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Distinct brain state dynamics of native and second language processing during narrative listening in late bilinguals.
- Source :
-
NeuroImage [Neuroimage] 2023 Oct 15; Vol. 280, pp. 120359. Date of Electronic Publication: 2023 Sep 03. - Publication Year :
- 2023
-
Abstract
- The process of complex cognition, which includes language processing, is dynamic in nature and involves various network modes or cognitive modes. This dynamic process can be manifested by a set of brain states and transitions between them. Previous neuroimaging studies have shed light on how bilingual brains support native language (L1) and second language (L2) through a shared network. However, the mechanism through which this shared brain network enables L1 and L2 processing remains unknown. This study examined this issue by testing the hypothesis that L1 and L2 processing is associated with distinct brain state dynamics in terms of brain state integration and transition flexibility. A group of late Chinese-English bilinguals was scanned using functional magnetic resonance imaging (fMRI) while listening to eight short narratives in Chinese (L1) and English (L2). Brain state dynamics were modeled using the leading eigenvector dynamic analysis framework. The results show that L1 processing involves more integrated states and frequent transitions between integrated and segregated states, while L2 processing involves more segregated states and fewer transitions. Our work provides insight into the dynamic process of narrative listening comprehension in late bilinguals and sheds new light on the neural representation of language processing and related disorders.<br />Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests in aspects of the finance, authorship, or publication of this article.<br /> (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1095-9572
- Volume :
- 280
- Database :
- MEDLINE
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 37661079
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2023.120359