title: Resting-state low-frequency fluctuations reflect individual differences in spoken language learning.
abstract: 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.
authors: Center for the Study of Applied Psychology and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong.
authors: Zhizhou
authors: Z
authors: Deng
authors: Department of Communication Sciences and Disorders, Moody College of Communication, The University of Texas at Austin, Austin, TX, USA; Department of Psychology, College of Liberal Arts, The University of Texas at Austin, Austin, TX, USA; Department of Linguistics, College of Liberal Arts, The University of Texas at Austin, Austin, TX, USA; Institute of Mental Health Research, College of Liberal Arts, The University of Texas at Austin, Austin, TX, USA; Institute for Neuroscience, The University of Texas at Austin, Austin, TX, USA.
authors: Bharath
authors: B
authors: Chandrasekaran
authors: Center for the Study of Applied Psychology and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China. Electronic address: wangsuiping@m.scnu.edu.cn.
authors: Suiping
authors: S
authors: Wang
authors: Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong; Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong. Electronic address: p.wong@cuhk.edu.hk.
authors: Patrick C M
authors: PC
authors: Wong
mesh_terms: D000328:Adult; D001288:Attention / Q000502:physiology; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods; D005260:Female; D006801:Humans; D007206:Individuality; D007802:Language; D007858:Learning / Q000502:physiology*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D009415:Nerve Net / Q000503:physiopathology; D012146:Rest / Q000502:physiology*
keywords: Individual differences; Low-frequency fluctuations; Resting-state; Spoken language learning; fMRI