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. 2017 Oct 5;6:e27576. doi: 10.7554/eLife.27576

Figure 2. Voxel-wise analysis for the box scrambling experiment.

(A) Shape sensitivity is projected on an inflated brain from a superior view (upper panel) and from a posterior-inferior view (lower panel). Warm colors signify voxels that are shape sensitive, with activation increasing as a function of object coherence. Conversely, cold colors reflect low shape sensitivity (negative slopes) or greater sensitivity for scrambled than intact images. The activation profile of four representative clusters (10 voxels each) is plotted and the color of the bars reflects the slope value of each cluster (left panel). The right panel is a 3D reconstruction of the shape sensitivity of all visual voxels along the two pathways. (B) Group-averaged piecewise regression analysis. Each dot signifies a voxel, at a particular y-coordinate, averaged across participants, and the black line shows the result of the piecewise regression (based on two linear components) obtained for the group average. In both the dorsal and ventral pathways, the first component showed that the voxel location on the posterior-anterior axis was significantly correlated with shape sensitivity, which depicts how shape selectivity increases moving forward from the occipital pole through extrastriate and inferior temporal areas. The second component reflects a decrease in shape selectivity. (C) Correlation coefficients for each component, computed for individual participants, reveal that the large-scale organization of the two pathways was reliable across participants. (D) The point of maximal-shape sensitivity (inflection point) differed between the two pathways. This was more posterior in the dorsal versus ventral pathway. (E) The average shape sensitivity of all shape-selective voxels was greater in the ventral than dorsal pathway. Figure 2—figure supplement 1 (upper panel) shows that similar results were observed when the analysis excluded pictures of tools and was restricted to images of objects with no visuomotor association. Figure 2—figure supplement 1 (lower panel) shows that similar results were obtained when the piecewise regression was based on distance that was calculated from the combination of the Y and Z coordinates (see Figure 2C for a comparison). Figure 2—figure supplement 2 shows the ROI analysis in which slope (shape sensitivity) is plotted as function of Region of Interest defined from atlases, separately for each pathway and hemisphere. Black asterisks signify that a ROI is significantly sensitive to shape (slope >0, q < 0.05 FDR corrected). The black vertical line separates the lateral and inferior ROIs of the ventral pathway. Error bars in all graphs represent the standard errors. For all figures, see also source data files for individual data points.

Figure 2—source data 1. Individual data points for Figure 2C.
DOI: 10.7554/eLife.27576.007
Figure 2—source data 2. Individual data points for Figure 2D.
DOI: 10.7554/eLife.27576.008
Figure 2—source data 3. Individual data points for Figure 2E.
DOI: 10.7554/eLife.27576.009
Figure 2—source data 4. Individual data points for Figure 2—figure supplement 1 (upper panel).
DOI: 10.7554/eLife.27576.010
Figure 2—source data 5. Individual data points for Figure 2—figure supplement 1 (lower panel).
DOI: 10.7554/eLife.27576.011
Figure 2—source data 6. Individual data points for Figure 2—figure supplement 2.
DOI: 10.7554/eLife.27576.012

Figure 2.

Figure 2—figure supplement 1. Two components analysis for the box scrambling experiment.

Figure 2—figure supplement 1.

(A) The large scale organization of the two pathways based on pictures of objects (and not tools). (B) The large scale organization of the two pathways based on the combination of Y (posterior-anterior) axis and Z (superior-anterior) axis.
Figure 2—figure supplement 2. Shape sensitivity for Dorsal left and Dorsal right and Ventral left and Ventral right.

Figure 2—figure supplement 2.