Elucidating a statistical learning brain network: Coordinate-based meta-analyses and functional connectivity profiles of artificial grammar learning in healthy adults.

Language rehabilitation centers on modifying its use through experience-based neuroplasticity. Statistical learning of language is essential to its acquisition and likely its rehabilitation following brain injury, but its corresponding brain networks remain elusive. Coordinate-based meta-analyses were conducted to identify common and distinct brain activity across 25 studies coded for meta-data and experimental contrasts (Grammatical and Ungrammatical). The resultant brain regions served as seeds for profiling functional connectivity in large task-independent and task-dependent data sets. Hierarchical clustering of these profiles grouped brain regions into three subnetworks associated with statistical learning processes. Functional decoding clarified the mental operations associated with those subnetworks. Results support a left-dominant language sub-network and two cognitive control networks as scaffolds for language rule identification, maintenance, and application in healthy adults. These data suggest that cognitive control is necessary to track regularities across stimuli and imperative for rule identification and application of grammar. Future empirical investigation of these brain networks for language learning in individuals with brain injury will clarify their prognostic role in language recovery.