Characterizing the extended language network in individuals with multiple sclerosis.

Area of Science:

• Neuroscience
• Cognitive Neurology
• Medical Imaging

Background:

• Language dysfunction is a common early symptom in multiple sclerosis (MS).
• Understanding the neural basis of language deficits in MS is crucial for diagnosis and treatment.
• Previous research has highlighted cognitive impairments in MS, but network-level language models are less understood.

Purpose of the Study:

• To develop a network-level model of language dysfunction in multiple sclerosis (MS).
• To investigate differences in the extended language network (ELN) between MS patients and healthy controls (HCs).
• To identify neuroimaging correlates of language performance in MS.

Main Methods:

• Acquired structural and functional brain MRI data from 54 MS patients and 54 HCs.
• Calculated summary measures of the extended language network (ELN) connectivity and derived structural imaging metrics.
• Assessed group differences in ELN connectivity and correlated these with language performance using multimodal neuroimaging features.

Main Results:

• MS patients exhibited poorer performance on semantic fluency and rapid naming tasks compared to HCs.
• The MS group showed increased within-ELN connectivity.
• A multimodal latent component derived via PCA correlated significantly with language performance in the MS group.

Conclusions:

• Identified network-level functional and structural measures that may characterize language dysfunction in MS.
• These findings suggest potential biomarkers for language deficits in MS.
• Further research using these features could elucidate MS-specific mechanisms and predictors of language impairment.