Ratzan Alexander S, Simani Leila, Dworkin Jordan D, Buyukturkoglu Korhan, Riley Claire S, Leavitt Victoria M
Cognitive Neuroscience Division, Department of Neurology, Columbia University Irving Medical Center, New York, NY.
Department of Computer Science & Engineering, Tandon School of Engineering, New York University, New York, NY.
medRxiv. 2025 Jul 11:2023.08.30.23294843. doi: 10.1101/2023.08.30.23294843.
Language dysfunction is increasingly recognized as a prevalent and early affected cognitive domain in individuals with MS.
To establish a network-level model of language dysfunction in MS.
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.
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).
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.
语言功能障碍在多发性硬化症（MS）患者中越来越被认为是一种普遍且早期受影响的认知领域。
建立MS患者语言功能障碍的网络水平模型。
从54例MS患者和54例匹配的健康对照（HC）中获取认知数据以及3T功能和结构脑MRI数据。计算扩展语言网络（ELN）的功能汇总指标（前部性、分离度、中间中心性、内部中心性）并得出结构成像指标。评估ELN连接性的组间差异。评估ELN连接性与语言表现之间的关联；在MS组中，采用无监督学习方法评估源自语言相关区域的多模态神经影像特征与语言任务表现之间的关系。
与HC相比，MS组在语义流畅性和快速自动命名测试中的表现更差（<0.005）。关于ELN指标，MS组的ELN内部连接性高于HC（<0.05）。主成分分析（PCA）产生了一个与语言表现具有独特相关性的多模态潜在成分（<0.05）。
我们确定了网络水平的功能和结构指标，以潜在地表征MS中的语言功能障碍。利用这些特征的进一步研究可能揭示MS特有的语言功能障碍的机制和预测因素。
