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Thickness of Cortical Grey Matter Predicts Face and Object Recognition


Outcome: Sophisticated techniques allowed for segmentation of human grey matter and estimates of regional cortical thickness. Individual differences in the cortical thickness of pea-sized regions in the inferior temporal could be predicted by behavioral recognition performance on faces and objects. While subjects with a thicker cortex performed better with vehicles, those with a thinner cortex performed better with faces and living objects.

Impact/benefits: The fusiform face area (FFA) is a brain region defined by its selectivity for faces. Several studies have shown that the response of FFA to non-face objects can predict behavioral performance for these objects. However, one possible account is that experts pay more attention to objects in their domain of expertise, driving signals up. By considering brain structure rather than function, we show an effect of expertise with non-face objects in FFA that cannot be explained by differential attention to objects of expertise.

Explanation: Regional cortical thickness estimates corresponded to face-selective regions in a group of 27 men who evidenced functional expertise effects for cars in FFA. Cortical thickness was measured from high-resolution structural images, calculating the distance between the white/grey matter boundary and the grey matter/cerebral spinal fluid boundary. Right hemisphere FFA was thicker for individuals with high performance on non-living categories (vehicles). In contrast, right hemisphere FFA was thinner for individuals with high performance on faces. The results point to a domain-general role of FFA in object perception and reveal an interesting double dissociation that does not contrast faces and objects, but rather living and non-living objects.

Cortical Thickness

McGugin, R.W., Van Gulick, A.E. & Gauthier, I. (2015). Cortical thickness in fusiform face area predicts face and object recognition performance. J Cogn Neurosci. 1-13.

(NSF Highlight 2015-2016)