Functional divisions of the left anterior and posterior temporoparietal junction for phonological and semantic processing in Chinese character reading
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| Publicat a: | NeuroImage vol. 311 (May 1, 2025) |
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| Autor principal: | |
| Altres autors: | , , , , , , |
| Publicat: |
Elsevier Limited
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
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| Resum: | Previous studies have shown that the left temporoparietal junction (TPJ) plays a critical role in word reading. Nevertheless, there is still controversy surrounding the phonological and semantic functions of the left TPJ. The parietal unified connectivity-biased computation (PUCC) model posits that the function of the left TPJ depends on both the neurocomputation of this local area and its long-range connectivity. To clarify the specific roles of different TPJ subregions in phonological and semantic processing of Chinese characters, the present study used connectivity-based clustering to identify seven subdivisions within the left TPJ, and conducted comprehensive analyses including functional and structural connectivity, univariate and multivariate analyses (i.e., representational similarity analysis, RSA) on multimodal imaging data (task-state fMRI, resting-state fMRI, and diffusion-weighted imaging [DWI]). Functional and structural connectivity analyses revealed that the left anterior TPJ had stronger connections with the phonological network, while the left posterior TPJ had stronger connections with the semantic network. RSA revealed that the left anterior and posterior TPJ represented phonological and semantic information of Chinese characters, respectively. More importantly, the phonological and semantic representations of the left TPJ were respectively correlated with its functional connectivity to the phonological and semantic networks. Altogether, our results provide a more elaborate perspective on the functional dissociation of the left anterior and posterior TPJ in phonological and semantic processing of Chinese characters, and support the PUCC model. |
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| ISSN: | 1053-8119 1095-9572 |
| DOI: | 10.1016/j.neuroimage.2025.121201 |
| Font: | Health & Medical Collection |