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Summary: Object and facial recognition abilities are associated with the same brain area but are characterized by different depths of cortical layers, which form at the age each ability was acquired.
Source: Vanderbilt University
Object recognition and facial recognition may seem like similar abilities, but new research finds that these behaviors are on the opposite ends of the spectrum when it comes to physical signatures in the brain.
According to a new study, published by a team of scientists at Vanderbilt, both recognition abilities are associated with the same area of the brain—the cortex—but are characterized by different depths of cortical layers—which, like tree rings, form at the age at which each ability was acquired.
The research—the first to uncover the ability to predict strength of cognitive function based on cortical layer structure—appears in the Journal of Cognitive Neuroscience.
“Our paper is novel in that we examine the formation and structure of specific layers of a cortical area known as the fusiform face area, and its relationship to unique cognitive functions,” said Isabel Gauthier, David K. Wilson Professor of Psychology and director of the Object Perception Lab at Vanderbilt. “As a result of both our theoretical and imaging work, we can report evidence of a newfound ability to predict facial and object recognition abilities based on the depth of these layers.”
In particular, the outer layers of the cortex may shrink over time and do so more in some people than others, thanks to processes in the brain that cause the connections to other brain areas to be pruned during early development. As such, skills like facial recognition are associated with thinner outer cortical layer, since humans begin learning faces in early childhood and a lot of pruning may result in the best connections in adults.
Meanwhile, object recognition (the researchers used cars in this study) is associated with a thicker fusiform face area, for all layers of the cortex. This is because we learn these objects later in life, and the mechanisms that support this learning is not specific to any one layer.
Using ultra high-resolution MRI technology, the team also achieved a first for medical imaging. Using a powerful 7-Tesla magnet, the team conducted meticulous scans of deep, middle and superficial cortical layers in 14 adult male individuals during the study, and combined the averages of their scans for high-definition images with clear outlines of brain layers.
“There is no precedent for the imaging and measurement work done by our team of researchers on this study,” said Rankin McGugin, research assistant professor of psychology in the Object Perception Lab at Vanderbilt and co-lead author on the study together with Allen Newton, research assistant professor of radiology and radiological sciences also at Vanderbilt. “As our brain imaging techniques continue to improve and we can achieve this sort of resolution in all of the brain at once, we will run larger studies, to better describe how other visual abilities relate to brain structure more generally.”
The study builds off the Object Perception Lab’s 2016 paper which first reported the correlation in cortical thickness variations to cognitive abilities, and sets the stage for future research into the brain and why individual differences occur in visual recognition.
Funding: The research is funded by the National Science Foundation.
Competing Interests: The authors declare no conflict of interest.
About this neuroscience research article
Source: Vanderbilt University Media Contacts: Spencer Turney – Vanderbilt University Image Source: The image is credited to Isabel Gauthier / Vanderbilt University.
Original Research: Open access (PDF) “Thickness of Deep Layers in the Fusiform Face Area Predicts Face Recognition”. by Rankin W. McGugin, Allen T. Newton, Benjamin Tamber-Rosenau, Andrew Tomarken and Isabel Gauthier. Journal of Cognitive Neuroscience doi:10.1162/jocn_a_01551.
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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