High-resolution imaging of the fusiform face area (FFA) using multivariate non-linear classifiers shows diagnosticity for non-face categories

How are objects represented in the human visual pathway? This question continues to elude the neuroimaging field due to at least two kinds of problems: first, the relatively low spatial resolution of fMRI and second, the bias inherent in prevailing statistical methods for analyzing the actual diagnosticity of cortical tissue. We collected high-resolution (1mm x 1mm) imaging data of the fusiform face area (FFA) from 4 subjects while they categorized images as ‘animal’, ‘car’, ‘face’, or ‘sculpture. ’ We performed exploratory analysis to determine the nature of the distributions over classes and the similarity structure between classes. The FFA was visualized using nonmetric multidimensional scaling revealing “string-like ” sequences of voxels, which appeared in small non-contiguous clusters of categories, intertwined with other categories. Since the feature space appeared highly nonlinear, we trained various statistical classifiers on the class conditional distributions (labelled) and separated the four categories with 100 % reliability (over replications) and generalized to out of sample cases with high significance (45 % to 51%; p<. 000001, chance=25%). The increased noise inherent in high-resolution neuroimaging data relative to standard resolution resisted an