Dynamic Language Network in Early and Late Cantonese-Mandarin Bilinguals

Xiaojin Liu; Liu Tu; Xiaoxi Chen; Miao Zhong; Meiqi Niu; Ling Zhao; Zhi Lu; Ruiwang Huang

doi:10.3389/fpsyg.2020.01189

Dynamic Language Network in Early and Late Cantonese-Mandarin Bilinguals

Front Psychol. 2020 Jun 18:11:1189. doi: 10.3389/fpsyg.2020.01189. eCollection 2020.

Authors

Xiaojin Liu¹, Liu Tu², Xiaoxi Chen³, Miao Zhong¹, Meiqi Niu¹, Ling Zhao¹, Zhi Lu⁴, Ruiwang Huang¹

Affiliations

¹ Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, China.
² College of Foreign Studies, Jinan University, Guangzhou, China.
³ School of Management, Jinan University, Guangzhou, China.
⁴ Guangdong Collaborative Innovation Center for Language Research and Services, Guangdong University of Foreign Studies, Guangzhou, China.

Abstract

The brain representation of language in bilinguals is sculptured by several factors, such as age of acquisition (AoA) and proficiency level (PL) in second language. Although the effect of AoA-L2 on brain function and structure has been studied, little attention is devoted to dynamic properties of the language network and their differences between early and late bilinguals. In this study, we acquired resting-state fMRI data from early and late Cantonese (L1)-Mandarin (L2) bilinguals with high PLs of verbal fluency in both languages. We then analyzed dynamic functional connectivity (dFC) by using the sliding-windows approach, estimated the dFC states by using the k-means clustering algorithm, and calculated the dynamic topological properties of the language network for the early and late bilinguals. We detected four dFC states, State 1, State 2, State 3, and State 4, which may be related to phonetic processing, semantic processing, language control, and syntactic processing, respectively. Compared to the late bilinguals, the early bilinguals showed higher dFC between the inferior frontal area and the temporal area in State 1 and State 2, while higher dFC between the cerebellum and other regions in State 3. The early bilinguals showed a higher clustering coefficient and local and global efficiency in State 1 and State 3, but lower characteristic path length in State 1, than the late bilinguals. Together, these results suggested that AoA-L2 affects temporal neural activation and dynamic topological properties of the language network. These findings provide new information to understand the effect of experience of L2 acquisition on language network in bilinguals.

Keywords: age of acquisition; bi-dialects; bilingual experience; dynamic functional connectivity; second language.