Characterizing the Extended Language Network in Individuals with Multiple Sclerosis.

Background: Language dysfunction is increasingly recognized as a prevalent and early affected cognitive domain in individuals with MS.

Objectives: To establish a network-level model of language dysfunction in MS.

Methods: Cognitive data and 3T functional and structural brain MRI were acquired from 54 MS patients and 54 matched healthy controls (HCs). Functional summary measures (anteriority, segregation, between-ness, within-ness) of the extended language network (ELN) were calculated and structural imaging metrics were derived. Group differences in ELN connectivity were evaluated. Associations between ELN connectivity and language performance were assessed; in the MS group, an unsupervised learning approach was used to assess relationships between multimodal neuroimaging features derived from language-related areas and performance on language tasks.

Results: The MS group performed worse on semantic fluency and rapid automized naming tests (<0.005) compared to HC. Regarding ELN measures, the MS group exhibited higher within-ELN connectivity than HCs (<0.05). Principal component analysis (PCA) yielded a multimodal latent component that uniquely correlated with language performance (<0.05).

Conclusion: We identified network-level functional and structural measures to potentially characterize language dysfunction in MS. Further studies leveraging these features may reveal mechanisms and predictors of language dysfunction specific to MS.