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首页> 外文期刊>Frontiers in Psychology >Dynamic Language Network in Early and Late Cantonese–Mandarin Bilinguals
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Dynamic Language Network in Early and Late Cantonese–Mandarin Bilinguals

机译:早期和晚期粤语 - 普通话的动态语言网络双语

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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 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 temporal area in State 1 and State 2, while higher dFC between cerebellum and other regions in State 3. The early bilinguals showed higher clustering coefficient, 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.
机译:双语语言的大脑表现在二语言中的几个因素(例如收购年龄(AOA)和熟练程度(PL))雕刻。虽然研究了AOA-L2对脑功能和结构的影响,但致力于语言网络的动态特性,较早和后期双语之间的差异很少。在这项研究中,我们从两种语言中从早期和晚期粤菜(L1) - Mandarin(L2)双语中获得了休息状态的FMRI数据。然后,通过使用滑动窗口方法分析动态功能连接(DFC),估计DFC状态通过使用K-Means聚类算法,并计算出早期和后期双语的语言网络的动态拓扑特性。我们检测到四个DFC状态,状态1,状态2,状态3和状态4,其分别可以与语音处理,语义处理,语言控制和句法处理有关。与后期双语,早期双语在第1状态和状态2的较差前面积和颞区域之间显示出更高的DFC,而大脑和状态3的其他地区之间的DFC较高。早期的双语表现出更高的聚类系数,本地和全球状态1和状态3的效率,但状态1的特征路径长度低于后期双语。这些结果表明,AOA-L2影响语言网络的时间神经激活和动态拓扑特性。这些调查结果提供了新的信息,以了解L2收购在双语中的语言网络经验的影响。

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