Poster No:
734
Submission Type:
Abstract Submission
Authors:
Teng Ieng Leong1,2,3, Victoria Lai Cheng Lei1,2,3, Ut Meng Lei1,2,3, Defeng Li1,2,3, Ruey-Song Huang1,2,4
Institutions:
1University of Macau, Macau, China, 2Centre for Cognitive and Brain Sciences, University of Macau, Macau, China, 3Faculty of Arts and Humanities, University of Macau, Macau, China, 4Faculty of Science and Technology, University of Macau, Macau, China
First Author:
Teng Ieng Leong
University of Macau|Centre for Cognitive and Brain Sciences, University of Macau|Faculty of Arts and Humanities, University of Macau
Macau, China|Macau, China|Macau, China
Co-Author(s):
Victoria Lai Cheng Lei
University of Macau|Centre for Cognitive and Brain Sciences, University of Macau|Faculty of Arts and Humanities, University of Macau
Macau, China|Macau, China|Macau, China
Ut Meng Lei
University of Macau|Centre for Cognitive and Brain Sciences, University of Macau|Faculty of Arts and Humanities, University of Macau
Macau, China|Macau, China|Macau, China
Defeng Li
University of Macau|Centre for Cognitive and Brain Sciences, University of Macau|Faculty of Arts and Humanities, University of Macau
Macau, China|Macau, China|Macau, China
Ruey-Song Huang
University of Macau|Centre for Cognitive and Brain Sciences, University of Macau|Faculty of Science and Technology, University of Macau
Macau, China|Macau, China|Macau, China
Introduction:
Investigating the intriguing question of directionality in oral interpreting through neuroimaging is vital for uncovering the complex cognitive processes involved in bilingual language transfer. Previous studies have revealed distinctive neurocognitive signatures for L1-to-L2 and L2-to-L1 translation directions, marked by stronger activation in the perisylvian and frontoparietal networks during L1-to-L2 translation (Munoz, et al., 2018; Zheng, et al., 2020). These studies focused exclusively on strength of activation or connectivity. However, characterizing the temporal patterns of brain engagement is also crucial for unraveling the complexity of directionality in oral interpreting. Here we used rapid phase-encoded fMRI designs to reveal fine-grained spatiotemporal brain dynamics during real-time naturalistic language production tasks. By effectively reducing head motion and scanner noise, we can now perform continuous image acquisition. This has enabled us to capture and decode sub-second neural activities during the entire oral interpreting process.
Methods:
Twenty-eight participants (20 females, 8 males; 20-42 years, Mean: 23.3, SD: 4) were included in the study. All of them were native Chinese speakers with English as their second language. Each fMRI session consists 12 scans of the following tasks:
(i) sentence sight interpreting: reading a sentence then translate it orally;
(ii) sentence consecutive interpreting: listening to a sentence and translate it orally;
(iii) digit consecutive interpreting: listening to six random digits and translate it digit by digit orally. Each task was scanned twice for both L1-to-L2 and L2-to-L1 directions. The sentence or digit stimuli were delivered periodically every 16 s using a phase-encoded design (Lei, et al., 2024). Functional images were analyzed with Fourier transform, and the signal amplitudes and phases at the task frequency (16 cycles/scan) were displayed on cortical surfaces reconstructed from structural images of each subject. To find out the temporal dynamics of brain engagement patterns, we computed hemodynamic surge profiles in 36 surface-based region-of-interests (sROIs) identified from the HCP-MMP1 parcellations.
Results:
Surge profiles were compared between L1-to-L2 and L2-to-L1 directions in the following sROIs: (i) Ten core domain-general multi-demand areas (Assem, et al., 2024), including IP1, IP2, PFm, 8BM, i6-8, IFJp, AVI, 8C, p9-46v and a9-46v; and (ii) Three groups of 26 language-related areas (Rolls, et al., 2022), including a semantic network Group 1: STSva, STSvp, TE1a, TGd, PGi, 10v, 9m, 10pp, 47s, 8Av, 8BL, 9a, 9p; a speech output network in Group 2: TGv, 44, 45, 47l, SFL, 55b; and another semantic network Group 3: A5, STGa, STSda, STSdp, PSL, STV, TPOJ1.
A directionality effect, predominantly in the left hemisphere, was observed for most of the sROIs in sentence interpreting tasks, where a L1-to-L2 direction resulted in earlier onset and offset and stronger activity than the L2-to-L1 direction. Specifically, for the domain-general multi-demand areas, all except IFJp showed significant effect. For language-related areas, approximately half of Group 1 showed an effect, while the rest (TE1a, TGd, 10v, 9m, 10pp, 47s, 9p) showed no difference . All except TGv and 55b in Group 2 showed a directionality effect and all except A5 showed an effect for Group 3. While some of the sROIs showed an effect, most remained no difference for the right hemisphere. Similarly, for digit interpreting tasks, most sROIs showed no difference between translation directions for both hemispheres.
Conclusions:
Rapid phase-encoded fMRI design allows detailed inspection of dynamic brain activation patterns during real-time translation tasks. Specifically, in addition to showing that L1-to-L2 translation involves greater cognitive and linguistic demands, our results also suggested heightened sensitivity to processing L1 source language as indicated by earlier engagement in both domain-general and linguistic areas.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Language:
Speech Production
Language Other 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cognition
Cortex
FUNCTIONAL MRI
Language
Other - Translation
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Free Surfer
Provide references using APA citation style.
Assem, et al., (2024). Basis of executive functions in fine-grained architecture of cortical subcortical human brain networks. Cerebral Cortex, 34, 1-19.
Chen, et al., (2019). Unraveling the spatiotemporal brain dynamics during a simulated reach-to-eat task. Neuroimage, 185, 58-71
Engel, S. A. (2012). The development and use of phase-encoded functional MRI designs. Neuroimage, 62(2), 1195-1200.
Lei, et al., (2024). Phase-encoded fMRI tracks down brainstorms of natural language processing with subsecond precision. Human Brain Mapping, 45(2), e26617.
Munoz, et al., (2018). Grounding translation and interpreting in the brain: what has been, can be, and must be done. Studies in Translation Theory and Practice, 27(4), 483-509.
Pa, J., & Hickok, G. (2008). A parietal-temporal sensory-motor integration area for the human vocal tract: Evidence from an fMRI study of skilled musicians. Neuropsychologia, 46(1), 362-368.
Rolls, et al., (2022). The human language effective connectome. Neuroimage, 258, 119352.
Zheng, et al., (2020). Semantic and attentional networks in bilingual processing: fMRI connectivity signatures of translation directionality. Brain and Cognition, 143, 105584.
No