Wellcome Department of Imaging Neuroscience, University College London, London WC1E 6BT, UK
Address correspondence to S. Zeki, Wellcome Department of Imaging Neuroscience, University College London, London WC1E 6BT, UK. Email: zeki.pa{at}ucl.ac.uk.
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Abstract |
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Introduction |
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Such a putative specialization opens up a host of interesting questions regarding the construction of forms by the brain, which we wanted to explore. But we wanted, first, to be certain of the specialization of KO and thus ascertain that the brain does indeed use a specialized cortical area to construct forms from kinetic contours. KO has not been studied for its involvement in the generation of forms and shapes from attributes other than luminance and kinetic contours, e.g. color. One aim of our imaging experiments was therefore to compare the responses of KO to simple shapes generated from kinetic boundaries and from isoluminant colors because much work suggests that these two systems are the ones that are most separate from each other in terms of cortical representation, even though there are cross-connections between them (Shipp and Zeki, 1989; Zeki and Shipp, 1989
; Lund et al., 1994
). If KO is specialized for kinetic contours, one would expect it to be less reactive, or even unreactive, to contours generated from static equiluminant colors. On the other hand, if an area specialized for the construction of forms is able to draw on signals from any source to undertake its function, one would expect that shapes constructed from equiluminant colors would also activate KO, as indeed would shapes generated from static textures.
We also hoped to resolve another uncertainty about KO, namely its relationship to other visual areas in its vicinity. There is little doubt from the published evidence that KO is distinct from the more anteriorly located V5, specialized for visual motion (Zeki et al., 1991; Watson et al., 1993
; Sereno et al., 1995
; Tootell et al., 1996
). What is more problematic is its relationship to areas of the V3 complex (V3 and V3A) (Shipp et al., 1995
; Tootell et al., 1997
) and to the complex of areas that constitute LO (Malach et al., 1995
). The former are areas that have been considered to constitute part of the (dynamic) form system of the visual brain (Zeki 1978
, 1991
). Their proximity to KO naturally raises the question whether KO, assuming it to be an entirely separate area, could plausibly belong to the V3 family of areas, as was indeed implied by the use of the alternative name for KO, area V3B by Smith et al. (Smith et al., 1998
). In the study of Van Oostende et al., activity in KO is not much different from that in V3A, except that the latter was found to be less reactive to luminance borders compared to KO (Van Oostende et al., 1997
). In the study of Smith et al., V3 and KO were equally active to second-order motion stimuli, which is what led Smith et al. to refer to KO as V3B (Smith et al., 1998
). The relationship of KO to the LO complex, an area that is critical for visual object recognition (Malach et al., 1995
), is also uncertain. If KO is indeed distinct from LO, then a compelling case for it has not been made by the published maps, which show a substantial and sometimes total overlap between the two (Van Oostende et al., 1997
). Hence, another possibility is that KO belongs to the LO family of areas.
To address this issue, we applied the independent component analysis (ICA) method of Bell and Sejnowski (Bell and Sejnowski, 1995) to functional imaging data acquired when humans viewed dynamic complex visual scenes (Bartels and Zeki, 2001
, 2003
). This method isolates cortical areas with respect to their activity time course (ATC), placing areas that have different ATCs in separate independent components (ICs). This time-based parcellation of the cortex is done without a priori hypotheses (McKeown et al., 1998
) and has the advantage of showing which areas have ATCs that correlate. Any evidence that the ATC of KO is distinct from those in LO, the V3 complex and the V5 complex would be a strong hint that KO operates independently from other areas and thus deserves a separate status, regardless of whether it is specialized for processing kinetic contours or not. On the other hand, there was always the possibility that the ATC of KO might correlate better with one of the other areas, such as V5 or V3 or LO, thus betraying its status as belonging to one family of areas or another.
Results obtained from these imaging experiments encouraged us to examine the responses of orientation-selective cells in the areas of the V3 complex (V3 and V3A) of the macaque to kinetic and luminance boundaries. The presence in the V3 complex of cells that are only selective for oriented lines when generated from kinetic contours would be strong suggestive evidence that a separate system, embedded within the V3 complex, processes oriented lines so generated. The choice of the V3 complex was not arbitrary. Apart from the functions imputed to it and alluded to above, the results of our imaging studies emphasized the need for a study of selectivities within the V3 complex.
Our overall conclusion from all these results is that KO (V3B) is not specialized for the processing of kinetic contours and that it belongs more properly to the V3 family of areas. We conclude that the brain does not devote a cortical area specifically to the processing of kinetic boundaries.
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Materials and Methods |
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Stimuli and Task for Functional Magnetic Resonance Imaging (fMRI) Experiment 1
Experiment 1 used eight normal subjects (six male, seven right-handed) to compare the patterns of activity in the brain when subjects viewed shapes derived either from color or from motion cues. The four types of stimulus, and the design of the experiment, are illustrated in Figure 1. Color shapes (CS), such as the keyhole in the center of Figure 1A
, were constituted from static isoluminant red and green oriented bars, by arranging the orientation of the bars inside the shape to be orthogonal to those outside. Color shapes could only be extracted by a visual subsystem that discriminates between isoluminant red and green. An equal number of color no-shapes (CN) were shown, in which the background alone was present, as in Figure 1B
. In both cases the orientation of the background bars was randomized between the two diagonals (tilting right or left) from trial to trial. Thus the critical difference between the two is the appearance of a simple recognizable shape in one (A) and its absence in the other (B).
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An example of a motion shape (MS) is shown in Figure 1C. Here the stationary shape is defined by kinetic contours, through the direction of motion of a field of dots, illustrated by smeared dots in this figure. Dots inside the shape moved in one of the four diagonal directions, determined at random, while those outside moved in an orthogonal direction. The parameters of the dots are shown in the legend to Figure 1
. The shape derived from kinetic contours was much more clearly visible in the moving display than in the stationary illustration given here, but it would be entirely invisible in any single stationary frame taken from the stimulus. As before, an equal number of motion no-shape (MN) displays were shown (Fig. 1D
), composed of the background alone, which in this case was simply a field of random dots all moving in the same direction (translational motion). The critical difference between the two, again, is the appearance of a recognizable shape in one (Fig. 1C
) and its absence in the other (Fig. 1D
). These four conditions form a 2 x 2 factorial design (Fig. 1
), in which the two factors are shapes versus no-shapes and motion versus color.
Subjects fixated a central black cross throughout the experiment, and displays in which this cross appeared on a uniformly gray background (gray stimuli) were included as a baseline condition. Trials were grouped into epochs consisting of 10 stimuli of the same configuration (e.g. color shapes). Each stimulus was presented for 500 ms, and followed by a gray screen for 1100 ms, up to the onset of the next stimulus. Before scanning, subjects were trained to recognize a keyhole shape as the target (Fig. 1A). During scanning, the subjects task was to press a button with their right index finger whenever they saw this target shape, but not to press to other distractor shapes. The target object was randomized with eight distractor shapes, giving a mean target frequency of 11% (1/9). Subjects were told beforehand that there would be periods during which no shapes would be present on the screen (i.e. during no-shape and gray epochs), and that no response was required during these periods.
All of the statistics in the present study are based on these four types of epoch: CS, CN, MS and MN. Four types of epochs with other stimulus configurations were also included as part of a separate study (Perry and Zeki, 2000). Epochs were pseudo-randomized with the constraint that, during the whole experiment, subjects saw nine epochs of each type, and 18 gray epochs. These 90 epochs were shown in two equal blocks, with a short rest between. Subjects were familiarized with the target shape, trained on the task, and given 1015 min of practice outside the scanner until they were confident. During scanning most of the target shapes were correctly identified (Table 1
). The subjects appeared to find extracting objects from motion more difficult. If anything, this would tend to enhance the response to motion objects relative to color objects, which was not observed in our data.
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The aim here was to learn the degree to which the ATC of V3B (KO) correlates with that of other areas and, if so, which ones. This is best achieved by studying the activity in V3B in complex free-viewing conditions, over a relatively prolonged period of time. Exposure to a complex stimulus has the advantage that many areas will be differentially active in time over the viewing period (Bartels and Zeki, 2001, 2003
). For this second study we therefore asked eight normal subjects (three male, all right-handed) to view the first 22 min 25 s of the James Bond movie Tomorrow Never Dies, including its sound track. The movie was interrupted eight times (i.e. every 2.5 or 3 min) with a blank period (black screen, no sound) lasting 30 s as a baseline condition. As part of a separate study, the movie changed from colour to black and white every 30 s.
Image Acquisition and Pre-processing (Experiments 1 and 2)
Subjects were scanned in a 2 T Magnetom Vision fMRI scanner with a head-volume coil (Siemens, Erlangen, Germany), while viewing the 26° x 19° visual display via an angled mirror. A gradient echo-planar imaging (EPI) sequence, with an echo time (TE) of 40 ms and a repeat time (TR) of 4.1 s was selected to maximize blood oxygen level dependent (BOLD) contrast. Each brain image was acquired in 48 slices, approximately 2 mm thick with 1 mm gaps in between, and each comprising 64 x 64 pixels. The number of EPI scans per subject was as follows: experiment 1, 180 scans; experiment 2, 324 scans (subjects 14) or 368 scans (subjects 58). The data were pre-processed using the SPM99 software (Wellcome Department of Imaging Neuroscience, London). In experiment 2, the data were realigned in time using sinc interpolation (Schanze, 1995). In both experiments the EPI images were spatially realigned and normalized to the Montreal Neurological Institute template provided in SPM99 [which approximates to the TalairachTournoux atlas (Talairach and Tournoux, 1988
)] and spatially smoothed with an isotropic Gaussian kernel of 10 mm (experiment 1) or 6 mm (experiment 2). After spatial pre-processing, non-physiological high-frequency noise was filtered out with a low-pass filter shaped to the spectral characteristics of the canonical hemodynamic response function (HRF) within SPM99. In experiment 1, an additional high-pass filter with a period of 512 s was used to remove low-frequency noise. The signal in every voxel was divided by the mean signal intensity of each whole image (global normalization), to compensate for fluctuations of the global signal.
Epoch-based Statistical Analysis (Experiment 1)
Each epoch type was modeled with a separate box-car function (convolved with SPM99s canonical HRF) in a multiple regression analysis (Friston et al., 1995). In addition, subjects responses during scanning were recorded, classified as hits or false positives, and modeled in the design matrix as events of no interest [using the HRF and its first temporal derivative (Josephs et al., 1997
)]. Misses were modeled in the same way, but correct rejections were not modeled explicitly, to avoid over-specifying the model. Group results are based on a fixed-effects analysis, in which the reliability of the observations is assessed relative to within -subject variance, as is appropriate for establishing typical features of the human brain (Friston et al., 1999
). The figures have been thresholded at P < 0.001 uncorrected to show the spatial extent of the clusters.
ICA Analysis (Experiment 2)
ICA analysis (Bell and Sejnowski, 1995) was done on each subject separately, to obtain spatially independent components (McKeown et al. 1998
). Based on information theory, ICA is capable of unmixing or decomposing any linear mixture of independent sources, which need not be known a priori. In the case of fMRI, we assume that all voxels belonging to one functionally specialized area or to a network of highly correlated areas form such an independent source, several of which are spatially mixed in the fMRI whole-brain images. During complex tasks, many different, and maybe temporally or spatially overlapping, sets of areas will be active, each with its own characteristic ATC (Bartels and Zeki, 2001
, 2003
). ICA identifies such sets by separating voxels that share similar ATCs from other voxels and saves them in a separate independent component (IC a whole-brain image). The total number of ICs is equal to the number of input-images, in our case 324 (for subjects 14) or 368 (for the subjects 58). Each IC has an associated time-course, which corresponds closely to the BOLD signal of the most significant voxels in that IC.
The methodology we used for the ICA analysis in this and in our earlier studies (Bartels and Zeki 2000) was similar to the one of McKeown et al. (McKeown et al., 1998
), who first applied ICA to fMRI data. After pre-processing, voxels lying outside the brain were removed by manual thresholding, done for each subject individually, in order to reduce the total data to be processed. Data were then converted into a large matrix with the number of rows corresponding to the number of scans (324 for subjects 14; 368 for subjects 58), and the number of columns corresponding to the number of voxels (between 60 000 and 70 000, depending on the subject). This matrix was submitted to the runica procedure supplied in the EEG package by Makeig (http://www.cnl.salk.edu/~scott/ica-download-form.html) (Makeig et al., 1997
), which applied ICA incorporating the natural gradient feature of Amari (Amari, 1998
), using the default parameters. In each subject, we identified those ICs that contained their most significant voxels within the visual cortex, based on their anatomical position. Among those, we selected areas whose most significant voxel came closest to the Talairach coordinates described for KO and V5 by Van Oostende et al. (Van Oostende et al., 1997
) and for V3v by DeYoe et al. (DeYoe et al., 1996
) (see Table 2
). In all subjects, areas V5 and V3v were more clearly separated by ICA and therefore easier to identify than area KO. The medians and the quartiles of the coordinates of the areas identified are given in Table 2
; they fall within a few millimeters of those reported in previous publications (see above). We isolated the BOLD signal time-courses from the most significant voxels in each area and calculated the correlation coefficients between them. In order to obtain ATCs related only to free viewing of the movie, those parts of the BOLD signal time-course that corresponded to the blank periods and the 30 s window following them were excluded.
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We recorded from 100 cells in 12 cynomolgus monkeys, using standard procedures (Zeki, 1978). All cells were histologically verified to have been located within the V3 complex (V3 or V3A) (see Fig. 2
). Monkeys were first given 25 mg/kg of ketamine hydrochloride and then anesthetized with pentobarbitone sodium and, after surgery, paralyzed with 0.4 mg/kg of pancuronium bromide, supplemented at the rate of 0.5 ml/h throughout the experiment. The electrocardiogram, rectal temperature and exhaled CO2 were monitored continuously and maintained at physiological levels, and additional doses of anesthetic were administered to maintain adequate levels of anesthesia. Retinoscopy and other procedures were as described before (Zeki, 1978
).
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For the orientation-selective cells studied here (the great majority), the bar was moved across the receptive field of the cell in eight directions and responses plotted at 45° intervals, although tests were also carried out at 15°, 30°, 60° and 90° intervals. At least two such direction tests were performed on each cell. In the first, the stimulus bar was solid white on a black background (contrast > 90%); in the second, the stimulus bar contained a texture matrix and the background a second one. The textures (Fig. 3) were composed of 50% black and 50% white randomly arranged squares so that there was no overall luminance contrast. The grain of the texture was one pixel, subtending
3' of arc at the eye. The number of times that the line moved through the receptive field varied, depending upon the background discharge rate of the cell; it was rarely less than twice, and commonly three times or more.
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Our aim in these recordings was limited (i) to determining whether the cells responded as specifically to the above kinetic stimuli as they had to luminance ones; this could be done by determining the tuning curve widths of the cells in both conditions. (ii) To learning whether the cells respond as well, in quantitative terms, to the two stimuli; we determined this by obtaining quantitative data about optimal discharge rates, in terms of spikes per second, while the stimuli traversed the receptive field, for both types of stimuli. For each cell, we presented a luminance bar and a kinetic contour bar at a variety of orientations, and measured the cells responses. Two or three repetitions were done for each orientation in a randomized block sequence. The pairs of tests were examined in detail and, for each test, a cell response profile was drawn, using a spline interpolation between the measured orientations. The profile was used to obtain the preferred orientation, cell firing rate to that orientation and the tuning width of the preferred orientation. For the tuning width, the half width at half height of the peak response was taken. We made two statistical checks on a cells response to the oriented bars: (i) the cell response to each oriented bar was compared to the background firing rate of the cell using a t-test; and (ii) the cells responses to the different orientations were compared using ANOVA. In our comparison of single cells responses to luminance and kinetic contour stimuli (Figs 810), only cells that showed significance at the 1% level for either kind of stimulus were included.
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Results |
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An area that gives its greatest response to kinetic boundaries could be doing so for one of two reasons: (i) it may be specialized for kinetic contours and thus give a response that is specific to them (Dupont et al., 1997; Van Oostende et al., 1997
); or (ii) it may give independent responses to contours, however derived, and to motion. If (ii) is true, then there would be no specificity for kinetic contours. The simplest way to distinguish between these two alternatives is to use a factorial design, in which responses to motion and to contours (the main effects) can be dissociated from the specific response to kinetic contours (the interaction). We designed experiments based on such a 2 x 2 factorial design (Fig. 1
). We were particularly interested in the extraction of contours in the context of shape recognition, so in our study the contours go to make up recognizable shapes.
We thought that area KO should be easily identifiable, since we expected it to be active when contours must be extracted to recognize shapes (even if this were only true for motion shapes). Figure 4a shows the contrast of all shapes (which included contours to be extracted) with no-shapes (in which no contours had to be extracted) in the group data, superimposed onto a horizontal section through the standard template brain, at the level of Talairach coordinate z = +2. In the left prestriate cluster there is a sub-peak, at [32, 92, 2] (indicated by an arrow), which coincides almost exactly with the median coordinates of area KO [32, 92, 0] (Van Oostende et al., 1997
). The corresponding peak in the right hemisphere (indicated by the other arrow) lies in a slightly more lateral position, at [40, 88, 2] {cf. [31, 92, 0] in Van Oostende et al.(1997)
} but the same cluster extends as far medially as [34, 92, 0], and is therefore also likely to be area KO.
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The main purpose of the experiment, however, was to determine if there are any areas specifically involved in extracting kinetic contours. Such areas should appear in the contrast of motion shapes versus motion no-shapes (Fig. 4b.I), but not in the contrast of color shapes versus color no-shapes (Fig. 4b
.II). However, they appear in both contrasts. As the direct comparison between these two contrasts shows (Fig. 4b
.III), there are no areas specifically involved in extracting kinetic contours in this region of prestriate cortex, nor could we demonstrate such areas anywhere else in the brain. KO does not, therefore, appear to have a specificity for kinetic contours.
This point is emphasized by examining the responses of KO to the four classes of stimuli (Fig. 5). The top row of this figure shows that KO tends to give a larger response to motion stimuli (MS and MN) than to color stimuli (CS and CN), and a larger response to shapes (including contours, MS and CS) than to no-shapes (MN and CN). However, the lower row shows that the shape-specific response is the same whether the contours of the shape are extracted from motion or from color. In any putative area with a specificity for kinetic contours, the shape-specific response in the context of motion (MS MN) should have been much larger than the shape-specific response in the context of color (CS CN). KO does not, therefore, appear to be such an area.
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Area KO has also been called area V3B by Smith et al., because their second-order motion stimuli activated areas V3 and V3B similarly (Smith et al., 1998). This similarity made us wonder whether V3B is functionally more related to area V5 (motion) or area V3 (depth and contours). The ICA method segregates cortical areas according to differences in their ATCs, assigning one or more areas to a single IC if they have similar ATCs and to different ICs if they have different ones. Many areas will be simultaneously and differentially active when viewing complex visual scenes, and we hoped that V3B would be among these. The ICA method would then place it in a separate IC or together with other ones, depending upon its ATC relative to that of other cortical areas. To quantify whether activity in V3B was more related to cortical form-processing or to motion-processing we took the correlation of the blood oxygen level-dependent (BOLD) time-courses between V3B, V5 and V3, obtained during free viewing conditions.
ICA never isolated area V3B together with the motion-sensitive area V5 in the same IC (Fig. 4II). Instead, in six out of eight subjects, it isolated V3B together with areas that have been previously implicated in form perception: with V3 in five subjects, and with LO in one subject. In two subjects it isolated V3B, V5 and V3 in separate ICs. The coordinates of areas V5, V3 and V3B (from eight subjects) are given in Table 2
and correspond closely to those published in earlier studies for these areas (see Materials and Methods). The comparison of the BOLD signal from the hottest voxel in each of these areas revealed that, across all eight subjects, activity in V3B correlated significantly more with that in V3 than with that in V5 (paired t-test, P < 0.0001) (Fig. 6
). We conclude that KO (V3B) belongs to the V3 family of areas and is better referred to as V3B, consistent with the conclusions of Smith et al. (Smith et al., 1998
).
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The above does not provide any evidence for a human visual area specialized for the processing of kinetic contours and shows that the properties of V3B, at least as judged by the similarity of its response to second-order motion stimuli (Smith et al., 1998) and by an analysis of its ATC relative to other areas, are similar to those of V3. It thus seemed interesting to compare these results with our recordings from cells of the third visual complex (V3 and V3A) in the macaque, with the specific aim of learning whether the orientation-selective cells there respond as well to oriented lines generated from luminance and from kinetic contours. In all, we isolated 100 cells that were histologically verified to be in V3 or V3A; all had receptive fields located within the central 10° of the visual field. Seventy-one were orientation selective, and, of these, 52 were studied in greater detail, the rest being excluded because they could not be maintained long enough to complete all tests. Of the 52, three had complex responses to different oriented bars, and seven responded differently to the kinetic and luminance stimuli. The remaining 42 cells responded in a similar way to both, although some preferred oriented lines derived from one or the other. Figure 7
shows the responses and orientation profiles of a cell that responded more vigorously to kinetic boundaries than luminance ones. Other cells had a preference for luminance boundaries. Figure 8
shows the preferred orientation for each of the 42 cells, with the preferred orientation to a luminance stimulus plotted along the x-axis and the preferred orientation to a kinetic stimulus plotted along the y-axis. The majority of points lie close to the line x = y; hence for most cells the preferred orientation is very similar regardless of whether the stimulus is a luminance bar or a kinetic one. Three cells had a bimodal cell response with peaks at 180° intervals and in these the dominant peak for luminance was 180° from the dominant peak for the kinetic contour. The cells maximum responses to kinetic and to luminance stimuli are shown in Figure 9
; even though the values are more spread on the scatterplot, they are centred along the line x = y. When the difference between the luminance and texture responses for each cell is taken, a symmetrical frequency distribution becomes evident (Fig. 9
, inset). The frequency distribution is centered near zero, with a slight bias in favor of the luminance stimuli (mean = 15, sample SD = 78). A t-test that the mean differs from zero yields a t-score of 1.25 (P = 0.11, df = 41, non-significant). Therefore, even though individual cells may show a strong preference for either luminance or kinetic stimuli, for most cells oriented lines generated from kinetic contours were as effective as those generated from luminance. The same general picture emerges when one looks at the tuning widths (Fig. 10
). These are spread out even more on the scatter-plot. The differences between the luminance and kinetic contour tuning widths for each cell show a broadly symmetrical frequency distribution (Fig. 10
, inset), thus suggesting that most cells have the same tuning widths, whether activated by luminance or kinetic stimuli, even if individual cells sometimes show substantial differences in their tuning widths; the frequency distribution is nevertheless still centered around zero (mean = 0.9429, sample SD = 6.8587). A t-test that the mean differs from zero yields a t-score of 0.8909 (P = 0.19, df = 41, non-significant).
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Long Penetrations through the Third Visual Complex
Since we were principally interested in the characteristics of the orientation-selective cells within the V3 complex, we did not study in detail the possibility that cells preferring oriented lines generated from luminance or from kinetic boundaries may be grouped together within the cortex. That this may be so is, however, suggested by long oblique penetrations such as the one illustrated in Figure 11. Of the 20 cells in this penetration (separated from each other by 100 mm on average), six responded better to oriented lines when generated from kinetic contours than when generated from luminance, and three (cells 9, 10, 15) were unselective in that they did not specifically respond to oriented lines. The six that preferred kinetic contours showed some tendency to group together. It is thus possible that there is a subgrouping of cells within the V3 complex, which might imply that a specialization for kinetic contours is not between areas but embedded within an area, an interesting possibility for future studies.
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Discussion |
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The evidence purporting to show a specialization of area KO for kinetic contours is not compelling. The kinetic stimuli reported to activate KO selectively also activated other areas, notably V5 and V3A (Van Oostende et al., 1997). Our data do not show that KO is specific to the presence of kinetic contours, since it gave equal responses to contours from motion and contours from colour (Fig. 5
). The data of Van Oostende et al. do not appear to provide evidence of such specificity either, in spite of these authors conclusions (Van Oostende et al., 1997
). According to their Figure 4
, KO shows a BOLD response to kinetic contours of 1.89%, compared with 1.03% for their preferred kinetic control stimulus (transparent motion without borders). The contour-specific response for kinetic stimuli was therefore 0.86%. The response to static, luminance contours was 1.03%, compared with 0% for their static control stimulus (0% since the other responses are expressed with respect to the response to the static control). The contour-specific response for luminance stimuli was therefore 1.03%, i.e. slightly larger than that observed in the kinetic context. Their evidence, thus analyzed, is therefore entirely consistent with ours in showing that V3B (their KO) does not give a specific response to kinetic contours. Indeed, together with our data, it shows that V3B responds equally well to isoluminant color contours, luminance contours and kinetic contours.
On the other hand, our ICA analysis shows that the ATC of KO correlates better with that of V3 than that of V5 in free viewing conditions, and, as implied by Smith et al. in their use of the term V3B (Smith et al., 1998), it may be better considered to belong to the V3 family of areas. We shall therefore use the term V3B to denote an area in the lateral occipital cortex, located posterior to V5 and in close proximity to the V3 complex, whose function cannot be specified at present, but which is closely involved in the processing and extraction of form, however derived, although it may have other functions as well (Poggio et al., 1988
; Galletti and Bataglini, 1989; Galletti et al., 1990
; Nakamura and Colby, 2000
; Adams and Zeki, 2001
; Backus et al., 2001
). Kinetic occipital does not reflect the function of V3B, and also suggests, erroneously, that there is a cortical specialization for the processing of kinetic contours. Smith et al. were probably right in saying that it is unsafe to name areas based on assumed functions (Smith et al., 1998
).
Attempts have been made recently to equate human KO with the dorsal part only of area V4 (V4d) in the macaque monkey. Tootell and Hadjikhani used the same stimuli as those of the Orban group to activate the visual brain and concluded that the human V4d topologue did respond selectively to kinetic motion boundaries (Tootell and Hadjikhani, 2001). They were led to this conclusion by comparing the activity produced by kinetic contours with that produced by translational motion. However, they too did not undertake the more critical comparison, of the brain activity produced when subjects view shapes produced from kinetic boundaries and from other attributes, such as color, for example. Their claim of a selectivity for kinetic motion boundaries is therefore also questionable, and is contradicted by our results.
The supposition that V4d alone is the homolog of KO implies that the latter registers activity in lower visual field alone, since V4d represents lower visual fields only. But, the retinotopic studies of Press et al. (Press et al., 2001) have shown that both quadrants of the contralateral hemifield are mapped in V3B (KO) and the same conclusion can be drawn by examining Figure 4
in the work of Smith et al.(1998)
, although the latter authors suppose that lower fields alone are mapped in V3B. The rationale for equating KO with V4d also rests on the unproven supposition that there are areas in the visual brain, such as VP (Burkhalter and Van Essen, 1986
) and V4v (Sereno et al., 1995
; Hadjikhani et al., 1998
) that represent one quadrant of the visual field alone. More recent evidence has, however, called into serious doubt the existence of such areas. The recent detailed retinotopic studies of Wade et al., for example, found no evidence for area V4v but only for an area V4 which abuts area V3 and in which both quadrants are mapped (Wade et al., 2002
). VP has also been shown not to exist as a separate area (Rosa et al., 2000
; Lyon and Kaas, 2001
, 2002
); it is instead the upper visual field representation of V3, as originally suggested (Cragg, 1969
; Zeki, 1969
). Tootell and Hadjikhani have been reassured by the precedent of areas such as VP and V4v into equating KO with V4d alone (Tootell and Hadjikhani, 2001
). But given the compelling evidence that areas representing one quadrant of the visual field alone do not exist, areas such as VP and V4v can no longer act as a precedent for other improbable areas and homologies (Zeki, 2003
).
That V3B is somewhat more active when simple shapes are generated from luminance or from equiluminant colors than when generated from kinetic contours finds an interesting reflection in the properties of the orientation-selective cells in the V3 complex of the macaque, most of which respond better to the preferred oriented lines when generated from luminance than from kinetic contours. This is also true of other studies (Albright, 1992; Geesaman and Andersen, 1996
; Marcar et al., 2000
). These have shown that even when a cell maintains its specificity to a stimulus when generated in different ways, the better response is usually to the stimulus generated from luminance differences.
It is interesting to note here a further parallel, that the kinetic contours in the imaging study of Van Oostende et al. (Van Oostende et al., 1997), which used oriented bars, and in ours, which used simple shapes generated from kinetic contours, did not result in any discernible activity within V1 and V2. This very likely reflects the paucity of cells in both areas that respond well to stimuli generated from kinetic contours. They are reported to be rare in V1 and no more than 10% in V2 (Leventhal et al., 1998
; Marcar et al., 2000
). On the other hand, the majority of orientation-selective cells that we have recorded from in the V3 complex responded to oriented stimuli, however generated. This would suggest that the V3 complex is more heavily involved in the processing of dynamic forms, as suggested from previous evidence (Zeki, 1993
).
What is not clear from this, and previous, studies is whether V3B has any homolog in the monkey brain. It would not be surprising if it does not, given the hugely expanded occipital lobe of the human brain. But a possible homolog cannot be ruled out. Some have supposed that the area known as V3A in the human should be subdivided (Press et al., 2001). One of the stranger aspects of this area as originally defined (Van Essen and Zeki, 1978
) is the presence of a double representation of central vision, one in the lunate sulcus and the other in the parieto-occipital sulcus. It is possible that each central representation belongs to a separate area, and that part of V3A may have to be fractioned off into a separate area, which may well constitute the monkey homolog of V3B. These are interesting questions to address in the future.
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References |
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Albright TD (1992) Form-cue invariant motion processing in primate visual cortex. Science 255:11411143.[ISI][Medline]
Amari S (1998) Natural gradient works efficiently in learning. Neural Comput 10:251276.[Abstract]
Backus BT, Fleet DJ, Parker AJ, Heeger DJ (2001) Human cortical activity correlates with stereoscopic depth perception. J Neurophysiol 86:20542068.
Bartels A and Zeki (2000) The architecture of the colour centre in the human visual brain: new results and a review. Eur J Neurosci 12:172193.[CrossRef][ISI][Medline]
Bartels A, Zeki S (2001) The chronoarchitecture of the human brain: the dissection of the human brain into functional subdivisions by ICA analysis of fMRI data collected during free viewing of a movie. Soc Neurosci Abstr 620.15
Bartels A, Zeki S (2003) The chronoarchitecture of the human brain. In preparation.
Bell AJ, Sejnowski TJ (1995) An information maximization approach to blind separation and blind deconvolution. Neural Comput 7:11291159.[Abstract]
Burkhalter A, Van Essen DC (1986) Processing of color, form and disparity information in visual areas VP and V2 of ventral extrastriate cortex in the macaque monkey. J Neurosci 6:2237351.
Burkhalter A, Felleman DJ, Newsome WT, Van Essen DC (1986) Anatomical and physiological asymmetries related to visual areas V3 and VP in macaque extrastriate cortex. Vision Res. 26:6380[CrossRef][ISI][Medline]
Callaway EM (1998) Local circuits in primary visual cortex of the macaque monkey. Annu Rev Neurosci 21:4774.[CrossRef][ISI][Medline]
Casagrande VA, Kaas JH (1994) The afferent, intrinsic and efferent connections of primary visual cortex in primates. In: Cerebral cortex (Peters A, Rockland K eds), Vol. 10, pp. 201-256. New York: Plenum Press.
CIE (1931) Commission Internationale de lEclairage (CIE)/ Proceedings, International Congress on Illumination, Cambridge: Cambridge University Press.
Cragg BG (1969) The topography of the afferent projections in circumstriate visual cortex studied by the Nauta method. Vision Res. 9:733747[CrossRef][ISI][Medline]
DeYoe EA, Carman GJ, Bandettini P, Glickman S, Wieser J, Cox R, Miller D, Neitz J (1996) Mapping striate and extrastriate visual areas in human cerebral cortex. Proc Natl Acad Sci USA 93:23822386.
Dupont P, de Bruyn B, Vandenberghe R, Rosier A, Michiels J, Marchal G, Mortelemans L, Orban GA (1997) The kinetic occipital region in human visual cortex. Cereb Cortex 7:283292.[Abstract]
ffytche DH, Skidmore BD, Zeki S (1995) Motion-from-hue activates area V5 of human visual cortex. Proc R Soc Lond B 260:353358.[ISI][Medline]
Frackowiak RSJ, Zeki S, Poline JB, Friston KJ (1996) A critique of a new analysis proposed for functional neuroimaging. Eur J Neurosci 8:22292231.[ISI][Medline]
Friston KJ, Frith CD, Frackowiak RSJ, Turner R (1995) Characterizing dynamic brain responses with fMRI a multivariate approach. Neuroimage 2:166172.[CrossRef][ISI][Medline]
Friston KJ, Holmes AP, Worsley KJ (1999) How many subjects constitute a study? Neuroimage 10:15.[CrossRef][ISI][Medline]
Galletti C, Battaglini PP (1989) Gaze-dependent visual neurons in area V3A of monkey prestriate cortex. J Neurosci 9:11121125.[Abstract]
Galletti C, Battaglini PP, Fattori P (1990) Real-motion cells in area V3A of macaque visual cortex. Exp Brain Res 82:6776.[ISI][Medline]
Geesaman BJ, Andersen RA (1996) The analysis of complex motion patterns by form/cue invariant MSTd neurons. J Neurosci 16:47164732.
Gouras P, Kruger J (1979) Responses of cells in foveal striate cortex of the monkey to pure color contrast. J Neurophysiol 42:850860.
Grill-Spector K, Kushnir T, Edelman S, Itzchak Y, Malach R (1998) Cue-invariant activation in object-related areas of the human occipital lobe. Neuron 21:191202.[ISI][Medline]
Gulyas B, Heywood CA, Popplewell DA, Roland PE, Cowey A (1994) Visual form discrimination from color or motion cues: functional anatomy by positron emission tomography. Proc Natl Acad Sci USA 91:99659969.
Hadjikhani N, Liu AK, Dale A, Cavanagh P, Tootell RBH. (1998) Retinotopy and color sensitivity in human visual cortical area V8. Nature Neurosci 1:235241.[CrossRef][ISI][Medline]
Josephs O, Turner R, Friston K (1997) Event-related fMRI. Hum Brain Mapp 5:243248.[CrossRef][ISI]
Kaiser PK (1991) Flicker as a function of wavelength and heterochromatic flicker photometry. In: Limits of vision (Kulikowski JJ, Walsh V, Murray IJ, eds), pp. 171190. Basingstoke: Macmillan.
Leventhal AG, Wang Y, Schmolensky MT, Zhou Y (1998) Neural correlates of boundary perception. Vis Neurosci 15:11071118.[CrossRef][ISI][Medline]
Lund JS, Yoshioka T, Levitt JB (1994) Substrates for interlaminar connections in area V1 of macaque monkey cerebral cortex. In: Cerebral cortex (Peters A, Rockland K, eds), Vol. 10, pp. 3760. New York: Plenum Press.
Lyon DC, Kaas JH (2001) Connectional and architectonic evidence for dorsal and ventral V3, and dorsomedial area in marmoset monkeys. J Neurosci 21:249261.
Lyon DC, Kaas JH (2002) Evidence for a modified V3 with dorsal and ventral halves in macaque monkeys. Neuron 33:453461.[CrossRef][ISI][Medline]
Makeig S, Jung TP, Bell AJ, Ghahremani D, Sejnowski TJ (1997) Blind separation of auditory event-related brain responses into independent components. Proc Natl Acad Sci USA 94:1097910984.
Malach R, Reppas JB, Benson RR, Kwong KK, Jiang H, et al. (1995) Object-related activity revealed by functional magnetic-resonance-imaging in human occipital cortex. Proc Natl Acad Sci USA 92:81358139.[Abstract]
Marcar VL, Raiguel SE, Xiao D, Orban GA (2000) Processing of kinetically defined boundaries in areas V1 and V2 of the macaque monkey. J Neurophysiol 84: 27862798.
McKeown MJ, Makeig S, Brown GG, Jung TP, Kindermann SS, et al. (1998) Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp 6:160188.
Nakamura K, Colby CL (2000) Visual, saccade-related, and cognitive activation of single neurons in monkey extrastriate area V3A. J Neurophysiol 84:677692.
Okusa T, Kakigi R, Osaka N (2000) Cortical activity related to cue-invariant shape perception in humans. Neuroscience 98:615624.[CrossRef][ISI][Medline]
Orban GA, Sary G, Vogels R, Orban GA (1993) Cue-invariant shape selectivity of macaque inferior temporal neurons. Science 260: 995997.[ISI][Medline]
Orban GA, Dupont P, DeBruyn B, Vogels R, Vandenberghe R, Mortelmans L (1995) A motion area in human visual cortex. Proc Natl Acad Sci USA 92: 993997.[Abstract]
Perry RJ, Zeki S (2000) Integrating motion and colour within the visual brain: an fMRI approach to the binding problem. Soc Neurosci Abstr 250.1.
Poggio GF, Gonzalez F, Krause F. 1988. Stereoscopic mechanisms in monkey visual cortex binocular correlation and disparity selectivity. J Neurosci 8:45314550.
Press WA, Brewer AA, Dougherty RF, Wade AR, Wandell BA (2001) Visual areas and spatial summation in human visual cortex. Vis Res 41:13211332.[CrossRef][ISI][Medline]
Roland PE, Gulyas B (1996) Assumptions and validations of statistical tests for functional neuroimaging. Eur J Neurosci 8:22322235.[ISI][Medline]
Rosa MG, Pinon MC, Gattass R, Sousa AP (2000) Third tier ventral extrastriate cortex in the New World monkey, Cebus apella. Exp Brain Res 132:287305.[CrossRef][ISI][Medline]
Saito H, Tanaka K, Isono H, Yasuda M., Mikami A (1989) Directionally selective response of cells in the middle temporal area (MT) of the macaque monkey to the movement of equiluminous opponent colour stimuli. Exp Brain Res 75:114.[ISI][Medline]
Sary G, Vogels R, Orba GA (1993) Cue-invariant shape selectivity of macaque inferior temporal neurons. Science 260:995997.[ISI][Medline]
Schanze, T (1995) Sinc interpolation of discrete periodic signals. IEEE Trans. Sig Process, 43:15021503.
Sereno MI, Dale AM, Reppas JB, Kwong KK, Belliveau JW et al. (1995) Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 268:889893.[ISI][Medline]
Shipp S, Zeki S (1989) The organization of connections between V5 and V1. Eur J Neurosci 1:333354.[ISI][Medline]
Shipp S, Watson JDG, Frackowiak RSJ, Zeki S (1995) Retinotopic maps in human prestriate visual cortex: The demarcation of areas V2 and V3. Neuroimage 2:125132.[CrossRef][ISI][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 18:38163830.
Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. Stuttgart: Thieme-Verlag.
Tootell RB, Dale AM, Sereno MI, Malach R (1996) New images from human visual cortex. Trends Neurosci 19:481489.[CrossRef][ISI][Medline]
Tootell RB, Hadjikhani N (2001) Where is dorsal V4 in human visual cortex? Retinotopic, topographic and functional evidence. Cereb Cortex 11:298311.
Tootell RBH, Mendola JD, Hadjikhani NK, Ledden PJ, Liu AK, Reppas JB, Sereno MI, Dale AM (1997) Functional analysis of V3A and related areas in human visual cortex. J Neurosci 17:70607078.
Van Essen DC, Zeki SM (1978) The topographic organization of rhesus monkey prestriate cortex. J Physiol 277:193226.[Abstract]
Van Oostende S, Sunaert S, Van Hecke P, Marchal G, Orban GA (1997) The kinetic occipital (KO) region in man: an fMRI study. Cereb Cortex 7:690701.[Abstract]
Wade AR, Brewer AA, Rieger JW, Wandell BA (2002). Functional measurements of human ventral occipital cortex: retinotopy and color. Phil Trans R Soc Lond B (in press).
Watson JDG, Myers R, Frackowiak RSJ, Hajnal JV, Woods RP, et al. (1993) Area V5 of the human brain: evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cereb Cortex 3:7994.[Abstract]
Zeki S (1969) Representation of central visual fields in prestriate cortex of monkey. Brain Res 14:271291.[CrossRef][ISI][Medline]
Zeki S (1978) The third visual complex of rhesus monkey prestriate cortex. J Physiol 277:245272.[Abstract]
Zeki S (1978) Uniformity and diversity of structure and function in rhesus monkey prestriate cortex. J Physiol 277:273290.[Abstract]
Zeki S (1991) Parallelism and functional specialization in human visual cortex. Cold Spring Harb Symp Quant Biol 55:651661.[ISI]
Zeki S (1993) A Vision of the Brain. Oxford: Blackwell.
Zeki S (2003) Improbable areas in the visual brain. Trends Neurosci (in press).
Zeki S, Shipp S (1988) The functional logic of cortical connections. Nature 335:311317.[CrossRef][ISI][Medline]
Zeki S, Shipp S (1989) The organization of connections between V4 and V2. Eur J Neurosci 1:494506.[ISI][Medline]
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 11:641449.[Abstract]