期刊
CORTEX
卷 76, 期 -, 页码 63-78出版社
ELSEVIER MASSON, CORP OFF
DOI: 10.1016/j.cortex.2015.11.020
关键词
Resting-state; fMRI; Low-frequency fluctuations; Spoken language learning; Individual differences
资金
- U.S. National Institutes of Health [R01DC008333, R01DC013315]
- Research Grants Council of Hong Kong [477513, 14117514]
- Health and Medical Research Fund of Hong Kong grant [01120616]
- Dr. Stanley Ho Medical Development Foundation
- Key Project of National Social Science Foundation of China [15AZD048]
- Key Project of National Natural Science Foundation of Guangdong Province [2014A030311016]
- U.S. National Institutes of Health grant [R01DC013315]
- Guangzhou Elites Project of Guangzhou Municipal Government [JY201245]
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. (C) 2015 Elsevier Ltd. All rights reserved.
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