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Is Region-of-Interest Overlap Comparison a Reliable Measure of Category Specificity?

¹ Brown University, ² Princeton University, ³ California State University, Fullerton

Reprint requests should be sent to Chun-Chia Kung, Department of Psychology, Green Hall, Princeton University, Princeton, NJ 08544, or via e-mail: chunkung{at}princeton.edu, or to Jessie J. Peissig, Department of Psychology, California State University, Fullerton, P.O. Box 34080, Fullerton, CA 92834-9480, or via e-mail: jpeissig{at}fullerton.edu.

Analysis of the degree of overlap between functional magnetic resonance imaging–derived regions of interest (ROIs) has been used to assess the functional convergence and/or segregation of category-selective brain areas. An examination of the extant literature reveals no consistent usage for how such overlap is calculated, nor any systematic comparison between different methods. We argue that how ROI overlap is computed, especially the choice of the denominator in the formula, can profoundly affect the results and interpretation of such an analysis. To do this, we compared the overlap of the FFA–FFA (fusiform face area) and FFA–FGA (fusiform Greeble-selective area) in a localizer study testing both Greeble novices and experts. When using a single ROI as the denominator, we found a significant difference in FFA–FFA versus FFA–FGA overlap, consistent with the result of a previous study arguing for face specificity of the FFA [Rhodes, G., Byatt, G., Michie, P. T., & Puce, A. Is the fusiform face area specialized for faces, individuation, or expert individuation? J Cogn Neurosci, 16, 189–203, 2004]. However, these ROI overlap differences disappeared when the denominator combined both of the involved ROIs, and the patterns of such overlap comparisons were dependent on given statistical thresholds. We also found proportionally decreasing FFA–FFA overlap with increasing center-of-FFA distance, resolving an apparent contradiction between the consistency of the location of the FFA and the seemingly low FFA–FFA overlap. Finally, Monte Carlo simulations revealed the most stable formula—the most resistant to ROI size variations—to be the average of the two single-ROI-denominator-based overlap indices. In sum, ROI overlap analysis is not a reliable tool for assessing category specificity, and caution should be exercised with regard to ROI overlap definition, underlying assumptions, and interpretation.

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