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High Resolution Imaging of the Fusiform Face Area (FFA) Using Nonlinear Classifiers Shows Diagnosticity for Nonface Categories

Stephen Hanson¹ and Arielle Schmidt²

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1. Rutgers University, Department of Psychology, Newark, USA
2. Rutgers University, Neuroscience Department, Newark, USA

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Document Type:

Manuscript

Date:

Received 26 August 2008 21:18 UTC; Posted 29 August 2008

Subjects:

Neuroscience, Bioinformatics

Tags:

• Classifiers
• FFA
• neuroimaging

Abstract:

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 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 one category but responded to all 4 categories with some high diagnosticity but at lower intensity. This work further verifies the hypothesis that the FFA is 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 artifact.

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License:

This document is licensed to the public under the Creative Commons Attribution 3.0 License

How to cite this document:

Hanson, Stephen and Schmidt, Arielle. High Resolution Imaging of the Fusiform Face Area (FFA) Using Nonlinear Classifiers Shows Diagnosticity for Nonface Categories. Available from Nature Precedings <http://hdl.handle.net/10101/npre.2008.2235.1> (2008)

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