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

Abstract

Does the “fusiform face area” (FFA) code only for faces? This question continues to elude the neuroimaging field due to at least two kinds of problems: first, the relatively low spatial resolution of fMRI in which the FFA was defined and second, the potential bias inherent in prevailing statistical methods for analyzing the actual diagnosticity of cortical tissue. Using high-resolution (1mm×1mm×1mm) imaging data of the fusiform face area (FFA) from 4 subjects who had categorized images as ‘animal’, ‘car’, ‘face’, or ‘sculpture’, we used multivariate linear and non-linear classifiers to decode the resultant voxel patterns. Prior to identifying the appropriate classifier we performed exploratory analysis to determine the nature of the distributions over classes and the voxel intensity pattern structure between classes. The FFA was visualized using non-metric multidimensional scaling revealing “string-like” sequences of voxels, which appeared in small non-contiguous clusters of categories, intertwined with other categories. Since this analysis suggested that feature space was highly non-linear, 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 (up to 50%; p<.000001, chance=25%). The increased noise inherent in high-resolution neuroimaging data relative to standard resolution resisted any further gains in category performance above ~60% (with FACE category often having the highest bias per category) even coupled with various feature extraction/selection methods. A sensitivity/diagnosticity analysis for each classifier per voxel showed: (1) reliable (with S.E.<3%) sensitivity present throughout the FFA for all 4 categories, and (2) showed multi-selectivity; that is, many voxels were selective for more than one category with some high diagnosticity but at submaximal intensity. This work is clearly consistent with the characterization of the FFA as a distributed, object-heterogeneous similarity structure and bolsters the view that the FFA response to “FACE” stimuli in standard resolution may be primarily due to a linear bias, which has resulted from an averaging artefact.