Description
Points you may want to address in your article analysis and presentation
Introduction
1. Authorship & Collaboration: who was involved in this study? Were the scientists involved in this study working in the same lab at the same institution? If a number of labs or institutions collaborated, in what way may that have been important to the study?
2. What is the specific area of research that this study is about?
3. Is it a hypothesis driven paper or observational/other?
4. Describe the pertinent background information that it is imperative that we know before we can understand the findings of this study.
5. What did you have to research further in order to understand this study and could it have been included in the manuscript?
Methods
1. Describe the main techniques used in the study – think big picture a diagram or table may help
2. What statistical analysis was used?
3. What flaws are there in the methods used in the study? What would you have liked to have also seen?
4. What questions do you still have, what is unclear or excluded?
Results
1. Describe the main findings and overview of the important results, you can use figures from the paper here
2. Discuss the most interesting results and what makes this study impressive
3. Are there errors in their data? Could things have been presented in a better way?
Conclusion/Discussion
1. What conclusions did the authors made from the results?
2. Are the authors mis-representing the significance of the data or are their claims grander than the data presented?
3. What is the next step after this study? What would you love to see them do next?
Unformatted Attachment Preview
J Neurophysiol 94: 4209 – 4223, 2005.
First published August 25, 2005; doi:10.1152/jn.01303.2004.
Arm Movements Evoked by Electrical Stimulation in the Motor
Cortex of Monkeys
Michael S. A. Graziano, Tyson N. S. Aflalo, and Dylan F. Cooke
Department of Psychology, Princeton University, Princeton, New Jersey
Submitted 17 December 2004; accepted in final form 22 August 2005
Graziano, Michael S. A., Tyson N. S. Aflalo, and Dylan F. Cooke.
Arm movements evoked by electrical stimulation in the motor cortex
of monkeys. J Neurophysiol 94: 4209 – 4223, 2005. First published
August 25, 2005; doi:10.1152/jn.01303.2004. Electrical stimulation
of the motor cortex in monkeys can evoke complex, multijoint
movements including movements of the arm and hand. In this study,
we examined these movements in detail and tested whether they
showed adaptability to differing circumstances such as to a weight
added to the hand. Electrical microstimulation was applied to motor
cortex using pulse trains of 500-ms duration (matching the approximate duration of a reach). Arm movement was measured using a
high-resolution three-dimensional tracking system. Movement latencies averaged 80.2 ms. Speed profiles were typically smooth and
bell-shaped, and the peak speed covaried with movement distance.
Stimulation generally evoked a specific final hand position. The
convergence of the hand from disparate starting positions to a narrow
range of final positions was statistically significant for every site
tested (91/91). When a weight was fixed to the hand, for some
stimulation sites (74%), the evoked movement appeared to compensate for the weight in that the hand was lifted to a similar final
location. For other stimulation sites (26%), the weight caused a
significant reduction in final hand height. For about one-half of the
sites (54%), the variation in movement of each joint appeared to
compensate for the variation in the other joints in a manner that
stabilized the hand in a restricted region of space. These findings
suggest that at least some of the stimulation-evoked movements
reflect relatively high-level, adaptable motor plans.
INTRODUCTION
It has long been debated whether the primate motor cortex
controls movement at a level of muscles and joints or at a
higher level of coordinated actions. Single neuron recording
studies have provided evidence for both low level and high
level encoding. Neuronal activity can be correlated with muscle activity, joint rotation, direction of reach, direction of wrist
rotation, and speed of the hand (e.g., Georgopoulos et al. 1986;
Holdefer and Miller 2002; Kakei et al. 1999; Reina et al. 2001;
Scott and Kalaska 1997). Neuronal activity in motor cortex can
even be correlated with learned sequences of movements (Lu
and Ashe 2005). These studies suggest that motor cortex is
heterogeneous and probably encodes a variety of movement
parameters, perhaps reflecting the range of movements that the
animal typically makes.
Recently we found that electrical microstimulation in the
motor cortex of monkeys, when stimulation is applied on a
time scale of one-half a second, can result in complex, multijoint movements (Cooke and Graziano 2004a; Graziano et al.
Address for reprint requests and other correspondence: M. Graziano, Dept.
of Psychology, Green Hall, Princeton Univ., Princeton NJ 08544 (E-mail:
[email protected]).
www.jn.org
2002a,b, 2003). These results also support the view that motor
cortex controls movement at a relatively complex level. However, the details and level of complexity of these stimulationevoked movements have not been examined previously. For
example, in normal voluntary movement, the hand follows a
smooth trajectory with a bell-shaped velocity profile (e.g.,
Bizzi and Mussa-Ivaldi 1989; Flash and Hogan 1985). If a
weight is fixed to the arm, the movement can show compensation, adapting flexibly to the added weight. If one joint
deviates from the desired angle, other joints in the arm can
compensate, thus bringing the hand closer to the goal position. Do the stimulation evoked movements show similar
complexities, or do they instead resemble uncoordinated muscle output?
Three general issues
We measured the stimulation-evoked arm movements in greater detail than had been
done previously, using a high-resolution three-dimensional
(3-D) tracking system to monitor the position of the hand and
the angles of individual arm joints.
DETAILS OF MOVEMENT TRAJECTORIES.
COMPENSATION. We fixed a weight to the wrist and tested
whether stimulation drove the hand to the same final height,
overcoming the weight, or if the added downward force caused
the hand to reach a lower final height. We also examined
whether the stimulation tended to bring the hand to a similar
final location despite trial-by-trial variability in joint angles. If
one joint varied from the optimal posture, would the other
joints vary in a compensatory fashion to stabilize the hand at a
specific location?
CORTICAL LOCALIZATION OF MOVEMENT TYPES. We examined
the clustering of movement types on the cortical surface. In our
previous publications (Cooke and Graziano 2004a,b; Graziano
et al. 2002a), we found a clustering of three types of movement: hand-to-mouth movements; movements resembling defensive or protective gestures; and movements resembling
manipulation of objects in central space. The mapping data in
the present experiment adds to this emerging picture of the
clustering of different types of stimulation-evoked movements.
METHODS
All husbandry, surgical, and behavioral procedures were approved
by the Princeton University Institutional Animal Care and Use Committee and the attendant veterinarian and were in accordance with
The costs of publication of this article were defrayed in part by the payment
of page charges. The article must therefore be hereby marked “advertisement”
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
0022-3077/05 $8.00 Copyright © 2005 The American Physiological Society
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4209
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M.S.A. GRAZIANO, T.N.S. AFLALO, AND D. F. COOKE
NIH and USDA guidelines. We studied two adult male Macaca
fascicularis (4.5 and 7.0 kg).
Surgery
For each monkey, an initial surgical operation was performed under
isoflurane anesthesia and strict aseptic conditions, during which an
acrylic skullcap was fixed to the skull with bone screws. A steel bolt
for holding the head and a 2.5-cm-diam steel chamber for neuronal
recording and electrical stimulation were also imbedded in the acrylic.
The recording chamber was positioned for a vertical (dorsal-ventral)
approach to the precentral gyrus. Each animal recovered from the
surgery within 1 wk but was given 2 additional wk to allow the skull
to grow tightly around the skull screws. In a subsequent procedure,
also under deep anesthesia and aseptic conditions, the recording
chamber was opened, and a hole !10 mm in diameter was drilled
through the layer of acrylic and the bone, exposing the dura.
Neuronal recording and stimulation
During the daily recording sessions, the monkey sat in a Lexan
primate chair with the head restrained by the head bolt. A hydraulic
microdrive (Narishige) was mounted to the top of the recording
chamber. A steel guide tube (an 18-gauge syringe needle) was
lowered through the hole in the skull and into the granulation tissue
that lay over the dura. The varnish-coated tungsten microelectrode
(impedance, 0.5–2 MOhm; Frederick Haer) was advanced from the
guide tube through the dura and into the brain. Typically an electrode
would start with an impedance of !2 MOhm. After repeated use on
multiple days, as the insulation began to wear off near the tip, the
impedance would begin to drop. When clear single neurons were no
longer easily isolable on the electrode, a new one was used for the
next penetration.
The introduction of the electrode into the cortex was confirmed by
monitoring neuronal activity on an oscilloscope and over a loudspeaker. The location of the top of the cortex was estimated as the
depth at which spiking neuronal activity was first found. The electrode
was typically advanced into the cortex 1–2 mm beyond that depth, and
electrical stimulation was tested. On some penetrations, multiple
depths were tested, separated by 0.5 mm. When the electrode was
advanced beyond the cortex and into the white matter, as indicated by
the drop in neuronal cellular spiking, electrical stimulation was no
longer tested, and the electrode was retracted. A systematic test of
different layers of cortex was not attempted in this experiment. In
previous experiments (Graziano et al. 2002a), we found that the
movement evoked by 500-ms stimulation is similar as the electrode is
advanced perpendicularly through cortex and that the movement
thresholds are typically lower in the deeper layers of cortex.
At each cortical site studied, stimulation was applied by an S88
stimulator and two SIU6 stimulus isolation units (Grass, West Warwick, RI). Stimulation consisted of a train of biphasic pulses presented
at 200 Hz. In some cases, 100, 150, and 250 Hz were also tested,
causing similar effects (see RESULTS). Each stimulation pulse had a
negative followed by a positive phase; each phase was 0.2 ms in
duration. In some cases, phases of 0.4 ms were also tested. For most
movements, a 500-ms stimulation train was used.
Current was measured by the voltage drop across a 1-KOhm
resistor in series with the return lead of the stimulus isolation units.
For each site, we varied the current until an evoked movement was
observed. The threshold, the current at which any visible movement
was evoked 50% of the time, was determined by two observers. These
threshold measurements were thus approximate. The average threshold measured in this fashion was 19 ” 14 (SD) !A. Thresholds were
generally lowest (sometimes as low as 5 !A) in the anterior bank of
the central sulcus. For quantification of the evoked movement, the
current was usually set between 25 and 100 !A. The current level was
adjusted until a clear, consistent, multijoint movement of the arm was
obtained, and the quantitative testing was begun.
Asanuma and Arnold (1975) found that extended trains of cathodal
pulses killed the cortical tissue around the electrode tip. With balanced
biphasic pulses, however, it is possible to stimulate for long durations
(seconds) and high currents (milliamps) without measurable damage
(e.g., Tehovnik 1996). Our stimulation parameters are within the
range for cortical stimulation studies in oculomotor, visual, and
somatosensory systems (e.g., Bruce et al. 1985; Freedman et al. 1996;
Gottlieb et al. 1993; Romo et al. 1998; Salzman et al. 1990; Tehovnik
and Lee 1993). To check whether the current damaged the brain, for
each site studied, after stimulating for many trials, we switched the
amplifier to neuronal-recording mode and confirmed that normal
neuronal spiking activity could still be obtained. Second, as Asanuma
and Arnold (1975) pointed out, electrical damage to the brain causes
the effect of the stimulation to disappear after several trials. We found
that the stimulation had a consistent effect over hundreds of trials with
no sign of degradation. Finally, in past experiments using the same
stimulation techniques (Cooke et al. 2003; Graziano et al. 2002a), on
histology we found no visible damage to the cortex associated with
the stimulation sites. These lines of evidence suggest that our trains of
biphasic pulses do not cause extensive cell death, although more
subtle damage cannot be ruled out.
Location of stimulation sites
The monkeys were not killed at the termination of this experiment,
thus the locations of the stimulation sites were reconstructed through
nonhistological means. The central and arcuate sulci were located first
by shining a bright light on the dura during the initial craniotomy
surgery. Both sulci were clearly visible through the dura. The microdrive was mounted to the recording chamber, and the locations of the
visualized sulci were measured with the tip of the guide tube. In this
way, the locations of the sulci were obtained in microdrive coordinates. The sulcus locations shown in Fig. 10 are based on this
procedure.
After the craniotomy surgery, during the daily experiments, the
measured location of the central sulcus was confirmed to within 0.5
mm by recording and stimulating to either side of the sulcus. Just
posterior to the sulcus, in primary somatosensory cortex, we observed
the expected small tactile receptive fields on the contralateral hand
and also the expected lack of effect of electrical stimulation. Just
anterior to the sulcus, we obtained the expected low stimulation
thresholds in primary motor cortex. In monkey 2, the location of the
arcuate sulcus was confirmed by stimulating just anterior to it and
obtaining no skeletomotor movements, but instead obtaining stimulation-evoked saccadic eye movements presumably in the frontal eye
fields. The locations of both the central and arcuate sulci were further
verified by using the pattern of cellular activity and silence obtained
on long electrode penetrations to reconstruct the arrangement of
cortex and white matter.
Within the precentral gyrus, the studied area encompassed the arm
and hand representation and was bracketed laterally by the face
representation and medially by a leg represention. At the most lateral
sites shown in Fig. 10, stimulation sometimes evoked movements of
the arm, hand, and mouth. Sites that were even more lateral (data not
shown) appeared to be in the orofacial region of motor cortex; no arm
or hand movements were evoked from these more lateral sites, only
facial or oral movements. At the most medial sites shown, stimulation
evoked movements of the arm, hand, and leg.
The medial-lateral, anterior-posterior, and dorsal-ventral location of
every tested site was supplied to a mapping program to construct a
3-D model of the cortical area studied. The model was flattened,
collapsing sites onto a 2-D surface and unfolding the anterior bank of
the central sulcus. The plots in Fig. 10 show these 2-D reconstructions, including the gyral surface between the arcuate and central sulci
and the unfolded anterior bank of the central sulcus.
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ARM MOVEMENTS EVOKED BY MICROSTIMULATION
Measurement of limb position
To study the effect of different starting postures, stimulation was
applied while the monkey performed a simple untrained reaching task.
A small piece of fruit was held with forceps by the experimenter at
one of many possible locations around the monkey, and the monkey
reached for the fruit. On approximately two-thirds of the reaches,
stimulation was applied by the experimenter as the hand approached
the target location to within about 1 cm but before the monkey had
grasped the fruit. The purpose was to stimulate at a moment when the
hand was relatively still and at a variety of locations in the workspace
and to do so in a manner that did not entrain the monkey to particular
postures. Stimulation was also applied during the monkey’s spontaneous reaches that placed the hand in different locations in the
workspace, and while the monkey was sitting quietly with the arm
stationary. In this way, stimulation could be tested during a range of
initial postures of the limb. All stimulation trials, whether they took
place during a reach to a fruit reward, during spontaneous reaches, or
when the monkey was resting, were combined in the data because the
resultant stimulation-evoked movement of the arm to a final posture
was similar for all of these cases. Data were collected continuously
during a 3-min block in which an average of 25 stimulation trials were
tested. Two to three blocks were tested for each stimulation site.
The positions of points on the limb were measured by means of an
Optotrak 3020 system (Northern Digital). This system tracks the 3-D
position of infrared light emitting diodes (LEDs). Each LED could be
separately tracked to a spatial resolution of 0.1 mm. The position was
measured every 14.3 ms. To create a marker that could be detected by
the Optotrak cameras from any angle, we glued five individual LEDs
together to produce an omni-directional marker ball. A marker ball
was taped to the monkey’s forefinger on the dorsal surface where it
would not interfere with grasping; on the thumb, again on the dorsal
surface where it would not interfere with grasping; on the back of the
hand, between the knuckles of the third and fourth digits; on the lateral
aspect of the elbow; and on the shoulder. In addition, 14 individual
markers were taped in a double ring around the monkey’s wrist, with
7 markers per ring and a 1-cm spacing between the rings. A marker
was also taped to the monkey’s lower jaw to measure the opening and
closing of the mouth and to measure the relative position of the hand
and face. A marker was taped to the side of the primate chair to
calibrate the position of the monkey with respect to the chair. For the
LEDs attached to the arm and hand, the wires were taped in a bundle
to the underside of the arm and draped behind the monkey. The
monkey’s chair was open at the front and side, allowing for almost
total range of movement of the arm. The monkey’s other arm,
ipsilateral to the stimulating electrode, was not studied with Optotrak
markers. To ensure that this hand would not reach for the fruit rewards
during trials or tear off the markers taped to the measured hand, this
untested hand was fixed to the side of the chair in an arm holder.
The marker balls on the index finger and thumb were used to
measure grip aperture. The marker ball on the back of the hand was
used to measure hand position.
The double ring of 14 markers around the wrist was subject to a
rigid body computation to calculate the center point and spatial
orientation of the wrist. In this computation, for each time-point, a 3-D
rigid model of the double ring of markers was fitted to the measured
positions of the currently visible markers, using a least-squares
method of optimal fit. The orientation and position of the model could
be used to estimate the orientation and center of the wrist. The center
of the wrist was taken to be the mean position of the 14 points in the
model.
Once the orientation and position of the wrist was calculated, the
elbow position in space could be calculated by assuming that the
elbow was a certain distance from the center of the wrist in a direction
specified by the orientation of the wrist. This calculation required
knowing the distance between the elbow and the wrist markers; this
reference distance was measured each day after the ring of markers
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was put on the wrist and was typically about 12 cm. The calculated
spatial position of the elbow, derived from the wrist markers, was
consistently 1–2 cm medial to the marker that was fixed to the lateral
aspect of the elbow, as expected. The calculated position was assumed
to be closer to the actual point of rotation of the elbow, internal to the
arm. For this reason, in reconstructing the posture of the arm, this
calculated elbow position was used rather than the location of the
marker on the surface of the elbow.
The position of the shoulder in space was calculated by analyzing
the position of the elbow over time. Over many time-points, the elbow
described a portion of a sphere, the origin of which was located at the
shoulder joint. For each 3-min block of data, a shoulder position was
calculated by fitting a sphere to the data using a least-squares best-fit
algorithm and using the center of the sphere as the shoulder location.
Because the shoulder is capable of small translational movements in
addition to rotations, this method of estimating shoulder joint location
is approximate but was sufficient for the purposes of this study. When
the shoulder position was calculated multiple times over different time
segments, it varied within #3 cm. Just as for the elbow, the calculated
position of the shoulder joint was assumed to be more accurate than
the measured position of the marker ball fixed to the shoulder. The
calculated position was at the actual point of rotation, as estimated by
the best fit algorithm, whereas the marker ball was fixed to the lateral
surface of the shoulder. Thus in reconstructing the posture of the arm,
the calculated shoulder position was used.
Three shoulder angles were computed: the elevation; the azimuth;
and the “twist” or internal/external rotation of the shoulder joint. We
also calculated the flexion of the elbow; the pronation of the forearm;
the extension of the wrist; the adduction of the wrist; and the grip
aperture. In total, eight degrees of freedom were calculated for the
arm. This model of the arm was verified by applying forward kinematics to estimate the position of the hand. This calculated position of
the hand matched the actual, measured position of the hand to within
an accuracy of 1.5 cm.
Testing the effect of a weight on the arm
On some blocks of trials, a 90-g lead bracelet was wrapped tightly
around the contralateral wrist just proximal to the ring of Optotrak
markers. A 90-g weight represents about 2% of body weight or about
25% of arm weight (arm weight ! 300 g). In pilot tests using heavier
weights such as 130 g, the monkey was unwilling to lift its hand, and
therefore we were not able to test different initial hand locations. In
contrast, with the 90-g weight, the monkey did reach for fruit rewards
and thus we could test a range of initial locations.
For each stimulation site, we first tested the stimulation-evoked
movement without a weight on the wrist in a 3-min block of stimulation trials (typically 25 trials). Then the weight was added, and a
second block of trials was run. This alternation of unweighted and
weighted blocks was continued, typically for four to six blocks.
To help test whether the system showed any evidence of compensation for the weight, we estimated the expected effect that the weight
should have if there was no compensation. To perform this calculation, we modeled the physics of the arm. The model involved two
hinged segments: an upper arm and a forearm. The shoulder had three
degrees of freedom of rotation, and the elbow had one degree of
freedom. The model incorporated the gravitational force on these
segments, a spring-like muscle force acting around each degree of
joint rotation, and a damping term for each degree of joint rotation.
Note that this type of model is deterministic; given the lengths and
weight distributions of the segments, the force of gravity, and the
spring constant, equilibrium angle, and damping force for each joint,
Newtonian mechanics fully specifies the equations of motion. We
used the Denavit-Hartenberg representation, a standard method for
simplifying the equations of a multilink arm (Denavit and Hartenberg
1955), and the recursive Newton-Euler method, a standard method to
transform between kinematic variables and dynamic variables in a
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M.S.A. GRAZIANO, T.N.S. AFLALO, AND D. F. COOKE
multilink arm (Luh et al. 1980; Walker and Orin 1982). The lengths
of the arm segments were taken directly from the Optotrak measurements of the arm. Each segment was approximated as a cylinder of
uniform width and density. The diameter of each segment was taken
as the approximate mean diameter of the monkey’s arm (6 cm). The
total arm weight was set to 300 g, which matched our estimate from
volumetric measures of the arm. Each degree of freedom of joint
rotation was modeled as a spring system and given a spring constant,
an equilibrium angle, and a damping term. These parameters were
different for different stimulation sites. They were determined in the
following way. For each stimulation site tested, we analyzed the data
collected from the arm on actual trials when no weight was present.
We used the joint angles, angular speed, and angular acceleration, and
applied a least-squares method of optimal fit to estimate the spring
constants, equilibrium angles, and damping terms that best characterized the movement for those trials. We incorporated those dynamic
terms into the model. Using the model, we were able to obtain
simulated trajectories that closely matched the real trajectories.
We ran the model with an added 90-g weight on the wrist and
obtained the calculated trajectory and final position of the hand. This
calculation showed, as expected, that the weight should have two
principal effects: because of the increased inertial mass, it should
reduce the peak acceleration of the movement, and because of the
greater downward gravitational force on the hand, it should cause the
final height of the hand to be lower. These effects predicted by the
model were always pronounced because of the fact that that weight
was concentrated at the wrist. We compared this calculated effect of
the weight to the actual data collected with the weight fixed to the
wrist. This comparison is detailed in RESULTS.
RESULTS
Stimulation-evoked hand trajectories: basic description
Hand trajectories were measured during electrical stimulation of 91 sites in motor cortex. Figure 1 shows examples of
hand trajectories evoked by stimulation of 18 typical sites. In
each case, stimulation evoked a movement of the arm toward
a final posture, and thus a movement of the hand toward a final
region of space. As can be seen across the 18 examples,
stimulation of different sites in cortex drove the hand toward
different spatial regions.
Was the convergence of the hand to a location caused by the
stimulation, or was the monkey was somehow cued by the
context to perform a learned movement? While testing the
stimulation site shown in Fig. 1S, after evoking a convergence
of the hand toward a final location, we disconnected the
stimulator from the electrode and tested sham stimulation. The
results are shown in Fig. 1T. The sham stimulation, just like the
actual stimulation, was applied as the hand neared a fruit
reward but before the monkey grasped the reward. Some hand
movement was observed during sham stimulation, but this
movement was minimal and was often directed in a divergent
fashion, away from central space, consistent with the hand
continuing its trajectory toward the fruit reward. The pattern
obtained during sham stimulation was therefore unlike the
pattern obtained during actual brain stimulation at that site and
unlike the pattern found at any stimulation site.
Figure 2A1 shows speed profiles for a typical stimulation
site. To calculate latency, we fit a straight line to the prestimulation baseline data using a least-squares fit, fit a straight line to
the rising phase of the speed, and determined the time at which
the two lines intersected. Calculated in this fashion, the latency
was 82.4 ms. (The average latency among the 91 sites was
80.2 ” 10.9 ms, with a range of 49.9 –103.3 ms).
The speed profiles were usually bell-shaped. This bellshaped property can be seen more clearly in Fig. 2A2. Here, the
same trials are aligned on peak speed. This example site shows
a statistically significant fit to a Gaussian curve (regression
analysis, F $ 405; P # 0.0001). This fit to a Gaussian was
statistically significant for all 91 sites (significance level of
0.05, Bonferroni-adjusted for 91 tests).
Figure 2A3 shows the peak speed as a function of the
distance that the hand traveled. The relationship is roughly
linear, in which greater peak speeds occurred during longer
movements. For this example site, the data show a significant
linear trend (regression analysis, F $ 235; P # 0.0001). A
significant linear trend was obtained for 90 of the 91 sites tested
(significance level of 0.05, Bonferroni-adjusted for 91 tests).
Figure 2, B and C, shows similar results for two more
example sites.
We typically tested sites using 200-Hz stimulation, a frequency
borrowed from the oculomotor literature (e.g., Robinson and
Fuchs 1969). For six cortical sites, we examined the effect of
different frequencies. Figure 3 shows the results for one site tested
with stimulation at 100, 150, 200, and 250 Hz. The movement of
the hand was similar across these different stimulation frequencies. In each case, the hand converged from a range of initial
positions toward a similar final region of space. As shown in Fig.
3E, the peak speed of movement varied somewhat with stimulation frequency. The lowest speeds were obtained with 100-Hz
stimulation; the highest speeds were obtained with 200- and
250-Hz stimulation. This effect of stimulation frequency on hand
speed was significant (F $ 15.53; P # 0.0001). These results
suggest that similar movements can be evoked with a broad range
of stimulation frequencies, although higher frequencies tended to
evoke somewhat higher speeds.
Convergence of the hand toward a location
The most consistent feature of the stimulation-evoked movements was the convergence of the arm toward a posture, and
thus the convergence of the hand toward a region of space.
This convergence can be seen in the 18 example sites depicted
in Fig. 1. Figure 4A shows this convergence in greater detail for
a typical site. The convergence of the hand is shown in each of
the three Cartesian dimensions. To study the degree of convergence further, we calculated the “mean-distance-to-center”
and analyzed how this metric evolved over time during the
stimulation. The mean-distance-to-center is a measure of dispersion around the mean. For each time increment, we first
calculated the mean hand position across all trials. Then, for
each trial, we computed the radial distance from the hand to
that mean position. Finally, we averaged these distances across
trials to arrive at the mean-distance-to-center.
Figure 4A4 shows that, for this example site, the meandistance-to-center began at a high level and dropped during
stimulation. This drop in mean-distance-to-center began about
80 ms after the onset of stimulation. It is important to note that
this 80 ms is not simply the latency for the hand to move.
Rather, it is the latency with which the hand began to converge
toward a tighter cluster of locations. To determine if the degree
of convergence was statistically significant, we compared the
mean-distance-to-center at the start of stimulation and at the
end of stimulation (paired t-test). The mean-distance-to-center
was significantly reduced at the end of stimulation (t $ 14.58;
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ARM MOVEMENTS EVOKED BY MICROSTIMULATION
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FIG. 1. Examples of hand movements evoked by microstimulation in motor cortex. A: monkey drawing indicates approximate size, location, and perspective of the
monkey within the square frame. Height of frame represents 50 cm. B–S: stimulation-evoked hand movements from 18 typical stimulation sites. Each thin black line
shows path of the hand during a stimulation train. %, start of movement. Black dot indicates end of movement. In a small number of trials, tracking markers were
transiently blocked from view of the camera because of the specific posture of the limb. In these cases, the trace is interrupted. T: result of mock stimulation in which
wires to the electrode were disconnected but all other aspects of testing were the same. Traces in S show result for this same cortical site when the wires were connected.
P # 0.0001), indicating that the stimulation caused a significant spatial convergence.
Figure 4, B and C, shows similar results for two more
example sites. These examples are typical of the population. Of
the 91 sites tested, all 91 showed a statistically significant
degree of convergence of the hand (significance level of 0.05,
Bonferroni-adjusted for 91 tests).
Figure 4D shows the mean result for all 91 sites tested. Just
as for the individual examples, for the group data, the mean-
distance-to-center began to drop after stimulation, and the
latency of this drop was !80 ms.
Effect of a weighted bracelet on the final stimulation-evoked
height of the hand
As shown above, for almost all cortical sites, stimulation
caused the hand to converge from any initial position toward a
final region of space. How is this final position affected by a
J Neurophysiol • VOL 94 • DECEMBER 2005 • www.jn.org
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M.S.A. GRAZIANO, T.N.S. AFLALO,
