1Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland 20892; and 2Department of Psychology, College of Social and Behavioral Sciences, University of Arizona, Tucson, Arizona 85721
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ABSTRACT |
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Kastner, Sabine, Peter De Weerd, and Leslie G. Ungerleider. Texture Segregation in the Human Visual Cortex: A Functional MRI Study. J. Neurophysiol. 83: 2453-2457, 2000. The segregation of visual scenes based on contour information is a fundamental process of early vision. Contours can be defined by simple cues, such as luminance, as well as by more complex cues, such as texture. Single-cell recording studies in monkeys suggest that the neural processing of complex contours starts as early as primary visual cortex. Additionally, lesion studies in monkeys indicate an important contribution of higher order areas to these processes. Using functional MRI, we have investigated the level at which neural correlates of texture segregation can be found in the human visual cortex. Activity evoked by line textures, with and without texture-defined boundaries, was compared in five healthy subjects. Areas V1, V2/VP, V4, TEO, and V3A were activated by both kinds of line textures as compared with blank presentations. Textures with boundaries forming a checkerboard pattern, relative to uniform textures, evoked significantly more activity in areas V4, TEO, less reliably in V3A, but not in V1 or V2/VP. These results provide evidence that higher order areas with large receptive fields play an important role in the segregation of visual scenes based on texture-defined boundaries.
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INTRODUCTION |
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The segregation of visual scenes based on contour information is a fundamental process of early vision. In natural scenes, contour boundaries are mainly extracted on the basis of simple cues, such as luminance, color, or motion, but they can also be defined by more complex cues, such as texture or illusory contours.
Single-cell recording studies have shed light on the mechanisms
underlying complex contour perception. Responses to oriented bars
presented within a neuron's receptive field (RF) and surrounded by an
array of bars at the same or orthogonal orientation outside the RF were
studied in V1 of anesthetized and awake animals (Kastner et al.
1997, 1999b
; Knierim and Van Essen
1991
; Nothdurft et al. 1999
). Neurons responded
more strongly to the bar inside the RF when it was surrounded by
orthogonal bars, that is, when it was perceptually salient, "popping
out" from the texture background. Similarly, neurons in V1 showed
stronger responses to texture elements belonging to a figure defined by
texture boundaries than to elements belonging to a background
(Lamme 1995
; Zipser et al. 1996
). Neurons
in V1 and V2 have also been found to respond to other complex contours,
such as illusory boundaries (Grosof et al. 1993
;
Sheth et al. 1996
; Von der Heydt and Peterhans
1989
). These findings suggest that the segregation of visual
scenes based on complex contour information starts at early stages of
cortical processing.
It remains unclear, however, how neurons in V1 and V2 integrate
information from regions beyond their RFs. One possibility is that
neural responses to complex contours are generated within early visual
areas via intrinsic long-range horizontal connections (Das and
Gilbert 1999). Another possibility is that these responses are
mediated via feedback projections from higher order visual areas that
integrate information from more extensive portions of the visual field
by virtue of their large RFs. Results from lesion studies support the
latter view. Monkeys with ablations of area V4 and cats with extensive
extrastriate lesions are impaired in the perception of illusory or
texture-defined contours (DeWeerd et al. 1993
,
1994
, 1996
; Merigan 1996
),
suggesting that higher order areas play an important role in the
perception of complex contours.
In humans, recent functional brain-imaging studies have demonstrated
that the perception of illusory contours, motion-defined contours, or
structure from motion is associated with activations in several
extrastriate visual areas (ffytche and Zeki 1996;
Grill-Spector et al. 1998
; Hirsch et al.
1995
; Mendola et al. 1999
; Reppas et al.
1997
; Tyler and Baseler 1998
; Van
Oostende et al. 1997
). However, little is known about the areas
activated by static texture-defined contours, as typically used in
single-unit and visually evoked potential studies (e.g., Bach
and Meigen 1992
; Lamme et al. 1992
). The goal of
the present study was therefore to investigate at which level neural
correlates of texture segregation can be found in the human visual
cortex. Activations evoked by line textures, which have been shown to
activate ventral occipitotemporal areas (Beason-Held et al.
1998
; Gulyas et al. 1998
; Puce et al.
1996
), were studied when presented as uniform textures compared
with segregated textures containing boundaries that formed a
checkerboard pattern. Part of this work has been published in abstract
form (De Weerd et al. 1998
).
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METHODS |
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Five subjects (3 females, aged: 22-35) gave their informed
written consent to participate in the study, which was approved by the
NIMH Institutional Review Board. A more detailed description of
scanning procedures, image processing, statistical procedures, and
retinotopic mapping is given in Kastner et al. (1998,
1999a
).
Visual task
Line textures, 8 × 8 deg in size, were presented to the
upper right quadrant centered at 8 deg eccentricity from a fixation point (element size: 0.4 × 0.05 deg with length and width
randomization of 30%; element spacing of 0.3 deg with spacing
randomization of 40%). Textures were presented in two different
conditions: uniform (UT) or segregated (ST) (Fig.
1). The uniform textures contained line
elements of identical orientation alternating at 1 Hz between 45 and
135 deg. The segregated textures consisted of nine squares (2.66 × 2.66 deg each) forming a checkerboard pattern. The boundaries of
each square were defined by the orientation contrast between line
elements at 45 and 135 deg alternating at 1 Hz. The density of the line
elements within each square was kept constant, resulting in an equal
distribution of mean luminance across the pattern. A 2D Fourier
analysis was performed to confirm the lack of Fourier components along
the texture boundaries. Visual stimuli were presented to the subjects
as videotapes rear-projected onto a translucent screen placed 40 cm
from the subjects' feet. Stimuli were viewed from inside the bore of
the magnet via a mirror system attached to the head coil. During a
given scan, uniform and segregated textures were presented in
alternating blocks of 18 s each interleaved with blank periods.
Each scan started with a blank period of 36 s and ended with a
blank period of 18 s. The subjects' task was to maintain fixation
at a central fixation point and to count the letters T or L, presented
there for 250 ms in random order at 4 Hz. The T/L task had a high
attentional load to ensure proper fixation and to prevent subjects from
covertly attending to the peripheral stimuli (Kastner et al.
1998). When debriefed after the experiment, subjects reported
that they were not aware of any differences between the texture
stimuli.
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Data acquisition and analysis
Fourteen contiguous, coronal, 5-mm-thick slices were acquired in 12 series of 60 images each, starting from the posterior pole (in-plane resolution 2.5 × 2.5 mm). Gradient echo, echo planar imaging was used (TR = 3 s, TE = 40 ms, flip angle = 90 deg) on a 1.5 Tesla GE magnet using a standard head coil. Functional images were coaligned with a high-resolution anatomic scan taken in the same session (3D SPGR, TR = 15 ms, TE = 7 ms, flip angle = 30 deg, 256 × 256 matrix, FOV = 160 × 160 mm, 28 coronal slices, thickness: 5 mm).
Images were motion-corrected (Woods et al. 1993),
spatially smoothed in-plane with a small Gaussian filter (FWHM of 1.2 voxel lengths), and ratio-normalized to the same global mean intensity (Friston et al. 1991
). Statistical analyses were
performed on both smoothed and unsmoothed data. Activations were
identified by means of multiple regression analysis of the time series
of MRI intensities in every voxel and two regressors of interest (Friston et al. 1995
), reflecting contrasts between
1) both texture presentations versus blank periods and
2) segregated texture versus uniform texture
presentations. Additional regressors were used to factor out variance
due to between-run changes in mean intensity and within-run linear
changes. The statistical significance (P < 0.05)
of activated regions was assessed by an analysis based on the spatial
extent of each region (Friston et al. 1994
;
Poline et al. 1997
). Activated voxels were assigned to
areas V1, V2, and VP, as identified on the basis of retinotopic mapping
of the horizontal and vertical meridians, and to areas V4, TEO, and V3A on the basis of upper (UVF) and lower visual field (LVF) topography, performed on all subjects in a separate scan session. High color- and
luminance-contrast checkered stimuli extending over the central 10-12
deg were presented along the meridia. The UVF and LVF representations are separated in V4 and located medially and laterally, respectively, on the fusiform gyrus in ventral occipitotemporal cortex, whereas this
separation is not seen in the region anterior to V4, which we term TEO.
Area V4 in this study likely corresponds to area V4 of McKeefry
and Zeki (1997)
and appears to overlap with V4v and V8
described by Hadjikhani et al. (1998)
. Unlike V4 and
TEO, V3A is located dorsally in occipital cortex, where the UVF and LVF
are represented (Tootell et al. 1997
). The fMRI time
series, averaged over all activated voxels in a given region during
texture presentations versus blank presentations (thresholded at a
Z score of 2.3) and over runs for each subject, are
presented as group data. Statistical significance was assessed with
repeated measures of analysis of variance (ANOVAs) on the six peak
intensities of the fMRI signal during a given presentation block.
Differences in responses (R) to the different texture
conditions were quantified by a texture segregation index [TSI = (RST
RUT)/(RST + RUT)]. For each subject, statistical maps
and structural images were transformed into Talairach space
(Talairach and Tournoux 1988
) by using the template from
SPM96b to obtain normalized coordinates of the activations.
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RESULTS |
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Segregated and uniform line textures as compared with blank
presentations evoked significant activity in visual areas V1, V2, VP,
V4, and V3A of the left hemisphere in all subjects, and in TEO of the
left hemisphere in four of five subjects. With the exception of V3A,
the locations of the activations were in the ventral parts of these
areas, consistent with the presentation of texture stimuli to the upper
right visual field. For area V3A, the activations were located dorsally
in the left hemisphere. This is illustrated for a single subject in
Fig. 2A. As the border between
V2 and VP could not be distinguished unequivocally in some of the
subjects, the combined region will henceforth be referred to as V2/VP.
Mean Talairach coordinates (and activated volumes), averaged across all
subjects, were as follows: V1 (1,000 mm3):
x = 2.5, y =
84, z = +10; V2/VP (1,328 mm3):
4,
83,
8; V4
(2,273 mm3):
17,
76,
17; TEO (1,760 mm3):
23,
57,
13; V3A (813 mm3):
22,
88, +24. For the subject shown in
Fig. 2, the texture segregation patterns evoked significantly more
activity than the uniform texture patterns in areas V2/VP and V4 (Fig.
2B). Stronger activation of V4 by segregated textures than
by uniform textures was found in all subjects. Such response
differences were also seen in other visual areas in individual
subjects, but less consistently.
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An analysis of the time series of the fMRI signal (Fig. 3) and the mean signal changes (Fig. 4A) averaged across subjects revealed that segregated textures evoked stronger responses in V4 (P < 0.01), TEO (P < 0.05), and V3A (P < 0.05) with response differences on the order of about 0.3%. In V1, the differences in responses to the two texture conditions were not significant. In V2/VP, there was a trend toward stronger responses to segregated textures (P = 0.08). The interaction between area and texture condition was significant (P < 0.01). These results are also reflected in the texture segregation index (Fig. 4B). The first presentation block (i.e., the segregated textures) evoked a stronger transient response in most of the areas than subsequent texture presentations, but the differences in responses to segregated and uniform texture were not due to this transient onset signal. First, the difference between the first and the second presentation of the segregated textures (block 1 and 3 in Fig. 3) was not significant in any visual area, and second, the response differences between the second presentations of segregated and uniform textures (block 3 and 4 in Fig. 3) were significant in V4 (P < 0.01) and TEO (P < 0.05). In V3A, the latter analysis did not reach significance (P = 0.08), so that the contribution of transient onset signals to the effect in this area cannot be excluded. These findings suggest an important role of higher order areas V4, TEO, and probably V3A in processing texture-defined boundaries.
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DISCUSSION |
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The present study investigated the level at which texture-defined
contours are being processed in the human visual cortex. Neural
correlates of texture segregation were identified in higher order areas
V4, TEO, and less consistently, V3A, which were more strongly activated
by segregated textures than by uniform textures. The processing of
complex contours defined by illusory boundaries has been explored in
recent functional imaging studies (ffytche and Zeki
1996; Hirsch et al. 1995
; Mendola et al.
1999
). Activations in areas V3A, V4/V8, and V7 (an area just
anterior to V3A) were shown to be associated with the processing of
illusory contours (e.g., a Kanizsa square). We found that similar areas
are involved in the processing of texture-defined contours. Further,
our results suggest a gradual increase in the responsiveness of visual
areas to texture-defined contours as one proceeds through the cortical hierarchy. Such a gradual increase in responsiveness to illusory contours has been shown most clearly by Mendola et al.
(1999)
. These findings indicate that a common set of visual
areas rather than a single one is involved in the perception of complex
contours defined by texture or illusory boundaries in the human visual cortex.
The demonstration of an involvement of V4, TEO, and V3A in texture
segregation underlines the important role of higher order areas in
scene segmentation, as suggested by lesion studies in monkeys. Monkeys
with ablations of area V4 show deficits in the perception of both
texture-defined and illusory contours, but not of contours that are
defined by simple cues, such as motion, color, or luminance
(DeWeerd et al. 1996; Merigan 1996
). From these findings, it may be concluded that higher order areas extract complex contour information, presumably by virtue of their large receptive fields, and modulate the responses of lower order areas to
complex contours via feedback inputs. This idea is supported by recent
findings that cortical feedback modulates both excitatory and
suppressive inputs to neurons in lower-order areas (Hupe et al.
1998
).
Single-cell recording studies in monkeys have identified neural
correlates of texture-defined boundaries at the level of V1. Neural
responses were shown to depend on the perceptual context in which
visual stimuli were presented. For example, neurons responded more
strongly to an oriented line when it was part of a "pop-out" texture than when it was part of a uniform texture (Kastner et al. 1997, 1999b
; Knierim and Van Essen
1991
; Nothdurft 1999
), or they responded more
strongly to texture elements when they were part of a texture segment
standing out against a background than when they were part of the
background (Lamme 1995
; Zipser et al.
1996
). Using fMRI, we failed to identify neural correlates of
texture segregation in V1. It may be argued that this negative finding
is due to the fact that the texture patterns were presented to the
periphery and subjects were involved in a high attentional load task at
fixation, so that they did not perceive the segregation effect.
However, neural correlates of pop-out and texture segregation have been
identified in anesthetized animals (Kastner et al.
1999b
; Nothdurft et al. 1992
,
1999
; but see Lamme et al. 1998
). A
second possibility is that the texture patterns used in the present
study did not effectively activate V1 neurons, because the patterns were comprised of many texture-defined contours rather than consisting of a single figure standing out from a background. Further single-cell recording studies are needed to rule out this possibility. A third possibility is that the dimensions of our display favored more anterior
extrastriate areas with larger RFs. However, several single-unit
studies have shown that texture boundary effects occur over large
spatial scales up to 10-12 deg in V1 (e.g., Knierim and Van Essen
1991
; Lamme 1995
). Hence, neurons in V1 should be well suited to detect
texture boundaries that were separated, at the most, by 2.7 deg in our
experiments. Finally, it may be that fMRI at 1.5 T is not sensitive
enough to obtain signals related to texture segregation in V1. Future
investigations using magnets at higher field strength will be needed to
clarify this issue.
The present findings and those from single-cell physiology in
anesthetized animals suggest that complex contour information can be
processed outside the focus of attention. This view is further
supported by studies in patients suffering from visual hemineglect. For
example, Mattingley et al. (1997) reported a patient
whose extinction was less severe when bilateral stimuli were arranged
to form an illusory Kanizsa-square, demonstrating that the patient used
the complex contour information from his neglected hemifield to form
the percept of a common surface. Thus the findings from single-cell
physiology, patients with attentional deficits, and functional
brain-imaging converge to support the view that visual scene
segmentation based on complex contour information requires little or no
attentional resources (Braun and Sagi 1990
; Treisman 1985
).
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ACKNOWLEDGMENTS |
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We thank R. Desimone and A. Rossi for invaluable discussions, L. Pessoa and A. Roque for help with the Fourier analysis of the texture patterns, I. M. Elizondo for assistance with scheduling of subjects and data preprocessing, and M. A. Pinsk for help with the preparation of the manuscript.
This study was supported in part by Deutsche Forschungsgemeinschaft Grant Ka 1284/1-1 to S. Kastner.
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FOOTNOTES |
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Address for reprint requests: S. Kastner, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bldg. 49, Rm. 1B80, Bethesda, MD 20892-4415.
The cost of publication of this article were defrayed inpart by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 2 September 1999; accepted in final form 10 December 1999.
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REFERENCES |
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