Language Switching Training Reveals an Adaptive Cerebellar Network for Bilingual Language Control
Poster No:
1049
Submission Type:
Abstract Submission
Authors:
Qianwen Chang1, Taomei Guo2
Institutions:
1Beijing Normal University, China, Beijing, 2Beijing Normal University, Beijing, Beijing
First Author:
Co-Author:
Introduction:
When bilinguals speak in one of their two languages, both languages are activated. Language control is recruited to select the appropriate language and inhibit interference from the other language. Previous studies have revealed that the cerebellum, especially the posterior cerebellum, plays an important role in bilingual language control. According to the Adaptive Control Hypothesis1, neural correlates underlying bilingual language control exhibit adaptive changes to meet the demands in different language contexts. However, most past work has focused on the plasticity of the cerebral regions. In the present study, we examined the involvement of the cerebellum in bilingual language control and the plasticity of the intracerebellar network using short-term language-switching training.
Methods:
Two groups of Chinese-English bilinguals performed language switching task in the pre-test and post-test sessions during functional magnetic resonance imaging (fMRI) scanning. After the pre-test, only the training group received an 8-day training in language switching. MRI data were collected by a 3T Siemens Trio Tim MRI scanner. Functional scanning with a T2-weighted gradient EPI sequence was acquired. After functional scanning, a high-resolution T1-weighted anatomical scanning was obtained. Whole-brain images were preprocessed using the DPABI toolbox2. We used the SUIT toolbox to isolate the cerebellum from the whole-brain image3. At the group level, a paired-sample t-test was performed to compare the cerebellum activation patterns between the pre-test and post-test in both groups with FDR correction. To further investigate the network-level changes after training, we constructed the networks in the pre-test and post-test using the euSEM method4. The seven cerebellar sub-regions obtained in the activation analysis were selected as ROIs. Network measures including global efficiency, local efficiency, transitivity and betweenness centrality were calculated using the Brain Connectivity Toolbox5. We performed specification curve analysis (SCA) with a 1000-time bootstrapping to further reveal the influence of network plastic changes on language control performance6.
Results:
For the training group, a significant increase in language control performance (switch cost) was observed after training, t = 3.388, p = 0.003. For the control group, there was no significant change after training, t = 1.832, p = 0.082. Activation analysis showed reduced activation in the bilateral lobules IV-V and VI, vermis IV-V, the right Crus I and VIIB after training for the training group. In contrast, there was no significant change for the control group. Intra-cerebellar language control networks in the pre-test and post-test for the training group are shown in Fig 1. Paired sample t-tests showed a significant increase in global efficiency, mean local efficiency, transitivity and betweenness centrality after training, ps < 0.001. Furthermore, SCA showed a significant negative relationship between the change of betweenness centrality of the hub node (right lobule IV-V) and the switch cost in the post-test (Fig 2).
Conclusions:
Bilingual language control activates an intra-cerebellar network including multiple posterior cerebellar sub-regions as well as the anterior cerebellum (i.e. lobules IV-V). Furthermore, the intra-cerebellar network exhibited adaptive changes by enhancing local neural efficiency and network connectivity after training. The reorganization of the intra-cerebellar network in the present study might be associated with better coordination in speech production7. Global and local properties of the network were also modulated by training. For the first time, our study revealed the plasticity of the intra-cerebellar network in bilingual language control.
Language:
Speech Production 1
Learning and Memory:
Neural Plasticity and Recovery of Function 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Connectivity (eg. functional, effective, structural)
Keywords:
Cerebellum
FUNCTIONAL MRI
Language
Plasticity
1|2Indicates the priority used for review
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