Muthuraman Muthuraman, Fleischer Vinzenz, Kolber Pierre, Luessi Felix, Zipp Frauke, Groppa Sergiu
Department of Neurology and Neuroimaging Center of the Focus Program Translational Neuroscience, University Medical Center of the Johannes Gutenberg-University Mainz Mainz, Germany.
Front Neurosci. 2016 Feb 2;10:14. doi: 10.3389/fnins.2016.00014. eCollection 2016.
Focal demyelinated lesions, diffuse white matter (WM) damage, and gray matter (GM) atrophy influence directly the disease progression in patients with multiple sclerosis. The aim of this study was to identify specific characteristics of GM and WM structural networks in subjects with clinically isolated syndrome (CIS) in comparison to patients with early relapsing-remitting multiple sclerosis (RRMS). Twenty patients with CIS, 33 with RRMS, and 40 healthy subjects were investigated using 3 T-MRI. Diffusion tensor imaging was applied, together with probabilistic tractography and fractional anisotropy (FA) maps for WM and cortical thickness correlation analysis for GM, to determine the structural connectivity patterns. A network topology analysis with the aid of graph theoretical approaches was used to characterize the network at different community levels (modularity, clustering coefficient, global, and local efficiencies). Finally, we applied support vector machines (SVM) to automatically discriminate the two groups. In comparison to CIS subjects, patients with RRMS were found to have increased modular connectivity and higher local clustering, highlighting increased local processing in both GM and WM. Both groups presented increased modularity and clustering coefficients in comparison to healthy controls. SVM algorithms achieved 97% accuracy using the clustering coefficient as classifier derived from GM and 65% using WM from probabilistic tractography and 67% from modularity of FA maps to differentiate between CIS and RRMS patients. We demonstrate a clear increase of modular and local connectivity in patients with early RRMS in comparison to CIS and healthy subjects. Based only on a single anatomic scan and without a priori information, we developed an automated and investigator-independent paradigm that can accurately discriminate between patients with these clinically similar disease entities, and could thus complement the current dissemination-in-time criteria for clinical diagnosis.
局灶性脱髓鞘病变、弥漫性白质（WM）损伤和灰质（GM）萎缩直接影响多发性硬化症患者的疾病进展。本研究的目的是确定临床孤立综合征（CIS）患者与早期复发缓解型多发性硬化症（RRMS）患者相比，GM和WM结构网络的特定特征。使用3T磁共振成像（MRI）对20例CIS患者、33例RRMS患者和40名健康受试者进行了研究。应用扩散张量成像，结合概率纤维束成像以及WM的分数各向异性（FA）图和GM的皮质厚度相关性分析，以确定结构连接模式。借助图论方法进行网络拓扑分析，以在不同社区水平（模块性、聚类系数、全局和局部效率）表征网络。最后，我们应用支持向量机（SVM）自动区分这两组。与CIS受试者相比，发现RRMS患者的模块连接性增加且局部聚类更高，突出了GM和WM中局部处理的增加。与健康对照相比，两组的模块性和聚类系数均增加。SVM算法使用从GM得出的聚类系数作为分类器时准确率达到97%，使用概率纤维束成像中的WM时准确率为65%，使用FA图的模块性时准确率为67%，以区分CIS和RRMS患者。我们证明，与CIS和健康受试者相比，早期RRMS患者的模块和局部连接性明显增加。仅基于单次解剖扫描且无需先验信息，我们开发了一种自动化且与研究者无关的范式，该范式可以准确区分这些临床相似疾病实体的患者，从而可以补充当前临床诊断的时间传播标准。
