Characterizing the extended language network in individuals with multiple sclerosis

Abstract

Background:

Language dysfunction is increasingly recognized as a prevalent and early affected cognitive domain in individuals with multiple sclerosis (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) were acquired from 54 MS patients and 54 matched healthy controls (HCs). Summary measures (anteriority, segregation, betweenness, withinness) 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, dimensionality reduction was used to assess relationships between language performance and multimodal functional, structural, and diffusion-based neuroimaging features derived from ELN-related areas.

Results:

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