recognised (Ashe & Georgopoulos,
1994).
A limitation of both epoch and time course regression is that the
correlation of the ®ring to the task parameters can be time-dependent
(Fu et al., 1995). Binwise regression addresses this limitation. It is
essentially an extension of epoch regression methodology wherein the
®ring in a brief time bin is ®tted to the parameters of interest (Fu
et al., 1995). In this study, binwise regressions were used to
determine the time course of the correlations with direction and
speed. An interesting variant of binwise regression methodology was
employed by Schwartz (1992) to determine the correlation of the
®ring of MI neurons with the tangential velocity of continuous ®nger
tracing movements. The neuronal ®ring residual from the directional
regression was found to vary with ®nger speed.
Firing correlations to direction and speed
In the present study, the ®ring of both PMd and MI neurons was
found to signal target direction and speed. Using the period average
regressions, neurons were found to be modulated by direction, speed
or both in the cue, track or both periods. Irrespective of period,
neurons modulated by only direction were by far the most numerous.
The second largest group comprised neurons modulated by both
direction and speed. The smallest group were neurons modulated only
by speed (see Fig. 7). Direction encoding has been demonstrated
repeatedly in the ®ring of PMd and MI neurons (Georgopoulos et al.,
1982, 1984; Caminiti et al., 1991; Schwartz, 1992; Fu et al., 1993;
Johnson et al., 1999). Speed/velocity coding has been reported for
the discharge of MI neurons (Hamada & Kubota, 1979; Hamada,
1981; Schwartz, 1992, 1993; Ashe & Georgopoulos, 1994). However,
there has been little information available concerning the PMd. While
the present ®ndings con®rm and extend these earlier results,
particularly to the PMd, there are some important differences. The
previous studies used primarily step-type movements and did not
systematically vary both direction and movement speed. The only
other tracking study (Schwartz, 1992, 1993) employed a task in which
the trajectories were characterized by continuously changing direction and speed. Speed and curvature varied inversely; the residuals
could thus correlate with either. This confound did not exist in the
current task, which minimized angular velocity by using straight,
error-constrained movements.
Another difference from previous reports is the differentiation
between speed and velocity coding. Neurons with simultaneous
direction and speed modulation were operationally de®ned as coding
velocity. The higher temporal resolution of the binwise regression
demonstrated that the overlap between direction and speed in single
neurons was minimal. The conjunction of the partial R2 pro®les of
direction and speed explained only half of the variability that the
partial R2 to speed did (Fig. 12). Therefore, while the ®ring of single
neurons signals both speed and direction, the two signals are not
combined. Presumably, other structures combine these two signals
Ó 1999 European Neuroscience Association, European Journal of Neuroscience, 11, 4433±4445
Motor cortical encoding of direction and speed 4443
into a velocity vector as recently shown for cerebellar Purkinje cells
(Coltz et al., 1999).
In agreement with previous studies, speed modulation, in either the
cue or track periods, had an equal probability of having either a
positive or negative slope. Bivalent modulation by speed or velocity
appears to be the rule rather than exception in the central nervous
system. Positive and negative speed/velocity slopes were found for
the ®ring of pyramidal tract neurons (Hamada, 1981), cerebellar
Purkinje cells (Marple-Horvat & Stein, 1987; Coltz et al., 1999) and
for neurons in the globus pallidus (Georgopoulos et al., 1983). One
possibility is that the positive and negative slopes re¯ect different
uses or targets of this information.
Temporal parcellation of speed and direction
The results from the period regressions suggested strongly that the
neural encoding of target speed was more prominent while the
monkey viewed the target during the cue period than during the actual
hand movement in the track period. Conversely, directional
modulation was more prominent in the track period. The percentage
of periods that were modulated only by direction increased by 26%
from cue to track. The percentage of periods modulated by both
direction and speed decreased by 28% from cue to track. Neurons
modulated only by speed decreased by 69% from the cue to track
periods. The binwise regression model con®rmed this ®nding,
demonstrating that the partial R2 for speed became signi®cant early
in the cue period and became nonsigni®cant by the track period onset
(Fig. 10). The correlation with direction became signi®cant about
halfway through the cue period and increased over the cue period to
become the major determinant of the ®ring in the track period. The
cumulative percentage of neurons with correlations to direction and
speed (Fig. 11) shows that the recruitment of neurons modulated by
speed occurred earlier in the trial sequence than the recruitment of
those modulated by direction.
A previous study of identi®ed pyramidal tract neurons reported that
the ®ring of a majority (21 out of 41) was correlated to wrist ¯exion
velocity during movement, but in only one neuron was there a
correlation with velocity prior to movement onset (Hamada, 1981).
The present ®nding of prominent speed modulation in the instructed
delay period would seem to be in sharp contrast. However, the ®nding
of little velocity modulation during the instructed delay period in the
earlier study may be instructive in terms of task and behavioural
demands on neuronal processing. The paucity of velocity or speed
signals may be attributed to two task differences. First, in the
previous study the `go' signal was explicit and movement onset was
not contingent on the intersection of a dynamically displayed cue
target with the hold box. Therefore, there was no need for the animal
to use speed to estimate the onset of the actual movement. Secondly,
in the previous study speed was not presented as a moving target, but
was symbolically displayed as three levels of lights. Therefore if,
prior to movement, speed is visually presented as a moving target and
the processing of this information is required to determine movement
onset, speed becomes an important determinant of the ®ring of PMd
and MI neurons.
It was assumed in the design of the paradigm that the monkeys
would use both speed and direction information presented during the
cue period to prepare the upcoming movement. The presence of
correlations to both direction and speed during the cue period
strongly suggests that both sources of information were used, possibly
re¯ecting the visuomotor transform required in preparation for the
movement phase of the task. PMd and MI neurons have been shown
to modulate according to go/no-go instructions (Wise et al., 1983;
Sakagami & Niki, 1994; Kalaska & Crammond, 1995). As the `go'
signal was implicit in this study, the visually deduced speed or rate of
closure of the moving target with the central hold box becomes
important for timing the onset of tracking. It is hypothesized that
target speed information is transformed into an internal `go' signal.
This would explain why the cortical neurons were modulated initially
by speed information, then by direction.
Psychophysical studies have lead to the hypothesis that different
parameters of movement are processed separately, including direction
and distance (Rosenbaum, 1980; Favilla et al., 1990; Soechting &
Flanders, 1991). However, at the level of the PMd and MI,
information about several parameters of movement is not segregated
into different neurons. The ®ring of PMd and MI neurons is
modulated by direction and distance (Fu et al., 1993, 1995) and the
discharge of MI neurons is modulated by direction and changes in
angular velocity or speed (Schwartz, 1993). Likewise, this study
found that PMd and MI neurons signal both direction and speed
during pursuit-tracking, but segregate this information in the temporal
domain. Therefore, temporal parcellation appears to be a general
multiplexing strategy in the PMd and MI.
Potential experimental and analytic confounds
The possibility exists that other behaviours such as eye movement
(Boussaoud, 1995; Boussaoud et al., 1998) or spatial attention (di
Pellegrino & Wise, 1993) may differentially modulate neuronal ®ring
in the cue relative to the track periods. Eye movements consisted
initially of saccades followed by smooth pursuit of the target (see
®g. 4 of Johnson et al., 1999). However, eye movement behaviour
was symmetric in the cue and track periods. Selective attention would
be expected to follow a similarly symmetric behaviour over the trial
sequence, initially directed over the entire workspace, then directed at
the moving target, and becoming again diffuse at the end of the trial.
Neither the modulation with direction nor the modulation with speed
demonstrated such symmetric behaviour between the cue and track
periods, demonstrating that neither eye movements nor attention can
account for the discharge modulation observed.
As can be seen by the tangential velocity pro®les (Fig. lC), the
initial track period was marked by an acceleration of the hand as it
went from rest to the constant target speed. The regressions to target
speed performed in this study did not take this parameter into
consideration. Two ®ndings downplay a potential relationship
between the ®ring and acceleration. First, ®ring transients during
the initial track period were virtually nonexistent. Secondly,
regressions comparing the speed slopes (b1) taken from ®rst 500 ms
with those from the last 500 ms of the track period demonstrated that
they were highly correlated.
Is the speed modulation in the cue period a confound due to
the coexisting directional tuning? Speci®cally, the directional
tuning during the cue has been shown to rotate and converge
toward the eventual preferred direction (PD) of the track period
(Johnson et al., 1999). Could this rotation of the PD, which is
most pronounced in the cue period, artifactually produce a
correlation with target speed? Two observations argue that this is
not the case. First, the PDs resulting from period average and
binwise regressions do not systematically change with changing
target speed (Johnson et al., 1999). Secondly, one last analysis
was undertaken. Binwise regressions (equation 3) were performed
on two groups of neurons, those with PDs diverging little between
cue and track (< 45°) and those with greatly divergent PDs
(> 45°). The resultant binwise pro®les of speed and direction
correlations were almost identical to those shown for the entire
population in Fig. 10. Therefore, the presence of directional tuning
in the cue period did not effect the speed coding.
Ó 1999 European Neuroscience Association, European Journal of Neuroscience, 11, 4433±4445
4444 M. T. V. Johnson et al.
Neural substrate for direction and speed coding
Direction and speed information must reach the PMd/MI during the
cue period, prior to movement onset. It has been postulated that the
source of the visual information is mediated through two channels.
One possible route is the transfer of this information via the pontocerebellar pathway and then to the motor cortex (Stein & Glickstein,
1992). Most recently, it has been hypothesized that the dorsal stream
mediates the transfer of visual information from the parietal areas to
the PMd and MI, with the parietal cortex serving as the bridge
between vision and movement (Milner & Goodale, 1995; Johnson
et al., 1996; Wise et al., 1997; Battaglia Mayer et al., 1998). Both
pathways may contribute; however, this discussion will focus on the
potential contribution of the dorsal stream.
Most parietal input to the PMd/MI originates from the superior
parietal lobule with additional projections from the inferior parietal
lobule and the parieto-occipital sulcus (Johnson et al., 1996; Wise
et al., 1997). Direction and speed relationships to neuronal ®ring
have been characterized for several of these extrastriate visual
association areas. Early visual directional responses (`signal' and
`set' responses) were found to be most prominent in the intermediate
and ventral part of the intraparietal sulcus (Johnson et al., 1996).
Neurons in the ventral intraparietal area are highly responsive to the
direction and speed of moving visual targets (Colby et al., 1993).
The adjacent middle temporal visual area also responds to
directionally-moving visual targets (Albright, 1984; Lagae et al.,
1993). Neurons with foveal receptive ®elds have low-pass speed
tuning curves with a peak at 1 °/s while those with peripheral
receptive ®elds have broad speed tuning curves with peaks around
10 °/s (Lagae et al., 1993). Not only visual, but also limb movementrelated neurons have been characterized in the parietal cortex.
Movement-related directional responses were most prominent in the
dorsal medial wall of the intraparietal sulcus (Johnson et al., 1996).
Neurons in the intraparietal sulcus are also modulated by the direction
and velocity of wrist joint movement (Burbaud et al., 1991).
Therefore, a variety of parietal and extra-striate cortical regions
could provide direction and speed information during the cue period.
Speed modulation during the visual instruction was found in cells
located predominantly in the PMd. Speed modulation during the
movement was preferentially seen in cells located in MI. Speed
coding during cue, track or both periods was equally probable in the
region close to the central sulcus. In the region 8±12 mm rostral to the
central sulcus, the percentage of speed-modulated cells in the cue
period increased to 67%, while the speed-modulated cells in both
periods or during only track fell to 23 and 11%, respectively (see
Fig. 8A). Thus a rostral±caudal gradient exists for the modulation of
cells by speed information from the visual instruction to the actual
movement phases of the trial sequence. This temporal±spatial
gradient parallels that seen for directional information in this same
task (Fig. 8B). Furthermore, this rostral±caudal gradient is similar to
that found for the prevalence of premovement activity (Weinrich
et al., 1984; Riehle & Requin, 1989; Alexander & Crutcher, 1990;
Johnson et al., 1996) and the prevalence of visuospatial vs.
movement encoding (Shen & Alexander, 1997a,b). This supports
the view that the PMd and MI are a rostral extension of the distributed
visuomotor system begun as the dorsal stream (Godschalk et al.,
1985; Johnson et al., 1996; Battaglia Mayer et al., 1998).
The ®nding that, prior to movement, visual target speed and
direction are processed in the PMd and MI further underscores the
visuomotor nature of the processing in these areas. During the cue
period, visual input has been shown to be an important contributor to
the ®ring of neurons within the PMd and MI. During instructed-delay
reaching tasks, the discharge of these cells is highly modulated for
prolonged periods prior to movement onset by both the visual and
motoric aspects of the task (Tanji & Evarts, 1976; Weinrich & Wise,
1982; Crammond & Kalaska, 1994; Johnson et al., 1996; Shen &
Alexander, 1997a,b; Zhang et al., 1997). Even more complex
sensorimotor processes occur in these traditionally motor areas,
including the control of visuospatial attention (di Pellegrino & Wise,
1993), registration of the sequences of successive visual directional
stimuli (Kettner et al., 1996; Carpenter et al., 1999), quantization of
vibrotactile stimulation speeds (Salinas & Romo, 1998), and mental
rotation (Georgopoulos & Pellizzer, 1995). Therefore, the visuomotor
transforms begun in the caudal dorsal stream are far from completed
in the parietal cortex, as considerable processing occurs in the frontal
motor areas.
Acknowledgements
We wish to thank Mike McPhee for assistance with graphics and histology and
Joan Bailey and Linda King for help with manuscript preparation. Ezra Ebner
assisted in programming. The work was supported by NIH grants 5R01NS31530, 2R01-NS18338 and 5F31-MH11430 and a grant from the Human
Frontier Science Program.
Abbreviations
cue, target movement without hand movement; MI, primary motor cortex; PD,
preferred direction; PMd, dorsal premotor cortex; track, visual pursuit-tracking
of the target with the hand.
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