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Individual differences in expertise with non-face objects has been positively related to neural selectivity for these objects in several brain regions, including in the fusiform face area (FFA). Recently, we reported that FFA’s cortical thickness is also positively correlated with expertise for non-living objects, while FFA’s cortical thickness is negatively correlated with face recognition ability. These opposite relations between structure and visual abilities, obtained in the same subjects, were postulated to reflect the earlier experience with faces relative to cars, with different mechanisms of plasticity operating at these different developmental times. Here we predicted that variability for faces, presumably reflecting pruning, would be found selectively in deep cortical layers. In 13 men selected to vary in their performance with faces, we used ultra-high field imaging (7 Tesla), we localized the FFA functionally and collected and averaged 6 ultra-high resolution susceptibility weighed images (SWI). Voxel dimensions were 0.194x0.194x1.00mm, covering 20 slices with 0.1mm gap. Images were then processed by two operators blind to behavioral results to define the gray matter/white matter (deep) and gray matter/CSF (superficial) cortical boundaries. Internal boundaries between presumed deep, middle and superficial cortical layers were obtained with an automated method based on image intensities. We used an extensive battery of behavioral tests to quantify both face and object recognition ability. We replicate prior work with face and non-living object recognition predicting large and independent parts of the variance in cortical thickness of the right FFA, in different directions. We also find that face recognition is specifically predicted by the thickness of the deep cortical layers in FFA, whereas recognition of vehicles relates to the thickness of all cortical layers. Our results represent the most precise structural correlate of a behavioral ability to date, linking face recognition ability to a specific layer of a functionally-defined area.
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Thickness of Deep Layers in the Fusiform Face Area Predicts Face Recognition
People with superior face recognition have relatively thin cortex in face-selective brain areas, whereas those with superior vehicle recognition have relatively thick cortex in the same areas. We suggest that these opposite correlations reflect distinct mechanisms influencing cortical thickness (CT) as abilities are acquired at different points in development. We explore a new prediction regarding the specificity of these effects through the depth of the cortex: that face recognition selectively and negatively correlates with thickness of the deepest laminar subdivision in face-selective areas. With ultrahigh resolution MRI at 7T, we estimated the thickness of three laminar subdivisions, which we term “MR layers,” in the right fusiform face area (FFA) in 14 adult male humans. Face recognition was negatively associated with the thickness of deep MR layers, whereas vehicle recognition was positively related to the thickness of all layers. Regression model comparisons provided overwhelming support for a model specifying that the magnitude of the association between face recognition and CT differs across MR layers (deep vs. superficial/middle) whereas the magnitude of the association between vehicle recognition and CT is invariant across layers. The total CT of right FFA accounted for 69% of the variance in face recognition, and thickness of the deep layer alone accounted for 84% of this variance. Our findings demonstrate the functional validity of MR laminar estimates in FFA. Studying the structural basis of individual differences for multiple abilities in the same cortical area can reveal effects of distinct mechanisms that are not apparent when studying average variation or development.
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- Award ID(s):
- 1640681
- PAR ID:
- 10201988
- Date Published:
- Journal Name:
- Journal of Cognitive Neuroscience
- Volume:
- 32
- Issue:
- 7
- ISSN:
- 0898-929X
- Page Range / eLocation ID:
- 1316 to 1329
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1162/jocn_a_01551
