Your browser doesn't support javascript.
loading
Spatial tuning of face part representations within face-selective areas revealed by high-field fMRI.
Zhang, Jiedong; Jiang, Yong; Song, Yunjie; Zhang, Peng; He, Sheng.
Afiliação
  • Zhang J; Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
  • Jiang Y; University of Chinese Academy of Sciences, Beijing, China.
  • Song Y; Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
  • Zhang P; University of Chinese Academy of Sciences, Beijing, China.
  • He S; Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
Elife ; 102021 12 29.
Article em En | MEDLINE | ID: mdl-34964711
ABSTRACT
Regions sensitive to specific object categories as well as organized spatial patterns sensitive to different features have been found across the whole ventral temporal cortex (VTC). However, it is unclear that within each object category region, how specific feature representations are organized to support object identification. Would object features, such as object parts, be represented in fine-scale spatial tuning within object category-specific regions? Here, we used high-field 7T fMRI to examine the spatial tuning to different face parts within each face-selective region. Our results show consistent spatial tuning of face parts across individuals that within right posterior fusiform face area (pFFA) and right occipital face area (OFA), the posterior portion of each region was biased to eyes, while the anterior portion was biased to mouth and chin stimuli. Our results demonstrate that within the occipital and fusiform face processing regions, there exist systematic spatial tuning to different face parts that support further computation combining them.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Reconhecimento Visual de Modelos / Mapeamento Encefálico / Reconhecimento Facial Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Reconhecimento Visual de Modelos / Mapeamento Encefálico / Reconhecimento Facial Idioma: En Ano de publicação: 2021 Tipo de documento: Article