 |
Previous Article | Next Article 
The Journal of Neuroscience, August 15, 1999, 19(16):7162-7174
Motion Opponency in Visual Cortex
David J.
Heeger1,
Geoffrey M.
Boynton1,
Jonathan B.
Demb1,
Eyal
Seidemann2, and
William T.
Newsome2
1 Department of Psychology, and 2 Howard
Hughes Medical Institute and Department of Neurobiology, Stanford
University, Stanford, California 94305-2130
 |
ABSTRACT |
Perceptual studies suggest that visual motion perception is
mediated by opponent mechanisms that correspond to mutually suppressive populations of neurons sensitive to motions in opposite directions. We
tested for a neuronal correlate of motion opponency using functional magnetic resonance imaging (fMRI) to measure brain activity in human
visual cortex. There was strong motion opponency in a secondary visual
cortical area known as the human MT complex (MT+), but there was little
evidence of motion opponency in primary visual cortex. To determine
whether the level of opponency in human and monkey are comparable, a
variant of these experiments was performed using multiunit
electrophysiological recording in areas MT and MST of the
macaque monkey brain. Although there was substantial variability in the
degree of opponency between recording sites, the monkey and human data
were qualitatively similar on average. These results provide further
evidence that: (1) direction-selective signals underly human MT+
responses, (2) neuronal signals in human MT+ support visual motion
perception, (3) human MT+ is homologous to macaque monkey MT and
adjacent motion sensitive brain areas, and (4) that fMRI measurements
are correlated with average spiking activity.
Key words:
MT; V1; neuroimaging; fMRI; motion; visual motion
perception; motion opponency; vision; visual cortex
 |
INTRODUCTION |
Computational theories of visual
motion perception typically include a motion opponent stage in which,
for example, the response of a hypothetical leftward-selective neuron
is subtracted from that of a rightward-selective neuron to yield a
neuronal signal for net rightward motion (van Santen and Sperling,
1984 , 1985 ; Qian et al., 1994b ; Adelson and Bergen, 1985 ; Simoncelli
and Heeger, 1998 ). The importance of opponent mechanisms for motion
perception is supported by a number of perceptual studies (Levinson and
Sekuler, 1975a ; Mather and Moulden, 1983 ; van Santen and Sperling,
1984 ; Stromeyer et al., 1984 ; Lubin, 1992 ; Qian et al., 1994a ; Zemany et al., 1998 ). For example, superimposing two identical sinusoidal grating patterns moving in opposite directions produces what is called
a counterphase grating. The counterphase grating appears to flicker in
place with no net motion, as if the motions of the two component
gratings canceled one another. Likewise, when a pair of superimposed
random dot fields are paired so that each dot moving in one direction
is always located near a dot moving in the opposite direction, they
appear to flicker (Qian et al., 1994a ). However, the results of some
other perceptual studies suggest that opposite directions of motion are
processed independently, inconsistent with opponency (Levinson and
Sekuler, 1975b ; Watson et al., 1980 ; Raymond and Braddick, 1996 ).
Motion opponency is believed to play an important role in the response
properties of neurons in the middle temporal area (MT or V5) of the
monkey brain, a region of visual cortex that is widely viewed as a
cornerstone of the neuronal pathways subserving visual motion
perception (Dubner and Zeki, 1971 ; Zeki, 1974 ; Albright, 1993 ).
Neuronal signals that carry motion information pass from the primary
visual cortex (V1) to MT, and then to adjacent motion-sensitive visual
areas, including areas MST and FST (Maunsell and Van Essen, 1983 ; Ungerleider and Desimone, 1986 ; Krubitzer and Kaas, 1990 ; Boussaoud et al., 1990 ; Movshon and Newsome, 1996 ). MT neurons respond
vigorously to a visual pattern moving in a preferred direction, but the
responses can be suppressed substantially by superimposing a second
pattern moving in a nonpreferred direction (Snowden et al., 1991 ;
Bradley et al., 1995 ). This suppression is particularly strong for
paired dot patterns (Qian and Andersen, 1994 ).
Human visual cortex contains an area in the lateral portion of the
occipital lobe (MT+, or V5) that may be homologous to monkey MT and
adjacent motion-sensitive areas such as MST and FST (Zeki et al., 1991 ;
Tootell and Taylor, 1995 ). The case for homology between monkey MT and
human MT+ rests on its general location with respect to other
identified visual areas in both species, its cytoarchitecture, and its
heightened sensitivity to low-contrast, moving stimuli in comparison to
other visual areas (see Discussion for additional references). In the
monkey, however, the primary physiological signature of MT is direction
selectivity measured at the single neuron and columnar levels (Zeki,
1974 ; Maunsell and Van Essen, 1983 ). No single neuron or columnar level
observations have yet been made for human MT+. One neuroimaging study
has provided indirect evidence for directional interactions in human
MT+, based on functional magnetic resonance imaging (fMRI)
measurements of the motion aftereffect (Tootell et al., 1995b ). Thus,
the proposed homology with monkey MT must be regarded as somewhat
tentative until more detailed physiological and anatomical data are acquired.
In the current study, we tested for a neuronal correlate of motion
opponency in the human visual cortex using fMRI (Ogawa et al., 1990 ,
1992 ; Belliveau et al., 1991 ; Kwong et al., 1992 ). The goals of the
study were twofold: (1) to determine whether functional activity in
human MT+ reflects motion-opponent mechanisms (and by inference,
directionally selective mechanisms), and (2) to compare motion-opponent
responses in human MT+ with motion-opponent responses in monkey MT to
gain further evidence concerning potential homologies between the two species.
 |
MATERIALS AND METHODS |
fMRI
Each subject participated in several MR scanning sessions: one
to obtain a standard, high-resolution, anatomical scan, one to identify
visual areas V1 and MT+, and several sessions (10 for subject gmb and
11 for subject djh) to measure fMRI responses in the various
experimental conditions. Each subject repeated each experiment between
four and eight times in separate fMRI scans. The figures below plot the
mean and SE of these repeated measurements.
Moving gratings versus counterphase gratings. Brain activity
was measured while subjects viewed moving sinusoidal gratings and
counterphase (or contrast-reversing) gratings. Counterphase gratings
were presented for 18 sec followed by 18 sec of moving gratings (each
18 sec half-cycle was divided into nine 2 sec "trials," as
described below). During each 252 sec fMRI scan, this
counterphase/moving cycle was repeated seven times. The contrast of
counterphase gratings was twice that of the moving gratings (where
contrast was defined in the usual way as the peak luminance of the
stimulus minus the minimum luminance, divided by twice the mean), i.e.,
the counterphase gratings were constructed by superimposing a pair of
moving gratings that moved in opposite directions.
We used several different combinations of spatial frequencies (0.4 cycle/° and 0.8 cycle/°), temporal frequencies (4 and 8 Hz), and
mean luminances (3 and 36 cd/m2). Stimulus contrasts
varied slightly within each trial, as described below, so that subjects
could perform a contrast discrimination task. The chosen contrasts
varied slightly above and below either 6 or 45% for the moving
gratings, and 12 or 90% for the counterphase gratings (Table 1).
MT+ activity can be modulated by attention (Corbetta et al., 1990 ;
O'Craven et al., 1997 ; Beauchamp et al., 1997 ; Gandhi et al., 1999 ).
To control for attentional state, subjects performed a contrast
discrimination task throughout each fMRI scan. Each 2 sec trial
consisted of a pair of 500 msec stimulus presentations, one with
slightly higher contrast than the other, separated in time by a 200 msec blank interval, followed by an 800 msec response interval. A
uniform gray field (equal to the mean luminance of the grating stimuli)
was presented during the blank and response intervals. During the
response interval, subjects pressed one of two buttons to indicate
which of the two preceding stimuli appeared to have the higher
contrast. Feedback was provided after each trial. Subjects practiced
the task extensively until their performance reached asymptotic levels.
The contrast increments that were used during the fMRI scans were
chosen based on these asymptotic performance levels so that subjects
would perform with an accuracy of ~80-90% correct (Table
1).
Because MT+ activity can exhibit a motion aftereffect (Tootell et al.,
1995b ), the stimuli were designed to minimize visual adaptation.
Specifically, alternating brief stimulus presentations with blank
intervals minimized any effects of contrast-dependent adaptation by
visual neurons. In addition, the gratings alternated orientation on
every trial (rightward or leftward diagonal), to minimize orientation-
and direction-specific adaptation.
Human MT+ is believed to be homologous to a collection of macaque
monkey brain areas, including some (e.g., MST) in which neuronal
activity can be modulated by eye movements (Newsome et al., 1988 ). This
would present a possible alternative explanation of our results if
subjects had moved their eyes when viewing the moving gratings. To
minimize eye movements, subjects fixated a small, high-contrast
fixation mark that was displayed continuously throughout each scan. In
addition, for the moving gratings, the grating stimulus was bisected
along a diagonal line parallel to the orientation of the grating bars,
and each half of the grating moved toward the fixation mark. Thus, the
overall motion of the grating served to "draw" the eyes toward the
fixation point, avoiding a single, powerful optokinetic stimulus.
The stimuli were displayed on a screen made of rear-projection
material, positioned at the opening of the bore of the magnet near the
subjects' knees. The subjects, lying on their backs, looked directly
up into an angled mirror to see the rear-projection screen. The display
subtended 14 × 14° of visual angle.
Moving/counterphase gratings versus blank. In one series of
scans, we measured brain activity while the stimuli alternated between
18 sec, during which moving gratings (0.4 cycle/°, 8 Hz, 3 cd/m2, contrast varied slightly around 44.25%) were
presented followed by 18 sec during which the screen was a uniform gray
field (equal to the mean luminance of the gratings). In a second series
of scans, the stimuli alternated between counterphase gratings (same spatial and temporal frequency, contrast varied slightly around 88.5%)
and a uniform gray field. The gratings were again presented in a series
of 500 msec intervals while subjects performed a contrast discrimination task. The orientations of the gratings were again alternated on every trial (rightward/leftward diagonal). For the moving
gratings, the two halves of the screen again moved toward the fixation
mark. Subjects did not perform a task during the half-cycles when no
stimuli (gray fields) were presented. Hence, we did not completely
control subjects' attention during these scans.
Supersaturation control. Brain activity was measured while
moving grating stimuli (0.8 cycle/°, 4 Hz, 36 cd/m2) were presented. The gratings were again
presented in a series of 500 msec intervals while subjects performed a
contrast discrimination task, 18 sec at high contrasts (varied slightly
around 95%) followed by 18 sec at medium contrasts (varied slightly
around 47.75%). The gratings' orientations were again alternated on
every trial (rightward/leftward diagonal), and the two halves of the
screen again moved toward the fixation mark.
Paired dots versus unpaired dots. Brain activity was
measured while subjects viewed paired and unpaired dot patterns. Dots were square in shape, 0.1° across, and white (120 cd/m2) on a black background (4 cd/m2). The dot density was varied to be relatively
sparse (~200 dots uniformly distributed across the 20 × 20°
field) or relatively dense (~2400 dots). Half the dots moved to the
right, and the other half moved to the left with an average speed of
2.5°/sec. Following Qian and Andersen (1994) , each pair of dots in
the paired dot patterns started at a separation of 0.19° on average,
moved toward and across each other, and was replotted at a new
randomized location when the separation was again 0.19° on average.
The dots, therefore, had an average lifetime of 150 msec before being
replotted. The start/end positions and lifetime were randomized by
±5% so that subjects could not use the initial/final dot separation
as a cue to the dot speed. The replotting of the dot pairs was
asynchronous with respect to each other. There was a blank disc with no
dots in the central region (2° radius) around the fixation mark. The unpaired dot patterns were identical to the paired ones, except that
the two dots in each pair were positioned independently and randomly.
The unpaired dots thus appeared to move past one another like two
semitransparent surfaces, whereas the paired dots appeared to flicker.
Subjects performed a speed discrimination task. Each 2 sec trial
consisted of a pair of 500 msec stimulus presentation intervals. The
dots moved with a base speed in one interval and a slightly faster
speed in the other interval. The screen was black between the stimulus
presentations. Subjects pressed a button to indicate which of the two
preceding stimuli appeared to move faster. Feedback was provided after
each trial. Subjects practiced the task extensively until their
performance reached asymptotic levels. The speed increments that were
used during the fMRI scans were chosen based on these asymptotic
performance levels so that subjects would perform with an accuracy of
~75-80% correct (Table 2).
The stimulus alternated between 18 sec during which paired dots were
presented followed by 18 sec during which unpaired dots were presented.
During each 252 sec fMRI scan, this paired/unpaired cycle was repeated
seven times.
The stimuli were displayed on a flat-panel display (NEC, multisynch LCD
2000) positioned just beyond the end of the patient bed. The display
was viewed through binoculars (Optolyth-Optik Alpin 8 × 30)
specially modified with all the steel parts removed and replaced with
beryllium-copper or brass. A pair of mirrors, angled at ~45°, were
attached to the binoculars just beyond the two objective lenses, to
enable the subjects to see the LCD display.
Data acquisition. MR imaging was performed on a standard
clinical GE 1.5 T Signa scanner with either a GE head coil (grating experiments on subject djh) or a custom designed dual surface coil
(grating experiments on subject gmb, dots experiments on both
subjects). The experiments were undertaken with the written consent of
each subject and in compliance with the safety guidelines for MR research.
Each fMRI scanning session began by acquiring a set of low-resolution,
sagittal, anatomical images used for slice selection. Eight adjacent
planes were selected with the most ventral slice positioned along the
boundary between the occipital lobe and the cerebellum. Approximately
the same slices were chosen in each scanning session. A set of
structural images were then acquired using a T1-weighted spin echo
pulse sequence (500 msec repetition time, minimum echo time, 90° flip
angle) in the same slices and at the same resolution as the functional
images. These inplane anatomical images were registered to the
high-resolution anatomical scan of each subject's brain so that all MR
images (across multiple scanning sessions) from a given subject were
aligned to a common three-dimensional coordinate grid. Then a series of
fMRI scans were performed using a spiral T2*-sensitive
gradient-recalled echo pulse sequence (1500 msec repetition time, 40 msec echo time, 90° flip angle, two interleaves, inplane
resolution = 1.02 × 1.02 mm, slice thickness = 4 mm)
(Noll et al., 1995 ; Glover and Lai, 1998 ). Spiral fMRI pulse sequences
compare favorably with echo-planar imaging in terms of spatial
resolution and sensitivity (Sawyer-Glover and Glover, 1998 ).
A bite bar stabilized the subjects' heads. The time-series of fMRIs
from each scan were visually inspected for head movements. No
post hoc motion correction was applied because there was no indication of head movements during any of the scans.
Data analysis. Each fMRI scan lasted 252 sec. Data from the
first 36 sec cycle were discarded: (1) to minimize effects of magnetic
saturation, (2) to minimize effects of visual adaptation, and (3) to
allow time for subjects to practice the task. During the remaining six
cycles of each scan, 72 functional images (one every 3 sec) were
recorded for each slice. For a given fMRI voxel (corresponding to a
1 × 1 × 4 mm brain volume), the image intensity changed
over time and comprised a time series of data.
The data were analyzed separately in each of two identifiable visual
areas, V1 and MT+. We computed the fMRI response amplitudes and phases
by: (1) removing the linear trend in the time series, (2) dividing the
time series of each voxel by its mean intensity, (3) averaging the
resulting time series over the set of voxels corresponding to the
stimulus representation within a visual area (V1 or MT+), and then (4)
calculating the amplitude and phase of the best fitting 36 sec period
sinusoid. The first step (removing the linear trend) compensates for
the fact that the fMRI signal tends to drift, for unknown reasons, very
slowly over time. The second step converts the data from arbitrary
image intensity units to units of percent signal modulation; this is
especially important because the mean image intensity varies
substantially with distance from the surface coil. Finally, we computed
the vector average and SD of the responses (amplitudes and phases)
across measurements that were repeated in separate scans.
Localizing V1. Following well-established methods (Schneider
et al., 1993 ; Engel et al., 1994 ; Sereno et al., 1995 ; DeYoe et al.,
1996 ; Engel et al., 1997 ), the polar angle component of the retinotopic
map was measured by recording fMRI responses as a stimulus rotated
slowly (like the second hand of a clock) in the visual field. To
visualize these retinotopy measurements, a high-resolution MRI of each
subject's brain was computationally flattened (Engel et al., 1997 ).
Area V1 within each hemisphere was identified as a large region of
cortex in/near the calcarine sulcus with a retinotopic map spanning
half the visual field.
Localizing MT+. Following previous studies (Zeki et al.,
1991 ; Watson et al., 1993 ; Tootell et al., 1995a ), area MT+ was
identified based on fMRI responses to stimuli that alternated in time
between moving and stationary dot patterns. The dots (small white dots on a black background) moved (10°/sec) radially inward and outward for 18 sec, alternating direction once every second. Then the dot
pattern was stationary for the next 18 sec. This moving/stationary cycle was repeated seven times. We computed the cross-correlation between the time series of each fMRI voxel and a sinusoid with the same
(36 sec) temporal period. We drew MT+ regions by hand around contiguous
areas of strong activation, beyond the retinotopically organized visual
areas. The MT+ regions were confined almost entirely within a single
sulcus in each hemisphere. Brain atlases typically do not provide a
name for this sulcus but it is easily identified in coronal slices as
the sulcus between the inferior occipital sulcus and the lateral
occipital sulcus.
Reference scans. The procedures to define V1 and MT+ were
performed only once per subject. Because the fMRI data recorded during
successive scanning sessions in a given subject were all aligned to a
common three-dimensional coordinate grid (see above), we could localize
both areas across scanning sessions.
The V1 and MT+ regions were further restricted based on responses to a
reference stimulus. The reference scan responses were used to exclude
unresponsive voxels, e.g., brain regions that would have responded to
visual field locations outside the 14 × 14° stimulus aperture,
and voxels that had too little overlap with gray matter. The reference
stimulus was the same moving versus stationary dot pattern used to
localize area MT+. A reference scan was run during each scanning
session, usually as the first fMRI scan of the session. Voxels that
were unresponsive in the reference scans were discarded in the analysis
of all subsequent scans in that scanning session. Responsive voxels
were defined as those that were strongly correlated (r > 0.4 and 1-5 sec time lag) with a 36 sec period sinusoid. Excluding
voxels based on the reference scan decreased the variability in the
data, but it was not critical for any of our conclusions; when this
step in the analysis was skipped, all but one of the statistically significant results reported below retained statistical significance, and many of the results attained the same significance level. Even the
one result that did fall below significance threshold (see Fig. 2,
subject gmb, filled circle) remained close
(p = 0.059).
Statistics. One-tailed t tests were used to
determine the statistical significance of the measured modulations in
brain activity. First, we computed the fMRI response amplitude and
phase for each repeat of each experiment (see above). Second, for each
subject and for each visual area, the responses to the aforementioned reference scans were averaged across scanning sessions. Third, we
computed the component of the fMRI responses with zero phase lag
relative to the responses from the reference scans. Fourth, we computed
the mean and SE of the resulting response amplitude components.
Finally, we tested the null hypothesis that the mean response amplitude
component was zero, i.e., that there was no modulation of brain activity.
This procedure is illustrated in Figure
1. Figure 1A plots fMRI
measurements of MT+ brain activity from 10 repeats of the reference
scan, one per scanning session. In the polar plot, fMRI response
amplitudes are represented by the radial distances from the origin, and
fMRI response phases are represented by the angles counterclockwise
from the horizontal axis. The dashed line passes through the vector
mean of the data points. We refer to the angle of the dashed line as
the reference phase.

View larger version (20K):
[in this window]
[in a new window]
|
Figure 1.
Statistical analysis of fMRI measurements.
A, fMRI measurements of MT+ activity from 10 repeats (1 per
scanning session) of the reference scan for subject djh. Response
amplitude (percent MR signal modulation) indicated by radial distance
from the origin and response temporal phase indicated by the angle from
the horizontal axis. Circles, Responses from the 10 individual scans. Dashed line indicating the reference
phase, passes through the vector mean of the 10 data points.
B, MT+ responses in one of the experimental test conditions
(high-density paired versus unpaired dots) for the same subject.
Circles, Responses from the eight individual scans.
Dashed line (copied from A) indicates the
reference phase. C, Histogram of fMRI response amplitude
components, produced from B by computing the orthogonal
projection of each data point onto the dashed line.
|
|
The reference phases in V1 were 29 and 31° for subjects djh and gmb,
respectively. In MT+ the reference phases were 29 and 33° for
subjects djh and gmb, respectively. Given that a 10° phase lag in our
36 sec period paradigm corresponds to a 1 sec delay, these reference
phases are consistent with the 2-4 sec temporal lag that is
characteristic of the hemodynamic delay (Boynton et al., 1996 ; Malonek
and Grinvald, 1996 , 1997 ).
Figure 1B is a polar plot of MT+ responses in one of
the experimental test conditions (high-density paired versus unpaired dots). The dashed line in Figure 1B is copied from
Figure 1A, i.e., it again indicates the MT+ reference
phase for that subject.
Figure 1C plots a histogram of response amplitude
components. The histogram was produced from Figure 1B
by computing the orthogonal projection of each data point onto the
dashed line that indicates the reference phase. Assuming that the
reference phase is an accurate estimate of the hemodynamic delay, the
mean of the resulting histogram is an unbiased estimate of the true
(noise-free) response amplitude. Statistical tests (one-tailed
t tests) were computed based on the mean and SE of the
response amplitude components, after projecting onto the reference
phase line in this way.
This procedure takes full advantage of the a priori
knowledge of the periodic design of the experiments. For example, if
MT+ had responded more strongly to the paired dots in the first half of
each temporal cycle, then the responses would have been in the top
right quadrant of the polar plot. Instead, MT+ responded more strongly
to the unpaired dots in the second half of each temporal cycle, so the
responses are in the bottom left quadrant of the polar plot. Any data
points that do not lie near the reference phase line must be dominated
by measurement noise.
Converting the bivariate (amplitude and phase) responses into
univariate response amplitude components enabled us to perform a
standard statistical test, but it was not critical for any of our
conclusions. Our conclusions are supported equally well using statistics on the bivariate distributions of response amplitudes and
phases, like the example shown in Figure 1B. The
circles in Figures 2, 4, and 5 represent 95% confidence intervals on
the bivariate response distributions.
For the bar graph in Figure 3, each bar indicates the mean and SE of
the response amplitude components, after projecting (as shown in Figure
1) the data onto the reference phase line. The response phases for the
data plotted in Figure 3 were all within 20° of one another. Hence,
although it was convenient to plot the response amplitude components,
our conclusions are equally well supported by the bivariate response
amplitudes and phases.
Electrophysiology
Electrophysiological experiments were conducted in order to
obtain data concerning motion opponency in monkey MT that could be
reasonably compared to the fMRI data obtained in humans. Our goal was
to estimate the overall level of activity in MT (which is what the fMRI
signal is thought to reflect), by pooling activity from numerous
recording sites in MT measured one at a time. Our electrophysiological
methods therefore departed from standard recording techniques in three
important ways. First, instead of measuring the activity of single MT
units, we focused primarily on multiunit recordings that reflect summed
activity of a population of neurons near the recording electrode.
Second, we attempted to obtain an unbiased sample of recording sites in
MT. Thus, we made long penetrations through MT, recording neuronal
activity at fixed intervals along each penetration (every 150-200
µm) irrespective of the exact physiological properties of each site.
In one animal, we recorded single units in addition to multiunit
activity whenever possible, but the recording sites were not selected
specifically to isolate single units. Finally, instead of tailoring the
visual stimulus to the response properties of the recorded neurons
(optimized for receptive field location, preferred spatial and temporal
frequency, etc.), we tested each recording site with a fixed battery of
visual stimuli similar to those used in the fMRI study.
Behavioral task and visual stimulus. Two rhesus monkeys (one
male and one female) were trained to fixate a central spot on a
computer monitor while viewing either a moving sinusoidal grating or
counterphase grating. The monkey's eye position was measured using a
scleral search coil system (CNC Engineering).
Visual stimuli were moving gratings and counterphase gratings with the
same spatial frequency (0.8 cycle/°) and temporal frequency (4 Hz).
The contrast of the moving gratings was either 3 or 50%, and the
contrast of the counterphase gratings was 6 or 100%. The gratings
subtended 14 × 14° of visual angle and were centered on the
fixation point as in the fMRI experiments. Grating stimuli were
presented on a uniform gray background of the same mean luminance (50 cd/m2). Receptive fields at all our MT recording
sites lay completely or partially inside the stimulus aperture.
Each trial began with the appearance of a small central fixation point.
Throughout each trial, which lasted 6.3 sec, the monkey was required to
maintain fixation within a small window (3 × 3° or smaller)
around the fixation point. Trials in which the monkey broke fixation
prematurely were aborted without reward and were excluded from our analysis.
Each trial lasted 6.3 sec, beginning with a 0.5 sec fixation interval.
After the initial fixation interval, a series of six 0.5 sec stimulus
intervals alternated with 0.5 sec blank intervals. The final blank
interval was shortened to 0.3 sec. We alternated brief stimulus
presentations with blank intervals in this way to minimize any effects
of contrast-dependent adaptation, and so that the stimulation would be
similar to that used in the fMRI experiments. The gratings were
presented during the stimulus intervals, and a uniform gray field with
the same mean luminance was presented during the blank intervals. The
same gray field was presented between trials and during the initial 500 msec fixation interval.
Moving gratings were presented on half of the trials, and counterphase
gratings were presented on the other half of the trials. For moving
grating trials, the direction of motion was reversed every second so
that the first, third, and fifth seconds contained motion in one
direction, and the second, fourth, and sixth seconds contained motion
in the opposite direction. The direction of motion was chosen from
eight possible directions (0-315° at 45° steps). Counterphase
gratings were chosen from four possible orientations (0, 45, 90, or
135°). One of these 12 stimulus conditions was chosen pseudorandomly
for each successive trial.
Visual stimuli were generated using a Cambridge Research graphics board
(VSG 2/3) and presented on a Nanao 17 inch Flexscan monitor (model
T2-17ts, 60 Hz screen refresh), placed 57 cm away from the monkey.
Electrophysiology. While the monkey performed the fixation
task, neuronal activity was recorded from areas MT and MST using parylene-coated tungsten microelectrodes (Micro Probe Inc., impedance 1-2 M at 1 kHz). Electrical signals were amplified and filtered, and action potentials from single and multiunits were detected with a
time resolution of 1 msec using a dual time-amplitude window discriminator (Bak Electronics). When multiunit activity alone was
recorded, an "event" was considered to be any excursion of the
voltage trace above a set threshold (this might correspond to an action
potential from a single neuron or a signal from several superimposed
spikes). The threshold was set by hand so that baseline activity (in
the absence of a stimulus) was 50-100 events per second.
When single and multiunit activity was recorded simultaneously,
separate time-amplitude windows were employed for each signal. The two
windows were nonoverlapping in the range of amplitudes accepted, so
that the single unit action potentials were excluded from the multiunit
activity. For each site, the multiunit receptive field location and the
"preferred" direction were first mapped using a random dot stimulus
that was controlled interactively by the experimenter.
Our multiunit measurement is likely to reflect the activity of a small
population of neurons near the tip of the recording electrode. In
previous studies from our laboratories, multiunit event counts obtained
by this method correlated well with the root-mean-square power of the
local field potential measured from the same electrode. For numerous
recording sites in MT, disparity tuning curves were computed from both
the multiunit and local field potential data. Preferred disparities
obtained from the two data sets were nearly identical (regression slope
near unity, r = 0.93; n = 258;
p < 0.0001), and the disparity tuning indices were
highly correlated as well (regression slope near unity,
r = 0.78; n = 396; p < 0.0001) (G. C. DeAngelis and W. T. Newsome, unpublished
results). Thus, although we chose to use the multiunit measurement in
the current study, the same results would have been obtained from local
field potential measurements.
In monkey S, microelectrodes were advanced from the occipital lobe and
penetrated MT tangentially. In monkey M, microelectrodes were advanced
from the frontal lobe and penetrated MT roughly normal to the surface
of the cortex. MT was identified based on its high percentage of
direction-selective units, its characteristic topography, and the
stereotyped sequence of gray matter, white matter, and sulci along the
electrode tracks. We also recorded four sites from MST in monkey M. Identifying MST in monkey M was relatively easy because electrode
penetrations advancing from the frontal lobe necessarily pass through
MST on the dorsal-anterior bank of the superior temporal sulcus before
entering MT on the ventral-posterior bank. We have no histological
confirmation of the recording sites because both monkeys are currently
being used in related experiments. Additional details regarding our
physiological methods can be found in Britten et al. (1992) .
All procedures used in this study conformed to guidelines established
by the National Institutes of Health for the care and use of laboratory animals.
Data analysis and statistics. The absolute magnitude of the
multiunit response is somewhat arbitrary because it depends on the
precise placement of the electrode tip and on the particular threshold
level used for selecting multiunit events (see above). Hence, we
computed a normalized response for each trial as:
The response for each trial was quantified as the total number
of events recorded during the 5.8 sec interval of stimulus presentation. The baseline activity was estimated as the total number
of events recorded during the 300 msec interval before stimulus onset.
These normalized responses were then averaged across trials and across
recording sites. One-tailed, paired t tests were used to
test the null hypothesis that the resulting mean response to moving
gratings was no greater than the mean response to counterphase gratings.
Data set. We recorded from a total of 61 multiunit sites in
MT (25 in monkey M and 36 in monkey S) and four sites in MST (all from
monkey M). In addition we recorded 12 single units from MT of monkey S.
 |
RESULTS |
Moving versus counterphase gratings
In a motion-sensitive brain area lacking interactions between
opposite motion directions, one would predict that the total neuronal
activity evoked by a counterphase grating would be greater than that
evoked by a single moving grating component; the two moving grating
components that make up the counterphase grating would evoke responses
in two separate subpopulations of direction-selective neurons. With
motion opponency, on the other hand, each neuronal subpopulation would
be suppressed by the component grating moving in its nonpreferred
direction. If the suppression were strong enough, then the total
neuronal activity (summed across both subpopulations) evoked by the
counterphase grating would actually be less than that evoked by either
of the moving grating components presented alone.
In our first set of human fMRI experiments, counterphase gratings were
presented in alternation with moving gratings. If V1 exhibits little
motion opponency, then we would expect the fMRI responses to first
increase during the counterphase grating presentations and then
decrease during the moving grating presentations. If MT+ exhibits
strong motion opponency, then we would expect the fMRI responses to
first decrease and then increase. In other words, the temporal phase of
the fMRI responses in V1 should be near 0°, whereas the temporal
phase in MT+ should be near 180°.
The results, plotted in Figure 2,
demonstrate strong motion opponency in MT+. In the polar plots, fMRI
response amplitudes are represented by the radial distances from the
origin, and fMRI response phases are represented by the angles
counterclockwise from the horizontal axis. For all three stimulus
conditions, the fMRI responses in MT+ (filled
symbols) were in phase with the presentation of moving gratings.
In other words, MT+ brain activity was reduced by taking a single
moving grating and superimposing a second grating moving in the
opposite direction implying strong motion opponency. Superimposing the
second grating increased the stimulus contrast, but nonetheless
elicited a reduced response (circles, p < 0.001 for
djh and p < 0.05 for gmb; squares, p < 0.001 for djh and p < 0.01 for gmb; triangles,
p < 0.001 for both subjects).

View larger version (23K):
[in this window]
[in a new window]
|
Figure 2.
Motion opponency in human MT+. fMRI responses to
stimuli that alternated between counterphase and moving gratings.
Response amplitude (percent MR signal modulation) indicated by radial
distance from the origin, and response temporal phase indicated by the
angle from the horizontal axis. Responses from V1 (open
symbols) are near 0°, in phase with the presentation of
counterphase gratings. Responses from MT+ (filled
symbols) are near 180°, in phase with the presentation of moving
gratings. Two panels correspond to the two subjects. Plot symbols
represent the vector average of between four and six measurements that
were repeated in separate scans. Large circles represent
95% confidence intervals on the bivariate distributions of response
amplitudes and phases. Circles: sf = 0.8 cycle/°;
tf = 4 Hz; mean luminance = 36 cd/m2; mean
moving grating contrast = 45.75%; mean counterphase grating
contrast = 91.5%; n = 5 for both subjects.
Squares: sf = 0.4 cycle/°; tf = 8 Hz; mean
luminance = 3 cd/m2; mean moving grating
contrast = 44.25%; mean counterphase grating contrast = 88.5%; n = 5 for djh; n = 4 for gmb.
Triangles: sf = 0.8 cycle/°; tf = 4 Hz; mean
luminance = 36 cd/m2; mean moving grating
contrast = 6.25%; mean counterphase grating contrast = 12.5%; n = 6 for djh; n = 5 for
gmb.
|
|
In V1 (open symbols), on the other hand, activity was
slightly (although not always statistically significantly) increased by
superimposing the second grating, implying little or no motion opponency on average. It is possible that a subset of
direction-selective V1 neurons do exhibit motion opponency, but that
our fMRI measurements reflect the lack of opponency in the majority of neurons.
To control for attentional state, subjects performed a contrast
discrimination task throughout each fMRI scan (see Materials and
Methods). Subjects reported that the task demanded more concentrated effort when it was made more difficult by reducing the contrast increments. For two of the conditions (circles and squares) the subjects' performance was somewhat better for counterphase gratings than for moving gratings (Table 1),
suggesting that the subjects may have attended more to the moving
gratings than to the counterphase gratings. In the third condition
(triangles), we specifically adjusted the contrast increments to force
subjects to attend more to the counterphase gratings than to the moving
gratings (Table 1). The results (Fig. 2, triangles) again
show greater MT+ activity to moving than to counterphase gratings,
implying that any effect of attention is outweighed by motion opponency.
We ignored one repeat of the condition represented by the squares in
Figure 2 for subject gmb. Although there was no obvious artifact in the
functional MR images, this one repeat was clearly an outlier. The MT
response (filled square) was 5.7 SDs (11.4 SEs) away
from the mean of the other four repeats. The V1 response (open
square) was 3.5 SDs (seven SEs) away from the mean of the other
four repeats. With this scan included, the motion opponency effect in
MT+ falls below statistical significance threshold
(p = 0.06), but only for this one condition
(Fig. 2, filled square) in this one subject (gmb).
Moving/counterphase gratings versus blank
The data in Figure 2 indicate the differences in the fMRI signal
amplitudes to the moving and counterphase gratings. These data cannot,
however, be compared with electrophysiological measurements because a
small amplitude could be caused by a small motion opponency effect, or
it could be that the stimuli evoked only small responses to begin with.
We therefore conducted a separate experiment to quantify the magnitude
of the effect by measuring responses to moving gratings and
counterphase gratings separately. Figure
3 plots the results. MT+ responses were
stronger for moving gratings (white bars) than for
counterphase gratings (gray bars): 80% stronger for
subject djh (p < 0.001) and 45% stronger for
gmb (p < 0.01).

View larger version (21K):
[in this window]
[in a new window]
|
Figure 3.
fMRI response amplitudes in visual areas V1 and
MT+ to stimuli that alternated between test gratings and a uniform gray
field. White bars, Moving gratings (sf = 0.4 cycle/°;
tf = 8 Hz; mean luminance = 3 cd/m2; mean
contrast = 44.25%; n = 7 for djh;
n = 8 for gmb). Gray bars, Counterphase
gratings (same sf, tf, and mean luminance, mean contrast = 88.5%;
n = 6 for djh; n = 8 for gmb). MT+
responses are greater to moving than to counterphase gratings. Bar
height, Mean component response amplitudes (see Materials and Methods).
Error bars indicate SEM.
|
|
For neither subject was there a reliable difference in V1 activity, in
spite of the contrast of the counterphase gratings being double that of
the moving gratings. The lack of increase in activity with contrast in
V1 might be caused by response saturation; the firing rates of
individual V1 neurons typically saturate (level off) at high contrasts
(see Discussion). However, it might also reflect a heterogeneity of
responses with some individual neurons in V1 exhibiting decreased
firing rates caused by motion opponency, whereas others were exhibiting
increased firing rates with the increased contrast.
Supersaturation control
We performed a control experiment designed to rule out
supersaturation of the responses as a possible alternative explanation of the results. fMRI measurements of brain activity in visual cortex
typically increase monotonically with contrast and then saturate at
high contrasts (Tootell et al., 1995a ; Boynton et al., 1996 , 1999 ; Demb
et al., 1997 , 1998 ). Using other techniques, however, researchers have
sometimes observed supersaturation in which increasing the contrast of
the stimulus beyond a certain level reduced response amplitudes (Li and
Creutzfeldt, 1984 ; Tyler and Apkarian, 1985 ; Burr and Morrone, 1987 ;
Bonds, 1991 ). Because the counterphase grating always had twice the
contrast of the moving grating component, the possibility remains that
increasing the contrast per se caused the decreased responses in Figure
3 and for the circles and squares in Figure 2 (supersaturation cannot explain the triangles in Fig. 2 because they correspond to low-contrast stimuli).
Figure 4 plots fMRI responses to moving
gratings that alternated between high (mean, 95%) contrast and medium
(mean, 47.75%) contrast. There was no significant modulation of MT+
brain activity in either subject (p > 0.8 for
djh; p > 0.12 for gmb). For subject gmb the activity
in V1 increased with contrast (p < 0.01), but for subject djh the increase in V1 activity was not statistically significant. That is, the responses showed some evidence of response saturation in both brain areas, but no evidence of supersaturation at
high contrasts.

View larger version (22K):
[in this window]
[in a new window]
|
Figure 4.
Supersaturation control. fMRI responses (same
format as Fig. 2) to moving grating stimuli that alternated between
high (mean, 95%) and medium (mean, 47.75%) contrasts. sf = 0.8 cycle/°; tf = 4 Hz; mean luminance = 36 cd/m2; n = 6 for djh;
n = 5 for gmb.
|
|
Previously reported fMRI measurements as a function of contrast exhibit
a clear dissociation between V1 and MT+. fMRI responses in MT+ show
extremely high gain at low contrasts and near complete saturation at
high contrasts (Tootell et al., 1995a ; Demb et al., 1998 ). Responses in
V1 increase more gradually with contrast, roughly as a power law with
an exponent of 0.3-0.4 (Tootell et al., 1995a ; Boynton et al., 1996 ,
1999 ), a compressive nonlinearity that does not completely saturate at
high contrasts. This is precisely the pattern of results evident in
Figure 4 for subject gmb. When the contrast was increased from 47.75 to
95%, V1 responses increased, but MT+ responses were completely
saturated. For subject djh, there was a tendency for the responses to
increase with contrast both in V1 and in MT+, although neither of these
increases was statistically significant. It is entirely possible (given
the size of the confidence intervals) that further repeated
measurements in djh would reveal the usual dissociation between V1 and
MT+ response saturation.
Paired versus unpaired dots
Qian et al. (1994a) have demonstrated that humans perceive
transparent motion in unpaired dot patterns, but that paired dots appear to flicker. This difference in the perception of motion to
paired versus unpaired dot patterns was reflected in our experiments by
the subjects' performance on the speed discrimination task. The speed
increments for the paired dots were much larger than those for the
unpaired dots, but the (percentage correct) performance of both
subjects to both stimuli was about the same (Table
2).
The fMRI data, plotted in Figure 5, again
show evidence for motion opponency in MT+. At both dot densities, and
in both subjects, MT+ activity (filled symbols) was
in phase with the presentation of the unpaired dots. That is, MT+ brain
activity was reduced by pairing the dots (p < 0.001 for both subjects).

View larger version (30K):
[in this window]
[in a new window]
|
Figure 5.
Motion opponency with random dot stimuli. fMRI
responses (same format as Fig. 2) to paired versus unpaired dots.
A, Low dot density (n = 8 for both
subjects). Both V1 (open symbols) and MT+
(filled symbols) responses are near 180°, in phase
with the presentation of unpaired dots. B, High dot density
(n = 8 for both subjects). MT+ responses
(filled symbols) are near 180°, in phase with the
presentation of unpaired dots.
|
|
V1 responses, at the low dot density, were also reduced by pairing the
dots (p < 0.01 for both subjects), but this was
not the case at the high dot density. For subject gmb, V1 responses were increased by pairing the dots at the high dot density
(p < 0.001). For subject djh, V1 responses were
about equal for paired and unpaired dots at the high density. The
results at the low dot density lead one to be concerned that the
apparent motion opponency in MT+ might simply be inherited from the V1
afferents. However, the dissociation between V1 and MT+ at the high dot
density implies that this is not the case.
Summary of fMRI results
In the previous sections, we have seen that the fMRI signals in
human V1 and MT+ showed different levels of motion opponency. Activity
in human V1 was similar or slightly larger for moving gratings than for
counterphase gratings. This agrees with previous electrophysiological
measurements in cat and monkey primary visual cortex, and it agrees
with computational models that have been used successfully to fit those
electrophysiological measurements (see Discussion). Likewise, human V1
activity did not change consistently for paired versus unpaired dots.
This agrees with previous electrophysiological experiments showing that
the average response in macaque V1 to paired dot patterns is not
significantly different from that to unpaired dot patterns (Qian and
Andersen, 1994 ).
fMRI responses in human MT+, on the other hand, consistently exhibited
strong motion opponency. MT+ responses were reduced for paired versus
unpaired dots. This agrees with previous electrophysiological experiments showing that the average response in macaque MT is significantly reduced by pairing the dots (Qian and Andersen, 1994 ).
Human MT+ responses were also reduced for counterphase versus moving gratings.
However, it is not clear from the published literature whether the
average activity in monkey MT would be reduced by adding a pair of
oppositely moving gratings. Consider, for example, presenting a
rightward moving grating while recording from a rightward-selective MT
column. Superimposing a leftward moving grating will typically reduce
the net activity in this column, but it will simultaneously increase
activity in an adjacent leftward-selective column. The key issue is the
relative sizes of these two effects. For the macaque MT physiology to
match the human MT+ fMRI, the reduction of activity in
rightward-selective columns must exceed the increase in activity in
leftward-selective columns, thus generating a net reduced response to
the counterphase grating. We tested this prediction directly with MT
multiunit recordings.
Electrophysiology
Figure 6 shows the response of a
single MT multiunit site to moving and counterphase gratings. The top
panel displays peristimulus time histograms (PSTHs) of responses to
high contrast stimuli; the bottom panel shows responses to low-contrast
stimuli. The six cycles of stimulus presentation can be seen clearly in
the PSTHs. The dark solid curve depicts responses to a grating stimulus alternating between motion at 45° (first) and motion at 225°
(second), whereas the light solid curve depicts responses to the
reverse sequence (225° first and 45° second). The dashed curve
shows the responses to a counterphase grating oriented at 135° (the
sum of the two moving gratings). For each of the six stimulus cycles, the 45° moving grating (the "preferred" direction) elicited a stronger response than did the counterphase grating. In other words,
the robust response to the preferred grating was reduced by
superimposing a second grating moving in the opposite (or null) direction, as expected from motion opponency. On the other hand, the
response to the counterphase grating was consistently stronger than the response to the null direction grating (225°). The
same general trend is evident in response to the low-contrast stimuli (Fig. 6B).

View larger version (33K):
[in this window]
[in a new window]
|
Figure 6.
Multiunit responses in monkey MT to moving and
counterphase grating stimuli shown as PSTH. A, Responses to
high-contrast stimuli. B, Responses to low-contrast stimuli.
Dark solid curves, Response to gratings moving first at
45° and than at 225°. Light solid curves, Response to
gratings moving first at 225° and then at 45°. Dashed
curves, Response to counterphase grating oriented at 135°.
Responses are aligned to stimulus onset (time 0) and are binned in 100 msec time bins.
|
|
Figure 7 summarizes the full data set
obtained in the experiment of Figure 6 (see Materials and Methods).
Figure 7A plots the data obtained at high stimulus
contrasts, whereas Figure 7B plots the equivalent data set
for low-contrast stimuli. The polar plots depict average neuronal
response as a function of direction of motion of single gratings (solid
line) and response as a function of orientation of counterphase
gratings (dashed line). The responses to the moving gratings in Figure
6 give rise to the data points at 45 and 225° in Figure 7. The
effects seen in the PSTHs of Figure 6 are apparent in their entirety in
Figure 7. Adding the second grating to create a counterphase grating
significantly reduced responses for three directions that responded
well to the single grating stimulus and increased responses for one
direction that responded poorly to the single grating. Responses at the
two directions orthogonal to the preferred null axis (135 and 315°)
were nearly unaffected.

View larger version (17K):
[in this window]
[in a new window]
|
Figure 7.
Polar plot depicting multiunit responses in monkey
MT (same site as in Fig. 6) to moving and counterphase gratings.
A, Responses to high-contrast stimuli. B,
Responses to low-contrast stimuli. The angle of the polar plot
indicates the direction of motion for the moving grating. For the
counterphase gratings, each orientation is plotted twice at the two
opposite directions from which the counterphase stimulus is composed
(i.e., vertical counterphase stimulus generates two points at 0 and
180°). Radial distance from the origin indicates the magnitude of the
response in events per second, scale given at the bottom
left. The response to each stimulus condition was measured as the
mean firing rate during the 500 msec starting 50 msec after stimulus
onset. Solid curves, Responses to moving stimuli.
Dashed curves, Responses to counterphase stimuli. The
gray polygon at the center indicates baseline
activity estimated as the average firing rate during the 300 msec
preceding stimulus onset.
|
|
The data in Figure 7 illustrate the responses of a single cluster of
directional neurons to eight directions of motion and to four oriented
counterphase gratings. If we assume that MT also contains matching
clusters of neurons whose responses are similar to these, but which
prefer other directions of motion, we can consider the recordings in
Figure 7 to reflect the responses of eight different clusters of
directional neurons to a single direction of motion and to a similarly
oriented counterphase grating. We may infer from these data, therefore,
that adding a second moving grating to produce a counterphase grating
has two effects in MT: because of motion opponency, activity decreases
in columns that respond well to the motion of the first grating, but
activity increases in columns that respond poorly to the first grating. As indicated above, the key issue is the relative size of these two effects.
To assess this issue quantitatively, we compared the average response
elicited at a given site by counterphase gratings to the average
responses elicited by moving gratings. We computed this average across
all four axes of motion employed in this experiment (e.g., Fig. 7).
This procedure is necessary because the direction-tuning curves of MT
neurons are broad, and any given stimulus elicits responses from
neurons tuned to a variety of different directions. All of these
neurons contribute, presumably, to the fMRI signal, which
reflects changes in blood oxygenation over all of MT+. If macaque MT
responds to the moving and counterphase gratings in a manner similar to
human MT+, the mean response to the counterphase gratings should be
smaller, on average, than the mean response to the moving gratings,
reflecting an overall reduction in neuronal activity.
In the experiment of Figure 7, the outcome of the analysis is
consistent with the human fMRI data for both low- and high-contrast stimuli. For the high contrast (Fig. 7A), the mean response
to single grating gratings was 95 events per second (after subtracting the baseline activity, see Materials and Methods), which is 24% greater than the mean response to counterphase gratings (76 events per
second). For the low contrast (Fig. 7B), the mean response to moving gratings (41 events per second) was 20% stronger than the
mean response to counterphase gratings (34 events per second).
Although many MT sites yielded evidence of motion opponency, other
sites showed little or no opponency, as illustrated in Figure
8. Here, the response to a single grating
moving in the preferred direction (90°) was reduced only slightly by
the counterphase grating, whereas the response to a grating moving in
the null direction (270°) was increased substantially. Averaged
across the four axes of motion, the mean response to counterphase
gratings at this site was actually stronger than the mean response to
moving gratings.

View larger version (20K):
[in this window]
[in a new window]
|
Figure 8.
Polar plot depicting multiunit responses (same
format as Fig. 7) to high-contrast moving and counterphase gratings at
a second site in monkey MT, which does not exhibit motion
opponency.
|
|
The data in Figures 7 and 8 were computed from the stimulus-evoked
responses (i.e. only the 0.5 sec intervals of stimulus presentations).
In the fMRI study, however, the signal was averaged throughout periods
that alternated between 0.5 sec stimulus presentations and blank
(uniform gray field) intervals (see Materials and Methods). Thus, a
more direct comparison between the fMRI and electrophysiological data
is to measure the overall activity in MT during the entire stimulus
presentation, including the blank intervals. We therefore computed the
mean neuronal activity over the entire trial duration (5.8 sec),
averaging as well over all stimulus directions (for moving gratings) or
orientations (for counterphase gratings). The mean response was then
normalized to the baseline activity (see Materials and Methods).
Figure 9 shows scatterplots of the mean
normalized response to moving gratings (x-axis) versus the
mean normalized response to counterphase gratings
(y-axis) for the two monkeys. Figure 9A
depicts the normalized responses of all sites in monkey S for low
contrast stimuli. Most sites lie below the diagonal line, indicating
that the average response to moving gratings was stronger than the
average response to counterphase gratings. The mean normalized response
for moving gratings (0.32) was significantly stronger (p < 0.001; paired t test) than the
mean normalized response for counterphase gratings (0.2). The result is
similar at the high stimulus contrast (Fig. 9B), although
the effect was somewhat weaker and more variable (means for moving and
counterphase gratings were 0.6 and 0.53, respectively;
p < 0.005, paired t test). The results in
monkey M were more noisy, but the overall pattern was the same (Fig.
9C,D). The mean responses to moving gratings, averaged across all recording sites, were significantly stronger than the mean
responses to counterphase gratings (p < 0.05)
at both high and low contrast (mean normalized responses for the
high-contrast moving and counterphase gratings were 0.29 and 0.22, respectively; means for the low-contrast moving and counterphase
gratings were 0.078 and 0.042, respectively). These results in monkey
M, especially at the high contrast, were dominated by the three sites
in MST (indicated by the plus signs) that showed pronounced motion
opponency. More recordings would be necessary to determine whether the
strong opponency interactions in these few sites reflect a genuine
property of MST. It is important to note that both MT and MST are
likely to contribute to the fMRI signal in human MT+.

View larger version (35K):
[in this window]
[in a new window]
|
Figure 9.
Scatter plot of the normalized responses to moving
and counterphase stimuli in all sites recorded from the two monkeys.
The response for each single or mulitunit site to moving stimuli was
computed as the mean firing rate during the entire 5.8 sec trial,
averaged over all directions of motion. Similarly, the response to the
counterphase stimuli was computed as the mean firing rate over all
counterphase orientations. These responses were than normalized
according to the baseline activity at that site (see Materials and
Methods). A, Low-contrast stimuli in monkey S. B,
High-contrast stimuli in monkey S. C, Low-contrast stimuli
in monkey M. D, High-contrast in monkey M. The four points
marked by + correspond to the four MST sites. E,
Normalized responses of 12 single units in MT of monkey S to
low-contrast stimuli. F, Same for high contrast.
|
|
Finally, Figure 9, E and F, shows the normalized responses of the 12 single units recorded from monkey S. Again, most points fall below the
diagonal, even though there is great variability between units. The
difference in the mean single-unit responses was statistically
significant at the high stimulus contrast (mean normalized responses
were 1.22 and 0.69 for moving and counterphase, respectively;
p < 0.05) but not at the low contrast (means were 0.35 and 0.28 for moving and counterphase, respectively; p = 0.25).
Consistent with previous observations, our data suggest that motion
opponency plays an important role in many, but not all sites in MT.
More importantly for present purposes, the overall responses to
counterphase gratings are smaller than those to single moving gratings,
consistent with the outcome of the human fMRI experiments.
 |
DISCUSSION |
MT+, motion opponency, and direction selectivity
The main result of this study is that we found evidence for motion
opponency in human MT+. In MT+, brain activity elicited by a single
moving grating was selectively reduced by superimposing a second
grating moving in the opposite direction (Figs. 2, 3). Activity was not
reduced when the two superimposed gratings moved in the same direction
(Fig. 4). Such direction-specific interactions imply the presence of
direction-selective signals. Specifically, the data are consistent with
the notion that the two superimposed gratings evoke responses in two
mutually inhibitory subpopulations of direction-selective neurons. Our
data therefore support the notion that MT+, like monkey MT, processes
directional signals, consistent with the conclusion reached by Tootell
et al. (1995b) based on fMRI measurements of the motion aftereffect.
MT+ and human motion perception
The role of human MT+ in motion perception has been addressed
previously using a variety of techniques. Patients with lesions that
include this brain area show deficits in motion perception (Zihl and
Cramon, 1983 ; Zihl et al., 1991 ; Vaina et al., 1994 , 1998 ).
Transcranial magnetic stimulation (TMS) near MT+ in healthy volunteers
interferes with motion perception (Beckers and Hoemberg, 1992 ; Hotson
et al., 1994 ; Beckers and Zeki, 1995 ). Functional neuroimaging studies
have shown that MT+ is strongly activated when subjects view stimuli
that appear to be moving (Zeki et al., 1991 ; Watson et al., 1993 ;
McCarthy et al., 1995 ; Tootell et al., 1995a ; Sereno et al., 1995 ;
DeYoe et al., 1996 ; Smith et al., 1998 ), even for illusory motion in
stationary displays (Zeki et al., 1993 ; Tootell et al., 1995b ).
Activity in MT+ can be modulated by instructing subjects to attend to
moving stimuli (Corbetta et al., 1990 ; Beauchamp et al., 1997 ;
O'Craven et al., 1997 ; Gandhi et al., 1999 ). MT+ responds selectively
when subjects simply imagine visual motion stimuli (Goebel et al.,
1998 ).
Our results provide additional evidence that activity in human MT+ is
correlated with the perception of motion. MT+ brain activity is reduced
when a pair of superimposed gratings appears to flicker in place with
no net motion (Figs. 2, 3). In addition, unpaired dot patterns that
appear to move past one another like two semitransparent surfaces evoke
greater activity in MT+ than do paired dot patterns that appear to
flicker (Fig. 5B).
Modeling: divisive normalization and subtractive opponency
Current computational theories provide a framework for
interpreting our results. We focus here on a model recently proposed by
Simoncelli and Heeger (1998) , although other models share many of the
same components.
The Simoncelli-Heeger model posits a particular kind of divisive
suppression in V1 in which the response of each individual V1 neuron is
divided by a quantity proportional to the summed activity of a large
pool of neighboring neurons (Robson, 1988 ; Bonds, 1989 ; Albrecht and
Geisler, 1991 ; DeAngelis et al., 1992 ; Heeger, 1992 , 1993 ; Carandini
and Heeger, 1994 ; Carandini et al., 1997 ; Nestares and Heeger, 1997 ;
Tolhurst and Heeger, 1997a ,b ). The suppressive cortical neighborhood
includes neurons with nearby receptive fields tuned for a range of
orientations, directions, and spatiotemporal frequencies.
The Simoncelli-Heeger model also posits an additional subtractive
inhibition in MT. The response of a velocity-selective MT neuron sums
the responses of V1 afferents with compatible speed-direction preferences and subtracts the responses of V1 neurons with incompatible speed-direction preferences. For example, a model MT neuron tuned for
up-rightward motion sums responses of V1 neurons tuned for up and right
and subtracts responses of V1 neurons tuned for down and left.
Subtractive inhibition in MT predicts motion opponency and is,
therefore, consistent with the key aspects of our results. According to
the model, MT activity elicited by a moving grating should be reduced
by superimposing a second grating moving in the opposite direction
(Figs. 2, 3, 9). MT activity should also be reduced for paired versus
unpaired dots (Fig. 5). According to some models, activity should be
completely canceled by superimposing motions in opposite directions
(Adelson and Bergen, 1985 ; van Santen and Sperling, 1985 ). Other models
predict only partial opponency, i.e., that neuronal activity should be
reduced but not abolished (Zemany et al., 1998 ). Our data, in both
humans and monkeys, are more consistent with the latter.
Consistent with the model, V1 activity was similar or slightly larger
for moving gratings than for counterphase gratings. The response of an
individual V1 neuron to a grating moving in its preferred direction can
be suppressed by superimposing a second grating moving in the opposite
direction, as predicted by the divisive suppression in the model
(Carandini et al., 1997 ; Tolhurst and Heeger, 1997a ). However,
superimposing the second grating also enhances the responses of other
V1 neurons. According to the model, the sum total neuronal activity in
V1 should depend only on the contrast energy of the stimulus,
regardless of its motion. Increasing the contrast energy by
superimposing a second grating should cause saturation of the sum total
neuronal activity at high contrasts (consistent with the data in Fig.
4), but should never elicit a reduction in activity (consistent with
the data in Figs. 2 and 3).
Human fMRI versus macaque electrophysiology
Previous studies have demonstrated the existence of
motion-opponent effects in monkey MT, but the consequences of opponency for the net activity of monkey MT (which presumably is the closest electrophysiological equivalent of the blood flow signal measured by
fMRI) could not be inferred from previously published data. To
determine whether superimposing a second moving grating would result in
a net decrease in average brain activity in MT, as we observed in human
MT+ we recorded multiunit responses in areas MT and MST to moving and
counterphase gratings similar to the ones employed in our fMRI study.
We were quite surprised to find that some MT recording sites (and
single units) gave stronger average responses to counterphase than to
moving gratings (Fig. 8). These sites (and single units) all showed
motion opponency in the conventional sense that responses to a
preferred direction were suppressed somewhat by superimposing a grating
in the opposite direction. The suppression at these recording sites,
however, was counterbalanced by the enhancement of the responses when
superimposing the grating moving in the preferred direction on the
nonpreferred stimuli, yielding stronger average responses to the
counterphase than to the moving gratings. The variability in the degree
of opponency between recording sites may reflect diversity between
known subpopulations of MT neurons. The model proposed by Simoncelli
and Heeger (1998) , for example, predicts that motion opponency should
be evident in the responses of so-called pattern motion-selective MT
neurons but not in the responses of component motion-selective MT
neurons (Movshon et al., 1986 ).
Despite the high variability in the electrophysiological measurements,
when results from all sites and units were combined, we found that the
average response was indeed suppressed in monkey MT as in human MT+.
This general agreement between the monkey and human data provides
further support for the homology between monkey MT and human MT+.
In addition, these results demonstrate that fMRI measurements are
correlated with average spiking activity. The sequence of events from
neuronal response to fMRI response is only partially understood
(Malonek and Grinvald, 1996 ,1997 ; Buxton et al., 1998 ). Continuing to
establish such links between fMRI signals in the human brain and the
more familiar electrophysiological measurements in the monkey brain
will be crucial in the further elaboration of human brain function.
 |
FOOTNOTES |
Received Dec. 22, 1998; revised June 1, 1999; accepted June 3, 1999.
G.M.B. supported by a National Institute of Mental Health (NIMH)
postdoctoral research fellowship. D.J.H. supported by an NEI
grant (R01-EY11794) and an NIMH grant (R29-MH50228). W.T.N. is an
Investigator of the Howard Hughes Medical Institute.
Special thanks to G. H. Glover (and the Richard M. Lucas Center for
Magnetic Resonance Spectroscopy and Imaging, supported by an National
Institutes of Health National Center for Research Resources grant) for
technical support.
Correspondence should be addressed to David Heeger, Department of
Psychology, Stanford University, Stanford, CA 94305-2130.
Dr. Boynton's present address: The Salk Institute for Biological
Studies, 10010 North Torrey Pines Road, La Jolla, CA 92037-1099.
Dr. Demb's present address: Department of Neuroscience, University of
Pennsylvania Medical School, Philadelphia, PA 19104-6058.
Dr. Seidemann's present address: Department of Neurobiology, Weizmann
Institute of Science, Rehovot 76100, Israel.
 |
REFERENCES |
-
Adelson EH,
Bergen JR
(1985)
Spatiotemporal energy models for the perception of motion.
J Opt Soc Am
2:284-299[Web of Science][Medline].
-
Albrecht DG,
Geisler WS
(1991)
Motion sensitivity and the contrast-response function of simple cells in the visual cortex.
Vis Neurosci
7:531-546[Web of Science][Medline].
-
Albright TD
(1993)
Cortical processing of visual motion.
In: Visual motion and its role in the stabilization of gaze (Miles FA,
Wallman J,
eds), pp 177-201. Amsterdam: Elsevier.
-
Beauchamp MS,
Cox RW,
DeYoe EA
(1997)
Graded effects of spatial and featural attention on human area MT and associated motion processing areas.
J Neurophysiol
78:516-520[Abstract/Free Full Text].
-
Beckers G,
Homberg V
(1992)
Cerebral visual motion blindness: Transitory akinetopsia induced by transcranial magnetic stimulation of human area V5.
Proc R Soc Lond B Biol Sci
249:173-178[Medline].
-
Beckers G,
Zeki S
(1995)
The consequences of inactivating areas V1 and V5 on visual motion perception.
Brain
118:49-60[Abstract/Free Full Text].
-
Belliveau JW,
Kennedy Jr DN,
McKinstry RC,
Buchbinder BR,
Weisskoff RM,
Cohen MS,
Vevea JM,
Brady TJ,
Rosen BR
(1991)
Functional mapping of the human visual cortex by magnetic resonance imaging.
Science
254:716-719[Abstract/Free Full Text].
-
Bonds AB
(1989)
Role of inhibition in the specification of orientation selectivity of cells in the cat striate cortex.
Vis Neurosci
2:41-55[Web of Science][Medline].
-
Bonds AB
(1991)
Temporal dynamics of contrast gain in single cells of the cat striate cortex.
Vis Neurosci
6:239-255[Web of Science][Medline].
-
Boussaoud D,
Ungerleider LG,
Desimone R
(1990)
Pathways for motion analysis: cortical connections of the medial superior temporal and fundus of the superior temporal visual areas in the macaque.
J Comp Neurol
296:462-495[Web of Science][Medline].
-
Boynton GM,
Engel SA,
Glover GH,
Heeger DJ
(1996)
Linear systems analysis of fMRI in human V1.
J Neurosci
16:4207-4221[Abstract/Free Full Text].
-
Boynton GM,
Demb JB,
Glover GH,
Heeger DJ
(1999)
Neural basis of contrast discrimination.
Vision Res
39:257-269[Web of Science][Medline].
-
Bradley DC,
Qian N,
Andersen RA
(1995)
Integration of motion and stereopsis in middle temporal cortical area of macaques.
Nature
373:609-611[Medline].
-
Britten KH,
Shadlen MN,
Newsome WT,
Movshon JA
(1992)
The analysis of visual motion: a comparison of neuronal and psychophysical performance.
J Neurosci
12:4745-4765[Abstract].
-
Burr DC,
Morrone MC
(1987)
Inhibitory interactions in the human vision system revealed in pattern-evoked potentials.
J Physiol (Lond)
389:1-21[Abstract/Free Full Text].
-
Buxton RB,
Wong EC,
Frank LR
(1998)
Dynamics of blood flow and oxygenation changes during brain activation: the balloon model.
Magn Reson Med
39:855-864[Web of Science][Medline].
-
Carandini M,
Heeger DJ
(1994)
Summation and division by neurons in primate visual cortex.
Science
264:1333-1336[Abstract/Free Full Text].
-
Carandini M,
Heeger DJ,
Movshon JA
(1997)
Linearity and normalization of simple cells of the macaque primary visual cortex.
J Neurosci
17:8621-8644[Abstract/Free Full Text].
-
Corbetta M,
Miezin F,
Dobmeyer S,
Shulman G,
Petersen S
(1990)
Attentional modulation of neural processing of shape, color, and velocity in humans.
Science
248:1556-1559[Abstract/Free Full Text].
-
DeAngelis GC,
Robson JG,
Ohzawa I,
Freeman RD
(1992)
The organization of suppression in receptive fields of neurons in the cat's visual cortex.
J Neurophysiol
68:144-163[Abstract/Free Full Text].
-
DeYoe EA,
Carman GJ,
Bandettini P,
Glickman S,
Wieser J,
Cox R
(1996)
Mapping striate and extrastriate visual areas in human cerebral cortex.
Proc Natl Acad Sci USA
93:2382-2386[Abstract/Free Full Text].
-
Demb JB,
Boynton GM,
Heeger DJ
(1997)
Brain activity in visual cortex predicts individual differences in reading performance.
Proc Natl Acad Sci USA
94:13363-13366[Abstract/Free Full Text].
-
Demb JB,
Boynton GM,
Heeger DJ
(1998)
FMR imaging of early visual pathways in dyslexia.
J Neurosci
18:6939-6951[Abstract/Free Full Text].
-
Dubner R,
Zeki SM
(1971)
Response properties and receptive fields of cells in an anatomically defined region of the superior temporal sulcus of the monkey.
Brain Res
35:528-532[Web of Science][Medline].
-
Engel SA,
Rumelhart DE,
Wandell BA,
Lee AT,
Glover GH,
Chichilnisky E-J,
Shadlen MN
(1994)
fMRI of human visual cortex.
Nature
369:525[Medline].
-
Engel SA,
Glover GH,
Wandell BA
(1997)
Retinotopic organization in human visual cortex and the spatial precision of functional MRI.
Cereb Cortex
7:181-192[Abstract/Free Full Text].
-
Gandhi SP,
Heeger DJ,
Boynton GM
(1999)
Spatial attention affects brain activity in human primary visual cortex.
Proc Natl Acad Sci USA
96:3314-3319[Abstract/Free Full Text].
-
Glover GH,
Lai S
(1998)
Self-navigating spiral fMRI: Interleaved versus single-shot.
Magn Reson Med
39:361-368[Web of Science][Medline].
-
Goebel R,
Khorram-Sefat D,
Muckli L,
Hacker H,
Singer W
(1998)
The constructive natures of vision: direct evidence from functional magnetic resonance imaging studies of apparent motion and motion imagery.
Eur J Neurosci
10:1563-1573[Web of Science][Medline].
-
Heeger DJ
(1992)
Normalization of cell responses in cat striate cortex.
Vis Neurosci
9:181-198[Web of Science][Medline].
-
Heeger DJ
(1993)
Modeling simple cell direction selectivity with normalized, half-squared, linear operators.
J Neurophysiol
70:1885-1898[Abstract/Free Full Text].
-
Hotson J,
Braun D,
Herzberg W,
Boman D
(1994)
Transcranial magnetic stimulation of extrastriate cortex degrades human motion direction discrimination.
Vision Res
34:2115-2123[Web of Science][Medline].
-
Krubitzer LA,
Kaas JH
(1990)
Cortical connections of MT in four species of primates: areal, modular, and retinotopic patterns.
Vis Neurosci
5:165-204[Web of Science][Medline].
-
Kwong KK,
Belliveau JW,
Chesler DA,
Goldberg IE,
Weiskoff RM,
Poncelet BP,
Kennedy DN,
Hoppel BE,
Cohen MS,
Turner R,
Cheng H-M,
Brady TJ,
Rosen BR
(1992)
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.
Proc Natl Acad Sci USA
89:5675-5679[Abstract/Free Full Text].
-
Levinson E,
Sekuler R
(1975a)
Inhibition and disinhibition of direction-specific mechanisms in human vision.
Nature
254:692-694[Medline].
-
Levinson E,
Sekuler R
(1975b)
The independence of channels in human vision selective for direction of movement.
J Physiol (Lond)
250:347-366[Abstract/Free Full Text].
-
Li CY,
Creutzfeldt O
(1984)
The representation of contrast and other stimulus parameters by single neurons in area 17 of the cat.
Pflügers Arch
401:304-314[Web of Science][Medline].
-
Lubin J
(1992)
Interactions among motion-sensitive mechanisms in human vision.
In: PhD thesis University of Pennsylvania.
-
Malonek D,
Grinvald A
(1996)
Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping.
Nature
272:551-554.
-
Malonek D,
Grinvald A
(1997)
Vascular imprints of neuronal activity: relationships between the dynamics of cortical blood flow, oxygenation, and volume changes following sensory stimulation.
Proc Natl Acad Sci USA
94:14826-14831[Abstract/Free Full Text].
-
Mather G,
Moulden B
(1983)
Thresholds for movement direction: two directions are less detectable than one.
Q J Exp Psychol
35A:513-518.
-
Maunsell JHR,
Van Essen DC
(1983)
The connections of the middle temporal visual area (MT) and their relationship to a cortical hierarchy in the macaque monkey.
J Neurosci
3:2563-2586[Abstract].
-
McCarthy G,
Spicer M,
Adrignolo A,
Luby M,
Core J,
Allison T
(1995)
Brain activation associated with visual motion studied by functional magnetic resonance imaging in humans.
Hum Brain Mapp
2:235-243.
-
Movshon JA,
Newsome WT
(1996)
Visual response properties of striate cortical neurons projecting to area MT in macaque monkeys.
J Neurosci
16:7733-7741[Abstract/Free Full Text].
-
Movshon JA,
Adelson EH,
Gizzi MS,
Newsome WT
(1986)
The analysis of moving visual patterns.
In: Experimental brain research supplementum II: pattern recognition mechanisms (Chagas C,
Gattass R,
Gross C,
eds), pp 117-151. New York: Springer.
-
Nestares O,
Heeger DJ
(1997)
Modelling the apparent frequency-specific suppression in simple cells responses.
Vision Res
37:1535-1543[Web of Science][Medline].
-
Newsome WT,
Wurtz RH,
Komatsu H
(1988)
Relation of cortical areas MT and MST to pursuit eye movements. II. Differentiation of retinal from extraretinal inputs.
J Neurophysiol
60:604-620[Abstract/Free Full Text].
-
Noll D,
Cohen J,
Meyer C,
Schneider W
(1995)
Spiral k-space MR imaging of cortical activation.
J Magn Reson Imaging
5:49-57[Web of Science][Medline].
-
O'Craven KM,
Rosen BR,
Kwong KK,
Treisman A,
Savoy RL
(1997)
Voluntary attention modulates fMRI activity in human MT-MST.
Neuron
18:591-598[Web of Science][Medline].
-
Ogawa S,
Lee TM,
Kay AR,
Tank DW
(1990)
Brain magnetic resonance imaging with contrast dependent on blood oxygenation.
Proc Natl Acad Sci USA
87:9868-9872[Abstract/Free Full Text].
-
Ogawa S,
Tank DW,
Menon R,
Ellermannm JM,
Kim S-G,
Merkle H,
Ugurbil K
(1992)
Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging.
Proc Natl Acad Sci USA
89:5951-5955[Abstract/Free Full Text].
-
Qian N,
Andersen RA
(1994)
Transparent motion perception as detection of unbalanced motion signals: II. Physiology.
J Neurosci
14:7367-7380[Abstract].
-
Qian N,
Andersen RA,
Adelson EH
(1994a)
Transparent motion perception as detection of unbalanced motion signals: I. Psychophysics.
J Neurosci
14:7357-7366[Abstract].
-
Qian N,
Andersen RA,
Adelson EH
(1994b)
Transparent motion perception as detection of unbalanced motion signals: III. Modeling.
J Neurosci
14:7381-7392[Abstract].
-
Raymond J,
Braddick O
(1996)
Responses to opposed directions of motion: continuum or independent mechanisms?
Vision Res
36:1931-1937[Web of Science][Medline].
-
Robson JG
(1988)
Linear and nonlinear operations in the visual system.
Invest Ophthalmol Vis Sci [Suppl]
29:117.
-
Sawyer-Glover AM,
Glover GH
(1998)
In: fMRI of the motor cortex: comparison of EPI and spiral pulse sequences Proceedings of SMRT Seventh Annual Meeting Sydney.
-
Schneider W,
Noll DC,
Cohen JD
(1993)
Functional topographic mapping of the cortical ribbon in human vision with conventional MRI scanners.
Nature
365:150-153[Medline].
-
Sereno MI,
Dale AM,
Reppas JB,
Kwong KK,
Belliveau JW,
Brady TJ,
Rosen BR,
Tootell RBH
(1995)
Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging.
Science
268:889-893[Abstract/Free Full Text].
-
Simoncelli EP,
Heeger DJ
(1998)
A model of neural responses in visual area MT.
Vision Res
38:743-761[Web of Science][Medline].
-
Smith AT,
Greenlee MW,
Singh KD,
Kraemer FM,
Hennig J
(1998)
The processing of first- and second-order motion in human visual cortex assessed by functional magnetic resonance imaging (fMRI).
J Neurosci
18:3816-3830[Abstract/Free Full Text].
-
Snowden RJ,
Treue S,
Erikson RG,
Andersen RA
(1991)
The response of area MT and V1 neurons to transparent motion.
J Neurosci
11:2768-2785[Abstract].
-
Stromeyer CF,
Kronauer RE,
Madsen JC,
Klein SA
(1984)
Opponent-movement mechanisms in human vision.
J Opt Soc Am A
1:876-884[Web of Science][Medline].
-
Tolhurst DJ,
Heeger DJ
(1997a)
Contrast normalization and a linear model for the directional selectivity of simple cells in cat striate cortex.
Vis Neurosci
14:19-26[Web of Science][Medline].
-
Tolhurst DJ,
Heeger DJ
(1997b)
Comparison of contrast normalization and threshold models of the responses of simple cells in cat striate cortex.
Vis Neurosci
14:293-310[Web of Science][Medline].
-
Tootell RBH,
Taylor JB
(1995)
Anatomical evidence for MT and additional cortical visual areas in humans.
Cereb Cortex
5:39-55[Abstract/Free Full Text].
-
Tootell RBH,
Reppas JB,
Kwong KK,
Malach R,
Born RT,
Brady TJ,
Rosen BR,
Belliveau JW
(1995a)
Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging.
J Neurosci
15:3215-3230[Abstract].
-
Tootell RBH,
Reppas JB,
Dale AM,
Look RB,
Sereno MI,
Malach R,
Brady TJ,
Rosen BR
(1995b)
Visual motion aftereffect in human cortical area MT revealed by functional magnetic resonance imaging.
Nature
375:139-141[Medline].
-
Tyler CW,
Apkarian PA
(1985)
Effects of contrast, orientation and binocularity in the pattern evoked potential.
Vision Res
6:755-766.
-
Ungerleider LG,
Desimone R
(1986)
Cortical connections of visual area MT in the macaque.
J Comp Neurol
248:190-222[Web of Science][Medline].
-
Vaina LM,
Grzywacz NM,
Kikinis R
(1994)
Segregation of computations underlying perception of motion discontinuity and coherence.
NeuroReport
5:2289-2294[Web of Science][Medline].
-
Vaina LM,
Makris N,
Kennedy D,
Cowey A
(1998)
The selective impairment of the perception of first-order motion by unilateral cortical brain damage.
Vis Neurosci
15:333-348[Web of Science][Medline].
-
van Santen JPH,
Sperling G
(1984)
Temporal covariance model of human motion perception.
J Opt Soc Am A
1:451-473[Web of Science][Medline].
-
van Santen JPH,
Sperling G
(1985)
Elaborated Reichardt detectors.
J Opt Soc Am A
2:300-321[Web of Science][Medline].
-
Watson AB,
Thompson PG,
Murphy BJ,
Nachmias J
(1980)
Summation and discrimination of gratings moving in opposite directions.
Vision Res
20:341-347[Web of Science][Medline].
-
Watson JDG,
Myers R,
Frackowiak RSJ,
Hajnal JV,
Woods RP,
Mazziotta JC,
Shipp S,
Zeki S
(1993)
Area V5 of the human brain: evidence from a combined study using positron emission tomography and magnetic resonance imaging.
Cereb Cortex
3:79-94[Abstract/Free Full Text].
-
Zeki S
(1974)
Functional organization of a visual area in the posterior bank of the superior temporal sulcus of the rhesus monkey.
J Physiol (Lond)
236:549-573[Abstract/Free Full Text].
-
Zeki S,
Watson JDG,
Lueck CJ,
Friston KJ,
Kennard C,
Frackowiak RSJ
(1991)
A direct demonstration of functional specialization in human visual cortex.
J Neurosci
11:641-649[Abstract].
-
Zeki S,
Watson JDG,
Frackowiak RSJ
(1993)
Going beyond the information given: The relation of illusory motion to brain activity.
Proc R Soc Lond B Biol Sci
252:215-222[Medline].
-
Zemany L,
Stromeyer CF,
Chaparro A,
Kronauer RE
(1998)
Motion detection on flashed, stationary pedestal gratings: evidence for an opponent-motion mechanism.
Vision Res
38:795-812[Web of Science][Medline].
-
Zihl J,
Cramon DV
(1983)
Selective disturbance of movement vision after bilateral brain damage.
Brain
106:313-340[Abstract/Free Full Text].
-
Zihl J,
Cramon DV,
Mai N,
Schmid CH
(1991)
Disturbance of movement vision after bilateral posterior brain damage: further evidence and follow up observations.
Brain
114:2235-2252[Abstract/Free Full Text].
Copyright © 1999 Society for Neuroscience 0270-6474/99/19167162-13$05.00/0
This article has been cited by other articles:

|
 |

|
 |
 
K. Moutoussis and S. Zeki
Motion processing, directional selectivity, and conscious visual perception in the human brain
PNAS,
October 21, 2008;
105(42):
16362 - 16367.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
J. Vinberg and K. Grill-Spector
Representation of Shapes, Edges, and Surfaces Across Multiple Cues in the Human Visual Cortex
J Neurophysiol,
March 1, 2008;
99(3):
1380 - 1393.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
V. M. Ciaramitaro, G. T. Buracas, and G. M. Boynton
Spatial and Cross-Modal Attention Alter Responses to Unattended Sensory Information in Early Visual and Auditory Human Cortex
J Neurophysiol,
October 1, 2007;
98(4):
2399 - 2413.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
B. Krekelberg and T. D. Albright
Motion Mechanisms in Macaque MT
J Neurophysiol,
May 1, 2005;
93(5):
2908 - 2921.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
P. Neri, H. Bridge, and D. J. Heeger
Stereoscopic Processing of Absolute and Relative Disparity in Human Visual Cortex
J Neurophysiol,
September 1, 2004;
92(3):
1880 - 1891.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
C. Kayser, M. Kim, K. Ugurbil, D.-S. Kim, and P. Konig
A Comparison of Hemodynamic and Neural Responses in Cat Visual Cortex Using Complex Stimuli
Cereb Cortex,
August 1, 2004;
14(8):
881 - 891.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
S. O. Dumoulin, C. L. Baker Jr, R. F. Hess, and A. C. Evans
Cortical Specialization for Processing First- and Second-order Motion
Cereb Cortex,
December 1, 2003;
13(12):
1375 - 1385.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
S.'y. Nishida, Y. Sasaki, I. Murakami, T. Watanabe, and R. B. H. Tootell
Neuroimaging of Direction-Selective Mechanisms for Second-Order Motion
J Neurophysiol,
November 1, 2003;
90(5):
3242 - 3254.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
J.-Y. Chatton, L. Pellerin, and P. J. Magistretti
GABA uptake into astrocytes is not associated with significant metabolic cost: Implications for brain imaging of inhibitory transmission
PNAS,
October 14, 2003;
100(21):
12456 - 12461.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
S. O. Murray, B. A. Olshausen, and D. L. Woods
Processing Shape, Motion and Three-dimensional Shape-from-motion in the Human Cortex
Cereb Cortex,
May 1, 2003;
13(5):
508 - 516.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
A. C. Huk, R. F. Dougherty, and D. J. Heeger
Retinotopy and Functional Subdivision of Human Areas MT and MST
J. Neurosci.,
August 15, 2002;
22(16):
7195 - 7205.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
G. Avidan, M. Harel, T. Hendler, D. Ben-Bashat, E. Zohary, and R. Malach
Contrast Sensitivity in Human Visual Areas and Its Relationship to Object Recognition
J Neurophysiol,
June 1, 2002;
87(6):
3102 - 3116.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
W. Bair, J. R. Cavanaugh, M. A. Smith, and J. A. Movshon
The Timing of Response Onset and Offset in Macaque Visual Neurons
J. Neurosci.,
April 15, 2002;
22(8):
3189 - 3205.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
A. Brechmann, F. Baumgart, and H. Scheich
Sound-Level-Dependent Representation of Frequency Modulations in Human Auditory Cortex: A Low-Noise fMRI Study
J Neurophysiol,
January 1, 2002;
87(1):
423 - 433.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
M. M. Churchland and S. G. Lisberger
Shifts in the Population Response in the Middle Temporal Visual Area Parallel Perceptual and Motor Illusions Produced by Apparent Motion
J. Neurosci.,
December 1, 2001;
21(23):
9387 - 9402.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
A. S. Tolias, S. M. Smirnakis, M. A. Augath, T. Trinath, and N. K. Logothetis
Motion Processing in the Macaque: Revisited with Functional Magnetic Resonance Imaging
J. Neurosci.,
November 1, 2001;
21(21):
8594 - 8601.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
B. T. Backus, D. J. Fleet, A. J. Parker, and D. J. Heeger
Human Cortical Activity Correlates With Stereoscopic Depth Perception
J Neurophysiol,
October 1, 2001;
86(4):
2054 - 2068.
[Abstract]
[Full Text]
[PDF]
|
 |
|

|
 |

|
 |
 
A. C. Huk and D. J. Heeger
Task-Related Modulation of Visual Cortex
J Neurophysiol,
June 1, 2000;
83(6):
3525 - 3536.
[Abstract]
[Full Text]
[PDF]
|
 |
|
|

|