Characterizing the Extended Language Network in Individuals with Multiple Sclerosis

Abstract

Background: Cognitive impairment is a pervasive, functionally limiting symptom of multiple sclerosis (MS), a disease of the central nervous system that is the most common non-traumatic cause of neurologic disability in young adults. Recently, language dysfunction has received increased attention 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 structural and functional brain magnetic resonance imaging (MRI) scans were acquired from 54 MS patients and 54 healthy controls (HCs). Summary measures of the extended language network (ELN) and structural imaging metrics were calculated. Group differences in ELN summary measures were evaluated. Associations between ELN summary measures and language performance were assessed in both groups; in the MS group, a two-step regression analysis was applied to assess relationships between additional language-specific imaging measures and language performance. Results: In comparison to the HC group, the MS group performed significantly worse on the semantic fluency and rapid automized naming tests (p < 0.005). Concerning the ELN summary measures, the MS group exhibited higher within-ELN connectivity than the HCs (0.11 +/- 0.02 vs. 0.10 +/- 0.01, p < 0.05, respectively). While no significant relationships between ELN summary measures and language function were observed in either group, the regression analysis identified a set of 17 imaging features that predicted performance on the rapid automized naming test (p < 0.05) and identified key white matter tracts predicting language function in individuals with MS. Conclusion: The derived functional network-level measures, combined with the identified structural neuroimaging metrics, constitute a comprehensive set of imaging features to characterize language dysfunction in MS. Further studies leveraging these features may uncover underlying mechanisms and clinically relevant predictors of language dysfunction, potentially leading to improved precision treatment strategies for cognitively impaired patients with multiple sclerosis.