Характеристика расширенной языковой сети у лиц с множественным склерозом.
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Ratzan Alexander S, Simani Leila, Dworkin Jordan D, Buyukturkoglu Korhan, Riley Claire S, Leavitt Victoria M
Аннотация
Оригинал публикации
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 (p<0.005) compared to HC. 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 (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.