Content area
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.
Details
Functional magnetic resonance imaging;
Phonological processing;
Brain research;
Semantic processing;
Information processing;
Automation;
Medical imaging;
Structure-function relationships;
Phonology;
Chinese languages;
Neural networks;
Semantics;
Characters;
Resting;
Information dissemination;
Networks;
Dissociation;
Functional connectivity;
Computation;
Semantic networks;
Clustering
; Feng, Yuan 1 ; Hu, Rui 1 ; Feng, Xiaoxue 1 ; Yang, Jingyu 1 ; Lin, Xingying 1 ; Mei, Leilei 1
1 Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, China, Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China, Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
2 Department of Psychological Science, University of California, Irvine, CA, USA