Adult language learning varies widely among individuals, with some learners quickly acquiring knowledge and skills while others struggle with specific components or overall proficiency despite similar exposure. This variability, once linked to frontotemporal language regions, is increasingly seen as originating from distributed networks involved in attention, control, and memory. The role and organization of these networks in explaining these differences remain unclear. We hypothesized that intrinsic multi-network connectivity underpins these variations, revealing potential neuromarkers of interactions among systems beyond language regions. We tested this in 101 healthy adults (72 females and 29 males) using multimodal neuroimaging before seven days of artificial language training across six tasks targeting auditory and speech categories, words, morphosyntax, and sentence structures. We identified one general component shared across tasks and five task-specific ones. Using cross-validated predictive modeling and graph-theoretic metrics, we found that the general component’s learning outcome (LO) and rate (LR) were primarily driven by the dorsal attention and frontoparietal networks. Their local efficiency was a strong predictor, highlighting local resilience and mesoscale segregation. Local connectivity dominated in association cortical networks, while global integration occurred in subcortical regions, reflecting a balance between segregation and integration influences learning. Only task-specific word learning was predictable, relying on default-mode and frontoparietal hubs. Single-modality predictions were weaker, emphasizing the value of multimodal approaches. These findings suggest that the intrinsic network topology underlies individual success in language learning, supporting a multiple-system model in which attention, default, and subcortical networks work together to shape learning trajectories and advance mechanistic understanding. Significance Statement Adult learners vary greatly in how effectively and successfully they learn a new language, but the neural basis for this variability remains unclear. Using multimodal MRI data collected before training and connectome-based graph-theoretic measures, we demonstrate that intrinsic brain network topology predicts both a general language-learning factor and learning speed across six artificial-language-learning tasks in 101 adults. The strongest predictive features were found in the dorsal attention and frontoparietal control networks, with nodal local efficiency emerging as the most consistent marker. A cortical-subcortical dissociation in local-efficiency and global-integration properties may underlie these individual differences. These findings highlight potential network-level neuromarkers for predicting adult language learning success beyond language areas.
Song et al. (Mon,) studied this question.