scholarly journals Thickness of Deep Layers in the Fusiform Face Area Predicts Face Recognition

2020 ◽  
Vol 32 (7) ◽  
pp. 1316-1329
Author(s):  
Rankin W. McGugin ◽  
Allen T. Newton ◽  
Benjamin Tamber-Rosenau ◽  
Andrew Tomarken ◽  
Isabel Gauthier
Keyword(s):  
Face Recognition ◽  
Deep Layer ◽  
Structural Basis ◽  
Fusiform Face Area ◽  
Brain Areas ◽  
Vehicle Recognition ◽  
Face Area ◽  
Deep Layers ◽  
Average Variation ◽  
The Right

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.

scholarly journals Thickness of deep layers in the fusiform face area predicts face recognition

2019 ◽  
Author(s):  
Rankin W. McGugin ◽  
Allen T. Newton ◽  
Benjamin Tamber-Rosenau ◽  
Andrew Tomarken ◽  
Isabel Gauthier
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Cortical Thickness ◽  
Neural Plasticity ◽  
Normal Population ◽  
Structural Basis ◽  
Fusiform Face Area ◽  
Vehicle Recognition ◽  
Face Area ◽  
High Resolution Mri

AbstractPeople with superior face recognition have relatively thin cortex in face-selective brain areas, while 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) for abilities 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 ultra-high resolution MRI at 7T, we estimated the thickness of three laminar subdivisions, which we term MR layers, in the right fusiform face area (rFFA) in 14 adult male humans. Face recognition was negatively associated with the thickness of deep MR layers, while 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) while the magnitude of the association between vehicle recognition and CT is invariant across layers. The total CT of rFFA 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.Significance StatementFace and object recognition vary in the normal population and are only modestly related to each other. The recognition of faces and vehicles are both positively related to neural responses in the fusiform face area (FFA), but show different relations to the cortical thickness of FFA. Here, we use very high-resolution MRI, and find that face recognition ability (a skill acquired early in life) is negatively correlated with thickness of FFA’s deepest MR-defined layers, whereas recognition of vehicles (a skill acquired later in life) is positively related to thickness at of all cortical layers. Our methods can be used in the future to characterize sources of variability in human abilities and relate them to distinct mechanisms of neural plasticity.

scholarly journals Neural Tuning for Face Wholes and Parts in Human Fusiform Gyrus Revealed by fMRI Adaptation

2010 ◽  
Vol 104 (1) ◽  
pp. 336-345 ◽  
Author(s):  
Alison Harris ◽  
Geoffrey Karl Aguirre
Keyword(s):  
Functional Mri ◽  
Holistic Processing ◽  
Fusiform Face Area ◽  
Single Population ◽  
Face Area ◽  
Neural Populations ◽  
The Face ◽  
Adaptation Response ◽  
The Right ◽  
Fmri Adaptation

Although the right fusiform face area (FFA) is often linked to holistic processing, new data suggest this region also encodes part-based face representations. We examined this question by assessing the metric of neural similarity for faces using a continuous carryover functional MRI (fMRI) design. Using faces varying along dimensions of eye and mouth identity, we tested whether these axes are coded independently by separate part-tuned neural populations or conjointly by a single population of holistically tuned neurons. Consistent with prior results, we found a subadditive adaptation response in the right FFA, as predicted for holistic processing. However, when holistic processing was disrupted by misaligning the halves of the face, the right FFA continued to show significant adaptation, but in an additive pattern indicative of part-based neural tuning. Thus this region seems to contain neural populations capable of representing both individual parts and their integration into a face gestalt. A third experiment, which varied the asymmetry of changes in the eye and mouth identity dimensions, also showed part-based tuning from the right FFA. In contrast to the right FFA, the left FFA consistently showed a part-based pattern of neural tuning across all experiments. Together, these data support the existence of both part-based and holistic neural tuning within the right FFA, further suggesting that such tuning is surprisingly flexible and dynamic.

scholarly journals No Change in the Size of the Right Fusiform Face Area between Age Five and Adulthood

2010 ◽  
Vol 10 (7) ◽  
pp. 493-493 ◽  
Author(s):  
D. D. Dilks ◽  
E. Dechter ◽  
C. Triantafyllou ◽  
B. Keil ◽  
L. L. Wald ◽  
...  
Keyword(s):  
Fusiform Face Area ◽  
Face Area ◽  
The Right

scholarly journals Representations of Facial Identity in the Left Hemisphere Require Right Hemisphere Processing

2012 ◽  
Vol 24 (4) ◽  
pp. 1006-1017 ◽  
Author(s):  
Sara C. Verosky ◽  
Nicholas B. Turk-Browne
Keyword(s):  
Left Hemisphere ◽  
Right Hemisphere ◽  
Hemispheric Specialization ◽  
Right Visual Field ◽  
Fusiform Face Area ◽  
Identity Information ◽  
Facial Identity ◽  
Face Area ◽  
The Right ◽  
Fmri Adaptation

A quintessential example of hemispheric specialization in the human brain is that the right hemisphere is specialized for face perception. However, because the visual system is organized contralaterally, what happens when faces appear in the right visual field and are projected to the nonspecialized left hemisphere? We used divided field presentation and fMRI adaptation to test the hypothesis that the left hemisphere can recognize faces, but only with support from the right hemisphere. Consistent with this hypothesis, facial identity adaptation was observed in the left fusiform face area when a face had previously been processed by the right hemisphere, but not when it had only been processed by the left hemisphere. These results imply that facial identity information is transferred from the right hemisphere to the left hemisphere, and that the left hemisphere can represent facial identity but is less efficient at extracting this information by itself.

scholarly journals Meta-analyses support the expertise hypothesis of the right fusiform face area

2019 ◽  
Vol 19 (10) ◽  
pp. 115a
Author(s):  
Edwin J Burns ◽  
Cindy Bukach
Keyword(s):  
Fusiform Face Area ◽  
Face Area ◽  
The Right ◽  
Meta Analyses

scholarly journals The bottom-up and top-down processing of faces in the human occipitotemporal cortex

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Xiaoxu Fan ◽  
Fan Wang ◽  
Hanyu Shao ◽  
Peng Zhang ◽  
Sheng He
Keyword(s):  
Facial Features ◽  
Fusiform Face Area ◽  
Functional Magnetic Resonance ◽  
Contextual Cues ◽  
Top Down ◽  
Bottom Up ◽  
Face Area ◽  
Occipital Face Area ◽  
Occipitotemporal Cortex ◽  
The Right

Although face processing has been studied extensively, the dynamics of how face-selective cortical areas are engaged remains unclear. Here, we uncovered the timing of activation in core face-selective regions using functional Magnetic Resonance Imaging and Magnetoencephalography in humans. Processing of normal faces started in the posterior occipital areas and then proceeded to anterior regions. This bottom-up processing sequence was also observed even when internal facial features were misarranged. However, processing of two-tone Mooney faces lacking explicit prototypical facial features engaged top-down projection from the right posterior fusiform face area to right occipital face area. Further, face-specific responses elicited by contextual cues alone emerged simultaneously in the right ventral face-selective regions, suggesting parallel contextual facilitation. Together, our findings chronicle the precise timing of bottom-up, top-down, as well as context-facilitated processing sequences in the occipital-temporal face network, highlighting the importance of the top-down operations especially when faced with incomplete or ambiguous input.

scholarly journals The fusiform face area is not sufficient for face recognition: Evidence from a patient with dense prosopagnosia and no occipital face area

2006 ◽  
Vol 44 (4) ◽  
pp. 594-609 ◽  
Author(s):  
Jennifer K.E. Steeves ◽  
Jody C. Culham ◽  
Bradley C. Duchaine ◽  
Cristiana Cavina Pratesi ◽  
Kenneth F. Valyear ◽  
...  
Keyword(s):  
Face Recognition ◽  
Fusiform Face Area ◽  
Face Area ◽  
Occipital Face Area
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