Pontillo Giuseppe, Prados Ferran, Wink Alle Meije, Kanber Baris, Bisecco Alvino, Broeders Tommy A A, Brunetti Arturo, Cagol Alessandro, Calabrese Massimiliano, Castellaro Marco, Cocozza Sirio, Colato Elisa, Collorone Sara, Cortese Rosa, De Stefano Nicola, Douw Linda, Enzinger Christian, Filippi Massimo, Foster Michael A, Gallo Antonio, Gonzalez-Escamilla Gabriel, Granziera Cristina, Groppa Sergiu, Harbo Hanne F, Høgestøl Einar A, Llufriu Sara, Lorenzini Luigi, Martinez-Heras Eloy, Messina Silvia, Moccia Marcello, Nygaard Gro O, Palace Jacqueline, Petracca Maria, Pinter Daniela, Rocca Maria A, Strijbis Eva, Toosy Ahmed, Valsasina Paola, Vrenken Hugo, Ciccarelli Olga, Cole James H, Schoonheim Menno M, Barkhof Frederik
Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK.
MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands.
Hum Brain Mapp. 2025 Jan;46(1):e70107. doi: 10.1002/hbm.70107.
Disruptions to brain networks, measured using structural (sMRI), diffusion (dMRI), or functional (fMRI) MRI, have been shown in people with multiple sclerosis (PwMS), highlighting the relevance of regions in the core of the connectome but yielding mixed results depending on the studied connectivity domain. Using a multilayer network approach, we integrated these three modalities to portray an enriched representation of the brain's core-periphery organization and explore its alterations in PwMS. In this retrospective cross-sectional study, we selected PwMS and healthy controls with complete multimodal brain MRI acquisitions from 13 European centers within the MAGNIMS network. Physical disability and cognition were assessed with the Expanded Disability Status Scale (EDSS) and the symbol digit modalities test (SDMT), respectively. SMRI, dMRI, and resting-state fMRI data were parcellated into 100 cortical and 14 subcortical regions to obtain networks of morphological covariance, structural connectivity, and functional connectivity. Connectivity matrices were merged in a multiplex, from which regional coreness-the probability of a node being part of the multiplex core-and coreness disruption index (κ)-the global weakening of the core-periphery structure-were computed. The associations of κ with disease status (PwMS vs. healthy controls), clinical phenotype, level of physical disability (EDSS ≥ 4 vs. EDSS < 4), and cognitive impairment (SDMT z-score < -1.5) were tested within a linear model framework. Using random forest permutation feature importance, we assessed the relative contribution of κ in the multiplex and single-layer domains, in addition to conventional MRI measures (brain and lesion volumes), in predicting disease status, physical disability, and cognitive impairment. We studied 1048 PwMS (695F, mean ± SD age: 43.3 ± 11.4 years) and 436 healthy controls (250F, mean ± SD age: 38.3 ± 11.8 years). PwMS showed significant disruption of the multiplex core-periphery organization (κ = -0.14, Hedges' g = 0.49, p < 0.001), correlating with clinical phenotype (F = 3.90, p = 0.009), EDSS (Hedges' g = 0.18, p = 0.01), and SDMT (Hedges' g = 0.30, p < 0.001). Multiplex κ was the only connectomic measure adding to conventional MRI in predicting disease status and cognitive impairment, while physical disability also depended on single-layer contributions. In conclusion, we show that multilayer networks represent a biologically and clinically meaningful framework to model multimodal MRI data, with disruption of the core-periphery structure emerging as a potential connectomic biomarker for disease severity and cognitive impairment in PwMS.
使用结构磁共振成像（sMRI）、扩散张量成像（dMRI）或功能磁共振成像（fMRI）测量发现，多发性硬化症患者（PwMS）存在脑网络中断，这凸显了连接组核心区域的相关性，但根据所研究的连接域不同，结果也不尽相同。我们采用多层网络方法，整合这三种成像方式，以描绘大脑核心-边缘组织的丰富表征，并探究其在PwMS中的变化。在这项回顾性横断面研究中，我们从MAGNIMS网络内的13个欧洲中心选取了拥有完整多模态脑MRI数据的PwMS患者和健康对照。分别使用扩展残疾状态量表（EDSS）和符号数字模式测验（SDMT）评估身体残疾和认知情况。将sMRI、dMRI和静息态fMRI数据划分为100个皮质区域和14个皮质下区域，以获得形态协方差网络、结构连接网络和功能连接网络。将连接矩阵合并为一个多重网络，从中计算区域核心度（节点成为多重网络核心一部分的概率）和核心度破坏指数（κ，核心-边缘结构的整体弱化）。在一个线性模型框架内，测试κ与疾病状态（PwMS与健康对照）、临床表型、身体残疾程度（EDSS≥4与EDSS<4）以及认知障碍（SDMT z评分<-1.5）之间的关联。除了传统的MRI测量指标（脑体积和病变体积）外，我们还使用随机森林排列特征重要性评估κ在多重网络和单层网络域中对预测疾病状态、身体残疾和认知障碍的相对贡献。我们研究了1048例PwMS患者（695例女性，平均±标准差年龄：43.3±11.4岁）和436例健康对照（250例女性，平均±标准差年龄：38.3±11.8岁）。PwMS患者的多重核心-边缘组织存在显著破坏（κ=-0.14，Hedges' g=0.49，p<0.001），与临床表型（F=3.90，p=0.009）、EDSS（Hedges' g=0.18，p=0.01）和SDMT（Hedges' g=0.30，p<0.001）相关。在预测疾病状态和认知障碍方面，多重κ是唯一能在传统MRI基础上增加预测价值的连接组学指标，而身体残疾程度还取决于单层网络的贡献。总之，我们表明多层网络是一个具有生物学和临床意义的框架，可用于对多模态MRI数据进行建模，核心-边缘结构的破坏成为PwMS疾病严重程度和认知障碍的潜在连接组学生物标志物。
