Sensory-motor network topology in multiple sclerosis: Structural connectivity analysis accounting for intrinsic density discrepancy.,Human Brain Mapping

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Sensory-motor network topology in multiple sclerosis: Structural connectivity analysis accounting for intrinsic density discrepancy.
Human Brain Mapping ( IF 3.5 ) Pub Date : 2020-05-15 , DOI: 10.1002/hbm.24989
Simona Schiavi _{1,

2} , Maria Petracca ₃ , Matteo Battocchio ₁ , Mohamed M El Mendili ₃ , Swetha Paduri ₃ , Lazar Fleysher ₄ , Matilde Inglese _{2,

4,

5} , Alessandro Daducci ₁

Affiliation

Graph theory and network modelling have been previously applied to characterize motor network structural topology in multiple sclerosis (MS). However, between‐group differences disclosed by graph analysis might be primarily driven by discrepancy in density, which is likely to be reduced in pathologic conditions as a consequence of macroscopic damage and fibre loss that may result in less streamlines properly traced. In this work, we employed the convex optimization modelling for microstructure informed tractography (COMMIT) framework, which, given a tractogram, estimates the actual contribution (or weight) of each streamline in order to optimally explain the diffusion magnetic resonance imaging signal, filtering out those that are implausible or not necessary. Then, we analysed the topology of this ‘COMMIT‐weighted sensory‐motor network’ in MS accounting for network density. By comparing with standard connectivity analysis, we also tested if abnormalities in network topology are still identifiable when focusing on more ‘quantitative’ network properties. We found that topology differences identified with standard tractography in MS seem to be mainly driven by density, which, in turn, is strongly influenced by the presence of lesions. We were able to identify a significant difference in density but also in network global and local properties when accounting for density discrepancy. Therefore, we believe that COMMIT may help characterize the structural organization in pathological conditions, allowing a fair comparison of connectomes which considers discrepancies in network density. Moreover, discrepancy‐corrected network properties are clinically meaningful and may help guide prognosis assessment and treatment choice.

中文翻译：

多发性硬化症中的感觉运动网络拓扑结构：考虑固有密度差异的结构连通性分析。

先前已应用图论和网络建模来表征多发性硬化症 (MS) 中的运动网络结构拓扑。然而，图形分析揭示的组间差异可能主要是由密度差异驱动的，在病理条件下，由于宏观损伤和纤维损失可能导致正确追踪的流线减少，密度差异可能会减少。在这项工作中，我们采用了微结构信息传输成像（COMMIT）框架的凸优化建模，该框架在给定一个传输图的情况下，估计每个流线的实际贡献（或权重），以最佳地解释扩散磁共振成像信号，过滤掉那些不可信或不必要的。然后，我们分析了 MS 中这种“COMMIT 加权感觉运动网络”的拓扑结构，以考虑网络密度。通过与标准的连通性分析进行比较，我们还测试了在关注更多“量化”网络属性时是否仍然可以识别网络拓扑中的异常。我们发现在 MS 中使用标准纤维束成像确定的拓扑差异似乎主要由密度驱动，而密度又受到病变存在的强烈影响。在考虑密度差异时，我们能够识别密度以及网络全局和局部属性的显着差异。因此，我们认为 COMMIT 可能有助于表征病理条件下的结构组织，允许对考虑网络密度差异的连接组进行公平比较。而且，

更新日期：2020-07-06

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