Global and localized network characteristics of the resting brain predict and adapt to foreign language learning in older adults

Abstract

Resting brain (rs) activity has been shown to be a reliable predictor of the level of foreign language (L2) proficiency younger adults can achieve in a given time-period. Since rs properties change over the lifespan, we investigated whether L2 attainment in older adults (aged 64–74 years) is also predicted by individual differences in rs activity, and to what extent rs activity itself changes as a function of L2 proficiency. To assess how neuronal assemblies communicate at specific frequencies to facilitate L2 development, we examined localized and global measures (Minimum Spanning Trees) of connectivity. Results showed that central organization within the beta band (~ 13–29.5 Hz) predicted measures of L2 complexity, fluency and accuracy, with the latter additionally predicted by a left-lateralized centro-parietal beta network. In contrast, reduced connectivity in a right-lateralized alpha (~ 7.5–12.5 Hz) network predicted development of L2 complexity. As accuracy improved, so did central organization in beta, whereas fluency improvements were reflected in localized changes within an interhemispheric beta network. Our findings highlight the importance of global and localized network efficiency and the role of beta oscillations for L2 learning and suggest plasticity even in the ageing brain. We interpret the findings against the background of networks identified in socio-cognitive processes.

Resting-state occipito-frontal alpha connectome is linked to differential word learning ability in adult learners

“…Secondly, from the perspective of functional connectome, our results suggest that alpha-band EEG coherence between the occipital and frontal regions could be a potential neural marker for word learning ability. Findings from this study not only corroborated the predicting effect of rsEEG connectivity on individual language learning abilities ( Fleck et al, 2016 ; Prat et al, 2019 ; Kliesch et al, 2022 ), but also extended prior findings that the association between alpha-band connectivity strength and word learning performance held even in the absence of neuromodulatory interventions ( Nicolo et al, 2016 ). Further, it is essential to consider the reasons why rsEEG connectivity between occipital and frontal regions is positively correlated with word learning performance since few studies have directly reported this association so far.…”

“…Albeit not many, studies into the relationship between resting-state electroencephalography (rsEEG) connectivity and language learning ability have, to date, yielded inconclusive findings. For instance, Prat et al (2019) observed that increased alpha/beta-band coherences within and between right frontotemporal/posterior networks were associated with better second language (L2) learning outcome (e.g., L2 learning rate, ultimate declarative memory), whereas a recent study conducted by Kliesch et al (2022) revealed that only the alpha-band connectivity between right-hemispheric fronto-temporal areas significantly correlated with the development of L2 complexity. The heterogeneous findings were probably caused by differences in sample age (i.e., younger and older groups), different measurements of language learning ability (i.e., L2 learning rate, memory, etc.…”

Foreign speech sound discrimination and associative word learning lead to a fast reconfiguration of resting-state networks

“…While several studies have examined functional ERP changes using EEG during phonetic discrimination ( Callan et al, 2003 ;Elmer et al, 2017 ;Raizada et al, 2010 ;Wong et al, 2007 ) and word learning tasks ( Dittinger et al, 2019 ;Ramos-Escobar et al, 2021 ;Takashima et al, 2014Takashima et al, , 2017, only a few focused on the relationships between RS brain activity and novel word or language learning ( Emberson et al, 2015 ;Kliesch et al, 2022 ;Kliesch et al, 2021 ;Küssner et al, 2016 ;Prat et al, 2016 ;Prat et al, 2019 ). In this context, some EEG studies have tested the suitability of different RS metrics at baseline as predictors of foreign language learning attainment, and provided evidence for an explained vari-ance in the range of 26-60 % ( Emberson et al, 2015 ;Kliesch et al, 2022 ;Kliesch et al, 2021 ;Küssner et al, 2016 ;Prat et al, 2016 ;Prat et al, 2019 ). For example, Prat and colleagues ( Prat et al, 2016 ) looked at associations between scalp RS power spectra at baseline and foreign language learning achievement in a sample of monolingual English speakers who were exposed for two month to French through an immersive virtual scenario.…”

“…Results showed that lower beta power across all pools of electrodes was related to language learning development, with an explained variance of about 33%. Interestingly, in a follow-up study with the same EEG data, Kliesch and colleagues ( Kliesch et al, 2022 ) used a source-based approach to examine whether RS functional connectivity measures provide a suitable marker for foreign language attainment, and to what extent connectivity metrics are modulated by proficiency indices. Results indicated that functional connectivity in the beta band at baseline was predictive of linguistic complexity, fluency and accuracy, whereas connectivity in the alpha band was inversely related to the development of linguistic complexity.…”

“…Although these previous EEG studies paved the way toward a better understanding of RS brain activity related to foreign language learning, in the present study we measured forty participants and specifically focused on the predictive value of different RS metrics as well as on source-based network and functional connectivity reconfigurations ( Kliesch et al, 2022 ) in association with a series of tasks which have previously been shown to facilitate the acquisition of new words, namely word discrimination, word-referent mapping and semantic generalization ( Dittinger et al, 2016 ;Dittinger et al, 2017 ;Dittinger et al, 2019 ;Elmer et al, 2021a ). With this purpose in mind, we first evaluated the sensitivity of RS scalp power spectra at baseline (RS1) in the delta (1-3 Hz), theta (4-7 Hz), lower alpha (8-10 Hz), upper alpha (10-12 Hz), lower beta (13-20 Hz) and upper beta (20-30 Hz) frequency range for predicting word discrimination skills, word-referent mapping and semantic generalization abilities using correlation analyses.…”