Es in PPA and FFA. Figures 1 and 5 suggest that activation profiles might be graded in FFA and PPA. This raises the question whether the category-average activation difference (by which these regions are functionally defined) can be accounted for by a continuously graded falloff without a step at the category boundary. Inspecting the noisy activation profile after ranking according to the same profile (Figs. 1, 2) cannot address this question (see Materials and Methods). Testing for a step-like drop in activation across the boundary requires joint modeling of category step and gradedness. A, Implementation of the category-step-and-gradedness analysis. We first rank the images within and outside the preferred category according to session 1 activation (step 1). We then order the session 2 activation profile according to the session 1 ranking (step 2). We define a linear falloff model consisting of four predictors: a positive ramp predictor for the preferred category (0 elsewhere), a negative ramp predictor for the nonpreferred category (0 elsewhere), a confound mean predictor (1 everywhere), and a category-step predictor (1 for preferred, 1 for nonpreferred) (step 3). The ramps were defined such that setting the category step to 0 would yield a piecewise linear falloff with a kink, but no step (no discontinuity), at the category boundary (gray dashed line). We then fit the model by ordinary Mequitazine cost least-squares to the activation profile estimated from session 2 and plot the model fit (step 4). The procedure is illustrated for the activation profile of right FFA defined at 128 voxels in one specific subject. The procedure was repeated using the sessions in reverse order, and the resulting estimates averaged. B, Model fits for FFA, PPA, and EVC. To assess the dependency of the estimates on the particular sample of stimuli, we bootstrap-resampled the stimulus set 10,000 times and performed the model-fitting procedure on each bootstrap sample in both directions. We computed a p value for the estimate of each predictor of the falloff model as the percentile of 0 within the bootstrap distribution of the estimates (one-sided tests). The panels show the fitted falloff model predictions with the color of each line section coding for the significance of the corresponding model component (gray, not significant; light pink, p 0.05; bright pink, p 0.01; red, p 0.0025). In the background, the 10,000 bootstrap model predictions are transparently overplotted in gray. Results show a large, significant category step in PPA (left and right); a small significant category step in right FFA; evidence for graded preferred activation profiles in FFA and right PPA; and evidence for graded nonpreferred activation profiles in right and left FFA and PPA. Activation profiles were first averaged across subjects; a modified version of this analysis that is sensitive to subject-unique activation profiles gave similar results.8660 ?J. Neurosci., June 20, 2012 ?32(25):8649 ?Mur et al. ?Single-Image Activation of Category RegionsPPA, then the monkey region should similarly exhibit essentially perfect categorical ranking and a pronounced category step. Activation profiles are graded with step, not binary Previous category-average studies left open whether order Saroglitazar Magnesium categoryselective regions simply act as a binary classifier or whether they show graded responses to individual exemplars of a category. This suggests three different possible scenarios. In the first scenario, the activation pro.Es in PPA and FFA. Figures 1 and 5 suggest that activation profiles might be graded in FFA and PPA. This raises the question whether the category-average activation difference (by which these regions are functionally defined) can be accounted for by a continuously graded falloff without a step at the category boundary. Inspecting the noisy activation profile after ranking according to the same profile (Figs. 1, 2) cannot address this question (see Materials and Methods). Testing for a step-like drop in activation across the boundary requires joint modeling of category step and gradedness. A, Implementation of the category-step-and-gradedness analysis. We first rank the images within and outside the preferred category according to session 1 activation (step 1). We then order the session 2 activation profile according to the session 1 ranking (step 2). We define a linear falloff model consisting of four predictors: a positive ramp predictor for the preferred category (0 elsewhere), a negative ramp predictor for the nonpreferred category (0 elsewhere), a confound mean predictor (1 everywhere), and a category-step predictor (1 for preferred, 1 for nonpreferred) (step 3). The ramps were defined such that setting the category step to 0 would yield a piecewise linear falloff with a kink, but no step (no discontinuity), at the category boundary (gray dashed line). We then fit the model by ordinary least-squares to the activation profile estimated from session 2 and plot the model fit (step 4). The procedure is illustrated for the activation profile of right FFA defined at 128 voxels in one specific subject. The procedure was repeated using the sessions in reverse order, and the resulting estimates averaged. B, Model fits for FFA, PPA, and EVC. To assess the dependency of the estimates on the particular sample of stimuli, we bootstrap-resampled the stimulus set 10,000 times and performed the model-fitting procedure on each bootstrap sample in both directions. We computed a p value for the estimate of each predictor of the falloff model as the percentile of 0 within the bootstrap distribution of the estimates (one-sided tests). The panels show the fitted falloff model predictions with the color of each line section coding for the significance of the corresponding model component (gray, not significant; light pink, p 0.05; bright pink, p 0.01; red, p 0.0025). In the background, the 10,000 bootstrap model predictions are transparently overplotted in gray. Results show a large, significant category step in PPA (left and right); a small significant category step in right FFA; evidence for graded preferred activation profiles in FFA and right PPA; and evidence for graded nonpreferred activation profiles in right and left FFA and PPA. Activation profiles were first averaged across subjects; a modified version of this analysis that is sensitive to subject-unique activation profiles gave similar results.8660 ?J. Neurosci., June 20, 2012 ?32(25):8649 ?Mur et al. ?Single-Image Activation of Category RegionsPPA, then the monkey region should similarly exhibit essentially perfect categorical ranking and a pronounced category step. Activation profiles are graded with step, not binary Previous category-average studies left open whether categoryselective regions simply act as a binary classifier or whether they show graded responses to individual exemplars of a category. This suggests three different possible scenarios. In the first scenario, the activation pro.
