Multi-network Topology Underlying Individual Language Learning Success

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 7 d 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 and rate 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 that 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 advance network-level understanding by showing that intrinsic network topology underlies individual success in language learning, supporting a multiple-system model in which control, attention, default, and subcortical networks work together to shape learning trajectories.