Abstract
Computational theories propose that attention modulates the topographical landscape of spatial 'priority' maps in regions of the visual cortex so that the location of an important object is associated with higher activation levels. Although studies of single-unit recordings have demonstrated attention-related increases in the gain of neural responses and changes in the size of spatial receptive fields, the net effect of these modulations on the topography of region-level priority maps has not been investigated. Here we used functional magnetic resonance imaging and a multivariate encoding model to reconstruct spatial representations of attended and ignored stimuli using activation patterns across entire visual areas. These reconstructed spatial representations reveal the influence of attention on the amplitude and size of stimulus representations within putative priority maps across the visual hierarchy. Our results suggest that attention increases the amplitude of stimulus representations in these spatial maps, particularly in higher visual areas, but does not substantively change their size.
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Acknowledgements
We thank E. Vul and S. Itthipuripat for assistance with statistical methods and M. Scolari and M. Smith for assistance with parietal cortex mapping protocols. This work was supported by a National Science Foundation Graduate Research Fellowship to T.C.S. and by US National Institutes of Health grant R0I MH-092345 and a James S. McDonnell Scholar Award to J.T.S.
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T.C.S. and J.T.S. designed the experiments and analysis method and wrote the manuscript. T.C.S. conducted the experiments and implemented the analyses.
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Integrated supplementary information
Supplementary Figure 1 Participants maintained fixation in the scanner during all three task conditions, related to Figure 2.
Average horizontal and vertical gaze position across each 3 s trial in each task condition. Neither horizontal nor vertical gaze varied as a function of either stimulus position or task demands. 2-way ANOVA for each gaze direction, with task condition and stimulus position (grouped into 6 bins corresponding to x or y coordinate for horizontal and vertical gaze plots, respectively) as factors: minimum p for main effects/interactions = 0.2725, which was for main effect of vertical stimulus position on vertical gaze. Note that data from null trials were not entered into the ANOVA, but subjects maintained steady fixation on these trials as well. Eyetracking data gathered in the scanner for 4 of the 8 participants. Error bars ±1 S.E.M. across subjects.
Supplementary Figure 2 One-dimensional cross-section of 2D basis function, related to Figure 3.
Cross-section through the center of a single basis function (Figure 3a). FWHM is the full-width at half-maximum. The size constant, s, was set to 5rstim (see Online Methods: Encoding model, Supplementary Fig. 3), where rstim is 1.163°. This corresponds to the distance from the center at which the filter amplitude reaches 0.
Supplementary Figure 3 The relationship between basis function size and spacing changes the smoothness of reconstructions, related to Figures 3 and 4.
(a) For a constant spatial filter separation distance of 2.09° (which matches that used in the main analysis), we varied the size parameter (Supplementary Fig. 2) of 2 neighboring spatial filters, then plotted their sum as a function of position in space and filter size (which was continuously varied). Summed response is indicated by the image colorscale. (b) A slice from (a) at the FWHM of the filters used in the main analysis (dashed line in panel a). This value resulted in smooth reconstructions to which we could accurately fit surfaces to quantify the spatial representations (see Online Methods: Curvefitting), but also resulted in sufficient filter separation so that adjacent filters did not excessively overlap (see below). Smaller FWHM values would result in speckled reconstructed spatial representations which would be poorly fit using a single surface (this would be seen as a dipped black solid line in panel b; see panel a at small filter size values), and larger FWHM values would result in poorly discriminable predicted channel responses because neighboring filters would account for much of the same variance in the signal due to a high degree of overlap (see a, high FWHM values). At high enough FWHM values, the model cannot be estimated because overly high correlations between adjacent filters result in a rank deficient design matrix (Equation 1 in Online Methods).
Supplementary Figure 4 Poor reconstructions during attend fixation condition for participant AG3, related to Figure 4.
Plotted as in Figure 4. All images on same color scale. Poor reconstructed spatial representations were measured during attend fixation runs across all ROIs, but more typical looking reconstructed spatial representations were observed for both of the other task conditions. Behavioral performance for this participant indicated they were awake and vigilantly performing the fixation task. This was the only participant with this issue, and their data were not included in Figures 4 or 5 (see Online Methods: Excluded participant). Note that the same estimated channel weight matrix was used here as was used to reconstruct spatial representations during the attend stimulus and spatial working memory tasks. Furthermore, note that these data support our conclusion of higher amplitude spatial priority maps with attention and they were excluded solely because of the noisy fits in the fixation condition. All of our reported effects would be more pronounced if this participant was included (see data included in the html version of this report).
Supplementary Figure 5 Encoding model does not overfit data and generalizes to novel stimuli, related to Figure 4.
(a) Reconstructions from all 36 stimulus locations under the attend stimulus condition across all 8 observers using voxels from only the ventral and dorsal aspects of V2 and V3. Color scale is identical to that used in Figure 4. Note that spatial reconstructions in the dorsal & ventral aspects of V2 and V3 are more robust in the lower and upper visual field, respectively. This pattern matches the known selectivity of dorsal and ventral areas V2 and V3. (b) Encoding model can be generalized to reconstruct novel stimuli that were not part of the training set. An encoding model trained using all attend fixation, attend stimulus & spatial working memory runs was able to accurately reconstruct a novel, untrained stimulus set acquired during a different scanning session on 7 of 8 participants presented in Figures 4,5 (novel test data was not available for this 8th participant, AA3). This novel stimulus display consisted of four half-circle stimuli presented at one of two eccentricities (see top row), and the model was able to reconstruct these four stimuli with a high degree of precision (see Online Methods: Stimulus reconstructions – novel stimuli for more details).
Supplementary Figure 6 sPCS exhibits larger responses, averaged across all voxels within the sPCS, in the attend stimulus and spatial working memory conditions, related to Figures 4 and 5.
Both left and right sPCS exhibit strong hemodynamic responses to stimuli, with increased averaged (i.e. univariate) responses during attend stimulus and spatial working memory task conditions compared to the attend fixation condition. Additionally, this mean signal increase under conditions of attention to the stimulus or spatial working memory likely accounts for much of the significant increase in the baseline offset in the reconstructed stimulus representations reported in Figure 5. Error bars ± SEM across subjects.
Supplementary Figure 7 IPS ROI primarily corresponds to IPS 0/1, related to Figures 6 and 7.
(a) Polar angle preferences for each voxel plotted on the inflated surface of 4 participants' cortical sheets. Maps are liberally thresholded to show any voxel with normalized power at the stimulus frequency > 0.005. Smooth polar angle transitions were used to delineate four retinotopic regions of IPS (termed IPS 0-3) in each of these 8 hemispheres. Dashed lines: lower vertical meridian (LVM); solid lines: upper vertical meridian (UVM). (b) For each participant and each hemisphere we compared the number of overlapping voxels between our original localizer-defined IPS ROI (see Online Methods: Mapping IPS subregions) and each of these 4 retinotopically mapped IPS subregions . The original IPS ROI primarily overlaps with areas IPS 0 and 1, and is therefore labeled as such in Figure 7. Blue: left hemisphere, Red: right hemisphere. (c) Fit parameters to reconstructed spatial representations estimated from activation patterns across each IPS subregion for these 4 participants (analysis identical to that implemented for Fig. 7). Critically, fit parameters in all regions are similar to those observed for the original IPS subregion (Fig. 7). Spatial representations of presented stimuli do not narrow in size when stimuli are attended or a target is remembered, but amplitude increases for representations in IPS0 (p = 0.012), and baseline increases in IPS3 (p = 0.002; statistics as in Figs. 5 & 7; error bars within-participant S.E.M.)
Supplementary Figure 8 Population receptive field analyses: example participant AA3B, related to Figure 7.
(a) Reconstructed pRFs and best-fit isotropic function for voxels at each interquartile boundary. White dashed circles are plotted at half-maximum of fit function. Quartiles were split by minimum R2 across all task conditions (see Online Methods: Population receptive field estimation). Above each column of pRFs is the minimum R2 value across the 3 conditions shown below. The right 3 columns (top 50%) are voxels that were included in subsequent analyses. White horizontal scale bars correspond to 1° visual angle. (b) Distribution of R2 (colored lines) and minimum R2 across conditions (black lines) for each voxel, plotted as a cumulative distribution. (c) Size vs. eccentricity for each condition for each ROI. Each data point corresponds to a single voxel. Black circles/lines are the mean size at each eccentricity bin which contains ≥ 5 voxels (these are the points which are included in Supplementary Fig. 9a). All slopes for this example participant are significantly > 0 after Bonferroni correction across all 48 tests (4 participants × 4 ROIs × 3 conditions, corrected α = 0.001), except hMT+, spatial working memory condition (p = 0.006, see Online Methods: Statistical Procedures). (d) Distribution of pRF size for each voxel across condition pairs. The percentage of voxels which lie above the unity line (that is, the percentage of voxels for which the size increases) within a ROI and condition pair is used to evaluate whether task demands significantly change pRF size (see Supplementary Fig. 9b, Supplementary Results, Online Methods).
Supplementary Figure 9 Population receptive fields increase size with attention, related to Figure 7.
(a) Summary of pRF size as a function of eccentricity across n = 4 participants. Each data point is plotted if ≥ 3 participants each had ≥ 5 voxels within that eccentricity bin. Error bars S.E.M. across included participants. (b) Summary of pRF size changes across each condition pair. For each ROI for each participant, we computed the percentage of voxels in which the pRF size was greater for the first condition than the second (e.g., cyan bars indicate the percentage of voxels in which pRF size was greater for the attend stimulus condition than for the attend fixation condition; this corresponds to the percentage of voxels which lie above unity when plotted as in Supplementary Fig. 8d). Black asterisks indicate significant size changes across a condition pair for an ROI, Bonferroni-corrected (two-tailed t-test, see Online Methods: Statistical procedures). Gray asterisks indicate a significant size change using a one-tailed t-test. Error bars indicate S.E.M. across participants (n = 4).
Supplementary Figure 10 Simulations demonstrate that uniform changes in voxel-level pRFs are reflected in changes in region-level spatial representations, related to Figure 7.
(a-b) For 500 simulated voxels, we generated data for 2 conditions in which we only manipulated the simulated pRF size (condition B uses pRFs that were on average 11% larger than pRFs in condition A, which corresponds to the measured increase in pRF size between “attend stimulus” and “attend fixation” conditions in hV4 across all 4 participants). Under these conditions, the size of the multivariate spatial representations scaled with pRF size (a, smaller spatial representation sizes in condition A than in condition B). However, note that in this scenario, there is no change in fit amplitude (b). This demonstrates that (1) multivariate spatial representations are sensitive to changes in pRF size, given that the changes occur uniformly across a region, and (2) that our analysis technique can detect size changes mediated by uniform changes in pRF size in the absence of amplitude changes, were they occurring. This rules out an important possibility that representation size changes might be occurring in our dataset, but they could be too small to measure (see Results: Size of spatial representations across eccentricity and ROI). (c-d) In panels a-b we demonstrate that multivariate region-level spatial representations can increase in size, reflecting uniform changes in the underlying univariate voxel-level pRFs. Here, we used the fit pRF parameters for 1 example participant (AA3B, shown in Supplementary Fig. 8) and 1 ROI (hV4), which undergo non-uniform size changes across conditions, to simulate data for all 3 task conditions in the main experiment. Even with pRF size increases observed across conditions (Supplementary Fig. 8d), multivariate spatial representations are shown to maintain a constant size (c), but increase in amplitude (d, mirroring our data in Figs. 5, 7). This pattern of results was also found in the other three participants (not shown). This demonstrates a decoupling of pRF size/amplitude and the size/amplitude of multivariate region-level spatial representations, and underscores the importance of exploiting all of the information available in a region to estimate the fidelity of spatial encoding.
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Sprague, T., Serences, J. Attention modulates spatial priority maps in the human occipital, parietal and frontal cortices. Nat Neurosci 16, 1879–1887 (2013). https://doi.org/10.1038/nn.3574
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DOI: https://doi.org/10.1038/nn.3574
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