Intrinsic Functional Connectivity in the Adult Brain and Success in Second-Language Learning

Abstract

There is considerable variability in an individual's ability to acquire a second language (L2) during adulthood. Using resting-state fMRI data acquired before training in English speakers who underwent a 12 week intensive French immersion training course, we investigated whether individual differences in intrinsic resting-state functional connectivity relate to a person's ability to acquire an L2. We focused on two key aspects of language processing-lexical retrieval in spontaneous speech and reading speed-and computed whole-brain functional connectivity from two regions of interest in the language network, namely the left anterior insula/frontal operculum (AI/FO) and the visual word form area (VWFA). Connectivity between the left AI/FO and left posterior superior temporal gyrus (STG) and between the left AI/FO and dorsal anterior cingulate cortex correlated positively with improvement in L2 lexical retrieval in spontaneous speech. Connectivity between the VWFA and left mid-STG correlated positively with improvement in L2 reading speed. These findings are consistent with the different language functions subserved by subcomponents of the language network and suggest that the human capacity to learn an L2 can be predicted by an individual's intrinsic functional connectivity within the language network.

The energy landscape underpinning module dynamics in the human brain connectome

“…Several studies have proposed statistical approaches to test for the presence of non-stationarity, including the assessment of the variance of the FC time series (Sakoğlu et al, 2010), test statistics based on the FC time series’ Fourier-transform (Handwerker et al, 2012), linear (e.g., variance of correlation series (Hindriks et al, 2016)) and nonlinear test statistics (Zalesky et al, 2014), among others (Chang and Glover, 2010; Keilholz et al, 2013; Laumann et al, 2016). The bulk of the evidence points to the non-stationary nature of BOLD FC, a conclusion that is consistent with recent work suggesting that non-stationarity in BOLD functional connectivity reflects changes in ongoing cognitive processes supporting learning, working memory function, linguistic processing, and executive function (Bassett et al, 2011; Braun et al, 2015; Chai et al, 2016, 2017; Hutchison et al, 2013a). Nonetheless, some of these reports are inconclusive, mainly because test statistics are commonly compared against that of null (stationary) time series and creating such time series with matching covariance structure, spectral properties, and stationary FC to this day remains a challenge (Hindriks et al, 2016).…”

Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages

“…Similar positive correlations between frontal beta power at rest and learning rate were found in both of our previous rsEEG investigations of natural language learning in adulthood 20,25 . These findings add to the increasing body of literature suggesting that characterizations of resting-state brain networks can be used to understand individual differences in executive functioning 26 and complex skill learning more broadly [19][20][21] .…”

“…To understand the relative predictive utility of such measures, we included factors known to relate to complex skill learning more generally (e.g., fluid reasoning ability, working memory, inhibitory control), and numeracy, the mathematical equivalence of literacy, as predictors. In addition, the current research adopted a neuropsychometric approach, leveraging information about the intrinsic, network-level characteristics of individual brain functioning which have proven to provide unique predictive utility in natural language learning [19][20][21] . Such an approach allows us to leverage the field of cognitive neuroscience to understand, in a paradigm-free manner, the cognitive bases of learning to program.…”

Functional connectivity between somatosensory and motor brain areas predicts individual differences in motor learning by observing

“…In a recent review article, Zatorre (2013) discusses findings showing how structural and functional neural connectivity patterns predict individual differences in musical training and speech learning. Other studies have shown similar predictability for a wide array of cognitive abilities including executive function (Barnes et al 2014;Reineberg et al 2015), reading (Koyama et al 2011;Wang et al 2013), second language acquisition (Chai et al 2016), visual perceptual discrimination (Baldassarre et al 2012), and memory recall (King et al 2015). In the motor domain, Tomassini et al (2011) demonstrated that individual differences in both functional and structural magnetic resonance imaging (MRI) measures correlate with the acquisition of a novel visuomotor tracking skill through active movement training.…”