Articles in PresS. J Neurophysiol (February 8, 2012). doi:10.1152/jn.01051.2011
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Bridging the gap: global disparity processing in the human
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visual cortex
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Benoit R. Cottereau
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Stanford University, Stanford, CA, USA
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Suzanne P. McKee
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Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA
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Anthony M. Norcia
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Stanford University, Stanford, CA, USA
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Corresponding Author: Benoit R. Cottereau
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Adress: Department of Psychology | Jordan Hall, Building 01-420 | Stanford
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University | 450 Serra Mall | Stanford, CA 94305
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Phone number: 650-725-2440
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e-mail address:
[email protected]
Fax number: 650-725-1876
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Running Title: Global disparity processing in the Human Visual System
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Acknowledgments
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This work was supported by National Eye Institute grants R01 EY018875, the Smith-
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Kettlewell Eye Research Institute and a Walt and Lilly Disney Amblyopia Research
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Award from Research to Prevent Blindness.
Copyright © 2012 by the American Physiological Society.
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Abstract
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The human stereoscopic system is remarkable in its ability to utilize widely
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separated features as references to support fine depth discrimination. In a search for
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possible neural substrates of this ability, we recorded high-density EEG and used a
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distributed inverse technique to estimate population-level disparity responses in five
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ROIs: V1, V3A, hMT+, V4 and LOC. The stimulus was a central modulating disk
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surrounded by a correlated ‘reference’ annulus presented in the fixation plane. We
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varied a gap separating the disk from the annulus parametrically from 0 to 5.5
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degrees as a test of long-range disparity integration. In the V1, LOC and hMT+ ROIs,
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the responses with gaps larger than 0.5 degrees were equal to those obtained in a
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control condition where the surround was composed of uncorrelated noise (no
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reference). By contrast, in the V4 and V3A ROIs, responses with gaps as large as 5.5
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degrees were still significantly higher than the control. As a test of the spatial
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distribution of the disparity-reference information, we manipulated the properties of
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the stimulus by placing noise between the center and the surround or throughout the
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surround. The V3A ROI was particularly sensitive to disparity noise between the
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center and annulus regions, suggesting an important contribution of disparity edge
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detectors in this ROI.
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Key words: binocular vision, disparity, EEG source modeling, long-range
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interactions.
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Introduction
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The general characteristics of disparity-tuned neurons in V1 are now well-
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established, thanks to more than three decades of single cell studies in awake-
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behaving primates (Poggio and Fischer, 1977; Cumming and DeAngelis, 2001;
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Prince et al. 2002; Parker, 2007; Roe et al, 2007). Some V1 neurons are remarkably
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sensitive to small changes in disparity, certainly sensitive enough to account for
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primate stereoacuity (Prince et al, 2000). Yet surprisingly, awake-behaving primates
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are unable to use the responses of these neurons to detect small changes in the
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disparity of an isolated target. Monkeys, like humans, require a second reference
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target in the visual field to respond precisely to changes in the disparity of a test
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target (Prince et al., 2000; Westheimer, 1979; McKee et al., 1990). Prince et al.
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(2000) concluded that a reference target was needed to remove ambiguity about
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whether the change in disparity was produced by a stimulus change or a change in
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convergence.
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If disambiguation were the primary function of reference targets, then any
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reference would suffice. However, psychophysical evidence shows that there are
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constraints on what serves as an optimum reference. For example, Farell (2006)
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found that stereoacuity for a Gabor target was best when the reference grating had
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the same orientation as the test. McKee et al. (1990) measured stereoacuity as a
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function of the distance separating test and reference lines, and found that
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stereoacuity with a reference was better than unreferenced stereoacuity for distances
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up to 5 degrees. Similarly, Read et al. (2010) found that stereoacuity for a referenced
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disk was superior to an unreferenced disk for distances as great as 10 degrees.
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These psychophysical constraints suggest that the calculation of disparity differences
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depends on specialized neural wiring, rather than on a generalized differencing
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operation that occurs between any two targets in the visual field.
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Here, we used high-density EEG source-imaging to study how the neural
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response to changes in disparity was affected by the separation of the disparity-
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modulated stimulus from a fixed reference disparity. In human subjects, we
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measured population responses in five visual ROIs (V1, V4, V3A, hMT+, and LOC).
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Our previous work has shown that the presence of an abutting reference stimulus
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greatly enhances the response to disparity modulation in extra-striate areas
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(Cottereau et al. 2011a, Cottereau et al., 2012). How does this enhancement depend
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on the distance to the reference stimulus and how does this dependence compare to
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comparable psychophysical measurements? Our measurements demonstrate that for
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large separations between center and surround (i.e. greater than 0.5 degrees), areas
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V1, LOC and hMT+ have responses equivalent to those of the control condition
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where the surround was composed of uncorrelated noise. In contrast, areas V3A and
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V4 have significantly larger responses than the control for gaps up to 5.5°. These
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areas therefore contain neural populations that integrate disparity information over
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long distances and these populations may support long-range perceptual
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judgements. In a second experiment, we introduced noise into the space between
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center and surround or throughout the surround. We showed that responses in area
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V3A are specifically affected by the presence of noise between the center and the
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surround. This result suggests that neurons acting as disparity edge detectors are
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present in this ROI.
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Methods and Materials
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The basic stimulus (see Figure 1) consisted of a central disk surrounded by an
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annulus; both were composed of dynamic random dots that changed positions every
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47 msec (21.25 Hz).
The diameter of the central disk was 4 deg. Its disparity
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alternated between 12.4 arcmin (uncrossed) and 0 arcmin every 470 msec (i.e. the
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disparity was driven by a square wave at 2.12 Hz) in a steady-state Visual Evoked
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Potential (SSVEP) paradigm. Vergence cannot follow disparity changes at this
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temporal frequency, so it cannot null out the changes in stimulus disparity (Rashbass
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and Westheimer, 1961; Erkelens and Collewijn, 1985). The disparity of the annular
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surround, when binocularly correlated, remained constant during a trial. For all
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studies, the EEG stimulus presentation lasted 10.35s (that is 22 cycles of the disk
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disparity modulation); the first 940ms (i.e. the first two cycles) of the data record was
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discarded to avoid start-up transients, leading to 9.41s trials (i.e. 20 cycles of the disk
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disparity modulation). Trials for all conditions were run interspersed in random order
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in 10-minute blocks. The blocks were repeated four times, producing a total of 20
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trials per condition.
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%%%%%% INSERT FIGURE 1 ABOUT HERE %%%%%%%%%%%%
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We used two different types of stereoscopes for these studies: a mirror
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stereoscope, and a beam-splitter based system. In the mirror stereoscope, the stimuli
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were presented on a pair of matched SONY monitors (Multiscan GS 220) viewed at a
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distance of 77 cm. At this viewing distance, the outer dimensions of the screens were
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23 deg high by 18 deg wide. The monitors and mirrors were angled for natural
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convergence at this viewing distance. The contrast of the bright dots was 90%. In the
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interval between trials, the screen was filled with bright static dots at zero disparity to
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prevent changes in light adaptation. In the beam-splitter system, orthogonally
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polarized images from two matched Sony Trinitron monitors (Model 110GS), were
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combined via a beam-splitter and viewed through appropriately oriented polarized
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filters placed immediately in front of the eyes. Each eye could see the image on only
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one screen; the viewing distance was 82 cm and the contrast of the bright dots was
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90%. Each separate experiment described below was, of course, conducted on a
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single stereoscope; this first experiment was performed in the mirror stereoscope,
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and the second one in the beam-splitter system. We checked whether the difference
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in stereoscopes affected our most sensitive measure, namely psychophysical
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stereoacuity thresholds. We found that the thresholds were not significantly different
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for the two types of stereoscopes. As will be apparent in the results below, the
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general pattern of EEG responses for comparable conditions was also unaffected by
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the change in stereoscopes.
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Subjects
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Twenty-one subjects (14 males, 7 females, age range 20-69 years)
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participated in the recording sessions. Thirteen subjects (9 males, 4 females) were
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involved in the first EEG experiment, 12 subjects (8 males, 5 females) participated in
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the second one and 4 subjects (2 males, 2 females) did the psychophysical
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measurements. All the subjects were volunteers, with normal stereopsis and normal
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or corrected-to-normal visual acuity. All subjects were given instructions and detailed
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information about the experiments. They provided written informed consent before
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participating in the study in accordance with Helsinki Declaration; the human subjects
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review committee of Smith-Kettlewell Eye Research Institute approved the study.
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Main Experiments
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1. Different gaps
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In six interspersed conditions, we measured the SSVEP to uncrossed disparity
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modulation of the central disk. The outer ring of the surround had an eccentricity of
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16 degrees (dot density: 20 dots/deg^2). The distance between the outer ring of the
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center and the inner ring of the surround varied from 0 to 5.5 degrees (see Figure 1).
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In a control condition, the surround consisted of uncorrelated disparity noise (without
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a gap). This stimulus was first used by Prince et al (2000) and has been used in our
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previous studies (Cottereau et al., 2011a; Cottereau et al., 2012) to measure a
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response driven mainly by neurons tuned to absolute disparity.
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For the six gap conditions, we presented a pair of nonius lines and a
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binocularly visible fixation point superimposed on the center of the disk-annulus
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stimulus. Subjects were asked to keep the nonius lines aligned during a recording
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trial. We eliminated the nonius lines and the fixation point for the uncorrelated
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surround condition to remove all immediate reference stimuli. In our last study
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(Cottereau et al., 2012), we tested if the absence of nonius line and fixation point
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during the stimulation produced a fixation bias for an equivalent condition. A
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psychophysical nonius alignment test demonstrated that if this bias existed, it was
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negligible and not sufficient to influence our results. The direct comparison between
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the EEG responses obtained with and without fixation point for this same condition
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pointed toward the same conclusion (Cottereau et al., 2012).
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2. Noise surround
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In a second experiment, we tested the influence of the nature of the space
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between center and surround on the EEG responses. The outer diameter of the
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surround annulus was diminished to 7.48° (dot density: 30 dots/deg^2). In the first
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two conditions, the distance between the outer ring of the center and the inner ring of
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the surround was 1°. The stimulus dimensions for these two conditions were chosen
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so that the area of the surround annulus and the area between center and surround
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were equal. The space between the center and surround was either empty (i.e. a
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gap) in condition 1 or filled with uncorrelated noise in condition 2. In a third condition,
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the surround touched the center but was filled with a mixture composed of 50% of
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correlated dots and 50% of uncorrelated noise. The noise amount was therefore the
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same in conditions 2 and 3. Nonius lines and a fixation point were displayed in the
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three conditions (see the ‘different gaps’ section).
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3. Psychophysical Thresholds We
also
measured
disparity
modulation
thresholds psychophysically.
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Observers judged in which of two intervals the disparity of a 4 deg central disk was
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modulated by a tiny amount in an uncrossed direction. The stimuli were presented for
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472 msec, equal to one period of our EEG presentations. The stimuli were presented
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in a mirror stereoscope and viewed at a distance of 86 cm. Four different modulation
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values were presented interspersed in a random sequence in a 100 trial block. We
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fitted a Weibull function to the percentages correct for these four values and
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estimated threshold as the disparity modulation corresponding to 81.6% correct. We
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measured thresholds for each subject and condition in two to four separate blocks
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(200 - 400 trials total); the thresholds were averaged from the blocks and are plotted
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as sensitivities (1/ threshold) in Figure 3.
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EEG signal acquisition and preprocessing
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The electroencephalogram (EEG) data were collected with 128-sensor
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HydroCell Sensor Nets (Electrical Geodesics, Eugene OR) and were band-pass
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filtered from 0.1 to 200 Hz. Following each experimental session, the 3D locations of
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all electrodes and three major fiducials (nasion, left and right peri-auricular points)
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were digitized using a 3Space Fastrack 3-D digitizer (Polhemus, Colchester, VT). For
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all observers, the 3D digitized locations were used to co-register the electrodes to
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their T1-weighted anatomical MRI scans and to construct the EEG forward model
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(see the “Forward modeling of the current sources” section below). Raw data were
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evaluated off line according to a sample-by-sample thresholding procedure to remove
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noisy sensors that were replaced by the average of the six nearest spatial neighbors.
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On average, less than five per cent of the electrodes were substituted. These
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electrodes were mainly localized in the front of the head or near the ears. The
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substitutions had a negligible impact on our results, as recordings at occipital and
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parietal locations mainly drive our estimates of the responses within the visual areas.
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After this operation, the EEG was re-referenced to the common average of all the
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sensors. Within each 9.41s trial, the data were segmented into five 1.82s long
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epochs (i.e. each of these epochs was exactly 4 cycles of the disk disparity
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modulation). EEG epochs that contained a large percentage of data samples
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exceeding a noise threshold (depending on the subject and ranging between 25 and
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50 micro volts) were excluded from the analysis on a sensor-by-sensor basis. This
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was typically the case for epochs containing artifacts such as blinks or eye
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movements. Finally, a Fourier analysis was applied on every remaining epoch using
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a discrete Fourier transform with a rectangular window. For each frequency bin, the
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Fourier coefficients were then averaged across all the epochs and all the trials.
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These average Fourier coefficients were therefore obtained from up to 100 (5 epochs
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* 20 trials) values.
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Structural and Functional Magnetic Resonance Imaging (MRI)
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Structural and functional MRI scanning was conducted at 3T (Siemens Tim
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Trio, Erlangen, Germany) using a 12-channel head coil. We acquired a T1-weighted
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MRI dataset (3-D MP-RAGE sequence, 0.8 x 0.8 x 0.8 mm3 and a 3-D T2-weighted
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dataset (SE sequence at 1 x 1 x 1 mm3 resolution) for tissue segmentation and
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registration with the functional scans. For fMRI, we employed a single-shot, gradient-
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echo planar imaging (EPI) sequence (TR/TE = 2000/28 ms, flip angle 80, 126
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volumes per run) with a voxel size of 1.7 x 1.7 x 2 mm3 (128 x 128 acquisition matrix,
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220 mm FOV, bandwidth 1860 Hz/pixel, echo spacing 0.71 ms). We acquired 30
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slices without gaps, positioned in the transverse-to-coronal plane approximately
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parallel to the corpus callosum and covering the whole cerebrum. Once per session,
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a 2-D SE T1-weighted volume was acquired with the same slice specifications as the
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functional series in order to facilitate registration of the fMRI data to the anatomical
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scan. The general procedures for these scans (head stabilization, visual display
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system, etc.) are standard and have been described in detail elsewhere (Brewer et
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al. 2005). The FreeSurfer software package (http://surfer.nmr.mgh.harvard.edu) was
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used to extract both gray/white and gray/cerebrospinal fluid (CSF) boundaries. These
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surfaces can have different curvatures. In particular, the gray/white boundary has
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sharp gyri (the curvature changes rapidly) and smooth sulci (slowly changing surface
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curvature), while the gray/CSF boundary is the inverse, with smooth gyri and sharp
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sulci. In order to avoid these discontinuities, we generated a surface partway
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between these two boundaries that has gyri and sulci with approximately equal
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curvature. This “midgray” cortical surface consisted in a triangular tessellation of
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20,484 regularly spaced vertices and was used to define the visual areas and the
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source space for the EEG current modeling (see the two sections below).
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Visual area definition
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Retinotopic field mapping using rotating wedges and expanding/contracting
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rings produced Regions-of-Interest (ROIs) defined by the visual cortical areas V1,
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V2v, V2d, V3v, V3d, V3A, and V4 in each hemisphere (Tootell and Hadjikhani 2001;
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Wade et al. 2002). ROIs corresponding to hMT+ were identified using low contrast
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motion stimuli similar to those described by Huk and Heeger (2002).
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The Lateral Occipital Complex (LOC) was defined using a block-design fMRI
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localizer scan. During this scan, the observers viewed blocks of images depicting
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common objects (12s/block) alternating with blocks containing scrambled versions of
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the same objects. The stimuli were those used in a previous study (Kourtzi and
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Kanwisher, 2000). The regions activated by these scans included an area lying
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between the V1/V2/V3 foveal confluence and hMT+ that we identified as LOC. This
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definition covers almost all regions (e.g. V4d, LOC, LOp) that have previously been
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identified as lying within object-responsive lateral occipital cortex (Kourtzi and
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Kanwisher, 2000; Tootell and Hadjikhani, 2001).
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Forward modeling of the current source
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For each subject, the EEG source space was given by his “midgray” cortical
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surface tessellation (see above) and consisted in 20,484 regularly spaced vertices.
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The distance between connected vertices was on average 3.7 mm, with a standard
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deviation of 1.5 mm, range .1–11 mm. Current dipoles were placed at each of these
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vertices. Their orientations were constrained to be orthogonal to the cortical surface
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to diminish the number of parameters to be estimated in the inverse procedure
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(Hämäläinen et al., 1993). The FSL toolbox (http://www.fmrib.ox.ac.uk/fsl/) was used
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to segment from the individual T1 and T2 weighted MRI scans, contiguous volume
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regions for the inner skull, outer skull and scalp. These MRI volumes were then
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converted into inner skull, outer skull, and scalp surfaces (Smith 2002; Smith et al.,
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2004) that defined the boundaries between the brain/CSF and the skull, the skull and
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the scalp, the scalp and the air. The source space, the 3D electrode locations, and
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the individually defined boundaries were then combined using the MNE software
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package
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characterize the electric field propagation using a 3-compartment boundary element
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method (BEM) (Hämäläinen and Sarvas, 1989). The resulting forward model is linear
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and links the activity of the 20,484 cortical sources to the voltages recorded by our
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EEG electrodes.
(http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php)
to
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Inverse modeling constrained by the visual ROIs
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Cortical current density estimates of the neural responses were obtained from
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an L2 minimum-norm inverse of the forward model described above (Hämäläinen et
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al., 1993). We used the definition of the visual ROIs to constrain these estimates by
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modifying the source-covariance matrix. Our aim was to decrease the tendency of
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the minimum-norm procedure to smooth activity over very large surfaces and across
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different functional areas. Two modifications were applied: 1) we increased the
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variance allowed within the visual areas by a factor of two relative to other vertices
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and 2) we enforced a local correlation constraint within each area using the first and
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second order neighborhoods on the cortical tesselation with a weighting function
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equal to 0.5 for the first order and 0.25 for the second. This correlation constraint
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therefore respects both retinotopy and areal boundaries and permitted us
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dissociate the signals from different areas unlike other smoothing methods such as
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LORETA that apply the same smoothing rule throughout cortex (Pascual-Marqui et
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al., 1994). The details of this approach can be found in Cottereau et al., (In press).
to
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ROI-based analysis of the Steady-state Visual Evoked Potential (SSVEP)
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For each subject and condition, our inverse approach was applied to the
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average Fourier coefficients (see the “EEG signal acquisition and preprocessing”
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section) and led to an estimation of these coefficients for each source of the cortical
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tessellation. Within each functionally defined ROI, the coefficients were then
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averaged across all the sources belonging to the area. We were particularly
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interested in their magnitude (i.e. their norm) at the odd (first and third) components
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of the steady-state frequency (2.12 Hz and 6.36 Hz). To take into account the
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difference of noise levels between the recordings from each of our subjects (Viallatte
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et al., 2009), we computed the signal-to-noise ratio (SNR) and divided these values
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by the root-mean square of the associated noise which is defined for a given
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frequency f by the average amplitude of the two neighbor frequencies (i.e. f – δf and f
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+ δf where δf gives the frequency resolution of the Fourier analysis). The
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corresponding SNRs are presented in decibels (20 X log10).
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Cross-Talk
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In a previous paper (Cottereau et al., 2011a), we described how we estimated
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the theoretical crosstalk among visual areas for a specific EEG study. Crosstalk
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refers to the neural activity generated in other areas that is attributed to a particular
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ROI, due to the smoothing of the electric field by the head volume. In brief, for each
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subject, we simulated the cross-talk by placing sources in one ROI and estimating
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their contribution to other ROIs, using the same forward and inverse methods
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described above. The global crosstalk matrix (i.e. averaged across all the subjects
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who participated in our EEG experiments) is shown in Figure 2 for seven ROIs (V1,
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V2, V3, V4, LOC, V3A & hMT+); the crosstalk magnitude shown in the matrix is
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proportional to activity originating in the ROI where the crosstalk is being estimated.
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%%%%%%%%%%% INSERT FIGURE 2 ABOUT HERE %%%%%%%%%%
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From our simulations, it was apparent that there was significant crosstalk in areas V2
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and V3 (the last two rows of the matrix). For this reason, we excluded these two ROIs
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from our analysis and focused on V1, V3A, V4, hMT+, and the LOC. These areas are
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more widely separated and their estimated activities are therefore more reliable.
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Within this subset of ROIs, the biggest crosstalk comes from a V1 contribution to V3A
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(55%), and a V3A contribution to V4 (50%). We discuss the influence of crosstalk
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coming from other regions that were not defined using fMRI in the “Effect of the gap”
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section of the results below.
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Statistical analysis
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Repeated measure analyses of variance (ANOVAs) were performed on the
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SNRs at the odd harmonics. We specifically tested effects of conditions, of ROIs and
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their interaction. Significant effects were followed-up by post-hoc pairwise Wilcoxon
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sign-ranks tests. The validity of the ANOVAs (equal variance hypothesis) was
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controlled using Mauchly’s sphericity test. The sphericity test showed no significant
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effect, so we did not correct the p-values of the ANOVA.
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Results
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Psychophysics:
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We measured stereoacuity thresholds for disparity modulation as a function of gap
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size in 4 subjects. The associated sensitivities (i.e. 1 / Threshold) are displayed in
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Figure 3.
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%%%%%% INSERT FIGURE 3 ABOUT HERE %%%%%%%%%%%%
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For all subjects, performance decreased when the gap was greater than one degree.
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Interestingly, three subjects were better for small gap values than without any
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separation, suggesting that they were uncertain about the location of the central disk
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in the absence of the demarcation provided by the small gap. Indeed, the edge of the
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disk was invisible to most observers at behavioral threshold in the zero gap condition;
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instead a central region of the disk appeared to dip slightly but at an unpredictable
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location from trial to trial. It is well known that uncertainty can reduce sensitivity
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(Green & Swets, 1966; Pelli, 1985) and that the addition of features that remove
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uncertainty about target location, such as the small gap surrounding the disk in this
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case, can improve sensitivity. For subjects RF and SPM, we were also able to
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measure a threshold for the control condition (uncorrelated surround). The two other
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subjects were unable to detect the change in disparity in the absence of the disparity
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reference. For all four subjects, the thresholds with a gap of 5.5 deg separating the
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disk from the correlated surround (the reference) were significantly better than the
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thresholds for the uncorrelated noise condition, but were elevated from the best value
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by a factor of 3 to 9, as shown by the decline in sensitivity apparent in Figure 3. The
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next section examines where these sensitivities are localized in visual cortex.
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SSVEP data:
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Figure 4 displays the amplitude spectrum for all the electrodes averaged across trials
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and subjects. Two conditions are presented, one where no gap separated the center
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and surround (0 deg) and another with a 4° gap.
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%%%%%% INSERT FIGURE 4 ABOUT HERE %%%%%%%%%%%%
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Over the low frequency range, the spectrum has multiple, narrow peaks that occur at
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integer multiples of the stimulus frequency. Their amplitudes range from 3 to 20 times
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the noise level (defined here by the amplitude at non-tagged frequencies). This result
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demonstrates that cortical responses are clearly entrained by the steady-state
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stimulation. The spectra also show a narrowband response at the dot refresh rate
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(i.e. f2 = 21.25Hz). The brain is therefore also responding in a synchronous fashion
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to the refresh of the local elements in the stimulus. When the center and surround
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regions are abutted (no gap, Figure 4a), the spectrum is dominated by first and third
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(odd) harmonics of the disparity update rate, which we refer to as 1f1 and 3f1. The
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introduction of a 4° deg gap strongly reduces the odd harmonic component of the
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response. Odd harmonics are produced by asymmetries in the responses to the two
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states of the stimulation, i.e., when the disk appears behind the surround and when it
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merges with it in the fixation plane. These asymmetries can be explained by the
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disparity tuning function in early visual areas. One of our previous EEG studies
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estimated these curves at the population level in V1, LOC, V4, hMT+ and V3A and
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found that in all these ROIs, responses increase from 0’ to 16’ and then go back to
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their baseline level (Cottereau et al., 2011a). Responses at 12’ are therefore stronger
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than those at 0’, leading to an asymmetry. In the 4° gap condition, the amplitudes at
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the odd harmonics and in the topography of the first harmonics suggest that, even
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though the cortical activity is diminished in this condition, the response is still mainly
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in the odd harmonics. The amplitude at the odd harmonics is therefore a good metric
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to characterize the cortical responses to the different conditions.
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Effects of the gap
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The grand average topographic maps corresponding to the first harmonic
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suggest that the underlying neural activity arises mainly in the parietal and occipital
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cortex. This is confirmed by the surface-based average reconstruction (Figure 4,
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right). Our question is a quantitative one --- how does disparity processing in different
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cortical areas depend on the spatial separation of features. These cortical images are
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shown here only for visualization purposes; quantitative analyses will be performed in
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the five regions of interest (ROIs) defined by fMRI retinotopy and standard localizers.
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%%%%%% INSERT FIGURE 5 ABOUT HERE %%%%%%%%%%%%
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Figure 5 displays a composite measure of the odd-harmonic amplitudes in the
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five ROIs as a function of the gap size. The responses obtained when the surround
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was uncorrelated (control condition) are displayed by the filled areas (mean and
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standard error). In V1 as well as in V4, LOC, hMT+ and V3A, the amplitudes in the
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absence of a gap are larger than those obtained in the control condition, suggesting
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that responses in these ROIs are enhanced by the presence of the correlated
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surround. In our previous work (Cottereau et al., 2011a, Cottereau et al, 2012), the
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V4, LOC, hMT+ and V3A ROIs were each found to be influenced by the addition of a
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disparity reference, i.e., the surround. Based on our previous findings and single cell
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studies (see e.g. Cumming and Parker 1999), we did not expect any surround
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(relative disparity) effects in V1. The activity observed with the 0° gap could arise
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from crosstalk from all the areas surrounding V1, but particularly the V3A area (see
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the ‘Resolution of source-imaged SSVEP’ section above for more detail on cross-
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talk) whose SNR for the no gap condition is particularly large.
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Taken across all the gap values, the presence of a correlated surround
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enhanced the SNRs at the odd harmonics when compared to the activity obtained
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with the uncorrelated surround. This was confirmed by a repeated measures (2
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surround conditions) x (5 ROIs) ANOVA where the two surround conditions
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(correlated and uncorrelated) corresponded to the average SNRs across the 6 gap
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values on the one hand and the SNR for the uncorrelated surround on the other
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hand. This ANOVA supported a surround effect (p < 0.05) and also a significant
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interaction (p < 0.01) between ROI and the nature of the surround. Post-hoc signed-
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rank tests showed that only V3A (p < 0.01) and V4 (p < 0.05) activity was significantly
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higher than the control when averaged across all gap conditions. The possibility that
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the responses in V4 are due to cross-talk from V3A is ruled out by the results of the
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second experiment (see the next section) that clearly demonstrate that the activity in
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V4 and V3A are different.
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Generally, the effect of the gap is to decrease the SNRs. This was confirmed
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by an (6 gap conditions) x (5 ROIs) ANOVA that led to a main effect of gap size (p
