Attending to What and Where: Background Connectivity Integrates Categorical and Spatial Attention

Abstract

Top-down attention prioritizes the processing of goal-relevant information throughout visual cortex based on where that information is found in space and what it looks like. Whereas attentional goals often have both spatial and featural components, most research on the neural basis of attention has examined these components separately. Here we investigated how these attentional components are integrated by examining the attentional modulation of functional connectivity between visual areas with different selectivity. Specifically, we used fMRI to measure temporal correlations between spatially selective regions of early visual cortex and category-selective regions in ventral temporal cortex while participants performed a task that benefitted from both spatial and categorical attention. We found that categorical attention modulated the connectivity of category-selective areas, but only with retinotopic areas that coded for the spatially attended location. Similarly, spatial attention modulated the connectivity of retinotopic areas only with the areas coding for the attended category. This pattern of results suggests that attentional modulation of connectivity is driven both by spatial selection and featural biases. Combined with exploratory analyses of frontoparietal areas that track these changes in connectivity among visual areas, this study begins to shed light on how different components of attention are integrated in support of more complex behavioral goals.

Figures

Figure 1.

Experimental design. (A) In Session 1, participants first completed two rest runs. They were instructed to fixate the dot in the center of the screen. (B) During attention runs, participants simultaneously viewed two streams of images and performed a repetition detection task on one stream. Faces were presented in one stream and scenes in the other. The location and category of the attended stream was manipulated such that participants attended a unique combination of spatial locations and categories in each run: faces on the left, faces on the right, scenes on the left, and scenes on the right. (C) In Session 2, participants completed six retinotopy scans: four polar scans with rotating wedges and two eccentricity scans with expanding/ contracting rings. These scans were used to identify V4 and ventral/dorsal V1–V3. (D) Participants then completed two functional localizers for FFA and PPA. These runs were identical to the attention runs, except only one stream was presented at a time. The blocks within each scan alternated between face and scene stimuli.

Figure 2.

Behavioral results. (A) Participants were faster to respond to repetitions in the left visual field. (B) Participants were more accurate with face versus scene repetitions in the LVF. *p < .05. Error bars reflect ±1 SEM.

Figure 3.

Evoked responses. (A) Time course of FIR parameter estimates for all conditions in every region, modeling the average evoked BOLD response. (B) Average FIR parameter estimates over “stimulated” time points, as an index of response amplitude. Shaded regions and error bars reflect ±1 within-subject SEM.

Figure 4.

Connectivity types. Connectivity was measured between four pairs of ROIs: left V4 and FFA, right V4 and FFA, left V4 and PPA, and right V4 and PPA. These pairs were labeled based on the relevance of the constituent regions to the attended stimuli. Left and right V4 were relevant to right and left attention, respectively. FFA and PPA were relevant to face and scene attention, respectively.

Figure 5.

Background connectivity. (A) Spatial and categorical attention enhanced background connectivity between the ventral temporal region that coded for the attended category and the V4 that coded for the attended location. (B) Background connectivity in each ROI pair by spatial and categorical selectivity. *p < .05. Error bars reflect within-subject SEM.

Figure 6.

Background connectivity in V1, V2, and V3. (A) The interaction between spatial and categorical relevance in V4 was marginally significant in ventral V3 but did not extend into V1 and V2. (B) In the dorsal stream, background connectivity with V1 and V2 was modulated by categorical relevance, but in the opposite direction of V4, with decreased coupling to the category-selective ventral temporal region. Overall, attention to the upper visual field (all conditions) had diverging effects relative to rest, with relatively enhanced connectivity for ventral areas and decreased connectivity for dorsal areas. Error bars reflect within-subject SEM.

Figure 7.

Temporal MUD analysis. (A) For each attention run, the normalized time courses from the right or left V4 were multiplied with the normalized time courses from FFA or PPA. The resulting time course represented the contribution of each time point to the background connectivity between the two regions (the sum of the products is the Pearson correlation coefficient). (A) The graphical intuition for this analysis is that if both ROI time courses have positive or negative normalized values at a given time point, then the time point falls in the first or third quadrant of a scatterplot relating the normalized activity of one region to another. Because of the mean-centering of both regions, the best-fit line passes through the origin and thus points in these quadrants support a positive slope. If one time course has a positive value and the other has a negative value, then the time point falls in the second or fourth quadrant, which supports a negative slope. The relative balance of points in quadrants 1/3 versus 2/4 thus determines the sign of the slope, and because of the variance normalization, the value of the slope is the Pearson correlation coefficient. (C) When examining voxels whose BOLD time courses correlated with the product time course for each condition, we observed significant clusters in the core attention network: bilateral IFG, bilateral IPS/SPL, and right TPJ. Contrasts corrected for multiple comparisons using cluster mass thresholding (p < .05; cluster-forming threshold z = 3).