Responses in early visual areas to contour integration are context dependent

Abstract

It has been shown that early visual areas are involved in contour processing. However, it is not clear how local and global context interact to influence responses in those areas, nor has the interarea coordination that yields coherent structural percepts been fully studied, especially in human observers. In this study, we used functional magnetic resonance imaging (fMRI) to measure activity in early visual cortex while observers performed a contour detection task in which alignment of Gabor elements and background clutter were manipulated. Six regions of interest (two regions, containing either the cortex representing the target or the background clutter, in each of areas V1, V2, and V3) were predefined using separate target versus background functional localizer scans. The first analysis using a general linear model showed that in the presence of background clutter, responses in V1 and V2 target regions of interest were significantly stronger to aligned than unaligned contours, whereas when background clutter was absent, no significant difference was observed. The second analysis using interarea correlations showed that with background clutter, there was an increase in V1-V2 coordination within the target regions when perceiving aligned versus unaligned contours; without clutter, however, correlations between V1 and V2 were similar no matter whether aligned contours were present or not. Both the average response magnitude and the connectivity analysis suggest different mechanisms support contour processing with or without background distractors. Coordination between V1 and V2 may play a major role in coherent structure perception, especially with complex scene organization.

Figure 1

(A) Example stimuli from the four test conditions with a circular contour detection task. (B) Example stimuli from the target (above) versus background (below) differential localizer scan.

Figure 2

Experimental procedure. (A) An example of block-designed functional localizer scans used to define target or background retinotopically corresponding ROIs. (B) Event-related scans with four experimental conditions.

Figure 3

An example of the psychophysiological interactions term. (A) The seed time series were first deconvolved based on the estimated HRF to obtain physiological responses. (B) To combine both the physiological and psychophysical effects we multiplied the seed physiological series by the condition code from each condition separately. (C) The interaction at the physiological level was convolved with the estimated HRF, so we could compare this BOLD level interaction (as shown in D) with residual time series from other ROIs.

Figure 4

Visual area mapping (A) and functional localizer results (B and C). (A) Angular visual field preference of one observer's left hemisphere obtained from rotating wedge stimulus (overlay on a flattened patch of the cortical surface centered on the occipital pole). The early visual areas are labeled. (B) On one single coronal EPI image, voxels significantly correlated with the block-alternation are color coded based on relative phases—the bluish voxels are in phase with the target presentation, while the orange voxels are in phase with the background stimulus. (C) Data in B was transformed to the flat patch, where a blue target-associated band and two orange background-associated bands could be seen among the early visual areas.

Figure 5

Estimated HRFs from four experimental conditions for one subject's tgV2 ROI. The HRFs were estimated for each voxel, and then averaged within each ROI.

Figure 6

Stimulus-related BOLD response differences among conditions. Each panel shows average data from the 15 observers in one ROI. For each panel, the differences of estimated HRF amplitudes between the aligned and unaligned contours when there was no background are shown on the left, and the differences when there was background clutter are shown on the right. Asterisks indicate statistical significance based on the two-sided permutation test at *p < 0.05, **p < 0.01, and ***p < 0.001. Error bars show ±1 SE.

Figure 7

Connectivity results using PPI. (A) The permutation test p values for interactions of PPI estimates between the Alignment and Background. A stronger interaction (smaller p value) is shown in darker color. Row indicates the seed region. The ROI is not tested on itself. (B) The differences of Beta estimates among conditions in tgV1 are shown when tgV2 was the seed. The mean difference from the 15 observers of estimated beta weights of PPI terms between the aligned and unaligned contours when there was no background is shown on the left, and the difference when there was background clutter was on the right. Error bars show ±1 SE. Asterisks show significant levels based on the permutation test at *p < 0.05.

Figure 8

Connectivity results using beta series correlations. (A) The permutation test p values for interactions of beta series correlation coefficients between the Alignment and Background. A stronger interaction (small p value) is shown in dark color. A significant interaction was observed among tgV1 and tgV2 ROIs. (B) The differences of correlation coefficients among conditions in tgV1 are shown when tgV2 was the seed. The mean difference of beta series correlations from the 15 observers between the aligned and unaligned contours when there was no background is shown on the left, and the difference when there was background clutter was on the right. With background, the aligned condition tended to show larger correlation coefficients between the tgV1 and tgV2 ROIs. Error bars show ±1 SE. Asterisks show significant levels based on the permutation test at *p < 0.05 and **p < 0.01.

Figure 9

(A) A circular shape could be grouped based on global features. (B) The same circle is not easily identified when surrounded by clutter. Local linkage cues are required. For example, in (C), the nearby pentagrams serve as local cues for the circular shape to be grouped.

Figure A1

Connectivity results in test ROIs using the PPI analysis when tgV2 was the seed ROI (dashed frame). Each panel shows the average results from the 15 observers in one ROI. Error bars show ±1 SE. In tgV1 ROI (the 1^st panel), with background clutter (on the right), the aligned condition has larger PPI connectivity estimates than the unaligned condition. The results in other ROIs are not different from zero.

Figure A2

Connectivity results in test ROIs using the beta series method when tgV2 was the seed region (dashed frame). Each panel shows the average results from the 15 observers in one ROI. Error bars show ±1 SE. In tgV1 ROI (the 1^st panel), with background clutter (on the right), the aligned condition has larger beta series correlation coefficients than the unaligned condition. The results in other ROIs are not different from zero.