Dataset Information

Impaired behavioral and neural representation of scenes in Williams syndrome.

ABSTRACT: Boundaries are crucial to our representation of the geometric shape of scenes, which can be used to reorient in space. Behavioral research has shown that children and adults share exquisite sensitivity to a defining feature of a boundary: its vertical extent. Imaging studies have shown that this boundary property is represented in the parahippocampal place area (PPA) among typically developed (TD) adults. Here, we show that sensitivity to the vertical extent of scene boundaries is impaired at both the behavioral and neural level in people with Williams syndrome (WS), a genetic deficit that results in severely impaired spatial functions. Behavioral reorientation was tested in three boundary conditions: a flat Mat, a 5 cm high Curb, and full Walls. Adults with WS could reorient in a rectangular space defined by Wall boundaries, but not Curb or Mat boundaries. In contrast, TD age-matched controls could reorient by all three boundary types and TD 4-year-olds could reorient by either Wall or Curb boundaries. Using fMRI, we find that the WS behavioral deficit is echoed in their neural representation of boundaries. While TD age-matched controls showed distinct neural responses to scenes depicting Mat, Curb, and Wall boundaries in the PPA, people with WS showed only a distinction between the Wall and Mat or Curb, but no distinction between the Mat and Curb. Taken together, these results reveal a close coupling between the representation of boundaries as they are used in behavioral reorientation and neural encoding, suggesting that damage to this key element of spatial representation may have a genetic foundation.

SUBMITTER: Ferrara K

PROVIDER: S-EPMC6888907 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Impaired behavioral and neural representation of scenes in Williams syndrome.

Ferrara Katrina K Landau Barbara B Park Soojin S

Cortex; a journal devoted to the study of the nervous system and behavior 20190920

Boundaries are crucial to our representation of the geometric shape of scenes, which can be used to reorient in space. Behavioral research has shown that children and adults share exquisite sensitivity to a defining feature of a boundary: its vertical extent. Imaging studies have shown that this boundary property is represented in the parahippocampal place area (PPA) among typically developed (TD) adults. Here, we show that sensitivity to the vertical extent of scene boundaries is impaired at bo ...[more]

PMID: 31655392

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Project description:Background: Williams-Beuren syndrome (WBS) is a rare genetic syndrome with a characteristic "elfin" facial gestalt. The "elfin" facial characteristics include a broad forehead, periorbital puffiness, flat nasal bridge, short upturned nose, wide mouth, thick lips, and pointed chin. Recently, deep convolutional neural networks (CNNs) have been successfully applied to facial recognition for diagnosing genetic syndromes. However, there is little research on WBS facial recognition using deep CNNs. Objective: The purpose of this study was to construct an automatic facial recognition model for WBS diagnosis based on deep CNNs. Methods: The study enrolled 104 WBS children, 91 cases with other genetic syndromes, and 145 healthy children. The photo dataset used only one frontal facial photo from each participant. Five face recognition frameworks for WBS were constructed by adopting the VGG-16, VGG-19, ResNet-18, ResNet-34, and MobileNet-V2 architectures, respectively. ImageNet transfer learning was used to avoid over-fitting. The classification performance of the facial recognition models was assessed by five-fold cross validation, and comparison with human experts was performed. Results: The five face recognition frameworks for WBS were constructed. The VGG-19 model achieved the best performance. The accuracy, precision, recall, F1 score, and area under curve (AUC) of the VGG-19 model were 92.7 ± 1.3%, 94.0 ± 5.6%, 81.7 ± 3.6%, 87.2 ± 2.0%, and 89.6 ± 1.3%, respectively. The highest accuracy, precision, recall, F1 score, and AUC of human experts were 82.1, 65.9, 85.6, 74.5, and 83.0%, respectively. The AUCs of each human expert were inferior to the AUCs of the VGG-16 (88.6 ± 3.5%), VGG-19 (89.6 ± 1.3%), ResNet-18 (83.6 ± 8.2%), and ResNet-34 (86.3 ± 4.9%) models. Conclusions: This study highlighted the possibility of using deep CNNs for diagnosing WBS in clinical practice. The facial recognition framework based on VGG-19 could play a prominent role in WBS diagnosis. Transfer learning technology can help to construct facial recognition models of genetic syndromes with small-scale datasets.

Dataset Information

Impaired behavioral and neural representation of scenes in Williams syndrome.

Publications

Impaired behavioral and neural representation of scenes in Williams syndrome.

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