Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri 63110
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ABSTRACT |
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Pruett, J. R., Jr., R. J. Sinclair, and H. Burton. Response Patterns in Second Somatosensory Cortex (SII) of Awake Monkeys to Passively Applied Tactile Gratings. J. Neurophysiol. 84: 780-797, 2000. This experiment explored the effects of controlled manipulations of three parameters of tactile gratings, groove width (1.07-2.53 mm), contact force (30-90 g), and scanning speed (40-120 mm/s), on the responses of cells in second somatosensory cortex (SII) of awake monkeys that were performing a groove-width classification task with passively presented stimuli. A previous experiment involving an active touch paradigm demonstrated that macaque SII cells code groove-width and hand-movement parameters in their average firing rates. The present study used a passive-touch protocol to remove somatosensory activation related to hand movements that accompany haptic exploration of surfaces. Monkeys maintained a constant hand position while a robotic device delivered stimulation with tactile gratings to a single stabilized finger pad. Single-unit recordings isolated 216 neurons that were retrospectively assigned to SII on histological criteria. Firing patterns for 86 of these SII cells were characterized in detail, while monkeys classified gratings as rough (1.90 and 2.53 mm groove widths) or smooth (1.07 and 1.42 mm groove widths), with trial-wise random, parametric manipulation of force or speed; the monkeys compared 1.07 versus 1.90 mm and 1.42 versus 2.53 mm in alternating blocks of trials. We studied 33 cells with systematic variation of groove width and force, 49 with groove width and speed, and four with all three variables. Sixty-three cells were sensitive to groove width, 43 to force (effects of random force in speed experiments contributed to N), and 34 to speed. Relatively equal numbers of cells changed mean firing rates as positive or negative functions of increasing groove width, force, and/or speed. Cells typically changed mean firing rates for two or three of the independent variables. Effects of groove width, force, and speed were additive or interactive. The variety of response functions was similar to that found in a prior study of primary somatosensory cortex (SI) that used passive touch. The SII sample population showed correlated changes (both positive and negative) in firing rates with increasing groove width and force and to a lesser degree, with increasing groove width and speed. This correlation is consistent with human psychophysical studies that found increasing groove width and force increase perceived roughness magnitude, and it strengthens the argument for SII's direct involvement in roughness perception.
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INTRODUCTION |
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The question of
SII's involvement in roughness discrimination motivated the present
investigation. This study used passively applied stimuli to
characterize the effects of tactile grating groove width,
contact force, and scanning speed on responses of macaque SII neurons.
Only two previous single-unit recording studies have investigated
SII's role in roughness perception (Jiang et al. 1997;
Sinclair and Burton 1993
).
Several investigators have used single-unit recording techniques to
explore involvement of macaque primary somatosensory cortex (SI) in
roughness perception (Ageranioti-Bélanger and Chapman 1992; Chapman and Ageranioti-Bélanger
1991
; Darian-Smith et al. 1982
, 1984
;
Phillips et al. 1988
; Sinclair and Burton 1988
,
1991a
,b
; Sinclair et al. 1996
; Tremblay
et al. 1996
). In a study where monkeys controlled their hand
movements during surface examination, mean firing rates for many SI
cells changed as grating groove width, force, and/or speed increased
(Sinclair and Burton 1991a
). Responses from many SI
cells displayed positive functions of increasing groove width, but a
small number of cells showed negative functions of groove width.
Responses from most SI cells followed changes in two or three
variables, groove width, force, and speed.
Sinclair and Burton described SII cells that coded roughness and/or
movement parameters in their average firing rates (Sinclair and
Burton 1993). The majority of these SII cells had response functions for these variables that resembled those found in SI (Sinclair and Burton 1991a
) and the ventral posterior
lateral nucleus of the thalamus (Sinclair et al. 1991
).
Factors that distinguished SII from SI cells appeared unrelated to the
processing of roughness. These included SII cells with larger receptive
fields, no passively definable receptive fields, responses to other
stimulus events in the task but not to gratings, and a general lack of
periodic entrainment to grating temporal periods.
Experiments involving passive touch removed the effects of hand
movement by scanning surfaces across the immobilized finger pad
(Jiang et al. 1997; Sinclair et al. 1996
;
Tremblay et al. 1996
). Hand motion during voluntary
movements extensively activates proprioceptive and cutaneous receptors
(Hulliger et al. 1979
), which may simultaneously affect
activity of individual SII cells, obscuring the contribution of any
single variable. The large number of connections between SII and motor
cortices (for reviews: Burton and Sinclair 1996
;
Felleman and Van Essen 1991
) creates a further complication from potential modulation contingent on motor execution and planning.
Jiang and colleagues employed a passive-touch paradigm to conduct a
limited exploration of roughness processing in macaque SII
(Jiang et al. 1997). They used embossed dots for
roughness stimuli but did not test effects of force and speed. They
reported that many SII cells responded in a nongraded fashion to
differences in roughness: a step increase in firing rate above versus
below some threshold dot spacing. Consequently, they argued that SII cell average firing rates only signal changes in roughness, providing no information about roughness magnitude. Using an active touch paradigm, however, Sinclair and Burton (1993)
found
graded changes in mean firing rates when grating groove width changed,
indicating that SII cell response functions provide information about
roughness magnitude. The present study with a passive-touch protocol
also found significant linear correlations between SII cell mean firing rates and magnitudes of grating groove width, force, and speed.
In this study, monkeys dichotomously classified the roughness of passively applied gratings with trial-wise random levels of applied force or speed. Recordings characterized the effects of force and speed on SII cell groove width response functions. Findings strongly implicate, in subsets of SII neurons, mean firing rate codes that follow the parameters of roughness stimuli, bolstering the contention that SII critically serves roughness perception.1
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METHODS |
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Subjects
The experimental subjects were two male rhesus monkeys that weighed 9 kg each and were 12 (MM23) and 7 (MM26) years old. During training and recording, they acquired their daily fluid (150-375 ml) by working for a fruit-flavored drink. The monkeys received bits of fruit for cooperation during receptive field mapping and liberal amounts of water and fruit on days off. Access to dry food was unrestricted.
Stimuli
Tactual grating stimuli consisted of a 22 × 70 mm, smooth,
raised start pad (~0.5 mm high) that was followed by a 22 mm wide by
50 mm long section of raised ridges (~0.5 mm high) (see Fig. 1, H and I). The
start pad and ridges were made of plastic (polyamide) on a metal
backing (Nyloprint; Process Color Plate, Chicago, IL). The start pad
minimized physical and neural transients that would occur when a moving
grating edge strikes the finger pad. The ridge land width was nominally
0.25 mm. The groove width between these ridges was the intended
variable for roughness. For gratings with fixed ridge width, the human
magnitude estimate of roughness is a positive function of increasing
groove width, up to ~3.0 mm (Lederman and Taylor 1972;
Sathian et al. 1989
; Sinclair et al. 1995
). Four different groove width gratings, 1.07, 1.42, 1.90, and 2.53 mm, were created for neural recordings, and these 1.07- to
2.53-mm groove widths, therefore, span the major ascending portion of
the human roughness magnitude estimation function (Connor et al.
1990
; Sinclair and Burton 1991b
; Sinclair
et al. 1995
). Stimuli were inspected by microscope. We used
gratings that were free of imperfections and that had ridge and groove
widths within ~0.03 mm of intended values.
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Stimulus control
As described previously (Sinclair et al. 1996), a
robotic device positioned a platter with different grating surfaces
underneath a single finger pad. Stepper motors (e.g., Fig.
1E) positioned stimuli vertically and horizontally during
each inter-trial interval. Before recording neural activity, an
iterative routine set platter heights to levels that gave desired force
readings. These could be modified interactively by the experimenter
during recording. During inter-trial intervals, the platter was
shuffled, thereby eliminating potential auditory or vibratory cues
about the platter location of the next grating. All parts of the
stimulator and stimulus surfaces were obscured from view of the
monkeys. The monkeys performed at chance, quit, or adopted a response
bias (e.g., repeatedly pulling the foot pedal up) if the grating
contact force approached 0 g, providing convincing evidence they
found no alternative strategies for performing the task. An audio
speaker, 2 m behind the monkey, delivered pink noise to mask room
noises during the experiment. Neural signals were monitored with
headphones. Oscilloscope displays could not be seen by the monkey.
Another motor (not shown) pulled hydraulic pistons (e.g., Fig. 1F), which mounted to the platter, moving the gratings across the finger pad. This arrangement smoothed the platter motion and damped potential vibrations produced by the motor, thereby eliminating unwanted vibratory stimuli. One personal computer (PC) controlled trial sequences, the stimulator, performance, and neural data acquisition; a second PC displayed behavioral control graphics on a computer monitor seen by the monkey.
A linear potentiometer (Fig. 1G, partly hidden by the platter) sampled position during the platter stroke with a resolution of 0.1 mm to calculate speed. Position was sampled at 500 Hz. Speeds were always within ~10% of the target values. Therefore for simplicity, some statistical analyses of the data used speed categories based on the intended speed values.
A strain gauge in the platter (Fig. 1D) measured average force with a resolution of ~1 gm and instantaneous force with a resolution of ~7 g. The force signal was low-pass filtered with a seventh-order Cauer elliptic filter (670 series, stop-band edge 147 Hz; Frequency Devices). Force was sampled at 500 Hz to resolve any fluctuations caused by the grating ridges during stimulation.
Behavioral control for passive touch
A hand and finger restraint was constructed for each monkey's
left hand using elastomer putty (A95B-2; Smith and Nephew Rolyan) that
was rigidly supported by fiber-glass, aluminum, and thermoplastic material (Hexcelite; Hexcel Medical Products). Prior to and during stimulation, the monkey had to insert its hand in this apparatus (Fig.
1B). The stimulated finger passed through an opening that provided access to the finger pad from below. The fingernail of the
stimulated finger engaged a plunger that depressed a tube-mounted micro-switch (Fig. 1, inset). The four nonstimulated fingers
depressed paddles made of thermoplastic, each of which closed a
micro-switch (e.g., Fig. 1A). Minor finger movements would
break switch contact. Maintained closure of these switches was
necessary for successful completion of a trial. The monkeys could
remove their hands at will. Generally, they maintained hand position in
this device for 40-60 min. Video of the hand and finger during
stimulation in training sessions verified that monkeys kept the hand
and fingers essentially immobile during stimulation. We stimulated the
left hand, only, and recorded cells in the right (contralateral)
hemisphere. We trained the monkeys to use the right (ipsilateral) foot
for the operant response, to confine any potential neural correlate for
the subsequent foot movement to part of the body representation farthest from the finger representation in SII on the recorded side. In
addition to closing the five finger switches, the monkey had to toggle
a three-position foot switch (Fig. 1C) to the middle (neutral) position with the right foot to initiate a trial. Maintenance of this neutral foot pedal position was also required for trial completion.
Trial sequence
A computer monitor, placed directly in front of the eyes, at a distance of ~1 m presented task related cues to the monkey. Figure 1 summarizes the events of a trial. A gray, inter-trial screen (between times 1 and 2, Fig. 1) followed platter repositioning for the next grating (before 1, Fig. 1). The monkey stopped moving the foot pedal and waited a random 1-4 s for a white, ready box. Moving the foot pedal during presentation of a gray screen activated an alarm tone and set the inter-trial interval to 4 s from the time of each offense. This feedback discouraged continued movements following the reward period from the previous trial. After a white, ready box appeared (between 2 and 3, Fig. 1), the monkey had to keep all five finger switches closed and toggle the foot pedal to neutral. When the fingers and foot were in place, a 200ms pause (between 3 and 4, Fig. 1) followed the appearance of a green, neutral box, which remained lit. The robotic stimulator, then, began to compress the finger with the start pad of the grating (4, Fig. 1), by raising the platter (Fig. 1D) to a predetermined height that gave the desired level of contact force during grating stimulation. Compression of the finger pad reached a maximum force level when the platter stopped at the specified height for this trial (between 4 and 5, Fig. 1). A 450-ms pause allowed peak static contact force to drop to a steady-state level as the skin of the finger pad deformed with compression. This wait period minimized potential neural effects that accompany the changing conformation of the elastic finger-pad tissue. Then the stimulator accelerated the grating start pad (5, Fig. 1) in a direction proximal to distal along the finger pad, reaching constant speed before the ridges contacted the finger (6, Fig. 1). Deceleration from constant speed (9, Fig. 1) did not begin until after the last ridge of the grating had moved out of contact with the finger. The grating therefore always moved at constant speed when in contact with the monkey's skin. A 700- or a 1,500-ms delay period (9-10, Fig. 1), assigned randomly, followed the cessation of platter motion. The monkey had to maintain finger switch contact and neutral foot position through this delay. These requirements provided additional assurance that neural activity seen during grating stimulation was not confounded by neural activity associated with postural adjustments in preparation for the motor response. Display of a white screen replaced the green, neutral box after this delay (10, Fig. 1). The monkey, then, had 1.5 s to make a behavioral response with the foot pedal. In this period, one monkey pulled the pedal up for rough and pushed down for smooth. For the other monkey, the pedal directions had the opposite meanings. Display of a yellow screen, a relay click, and ~1 ml of fruit flavored drink followed a correct roughness classification. A red error screen, an alarm tone, and a 10-s inter-trial penalty time followed an error. The monkey could move freely at operant response time and for ~4 s while the platter was repositioned for the next trial. If the monkey broke micro-switch contact or moved the foot pedal from neutral before the white response screen, that trial was aborted. The error tone sounded, the red abort box appeared, the platter dropped, and a 2-s time penalty was added to the inter-trial interval. If the monkey exceeded the 1.5 s allowed for the operant response, the error tone sounded, the red screen appeared, the platter dropped, and a 2-s time penalty was added to the inter-trial interval.
Experimental design
The monkeys learned to classify tactual gratings as rough or
smooth (wider or narrower groove width) regardless of applied force or
scanning speed. Short blocks of 24-36 trials with different grating
pairs alternated while recording isolation persisted to create groove
width response functions for each cell. One block of trials used the
1.07 mm (smooth) and 1.90 mm (rough) gratings, and the other used the
1.42 mm (smooth) and 2.53 mm (rough) gratings. The grating pairs in
each block were equally discriminable; the smooth grating had 44%
narrower grooves than the rough grating (defined as the difference in
groove widths divided by the maximum), and over this range of groove
widths, equal percent differences have closely perceived differences in
roughness for both humans and monkeys (Sinclair and Burton
1991b). In each block of trials, gratings were presented, one
at a time, with the random scheduling of rough and smooth grating
presentations constrained so that the same operant response was never
required more than four trials in a row, which discouraged adoption of
response bias (i.e., predilection to choose a particular pedal response).
Two different experiments were run, and subsequent statistical analyses
treated both as four by three factorial designs, with an additional
analysis model that included force as a continuous variable when speed
and groove width were manipulated (see following text). In the
force/groove-width experiment, each of the four gratings was delivered
at three target force levels, 30, 60, or 90 g, which were
trial-wise randomized, while speed was held constant at 80 mm/s. In the
speed/groove-width experiment, three levels of grating scanning speed,
40, 80, and 120 mm/s, (proximal to distal on the finger pad), were
trial-wise randomized, with a nominal force of 60 g. Force and
speed levels were chosen from the range values previously used by
humans (Lederman 1974) and monkeys in active touch
studies (Sinclair and Burton 1991a
,b
, 1993
). Engineering
constraints placed an upper limit on the range of speed used. In each
block the 12 trial types (a sub-block), consisting of one rough and one
smooth grating at three forces or speeds and at two response delays
(700 or 1,500 ms), repeated two to three times. Typically, recording
isolation stability only allowed enough time for either the
force/groove-width or speed/groove-width experiment to be run on any
one cell. Mechanical repositioning of gratings between trials dictated
a maximum stimulation rate of two to three trials per minute.
Both monkeys typically performed 80% correct in both experiments. If the monkey caused a trial abort or did not make a foot pedal choice before the response period elapsed, the stimulus combination used on that trial was repeated at the end of that sub-block. The data from aborted trials were excluded from statistical analyses. These measures forced the monkeys to classify groove width for all stimulus combinations and helped ensure balanced data sets. The monkeys usually aborted fewer than two trials in any recording session.
Single-neuron recording procedures
Surgical and postoperative care procedures for placing a
recording chamber over lateral parietal cortex were similar to those described previously (Burton et al. 1997;
Sinclair and Burton 1993
). A grid pattern of burr marks
was placed on the bone surface in the recording chamber. The centers of
the recording chambers were approximately: AP +11 mm, ML +27 mm at
60° tilted away from sagittal. All surgical, preoperative, and
postoperative procedures were in accord with the National Institutes of
Health Guide for the Care and Use of Laboratory Animals.
A stepper-motor hydraulic microdrive (Model 650; David Kopf) advanced
locally produced, glass-coated, platinum-iridium micro-electrodes through ~2-mm-diam craniotomy holes, that were opened as the
experiment proceeded. Electrodes measured 1.2-4.5 M at 1 kHz (Model
IMP-1; impedance meter, BAK, Rockville, MD).
Neural action potentials were amplified and filtered between 300 and 3,000 Hz. A commercial hardware and software system (DataWave, Longmont, CO) simultaneously recorded on separate channels the filtered analog neural signal, acceptance pulses from a dual amplitude and time window discriminator (BAK), video sync from the behavioral control graphics monitor, force on the platter, and position of the gratings. The first three channels were sampled at 20 kHz. This commercial system applied time stamps to recorded events with 100-µs resolution. Off-line waveform processing of the analog neural signal provided a means to verify the window discriminator acceptance pulses. Rarely, a second, distinct waveform could be isolated in this manner. Extremely strict criteria for single-unit isolation were used because of the quantitative nature of the analyses.
Selection criteria and receptive field assessments
Repeated penetrations were made at the same locations over successive days. Sampling proceeded from superficial to deep locations to maximize data yield from shallow depths before penetrations were driven through for deeper recording loci. Routinely cells were isolated for study while the animals worked. We selected cells that appeared to be engaged by the task. Bias, if any, was toward cells with high firing rates during application of the gratings. After collecting data during stimulation with gratings, we determined the spatial extent of receptive fields by manual palpation and examination with probes and brushes. Adaptation to manually applied punctate indentation was used to classify cells as slowly, intermediately, or rapidly adapting. Cells were classified as Pacinian or non-Pacinian based on responses to tuning forks. We assigned lateralities (contralateral, contralateral best, or bilateral) and submodalities (cutaneous, joint, hair, mixed) where possible. Force thresholds were determined with calibrated nylon filaments (Stoelting) for a subset of the recorded cells.
Data analyses
A custom program sorted the data and created rasters and histograms for visual inspection. From these we manually selected portions of response trains from each trial for statistical analyses in the following way. First, the end of the analysis interval (window) was defined as the time in each trial when the force first began to drop because the trailing edge of the grating was moving past the compressed finger pad (8, Fig. 1). Force then fell to 0 g as the grating moved completely past the finger (between 8 and 9, Fig. 1). Next, we set the start of this same analysis window to an earlier time that corresponded to a position on the grating 40 mm closer to the beginning of the ridges (7, Fig. 1). The proximal-distal axis of the monkey's compressed finger pad was ~7-9 mm. Therefore this last 40 mm of a 50-mm grating was chosen for statistical analysis. We chose these start and stop points because for part of the first 10 mm of grating (6-7, Fig. 1), the finger was in transition from the start pad (unfilled portion of grating box schematic before 6, Fig. 1) to the ridged grating surface (solid portion of grating box, 6-8, Fig. 1). The compressed part of the finger pad was, therefore wholly and only in contact with a grating during this analysis period (7-8, Fig. 1). As a final step, we inspected the responses during the defined window and adjusted its limits to ensure that they excluded transients, such as phasic bursts at the beginning and end of the gratings. A 40-mm grating spatial extent corresponded to 500 ms of stimulation at 80 mm/s and 1,000 ms and 333 ms at 40 and 120 mm/s, respectively. Windows shorter than 200 ms were not used for statistical analyses. Average force, speed, and neural firing rates were computed during the selected analysis intervals.
Further statistical analyses were carried out using SAS (SAS v6.03 and v6.12, SAS Institute). In the force/groove-width experiments, GLM ANOVA models (robust to unbalanced designs) examined effects of groove width and force and first-order interaction effects of groove width and force on average firing rate (AFR). Force varied from the intended 30, 60, and 90 g. Therefore each trial was assigned to one of five force bins, 0-25, 26-50, 51-75, 76-100, and 100+ g. These bins were defined for graphs in the figures in RESULTS by their centers: 12.5, 37.5, 62.5, 87.5, and 112.5 g. The force bins became levels of a class variable for force effects in the ANOVAs.
The speed/groove-width experiments were analyzed with both class
variable GLM ANOVA models (different groove widths and speeds were the
levels of class variables) and class and continuous variable GLM ANOVA
models (groove width and speed were classes, and force was run as a
continuous variable). The models tested for main effects of groove
width, speed, and force and first-order interactions between these
independent variables with AFR as the dependent variable. In the
speed/groove-width experiments, some variation around the intended
60 g level allowed the assessment of force effects. In these
experiments, the model with force as a continuous variable tested for
global effects of force and for homogeneity of the slopes of the
functions of AFR versus force (continuous variable) across all
combinations of the two classification variables, groove width and
speed. For both the force/groove-width and the speed/groove-width
experiments, ANOVA models containing a block-replicate term (when sets
of both pairs of gratings had been repeated 2 times) were considered
as needed.
Partial Pearson correlation coefficients (SAS), which measure the
strength of the linear relationship between two variables with effects
of a third variable removed (McNemar 1969), determined the strengths of the linear relationships between AFR and groove width,
AFR and force, and AFR and speed. Partial correlation analyses included
groove width, force, and average firing rate terms for the
force/groove-width experiments and groove width, force, speed, and
average firing rate terms for the speed/groove-width experiments. These
analyses assessed linear components in the SII cell response functions.
A significant partial Pearson correlation coefficient does not exclude
the presence of significant nonlinear components in the response
functions. However, with only four groove widths, the precise
characterization of the nonlinear components in the SII cell groove
width response functions was not meaningful. The partial Pearson
correlation coefficients, therefore approximated the degrees of
response function linearity. Cells were then assigned as having
positive or negative response functions for groove width, force, or
speed, if the class-variable GLM ANOVA effect of groove width, force,
or speed was significant (P < 0.05) and the partial Pearson correlation coefficient between AFR and the variable of interest was also significant (P < 0.05). The
class-variable ANOVAs simply tested for differences in AFR, and the
partial correlation analyses tested for the presence, strength, and
directionality of linear components in the cell response functions;
i.e., a cell could have a significant ANOVA effect but no significant
partial correlation results if its AFR function was peaked. The sign of the AFR response function designation, positive or negative, was determined by the sign of the partial correlation coefficient. A simple
correlation analysis looked for correlations between these partial
Pearson correlation coefficients, themselves, in the sample cell
population with significant ANOVA effects (i.e., the sample that was
sensitive to groove width, force, and/or speed).
Assessment of SII cells' abilities to distinguish between changes in the magnitude of groove width within the categories of smooth (1.07 and 1.42 mm) and rough (1.90 and 2.53 mm) was achieved as follows. t-tests examined, post hoc, the significance of mean firing rate differences between adjacent pairs of gratings, 1.07 and 1.42 mm, 1.42 and 1.90 mm, and 1.90 and 2.53 mm, (1-tailed) Bonferroni corrected for the three grating comparisons. Means were compared in this way at or across force and/or speed levels. If a cell distinguished between 1.07 and 1.42 mm and/or 1.90 and 2.53 mm, regardless of its ability to distinguish between 1.42 and 1.90 mm, then that cell was classified as providing information about groove-width magnitude. If, alternatively, the cell AFR function only distinguished the 1.42 and 1.90 mm groove widths, that cell could only strictly be classified as discriminating rough and smooth categories, a possibly behaviorally related effect, and not groove width magnitude, a more stimulus-level effect. These tests proved to impose extremely strict criteria for discrimination of groove-width magnitude, because of the low numbers of trials (hence the high AFR variance) at the individual groove width and force or groove width and speed combinations.
Histology
Monkeys were killed with an intravenous lethal overdose of pentobarbital sodium after sedation with ketamine hydrochloride (5-10 mg/kg). Transcardiac perfusion with buffers and aldehydes fixed the brains. The blocked brains were frozen and sectioned at 50 µm. Serial sections through the recorded area were stained with thionin and examined for evidence of electrode tracks and electrolytic lesions. The Animal Care and Use Committee at Washington University approved the methods used to kill the monkeys, methods that are consistent with the recommendations of the Panel on Euthanasia of the American Veterinary Medical Association.
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RESULTS |
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Overview
We determined receptive field properties for 300 cells recorded near the upper bank of the lateral sulcus. Histological recovery of electrode tracks, electrolytic lesions, ink, and dye marks allowed 216 of these to be assigned to SII, 17 to area 2, 47 to area 7b, 7 to the lower bank of the lateral sulcus. The other 13 cells with defined receptive fields could not be assigned to a cortical area with any certainty. No electrode tracks crossed into the insula. Figure 2 shows representative sections where electrode tracks passed through relative anterior, B and D, and posterior, A and C, parts of SII in both monkeys.
|
One hundred and sixty-two cells were studied with 20 trials in either
a force/groove-width or a speed/groove-width experiment. Recording
isolation persisted for
40 trials in 102/162 (63.0%) (well-characterized) cells. The histologies placed 86 of these in SII,
8 in 7b, and 8 could not be assigned. We could not separate the eight
7b and eight unassigned cells from the SII group based on responses to
groove width, force, and speed. They do not constitute statistically
reliable samples and will not be considered further. The
well-characterized sample of 86 SII cells is the subject of this
discussion because multiple force or speed levels were used for all
four gratings. Effects seen in experiments with fewer than 40 trials
were often qualitatively similar to those seen for cells with
40
trials. The force/groove-width experiment was run on 33/86 (38.4%)
cells, the speed/groove-width experiment was run on 49/86 (57.0%)
cells, and
40 trials of both experiments were run on 4/86 (4.7%)
cells (i.e., 37 force/groove-width and 53 speed/groove-width
experiments on 86 cells; 50 cells were from monkey MM23, and
36 cells were from monkey MM26). We observed significant
ANOVA effects of groove width, force, and/or speed on AFR in 72/86
(83.7%) of these SII cells, and the results from the two monkeys were similar.
Most of these SII neurons responded to cutaneous stimulation of the
glabrous skin on the hand contralateral to the recorded hemisphere. We
normally encountered either small receptive fields, covering from part
of one finger to all of one or more finger(s), or larger receptive
fields that included the entire hand and digits. Most cells were
rapidly adapting, and many responded, at least weakly, to manual
stimulation of the mirror-symmetric site, ipsilateral to the recorded
hemisphere. Specifically, 22/86 (25.6%) cells responded exclusively to
contralateral stimulation; 24/86 (27.9%) responded more strongly to
contralateral than to ipsilateral stimulation; 14/86 (16.3%) responded
equally to contralateral and to ipsilateral stimulation; and none of
the cells responded exclusively to or better to ipsilateral
stimulation. In 21/86 (24.4%) cells, recording isolation was lost
before laterality could be carefully evaluated, and in 5/86 (5.8%),
laterality could not be determined. In the sample, 25/86 (29.1%) cells
responded exclusively to cutaneous stimulation, 7/86 (8.1%) responded
primarily to joint stimulation, 15/86 (17.4%) responded to mixed modal
stimulation, 13/86 (15.1%) had a better response when the monkey
actively engaged a stimulus and were not well engaged by passively
applied stimuli, and 1/86 (1.2%) had an inhibitory response to
manually applied stimuli (in 20/86 or 23.3% of cells, recording
isolation was lost before submodality could be carefully evaluated, and
in 5/86 or 5.8%, submodality could not be determined). Consistent with
previous findings (Sinclair and Burton 1993), periodic
entrainment to grating temporal periods, measured by inspection of
autocorrelation histograms, was essentially absent in SII. For 19 SII
cells, isolation persisted through force threshold determination with
hand-held, calibrated nylon filaments. Force threshold mean and
standard error of the mean (SE) were 422 ± 151 mg: minimum = 20 mg, maximum = 2,950 mg, median = 258 mg. Sampling
procedures were biased toward cells responding to gratings. Therefore
this sample may not reflect the general characteristics of SII. The
sample was heterogeneous, but statistical analyses found no significant
correlations between receptive field characteristics and the effects of
groove width, force, and speed.
Positive and negative response functions for increases in groove width
Sixty-three of 86 SII cells had significant differences in AFRs
when groove width changed (ANOVA P < 0.05). In 31/63
cells, these effects were additive or isolated (ANOVA main effects); 25 of these had effects of groove width and at least one other variable,
and 6 had isolated effects of groove width. In 32/63 cells, these
effects were nonadditive with effects of force and/or speed (ANOVA
interactions). The cells with additive or isolated effects of groove
width will be considered first (followed by those of force and speed),
and the nonadditive effects will be explained and described later. Of
the 31 SII cells with additive or isolated effects of groove width, 27 cells had response functions that could be described as either positive
or negative (see METHODS: significant ANOVA and significant
positive or negative partial Pearson correlation coefficient between
AFR and groove width, rAFR/GW). Four of 31 cells
showed significant differences in AFR as groove width changed, but
their rAFR/GW's were nonsignificant. There were roughly equal numbers of positive and negative cells in the
sample: 14 of the 27 assigned cells had positive AFR response functions
for groove width, rAFR/GW (mean ± SE) = 0.49 ± 0.03, and the other 13 cells had negative
AFR response functions, rAFR/GW = 0.55 ± 0.05.
Figure 3 shows an example of a cell with
a positive response function for groove width. Its AFR rose from
55.1 ± 4.4 to 100.8 ± 3.5 (SE) spikes/s (averaged across
all forces) as groove width increased from 1.07 to 2.53 mm
[F (3, 51) = 24.67, P = 0.0001, rAFR/GW = 0.70, P < 0.0001]. On average,1 as can be inferred from
the graph (Fig. 3A), this cell's AFR differences could have
provided the monkey with sufficient information to classify correctly
the groove widths of the comparison gratings (1.07 vs. 1.90 and 1.42 vs. 2.53 mm). Examination of AFR differences for adjacent gratings,
1.07 versus 1.42, 1.42 versus 1.90, and 1.90 versus 2.53 mm groove
widths, (see METHODS) showed that this cell additionally
provided statistically reliable information about changes in groove
width magnitude within the smooth and/or rough categories (smooth = 1.07 and 1.42 mm, rough = 1.90 and 2.53 mm) at two force levels.
Specifically, it showed significant (P < 0.05, Bonferroni corrected for the 3 grating comparisons) changes in AFR from
1.07 to 1.42 mm, 42.8 ± 6.5 to 68.7 ± 2.5 spikes/s
[t = 3.30, df = 12, P < 0.01 (all Ps in this format are Bonferroni corrected for the 3 grating comparisons)] in the 37.5-g force bin and from 1.90 to 2.53 mm, 59.5 ± 7.9 to 86.7 ± 3.3 spikes/s (t =
2.82, df = 12, P < 0.03) in the 37.5-g force bin and 87.7 ± 5.3 to 115.3 ± 2.8 spikes/s
(t =
4.63, df = 10, P < 0.002)
in the 87.5-g force bin.
|
The example depicted in Fig. 3 had an effect of groove width on AFR that was additive with an effect of force on AFR, which is visible in the graph as the upward shift of the AFR functions for groove width at increasing force bins. Its AFR rose from 62.5 ± 4.2 to 89.2 ± 4.2 spikes/s (averaged across all groove widths) as force increased from the 37.5 to the 87.5-g bin [F (4, 51) = 14.92, P = 0.0001].2 Its partial Pearson correlation coefficient for AFR and force (rAFR/FORCE) was 0.64, P < 0.0001. The cell's groove width response functions have approximately the same shapes and slopes, illustrating the additive nature of this cell's ANOVA main effects. Also note for this cell that AFR changed 45 spikes/s for a 2.4-fold change in groove width, but it only changed by ~27 spikes/s for a 2.3-fold change in force.
Figure 4 shows an example of a cell with
a negative response function for groove width. Its AFR dropped from
46.7 ± 2.4 to 14.0 ± 1.3 spikes/s as groove width increased
from 1.07 to 2.53 mm [F (3, 51) = 84.14, P = 0.0001, rAFR/GW = 0.81, P < 0.0001]. This cell's AFR functions
provided information about groove width magnitude for the 1.07-, 1.42-, and 1.90-mm groove-width gratings, but its AFR functions did not
discriminate between 1.90 and 2.53 mm at any force level. It had
significant changes in AFR from 1.07 to 1.42 mm, 48.8 ± 3.8 to
35.6 ± 1.9 spikes/s (t = 3.13, df = 6, P < 0.05) in the 62.5-g force bin, 44.3 ± 1.6 to
31.1 ± 1.1 spikes/s (t = 6.83, df = 12, P < 0.0001) in the 87.5-g force bin, and from 1.42 to
1.90 mm, 39.0 ± 5.0 to 15.0 ± 2.9 spikes/s (t = 3.41, df = 6, P < 0.03) in
the 37.5-g force bin, 35.6 ± 1.9 to 11.9 ± 2.6 spikes/s
(t = 7.45, df = 6, P < 0.0005) in
the 62.5-g force bin, and 31.1 ± 1.1 to 12.5 ± 3.4 spikes/s
(t = 5.93, df = 10, P < 0.0002)
in the 87.5-g force bin. As with the example in Fig. 3, the functions
of AFR versus groove width at each force level in Fig. 4A
have similar shapes and slopes. In contrast, many other cells in the
sample (see following text) showed changes in the slopes and/or shapes
of the firing rate functions for groove width as force or speed
changed.
|
Figure 5 shows two examples of the
minority of recorded SII cells that only showed significant effects of
groove width (i.e., no effects of force and/or speed). The cell in
Figs. 5A had a positive AFR response function for groove
width [F (3, 67) = 12.96, P = 0.0001, rAFR/GW = 0.45, P < 0.0001], and the cell in Fig. 5B had a negative response
function [F (3, 72) = 34.43, P = 0.0001, rAFR/GW = 0.69,
P < 0.0001]. Each curve on these graphs represents the AFR versus groove width function for trials run at a different speed. There were no significant differences in AFR for the speed levels used, and the shapes and slopes of the AFR versus groove width
functions are equivalent for all three speeds. The AFR functions depicted for these cells provide other examples whose AFRs
discriminated between changes of groove width within (and/or across)
the behavioral response categories. The cell in Fig. 5A had
a significant change in AFR from 1.07 to 1.42 mm, 26.6 ± 2.9 to
38.9 ± 4.0 spikes/s (t =
2.55, df = 42, P < 0.03) for trials at all three speeds. The cell in
Fig. 5B showed significant changes in AFR from 1.07 to 1.42 mm, 27.9 ± 2.0 to 19.1 ± 1.3 spikes/s (t = 3.69, df = 43, P < 0.001) for trials at all three
speeds and from 1.42 to 1.90 mm, 19.1 ± 1.3 to 12.7 ± 0.9 spikes/s (t = 4.12, df = 45, P = 0.0003) for trials at all three speeds.
|
Figure 6, top, plots the 27 cells with positive or negative response functions of increasing groove width. Note the similar range of slopes for the positive and negative functions. Figure 6, middle and bottom, present data for force and speed in the same manner. This data will be discussed in the following text.
|
A majority of the 63 cells that showed significant effects of groove
width had AFR functions that discriminated between either the groove
widths of the two gratings designated smooth (1.07 and 1.42 mm) or
those designated rough (1.90 and 2.53), with or without discriminating
between adjacent groove widths of the gratings designated as smooth and
rough (1.42 vs. 1.90 mm).1 Specifically, 58/63
cells showed significant effects of changing groove width from 1.07 to
1.42, 1.42 to 1.90, or 1.90 to 2.53 mm for at least one force or speed
level (SAS v6.12: Proc T Test). The other 5/63 cells failed to
discriminate any adjacent grating pairs. Forty-one of 58 cells (70.7%)
that discriminated adjacent grating groove width levels did so for
either changes from 1.07 to 1.42 mm or from 1.90 to 2.53 mm, i.e.,
within (and, in 21/41 cases, also across) the categories of smooth or
rough. Seventeen of 58 cells (29.3%) had AFR functions that by these
criteria could only distinguish adjacent groove widths across smooth
and rough categories (1.42 vs. 1.90 mm). Visual inspection of response
trends in the data, however, allowed 60/63 cells to be classified as discriminating withinand/or across
behavioral response categories (3/63 cells had effects of multiple variables that were difficult to
categorize). Forty-eight of these 60 cells (80%) showed AFR changes as
groove width changed from 1.07 to 1.42 mm or from 1.90 to 2.53 mm for
at least one force or speed level, and 12/60 (20%) of these cells
showed AFR changes as groove width increased from 1.42 to 1.90 mm but
not from 1.07 to 1.42 mm or from 1.90 to 2.53 mm.
Positive and negative response functions for increases in contact force
Twenty-nine of the 37 cells that were studied with systematic
variation of force and groove width had significant (class variable ANOVA P < 0.05) effects of force. In 16/29 cells,
these effects were additive with effects of groove width (ANOVA main
effects of force and groove width), and 1 cell had an isolated effect of force (total n = 17). The remaining 12/29 cells had
interactive effects and will be discussed later. Fifteen of the 17 SII
cells with ANOVA main effects of force had response functions that we designated either positive or negative (significant ANOVA and significant rAFR/FORCE). Two of the 17 cells showed significant differences in AFR for changing force, but
their rAFR/FORCE's were
nonsignificant. As with groove width, there were roughly equal numbers
of positive and negative cells in the force sample. Seven of the 15 assigned cells had positive AFR response functions for force,
rAFR/FORCE (mean ± SE) = 0.47 ± 0.07, and the other 8 cells had negative AFR response
functions, rAFR/FORCE = 0.42 ± 0.05.
Fourteen of 49 (53 speed/groove-width experiments minus the 4 cells
studied with systematic variation of force, speed, and groove width)
cells studied with systematic variation of speed and groove width had
significant effects related to chance variation around the intended
60 g of target force. For these cells, it was not possible to
construct 25-g force bins with equal numbers of samples in each.
However, the effects were robust and clearly visible over a limited
range of force. For these reasons, the class and continuous variable
ANOVA model described in METHODS was employed to analyze
these cells, where force was a continuous rather than a class variable.
For these 14 cells, means ± SE were for force ranges 43.0 ± 2.0 g, for force minima 38.5 ± 2.2 g, for force maxima
81.5 ± 1.8 g, for force means 58.6 ± 1.2 g, and for force standard deviations 8.9 ± 0.3 g. Six of these 14 cells showed nonadditive effects of force with at least one other
variable. Two of the remaining eight cells showed significant
differences in AFR for changing force, but their
rAFR/FORCE's were nonsignificant. Three of the remaining six cells had positive AFR response functions for force, rAFR/FORCE (mean ± SE) = 0.26 ± 0.01, and the other three cells had negative
AFR response functions, rAFR/FORCE = 0.39 ± 0.09. These 14 cells corroborated the findings from the 29 cells with force effects in the force/groove-width experiments; in
total, 43 SII cells had significant differences in AFRs when force changed.
Figure 7 shows data from a cell that had
a positive AFR response function for increasing force. It increased its
AFR from 55.5 ± 3.4 to 80.5 ± 6.6 spikes/s (averaged across
all groove widths) as force increased from the 37.5- to the 112.5-g bin
[F (4, 81) = 22.85, P = 0.0001, rAFR/FORCE = 0.70, P < 0.0001].2 The points on the force axis in
Fig. 7 represent the centers of 25-g force bins. The graph shows that
this cell's force response functions have approximately the same
shapes and slopes for each level of groove width. This cell's force
effect was additive with a significant effect of groove width. It
changed its AFR from 48.3 ± 2.5 to 71.1 ± 2.6 spikes/second
(averaged across all forces) as groove width increased from 1.07 to
2.53 mm [F (3, 81) = 30.12, P = 0.0001, rAFR/GW = 0.64, P < 0.0001]. The upward shift of the curves for
trials on the 1.90- and 2.53-mm groove-width gratings relative to those
on the 1.07- and 1.42-mm gratings illustrates this positive effect.
This cell is representative of the minority in the sample that changed
AFR for groove width in a way that only distinguished between the
smooth and rough groove-width categories with no differentiation within
those groups. Its AFRs distinguished the 1.42- and 1.90-mm groove
widths at 35.3 ± 4.4 and 70.4 ± 3.8 spikes/s
(t = 5.90, df = 6, P < 0.002)
in the 37.5-g force bin, at 54.3 ± 3.1 and 80.2 ± 5.6 spikes/s (t =
4.30, df = 19, P < 0.001) in the 62.5-g force bin, and at 59.2 ± 5.6 and
80.9 ± 6.0 spikes/s (t =
2.54, df = 10, P < 0.05) in the 87.5-g force bin.
|
An example of a cell that had a negative response function for
increasing force appears in Fig. 8. Its
AFR fell from 66.9 ± 4.5 to 40.4 ± 2.9 spikes/s as force
increased from the 12.5to the 87.5-g force bin [F (4, 69) = 19.46, P = 0.0001, rAFR/FORCE = 0.47, P < 0.0001].2 The similarly shaped, decreasing
functions of force for each grating in Fig. 8A illustrate
this effect. This cell was unusual (as will be seen below) because it
had a negative force effect and a positive groove width effect, which
is visible as the separation of force curves for the gratings in the
graph, especially obvious at a force of 87.5 g (interactive
effects of multiple variables will be considered in the following text;
the interaction between force and groove width for this cell failed to
reach significance). Its AFR increased from 37.0 ± 3.2 to
53.7 ± 2.5 spikes/s as groove width increased from 1.07 to 2.53 mm [F (3, 69) = 13.59, P = 0.0001, rAFR/GW = 0.38, P < 0.0002].
|
The 15 cells with positive and negative effects of force that were studied with systematic variation of force and groove width are plotted in Fig. 6 (middle). AFRs were scaled for force as they were for groove-width (Fig. 6, top). These scaled average rates were plotted as functions of the centers of bins used to categorize force for the ANOVA models, positive functions on the left, negative on the right. As with groove width, a similar range of slopes was observed for positive and negative responses to force.
Positive and negative response functions for increasing scanning speed
Thirty-four of 53 SII cells studied in the speed/groove-width
experiments had significant differences in AFRs when speed changed. Fourteen of 34 of these effects were additive or isolated; 11 of these
had effects of speed and at least one other variable, and 3 had
isolated effects of speed. In 20/34 cells these effects were
nonadditive with effects of groove width and/or force (ANOVA interactionsto be discussed later). Of the 14 cells with additive or
isolated effects of speed, 12 cells had positive or negative response
functions (significant ANOVA and significant partial Pearson
correlation coefficient between AFR and speed,
rAFR/SPEED). Two of 14 cells showed significant
differences in AFR as speed changed without significant
rAFR/SPEED's. Eight of the 14 cells had positive AFR response functions for speed,
rAFR/SPEED (mean ± SE) = 0.57 ± 0.06, and the other four cells had negative AFR response
functions, rAFR/SPEED =
0.37 ± 0.10.
Figure 9 shows data from a cell that had a positive response function for speed. Its AFR increased from 44.9 ± 3.6 to 70.6 ± 5.9 spikes/s (averaged across all groove widths) as speed increased from 40 to 120 mm/s [F (2, 34) = 11.53, P = 0.0002, rAFR/SPEED = 0.44, P < 0.005]. The curves plotted in Fig. 9A also show an upward shift with increasing groove width, demonstrating that groove width also had a significant positive incremental effect on the responses of this cell. AFR rose from 39.7 ± 5.5 to 67.1 ± 4.5 spikes/s (averaged across all speeds) as groove width increased from 1.07 to 2.53 mm [F (3, 34) = 14.79, P = 0.0001, rAFR/GW = 0.62, P < 0.0001]. Even though the curves in Fig. 9A are not quite parallel, the speed-by-groove width interaction term in the ANOVA model was not significant, meaning there was no statistically reliable change for the slopes of this cell's AFR versus speed functions as groove width changed.
|
The example cell shown in Fig. 10 had a
negative response function for increasing speed. This cell decreased
its AFR from 62.1 ± 2.6 to 39.1 ± 3.5 spikes/s (averaged
across all groove widths) as speed increased from 40 to 120 mm/s
[F (2, 77) = 30.07, P = 0.0001, rAFR/SPEED = 0.66, P < 0.0001]. This effect was consistent on all four gratings (Fig.
10A). A negative effect of groove width can be appreciated
as a downward shift in the AFR versus speed functions for gratings with
wider grooves. AFR fell from 70.9 ± 2.6 to 33.7 ± 2.9 spikes/s (averaged across all speeds) as groove width increased from
1.07 to 2.53 mm [F (3, 77) = 11.03, P = 0.0001, rAFR/GW =
0.80,
P < 0.0001]. There was no statistically significant change for the slopes of this cell's AFR versus speed functions as
groove width changed (i.e., no speed-by-groove width ANOVA interaction). The negative speed effect is clearly evident in the
decreasing raster density and histogram bin heights, without additional
sorting by groove width (Fig. 10B).
|
Figure 6, bottom 2 graphs, shows response functions for the 12 cells with positive and negative response functions for increasing scanning speed. These plots of scaled AFR versus speed illustrate the comparable range of slopes for positive and negative functions.
Multiple and nonadditive effects
Most SII cells showed effects of groove width and force, groove width and speed, or all three factors. The distributions of multiple effects for the two kinds of experiments were as follows. In the 37 force/groove-width experiments, 28/32 (87.5%) cells, with significant ANOVA effects of groove width or force, had significant effects for both variables. Three cells had isolated effects of groove width, one cell had an isolated effect of force. The five other studied cells were not sensitive to either groove width or force. In the 53 speed/groove-width experiments, 34/43 (79.1%) cells, with significant effects of groove width, force, or speed, had significant effects for combinations of two or three variables. Ten of these cells had significant effects of groove width, force, and speed. One cell had effects of speed and force, 20 cells had effects of groove width and speed, and three cells had effects of groove width and force. Three cells had isolated effects of speed, three cells had isolated effects of groove width, and three cells had isolated effects of force. The remaining 10 cells studied with speed and groove width had no statistically significant effects of any variable. All four cells that were well characterized for both force and groove width and speed and groove width responded to multiple stimulus components. One of these four cells had significant ANOVA effects of groove width and force, and three of these cells had significant effects of groove width, force, and speed. These four cells contributed to the frequencies reported in the preceding text. Only 6/63 (9.5%) cells that had a significant ANOVA effect of groove width responded to groove width in isolation; i.e., to groove width but not to force or speed. This number is probably an overestimate of the true number of groove width-independent responses in this sample population, because effects of speed could not be evaluated in the force/groove-width experiments, and effects of force could only be evaluated in a subset of the speed/groove-width experiments (see METHODS).
We found roughly equal numbers of additive or nonadditive combined effects: 31 additive versus 32 nonadditive effects for groove width, 25 versus 18 for force, and 14 versus 20 for speed. That is, SII cells in the sample typically had either ANOVA main effects of two or three variables (additive effects, where changing 1 variable shifted the AFR function of another variable up or down) or ANOVA interactions (nonadditive effects, where changing 1 variable changed the slope of the AFR function of the other variable, with or without shifting the absolute level of that function). The dual effects that were graphed in the previous figures were additive.
Figure 11 provides an example of a cell
with nonadditive effects. This cell was studied in a speed/groove-width
experiment. In Fig. 11A the slope of the AFR versus speed
function increases with progressively rougher gratings. In Fig.
11B, there was essentially no change in raster dot density
at different speeds on the 1.07 mm grating, but there was an
increasingly steeper rise in raster density with greater speeds on
rougher gratings (1.42-2.53 mm). This cell had a significant
groove-width-by-speed interaction [F (6, 81) = 9.49, P = 0.0001, rAFR/GW = 0.72, P < 0.0001, rAFR/SPEED = 0.46, P < 0.0001]. The AFR response function depicted for this cell in Fig.
11A provides an example whose AFR discriminated groove widths between categories of rough and smooth at 80 and 120 mm/s but
also within the rough category at 120 mm/s. It showed significant changes in AFR from 1.42 to 1.90 mm, 33.5 ± 3.1 to 57.3 ± 4.3 spikes/s (t = 4.57, df = 15, P < 0.001), for trials at 80 mm/s, and 40.4 ± 3.6 to 63.8 ± 3.2 spikes/s (t =
4.86, df = 16, P < 0.0005), for trials at 120 mm/s. It
discriminated between 1.90 and 2.53 mm at 120 mm/s, with AFRs of
63.8 ± 3.2 to 86.5 ± 8.3 spikes/s (t =
2.67, df = 15, P < 0.03); a trend for
discriminating all the gratings is clearly visible by the separation of
the points at 120 mm/s.
|
The distribution of positive and negative AFR functions for cells
showing interaction effects resembled that of the group with purely
ANOVA main effects. For the cell in Fig.
12A, curves representing the
AFR versus groove width functions shift upward with increasing force.
Note that the slope of the curve for the 87.5 g force bin was
steeper than the slopes for the other two force bins. Responses of this
cell increased significantly with groove width [F (3, 130) = 18.76, P = 0.0001, rAFR/GW = 0.50, P < 0.0001] and force [F (3, 130) = 12.74, P = 0.0001, rAFR/FORCE = 0.48, P < 0.0001], and this cell had a significant
groove-width-by-force interaction [F (7, 130) = 2.90, P < 0.01]. The example cell in Fig. 12B
showed less activity on rougher surfaces and at higher force levels,
and it showed more steeply sloped negative effects of groove width at
the lightest applied forces. It had negative groove width
[F (3, 123) = 8.37, P = 0.0001, rAFR/GW = 0.24, P < 0.005] and force [F (4, 123) = 49.96, P = 0.0001, rAFR/FORCE =
0.63, P < 0.0001] main effects and a
groove-width-by-force interaction [F (11, 123) = 4.72, P = 0.0001].
|
We also observed both positive and negative interaction effects in
speed/groove-width experiments. The cell shown in Fig. 13A had higher AFRs on
rougher surfaces [F (3, 35) = 20.53, P = 0.0001, rAFR/GW = 0.66, P = 0.0001] and at higher speeds [F (2, 35) = 33.14, P < 0.0001, rAFR/SPEED = 0.65, P < 0.0001]. However, the interaction [F (6, 35) = 4.44, P < 0.002] was such that the slope of the AFR
versus groove width function was the least at 120 mm/s. This cell may
have started to saturate its response 120 spikes/s, so that at 120 mm/s AFR could not rise much higher as groove width increased.
Differentiation for groove width would, therefore be better for this
cell at lower speeds. This interaction was of a different nature than
that of the cell illustrated in Fig. 11. In Fig. 11, that cell's AFR
versus speed and AFR versus groove width functions showed increasing
slopes with increasing levels of the other variable. An example of a
negative groove-width-by-speed interaction appears in Fig.
13B. This cell showed decreased AFR on rougher surfaces
[F (3, 35) = 29.20, P = 0.0001, rAFR/GW = 0.77, P < 0.0001] and at higher speeds [F (2, 35) = 15.17, P = 0.0001, rAFR/SPEED =
0.52, P = 0.0002] with more steeply negatively sloping AFR versus groove width functions at lower speeds
[F (6, 35) = 4.35, P = 0.0022]. While
the cells shown in Fig. 13, A and B, have
opposite effects, they both have more sensitive response functions for
groove width at lower speeds. These two cells contrast with the example
speed-by-groove width cell shown in Fig. 11, which had the steepest
response function for groove width at 120 mm/s.
|
Correlated firing rate changes in SII
Figure 14A illustrates the relationship between groove width effects and force effects for the sample of SII cells that had class variable3 ANOVA effects of groove width and/or force. Each point in the scatter plot is assigned a position based on its partial Pearson correlations between AFR and groove width (rAFR/GW) and AFR and force (rAFR/FORCE). These correlation coefficients were, themselves, correlated [r = 0.63, P = 0.0001]. Except for a few outliers, these cells scatter to the bottom left and top right quadrants of the graph. Cells with positive AFR versus groove width functions tended to have positive AFR versus force functions, and cells with negative AFR versus groove width functions tended to have negative AFR versus force functions. A linear regression line with 95% confidence limits has been drawn through these points, and the slope of this regression equation, 0.57 ± 0.13, was significantly different from 0 (t = 4.40, P = 0.0001). Cells, also, tended to show larger and more linear changes in AFR with changing groove width than with changing force. This observation can be further appreciated by noting that while the regression line in this figure is >0, it is <1. Less than unity slope means, for example, that a SII force- or groove width-responsive cell that has a strong positive linear relationship between AFR and groove width will likely have a positive linear relationship between AFR and force that is of lesser strength, over the range of the parameters that were tested in these experiments.
|
A similar but somewhat less robust pattern existed for the AFR versus groove width and AFR versus speed functions. Figure 14B demonstrates that the partial Pearson correlations between AFR and groove width (rAFR/GW) and AFR and speed (rAFR/SPEED), were themselves, correlated (r = 0.42, P < 0.01). In general, cells scatter to the bottom left and top right of the graph. The slope of the linear regression line through these points, 0.37 ± 0.13, was significantly different from 0 (t = 2.86, P < 0.01), but proportionately more points fell outside the confidence envelope, here, than in Fig. 14A. This correlation was, therefore weaker than that seen for the partial Pearson correlations between AFR and groove width (rAFR/GW) and AFR and force (rAFR/FORCE) (r = 0.63), shown in Fig. 14A. Cells with positive or negative AFR versus groove width functions, respectively, tended to have positive or negative AFR versus speed functions.
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DISCUSSION |
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The present experiment is the first to examine SII during
controlled, parametric manipulation of multiple stimulus features related to roughness. These recordings from single neurons in macaque
SII reveal a range of response functions that collectively provide
information about roughness stimuli. The majority of cells showed
statistically significant changes in average firing rates when groove
width, force, and/or speed changed. The changes in mean firing rates in
sub-samples of SII cells provide metrics for the physical parameters
associated with roughness perceptions and support findings from an
active touch study (Sinclair and Burton 1993) and from
lesion-behavior experiments (Carlson and Burton 1988
;
Murray and Mishkin 1984
; Ridley and Ettlinger
1976
, 1978
) that have implicated SII in roughness
discrimination. Most SII cell mean firing rates changed in positive or
negative ways with increases in groove width and force or groove width
and speed, and roughly equal numbers of these combinatorial effects
were additive or nonadditive. A major finding of the present study was
the high proportion of cells that displayed negative response functions
for each of the factors (groove width, contact force, and scanning
speed). This has not been reported previously in recording from
somatosensory cortex (Chapman and Ageranioti-Bélanger 1991
; Darian-Smith et al. 1982
; Jiang et
al. 1997
; Sinclair and Burton 1991a
, 1993
;
Sinclair et al. 1996
; Tremblay et al.
1996
) or thalamus (Sinclair et al. 1991
).
Another major finding was the sensitivity of most cells to two or more
variables. There was little evidence, six cells, for the representation
of groove width in isolation from contact force or scanning speed in
the responses. In addition, there was a sample-wide correlation between
groove width and force and groove width and speed. Typically average
firing rates increased or decreased to both groove width and one other
variable: force or speed. Correlated changes in firing rates for groove
width and force are consistent with psychophysical data that show
increasing groove width or force increases the magnitude of perceived
roughness for gratings (Lederman 1974; Lederman
and Taylor 1972
; Taylor and Lederman 1975
). The
weaker correlation between the effects of changes in groove width and
scanning speed may similarly reflect psychophysical findings of less
disruption in roughness perceptions when surfaces are scanned at
different speeds (Lederman 1974
).
In this study, passive touch removed hand movement as a source of cell
drive, eliminating potential confounds from proprioceptive and
kinematic factors in the interpretation of SII cell responses to
roughness stimuli. Passive stimulation also allowed for parametric manipulation of grating groove width, contact force, and scanning speed, three features related to tactile roughness (Lederman
1974; Lederman and Taylor 1972
; Sathian
et al. 1989
; Sinclair and Burton 1991b
;
Sinclair et al. 1995
; Taylor and Lederman
1975
). The gratings, 1.07-2.53 mm, were designed to span the
ascending portion, only, of the roughness magnitude estimation
function, where the meaning of different grating groove spacings as
"roughness stimuli" is monotonic (Connor et al.
1990
; Sathian et al. 1989
) and, also, to yield
results comparable to existing neurometric (Sinclair and Burton
1991a
,b
, 1993
; Sinclair et al. 1991
, 1996
) and
psychometric data (Sathian et al. 1989
; Sinclair
and Burton 1991b
). Therefore the approximately linear (as
quantified by partial correlation coefficients) changes in average
firing rates for many SII cells in this study potentially represent
neural correlates for the percept of roughness.
Analyses of average firing rate differences for adjacent pairs of gratings allowed us to examine whether SII cell average firing rates changed simply between the behavioral response categories of smooth and rough or within the categories of smooth or rough. If a cell showed significant changes in average firing rate between 1.42 mm, smooth, and 1.90 mm, rough, but not between 1.07 and 1.42 mm, both smooth, or 1.90 and 2.53 mm, both rough, then by objective criteria its response function is ambiguous with respect to whether it is representing changes in the categories of rough or smooth or changes in the magnitudes of groove width from 1.42 to 1.90 mm. In this case, finer sampling of groove widths between 1.42 and 1.90 might have shown a gradation of average firing rates or it might have shown a step change corresponding to rough or smooth. If a cell showed significant changes in average firing rate between 1.07 and 1.42 or between 1.90 and 2.53 mm, then that cell's average firing rate discriminated magnitudes of groove width without regard for their relationship to the rough and smooth categories; i.e., a more stimulus-level process. This analysis showed that 70% of the sample with groove width ANOVA effects had response functions that discriminated within smooth or rough categories, i.e., discriminated groove width magnitude at one or more force or speed level. Our t-test analysis may have overinflated the relative proportion of categorical (rough/smooth) cells. Visual inspection of our data supports an even higher proportion, 80%, of cells representing groove width magnitude.
The changes in average firing rates with increasing groove width,
force, or speed, and the strengths of the linear partial correlation
coefficients between average firing rates and these grating parameters
exhibited by many cells in the present study, were quite comparable to
reported findings in SII during active touch, SI during active
(Sinclair and Burton 1991a) and passive touch
(Sinclair et al. 1996
), ventral posterior lateral
nucleus of the thalamus (VPL) (Sinclair et al. 1991
),
and peripheral nerve (Darian-Smith et al. 1980
;
Goodwin and Morley 1987a
,b
; Goodwin et al.
1989
; Sathian et al. 1989
). That is, mean firing
rates of many SII cells correlated with magnitudes of these three
stimulus dimensions. Since many SII cells showed changes in average
firing rates that correlated in linear ways with changes in the
magnitudes of force or speed, it would seem parsimonious they would
also process groove width in a similar way, were these stimuli
completely trial-wise randomized and not pair-wise randomized in
blocks. Sinclair and Burton's active touch study of SII
(Sinclair and Burton 1993
), which employed fully
randomized grating presentations, found strong linear relationships
between SII cell average firing rates and groove width.
However, in a prior study of SII that used passive application of
embossed surfaces, Jiang and colleagues found a minority of cells with
linear relationships between average firing rates and tactile stimulus
spatial periods (Jiang et al. 1997). Their observation
was that a majority of SII cells only signaled changes in spatial
period but did not provide information about the magnitude of spatial
period. They argued from their findings that SII contains "a
higher-order representation of surface texture" (Jiang et al. 1997
) than exists in SI, where they found predominantly linear relationships between cell firing rates and changes in spatial period
for those same stimuli. Our finding of a minority of binary response
functions in SII argues one of two things that are contrary to Jiang et
al.'s stated conclusion. Either the opposite conclusion is true, that
SII cells disregard roughness category changes and represent roughness
magnitudes, as in active touch (Sinclair and Burton
1993
), or both are true, and some cells in SII represent roughness category changes, while others, in our sample the majority, represent levels of groove width without concern for categories. The
shapes of some response functions in our sample, 20% by our visual
inspection e.g., Fig. 7, agree with the findings of Jiang et al.
(1997)
. Our blocked design with grating pairs and effects of
force and speed may have contributed to overestimation of the relative
proportions of these binary response functions.
Possibly several procedural differences contributed to different
results. Jiang et al. (1997) did not manipulate or
account for the effects of force or speed in their analyses (our Figs. 11-13 demonstrate the importance of doing so), and they based their conclusion on a sample of 19 texture-responsive cells from SII in one
hemisphere of one monkey, in which findings from 12/19 cells support
their contention. Jiang et al. (1997)
may have recorded a different subpopulation of SII cells, as they report 17/19 (89.5%) cells with receptive fields exclusively on the contralateral hand and
2/19 with bilaterally symmetric receptive fields. We found that only
22/86 (25.6%) SII cells responded to exclusively contralateral stimulation.4 Sinclair and
Burton's (1993)
study used a groove width discrimination task;
Jiang et al.'s study used a detection of texture change task; and the
present study used a roughness classification task. Jiang et
al.'s (1997)
monkey used multiple fingers to contact the
surfaces and responded with the hand contralateral to the site of
stimulation. Our monkeys used a single finger pad and responded with
the foot contralateral to the stimulated hand.
Choice of stimuli may explain a large part of the discrepancy between
the findings of the present study and those of Jiang et al. In their
SII study, Jiang and colleagues used rows of embossed dots instead of
gratings, and the dots ranged in row-to-row spacing from 2-5 mm in
1-mm increments. They saw increases in average firing rates for 3-5 mm
relative to 2 mm, but no graded changes in average firing rates over
the 3-5 mm range. The human roughness magnitude estimation function
for dot stimuli inverts above 3 mm center-to-center dot spacing for
dots in tetrahedral arrays (Connor et al. 1990;
Sinclair et al. 1995
); i.e., surfaces feel increasingly
rougher up to ~3 mm and then start to feel smoother as dot spacing
increases further. The mean firing rate functions for slowly adapting
(SA), rapidly adapting, and, to a much lesser extent, Pacinian
peripheral mechanoreceptive fibers, also, invert near 3-mm spacings
(Connor et al. 1990
). An early psychophysics study by
Burton and colleagues (Burton et al. 1996
) found
evidence for inversion or saturation of the human roughness magnitude
estimation function at ~3.5-mm row-to-row spacing for rows of dots,
similar to those used by Jiang et al. (1997)
. That
finding implies equivalence of the center-to-center and row-to-row
spacing metrics for tetrahedral dot arrays and rows of dots.
At wide (e.g., >3 mm) spacings the finger "bottoms-out" in grating
grooves (or spaces between dots), preventing the skin from deforming
and contributing to a "smoother" percept (Taylor and Lederman 1975). If average firing rates for populations of SII cells subserve, in part, the percept of roughness, then they may also
show inverting or saturating functions over the range of dot spacings
Jiang and colleagues used. With coarse sampling of the stimulus space,
graded changes in firing could have been missed. Strong linear
correlations between average firing rates and groove width, over a
range of 1.07-2.53 mm, and average firing rate differences within the
categories of smooth and or rough, within this same range of groove
widths, in a large sub-sample of SII cells in the present study support
this explanation.
Positive and negative response functions
One of the most surprising findings in this study was the high
proportion of SII cells that had negative response functions for groove
width, force, and speed. This sample contained equal numbers of cells
with negative and positive functions for groove width and force and
several clear examples of negative functions for speed. Several other
studies have reported negative response functions in SII and SI but not
in these proportions. Sinclair and Burton found negative response
functions for groove width in about one fourth of SII cells that had
significant groove width effects studied during active touch
(Sinclair and Burton 1993). Jiang and colleagues found a
small number of SII cells that decreased firing for rougher surfaces
(Jiang et al. 1997
).
The present data do not identify the source of negative graded response
functions. In their active touch study, Sinclair and Burton found that
negative effects of force and speed comprised about one-tenth of a
large sample of SI cells (Sinclair and Burton 1991a).
They speculated that negative speed and force effects were related to
behavior because the monkeys often used light touch on rough gratings
and slower hand movements at high contact forces. The current study
eliminated behavioral strategy as a potential confound by employing a
passive touch paradigm. The negative response functions seen in this
study likely reflect cortical phenomena because Sinclair and Burton
found scant evidence for negative response functions in VPL during
active touch (Sinclair et al. 1991
). Possibly, negative
response functions for groove width, contact force, and scanning speed
in SII arise from inhibition from other cells in SII, SI, or elsewhere.
For example, cells that receive inhibitory synapses from others that
have positive response functions for groove width would show decreased
mean firing rates as groove width increased.
A lack of speed sensitivity in SI cells with negative response
functions for groove width argued against a mechanism based on effects
of changing stimulus temporal period (Sinclair and Burton
1991a). Available data, however, cannot exclude the possibility that one-for-one phase-locking to grating temporal periods in SI (e.g.,
Sinclair et al. 1996
) or the periphery (e.g.,
Darian-Smith and Oke 1980
) contributes to negative
response functions for groove width in SII. With one-for-one
phase-locking, average firing rates would decrease because grating
temporal frequency decreases with increasing groove width. Most
reported peripheral mechanoreceptive afferent responses, however,
reflected increases in mean firing rate with increasing spatial periods
(Darian-Smith et al. 1980
; Goodwin and Morley
1987a
,b
; Goodwin et al. 1989
; Sathian et
al. 1989
). With controlled passive grating stimulation,
Sinclair and colleagues found negative response functions for speed and
force in SI but no negative response functions for groove width
(Sinclair et al. 1996
). Phase-locking cannot account for
negative response functions to force and speed. Therefore inhibition in
SII and/or SI seems the most parsimonious explanation. It is unclear
why the current study found such high frequencies of negative response functions in the SII sample. Task-specific factors, methodological differences, differential sampling, and different
electrodes5 remain possibilities.
Additive and nonadditive effects of multiple features
To classify grating groove width correctly in this task, the
monkeys had to extract information about spatial period from stimuli
with components of spatial period, contact force, and scanning speed.
One objective of the present study was to assess the relative frequency
of SII cells that represented groove width independent of force and
speed during passive touch. As in peripheral nerve, firing rates in VPL
thalamus follow changes in groove width, force and, sometimes, speed in
active touch of tactile gratings (Sinclair et al. 1991).
The mean firing rate of any VPL cell could, therefore, reflect many
possible combinations of groove width, force, and speed on the finger
pad. With the same task Sinclair and Burton found subpopulations of SI
and SII cells that changed firing rates with changing groove width but
not with changing force or changing speed (Sinclair and Burton
1991a
, 1993
). They argued on this basis that the independent
neural representation of grating groove width is a cortical phenomenon
and that it may provide a neural substrate for roughness perception and
perceptual constancy in the face of changing speed and force.
The present study found that 9.5% of groove width-responsive SII cells
responded to groove width independent of force or speed during passive
touch (recording isolation time typically allowed either the
force/groove-width or the speed/groove-width experiment, but rarely
both, for any one cell, so the untested parameter could have caused
changes in mean firing rates). This fraction is small compared with the
~35% seen in active touch in SII (Sinclair and Burton
1993) and the comparable fraction seen in SI during active touch (Sinclair and Burton 1991a
). It compares more
closely with the absence of groove width response independence observed
in SI with passive touch grating stimulation, using this same equipment (Sinclair et al. 1996
). This suggests a potential active
versus passive touch distinction. However, a number of other factors need to be considered. Stimulus control measures dictated a different experimental design and, as a consequence, different statistical analyses for the current study than for the active touch SII study. More likely than a physiological difference in SII related to passive
versus active touch, our passive touch experiments have brought a
greater level of stimulus control to the study of cortical processing
of roughness stimuli. Furthermore, the current study's SII cell
response functions in comparison to those reported for SI cells during
passive touch (Sinclair et al. 1996
), suggests processing of roughness information in SII that is partly in parallel with that in SI or that operates over a high-fidelity serial link from SI.
While a typical SII cell's firing rate was ambiguous, linear statistics applied to recording data taken during parametric manipulation of groove width, force, and speed allowed description of the shapes of its different response functions for these variables. Roughly equal numbers of SII cells showed additive or nonadditive effects (i.e., significant ANOVA main effects or interactions) of two or three of these variables. This distinction might more appropriately reflect ends of a continuum. There were obvious instances of additive and nonadditive effects, but there were also cases where nonsignificant trends for interactions might have reached significance with more data and cases where significant interactive effects were weak in comparison to main effects for those same variables. The interactions created more sensitive response functions for groove width over narrower ranges of force and speed; e.g., a cell with a groove width by force interaction might have a more steeply rising average firing rate function for groove width at higher force levels. There did not, however, appear to be any optimal level of force or speed where response functions for groove width were sharpest across the sample of SII cells. In fact, the SII sub-sample with interactive effects contained a broad distribution of instances where high and low force and speed produced steeper mean firing rate functions for groove width.
Correlated firing rate changes in SII
Changes in SII cell average firing rates for changing groove width
were correlated with changes in average firing rates for force and to a
lesser degree for speed. The finding that SII cells that had positive
(negative) response functions for groove width also tended to have
positive (negative) response functions for force is consistent with
Lederman's finding that increasing groove width or contact force
increases perceived roughness magnitude (Lederman 1974;
Lederman and Taylor 1972
) and provides strength for the
argument that mean firing rates in populations of SII cells code
roughness. This correlation between groove width and force in the
responses of cells in SII might reflect dependence on input from
peripheral SA mechanoreceptors. Goodwin and Morley demonstrated that SA
peripheral afferent fibers show increased firing rates for both
increasing grating spatial periods and increasing force (Goodwin
and Morley 1987b
). More recently, Connor and Johnson demonstrated that spatial variation of firing in afferent fibers innervating an area of skin of 1-2 mm strongly correlates with perceived roughness magnitude for arrays of raised dots (Connor and Johnson 1992
; Connor et al. 1990
), and Blake
and colleagues found the tightest correlation between perceived
roughness and spatial variation of firing for SA afferent fibers
(Blake et al. 1997
). In the present study, the graded
changes in SII cell average firing rates with increasing groove width
might reflect the translation of spatial variation of firing in the
peripheral SAs into a cortical mean rate code for roughness.
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ACKNOWLEDGMENTS |
---|
We thank J. Kreitler, S. Kumar, and G. Perry for technical expertise.
This work was supported by National Institute of Neurological Disorders and Stroke Grant NS-31005.
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FOOTNOTES |
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1 The precise relationship between SII cell average firing rates (AFRs) at different stimulus combinations and the monkeys' trial-wise decisions about rough and smooth grating classifications in the force/groove-width and speed/groove-width experiments is complex and is the subject of a companion paper (submitted).
2 In some places in the text, ANOVA model degrees of freedom may not be consistent with the number of data points in the figures. In the figures conditions with fewer than two or three trials were omitted for clarity of illustration (see figure legends); e.g., several trials from the 12.5- and the 112.5-g force bins have been omitted from Fig. 3. The ANOVAs used all data, and the GLM (SAS v6.03 and v6.12) versions of the ANOVA procedure, which are robust to imbalanced data sets, were employed to account for any inequality of design cell sizes.
3 The class variable ANOVA measures differences in mean AFR associated with class variable levels. It makes no assumptions about linearity between AFR and groove width or force. Linear or nonlinear relationships can produce significant results because this kind of model is sensitive to differences between means for any combination of class variable levels; e.g., "V-shaped" response functions could have significant effects in these models but might have nonsignificant linear correlation coefficients.
4 Statistical analyses found no correlations between receptive field characteristics and effects of groove width, force, and speed in our sample.
5
Glass-coated platinum-iridium electrodes in the
present study versus varnish-coated tungsten electrodes in Sinclair and
Burton's prior studies and glass-coated tungsten electrodes in
Jiang et al.'s (1997) study.
Address for reprint requests: J. R. Pruett, Jr., Dept of Anatomy and Neurobiology, Campus Box Number 8108, Washington University School of Medicine, 4566 Scott Ave., St. Louis, MO 63110.
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.
Received 10 June 1999; accepted in final form 28 April 2000.
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REFERENCES |
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