Thirteen neurologically intact individuals (mean age 34), with normal or corrected-to-normal visual acuity, participated in this experiment. Two subjects were excluded from the final analysis due to poor signal-to-noise ratio in their EEG data. Informed consent was obtained prior to experimentation under a protocol that was approved by the Institutional Review Board of the California Pacific Medical Center.
Stimulus Construction and Frequency Tagging Procedure
In the stimuli used in this experiment, periodic temporal modulation (‘frequency-tagging’) was applied to separate regions of the visual input, which allowed us to distinguish the responses arising from the two simultaneously present regions by spectrum analysis. These stimuli included one spatially asymmetric arrangement consisting of a small figure region surrounded by a large background () and four spatially symmetric configurations () in which the two tags modulated spatially equivalent regions of space that contained no size or configuration cues to predetermine the relative figure-ground assignment of the two regions.
Figure 1 Schematic illustration of (a) spatially asymmetric surrounded figure-ground arrangement and (b-e) four types of spatially symmetric stimulus arrangements used in this experiment. Note: B and C are identical except modulated according to sine wave and (more ...)
The asymmetric, spatially surrounded stimulus used in this experiment was identical to the “phase-defined form” stimulus utilized in our previous experiments (Appelbaum & Norcia, 2009
; Appelbaum, et al., 2008
; Appelbaum, et al., 2006
). This stimulus, shown in , consisted of a 5° diameter circular figure region surrounded by a 20° × 20° background in which the figure was defined by local discontinuities present at the border of horizontally oriented textures. The textures comprising both figure and background regions consisted of one-dimensional random-luminance bars with a minimum bar width of 6 arc min and texture contrast of 80%. In this stimulus the figure region modulated by 180° at 3.0 Hz while the background region modulated 180° at 3.6 Hz. Because the figure region was composed of the same texture as the background region, it either blended seamlessly into the background when both figure and background were in their un-rotated state, or appeared to be segmented from the background when the rotation state of the figure or background differed. Because the orientation of the figure and background regions was always horizontal, the segmentation was defined both by local luminance discontinuities that occurred at the figure/background border and by the temporal structure imposed by the texture modulations.
Four types of spatially symmetric stimuli were used to test the influence of the spatial organization of the inputs on the routing of scene information through visual cortex. show two types of stimuli comprised of fields of randomly oriented ‘texture doublets’ composed of pairs of equal size grating patches surrounded by a mean gray background. Within each pair, one patch was made to modulate contrast at 3.0Hz and the other at 3.6Hz, causing the two local grating patches to cycle into and out of phase with each other and therefore generating a kinetic border between the two parts of the stimulus that intermittently appeared and disappeared. These stimuli were made to modulate in counter phase (pattern reversal) with a temporal waveform that was either a square wave () or a sine wave (). On each successive trial a new field of randomly oriented textures was presented.
shows a single symmetric grating pair (texture doublet) composed of two patches each extending 2.5° vertically and 2.5° horizontally from the midline and surrounded with a mean gray background. Within this pair of patches the left grating modulated at 3.0 Hz, and the right grating at 3.6 Hz, according to a counter phase sine wave reversal. This stimulus presents two regions that are of similar size to the figure region in the asymmetric figure-ground stimulus and provides a test of whether small, centrally presented stimuli are sufficient to preferentially activate the LOC.
shows two frames of a full-field square wave modulated square-wave grating stimulus. This stimulus consists of abutting rows of alternating black-and-white vertical bars. The even rows reversed contrast (square-wave) at 3.0 Hz, and the odd rows at 3.6 Hz. As with the texture doublet patches, the two reversal frequencies imposed on this grating stimulus caused the alternating rows to cycle into and out of phase with each other, generating a kinetic border between the two parts of the stimulus that intermittently appeared and disappeared. The figure-ground arrangement of these rows was ambiguous as the regions partitioned by these borders were balanced for size and other configural cues.
Stimulus generation was performed by in-house software, running on the Power Macintosh platform and stimuli were presented on a Sony multi-synch video monitor (GDP-400) at a resolution of 800 × 600 pixels, with a 72 Hz vertical refresh rate. Participants were instructed to fixate a fixation mark at the center of the display and to distribute attention evenly over the entire display. Stimuli were viewed in a dark and quiet room with individual trials lasting 16.7 seconds and conditions randomized within a block. A typical session lasted roughly 45 minutes and consisted of 15 blocks of randomized trials in which the observer paced the presentation and was given opportunity to rest between blocks.
EEG signal acquisition and source imaging procedure
The procedures for this experiment (EEG signal acquisition, head conductivity modeling, source estimation, visual areas definition, region-of-interest quantification, and statistical analysis) are similar to those utilized in our previous studies. In the interest of brevity, we provide an overview of these methods, and reference the reader to our previous work (Ales & Norcia, 2009
; Appelbaum, et al., 2008
; Appelbaum, et al., 2006
) for a more a more detailed description.
As in our previous experiments, the electroencephalogram (EEG) was collected with 128-sensor HydroCell Sensor Nets (Electrical Geodesics, Eugene OR) and was band-pass filtered from 0.1 to 200 Hz. Following each experimental session, the 3D locations of all electrodes and three major fiducials (nasion, left and right peri-auricular points) were digitized using a 3Space Fastrack 3-D digitizer (Polhemus, Colchester, VT). For all observers, the 3D digitized locations were used to co-register the electrodes to their T1-weighted anatomical MRI scans.
Raw data were evaluated off line according to a sample-by-sample thresholding procedure to remove noisy sensors that were replaced by the average of the six nearest spatial neighbors. Once noisy sensors were substituted, the EEG was re-referenced to the common average of all the sensors. Additionally, EEG epochs that contained a large percentage of data samples exceeding threshold (25-50 micro volts) were excluded on a sensor-by-sensor basis. Preliminary analysis of the statistical significance of individual observer data was conducted using the Tcirc2
methods described by (Victor & Mast, 1991
Structural and Functional Magnetic Resonance Imaging (MRI)
Structural and functional MRI scanning was conducted at 3T (Siemens Tim Trio, Erlangen, Germany) using a 12-channel head coil. We acquired a T1-weighted MRI dataset (3-D MP-RAGE sequence, 0.8 × 0.8 × 0.8 mm3 and a 3-D T2-weighted dataset (SE sequence at 1 × 1 × 1 mm3 resolution) for tissue segmentation and registration with the functional scans. For fMRI, we employed a single-shot, gradient-echo EPI sequence (TR/TE = 2000/28 ms, flip angle 80, 126 volumes per run) with a voxel size of 1.7 × 1.7 × 2 mm3 (128 × 128 acquisition matrix, 220 mm FOV, bandwidth 1860 Hz/pixel, echo spacing 0.71 ms). We acquired 30 slices without gaps, positioned in the transverse-to-coronal plane approximately parallel to the corpus callosum and covering the whole cerebrum. Once per session, a 2-D SE T1-weighted volume was acquired with the same slice specifications as the functional series in order to facilitate registration of the fMRI data to the anatomical scan.
The FreeSurfer software package (http://surfer.nmr.mgh.harvard.edu
) was used to perform gray and white matter segmentation and a mid-gray cortical surface extraction. This cortical surface had 20,484 isotropically spaced vertices and was used both as a source constraint and for defining the visual areas. The FreeSurfer package extracts both gray/white and gray/cerebrospinal fluid (CSF) boundaries, but these surfaces can have different surface orientations. In particular, the gray/white boundary has sharp gyri (the curvature changes rapidly) and smooth sulci (slowly changing surface curvature), while the gray/CSF boundary is the inverse, with smooth gyri and sharp sulci. In order to avoid these discontinuities, we generated a surface partway between these two boundaries that has gyri and sulci with approximately equal curvature.
Individual Boundary Element Method (BEM) conductivity models were derived from the T1 and T2 weighted MRI scans of each observer. The FSL toolbox (http://www.fmrib.ox.ac.uk/fsl/
) was also used to segment contiguous volume regions for the scalp, outer skull, and inner skull and to convert these MRI volumes into inner skull, outer skull, and scalp surfaces (Smith, 2002
; Smith, et al., 2004
Visual area definition
Rotating wedge stimuli were used to map polar angle sensitivity and expanding and contracting ring stimuli were used to map retinal eccentricity up to 3.5°. Complete cycles lasted 24 sec and a total of 10 cycles in each of 3 scans were collected in each participant. Fourier analysis was used to extract the magnitude and phase of the BOLD signal, which was visualized on a flattened representation of the cortical surface. Retinotopic field mapping produced regions-of-interest (ROIs) defined for each participant's visual cortical areas V1, V2v, V2d, V3v, V3d in each hemisphere (Engel, Glover, & Wandell, 1997
). A contrast between scrambled versus intact objects (block design 12 sec intact/12 sec scrambled; 10 cycles (240 sec per scan, 2 to 3 scans) was used to define the LOC. The stimuli of (Kourtzi & Kanwisher, 2000
) were used. These stimuli result in an activation that extends onto both the lateral and ventral surfaces (Vinberg & Grill-Spector, 2008
). Only the portion lying on the lateral surface, posterior and adjacent to hMT+ was included in our definition. Activations in ventral areas were more variable and sources in these areas are less visible in the EEG due to their greater depth. Representative functional ROIs are shown in for all subjects.
ROI locations for all subjects color coded and shown from posterior and inferior perspectives.
Cortically constrained inverse
An L2 minimum norm inverse was computed with sources constrained to the location and orientation of the cortical surface. In addition, we modified the source covariance matrix in two ways to decrease the tendency of the minimum norm procedure to place sources outside of the visual areas. These constraints involved; 1) increasing the variance allowed within the visual areas by a factor of two relative to other vertices, and 2) enforcement of a local smoothness constraint within an area using the first- and second-order neighborhoods on the mesh with a weighting function equal to 0.5 for the first-order and 0.25 for the second-order relationships. The smoothness constraint therefore respects areal boundaries unlike other smoothing methods such as LORETA that apply the same smoothing rule throughout cortex.
ROI-based analysis of the Steady-state Visual Evoked Potential (SSVEP)
A Discrete Fourier Transform was used to estimate the response magnitude associated with each region tag within each functionally defined ROI. An estimate of the average response magnitude for each ROI was computed by coherently averaging across all nodes within that ROI. This averaging was performed on the complex Fourier components and therefore preserved phase information. Averaging across hemispheres and observers was then performed for each individual frequency component again maintaining the complex phase of the response. Finally, for each ROI, the total amplitude of the 2nd
, and 8th
harmonics was computed for each region tag by summing the powers of the individual harmonics and then taking the square root
To test for differences in region selectivity due to stimulus configuration across ROIs, response magnitude ratios were computed as the mean activity occurring in the first-tier ROIs (Tier 1; V1v, V1d, V2v, V2d, V3v, V3d, averaged over the left and right hemispheres), divided by the mean activity in the left and right LOC ROIs. Two-way, within-subject, repeated measures analysis of variance (rANOVA) was used to determine significant main effects of, and interactions between, stimulus arrangement (5 levels) and harmonic components (nf1 or nf2) for the Tier1-to-LOC ratio. Paired t-tests were performed between components for each stimulus condition to further probe effects of stimulus arrangement.