Resting-state low-frequency fluctuations reflect individual differences in spoken language learning.
Resting-state low-frequency fluctuations reflect individual differences in spoken language learning.
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DOI：
10.1016/j.cortex.2015.11.020
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发表时间：
2016-03
期刊：
影响因子：
--
通讯作者：
Wong PC
中科院分区：
文献类型：
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作者：
Deng Z;Chandrasekaran B;Wang S;Wong PC
语言学习研究的一个主要挑战是确定客观的，预先培训的成功预测。通过静息态功能磁共振成像（RS-fMRI）测量的自发脑活动的低频波动（LFF）的变化已被发现反映了认知测量的个体差异。在本研究中，我们的目的是调查在何种程度上初始自发脑活动与口语学习的个体差异。我们获得了RS-fMRI数据，随后对参与者进行了音词学习范式的培训，在该范式中，他们学会了使用外国音高模式（来自中国普通话）来表示词义。我们进行了自发低频波动（ALFF）分析，图论为基础的分析，和独立成分分析（伊卡），以确定在静息状态下的LFF的功能组件的幅度。首先，我们研究了ALFF作为一个区域的措施，并表明，区域ALFF在左上级颞回与学习成绩呈正相关，而ALFF在默认模式网络（DMN）地区与学习成绩呈负相关。此外，基于图论的分析表明，左侧上级颞回的程度和局部效率与学习成绩呈正相关。最后，默认模式网络和几个任务积极的静息态网络（RSN）的识别通过伊卡。“竞争”（即，负相关）与学习成绩呈负相关。我们的研究结果表明，a）自发脑活动可以预测未来的语言学习结果，而无需事先假设（例如，感兴趣区域的选择-ROI）和B）静息脑中的区域动力学和网络水平的相互作用可以解释未来口语学习成功的个体差异。
A major challenge in language learning studies is to identify objective, pre-training predictors of success. Variation in the low-frequency fluctuations (LFFs) of spontaneous brain activity measured by resting-state functional magnetic resonance imaging (RS-fMRI) has been found to reflect individual differences in cognitive measures. In the present study, we aimed to investigate the extent to which initial spontaneous brain activity is related to individual differences in spoken language learning. We acquired RS-fMRI data and subsequently trained participants on a sound-to-word learning paradigm in which they learned to use foreign pitch patterns (from Mandarin Chinese) to signal word meaning. We performed amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory-based analysis, and independent component analysis (ICA) to identify functional components of the LFFs in the resting-state. First, we examined the ALFF as a regional measure and showed that regional ALFFs in the left superior temporal gyrus were positively correlated with learning performance, whereas ALFFs in the default mode network (DMN) regions were negatively correlated with learning performance. Furthermore, the graph theory-based analysis indicated that the degree and local efficiency of the left superior temporal gyrus were positively correlated with learning performance. Finally, the default mode network and several task-positive resting-state networks (RSNs) were identified via the ICA. The “competition” (i.e., negative correlation) between the DMN and the dorsal attention network was negatively correlated with learning performance. Our results demonstrate that a) spontaneous brain activity can predict future language learning outcome without prior hypotheses (e.g., selection of regions of interest – ROIs) and b) both regional dynamics and network-level interactions in the resting brain can account for individual differences in future spoken language learning success.