Martinez-Heras Eloy, Solana Elisabeth, Vivó Francesc, Lopez-Soley Elisabet, Calvi Alberto, Alba-Arbalat Salut, Schoonheim Menno M, Strijbis Eva M, Vrenken Hugo, Barkhof Frederik, Rocca Maria A, Filippi Massimo, Pagani Elisabetta, Groppa Sergiu, Fleischer Vinzenz, Dineen Robert A, Bellenberg Barbara, Lukas Carsten, Pareto Deborah, Rovira Alex, Sastre-Garriga Jaume, Collorone Sara, Prados Ferran, Toosy Ahmed, Ciccarelli Olga, Saiz Albert, Blanco Yolanda, Llufriu Sara
Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain.
MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
J Neurol Neurosurg Psychiatry. 2023 Nov;94(11):916-923. doi: 10.1136/jnnp-2023-331531. Epub 2023 Jun 15.
We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes.
Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups.
Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes.
In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.
我们旨在描述随着多发性硬化症（MS）进展，基于脑扩散的连接性变化的严重程度，以及这些网络与不同MS表型相关的微观结构特征。
在8个MAGNIMS中心收集了221名健康个体和823名MS患者的临床信息和脑部MRI。患者被分为四种临床表型：临床孤立综合征、复发缓解型、继发进展型和原发进展型。采用先进的纤维束成像方法获得连接矩阵。然后，分析了全脑和基于节点图的测量指标以及组间连接的分数各向异性差异。使用支持向量机算法对组进行分类。
临床孤立综合征和复发缓解型患者相对于对照组有相似的网络变化。然而，与其他组相比，继发进展型患者的大多数全局和局部网络属性存在差异，大多数连接的分数各向异性较低。与临床孤立综合征和复发缓解型患者相比，原发进展型参与者在全局和局部图测量指标上的差异较少，分数各向异性仅在少数连接中明显降低。基于连接将患者与健康对照区分开的支持向量机准确率为81%，在区分临床表型时准确率在64%至74%之间。
总之，MS患者的脑连接性受到破坏，并且根据表型具有不同的模式。继发进展型与连接性更广泛的变化相关。此外，分类任务可以区分MS类型，皮质下连接是最重要的因素。
