Frigo Matteo, Deslauriers-Gauthier Samuel, Parker Drew, Aziz Ould Ismail Abdol, John Kim Junghoon, Verma Ragini, Deriche Rachid
Athena Project-Team, Inria Sophia Antipolis-Méditerranée, Université Côte D'Azur, Nice, France.
Penn Applied Connectomics and Imaging Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States of America.
J Neural Eng. 2020 Nov 11;17(6). doi: 10.1088/1741-2552/abc29b.
The use of non-invasive techniques for the estimation of structural brain networks (i.e. connectomes) opened the door to large-scale investigations on the functioning and the architecture of the brain, unveiling the link between neurological disorders and topological changes of the brain network. This study aims at assessing if and how the topology of structural connectomes estimated non-invasively with diffusion MRI is affected by the employment of tractography filtering techniques in structural connectomic pipelines. Additionally, this work investigates the robustness of topological descriptors of filtered connectomes to the common practice of density-based thresholding.We investigate the changes in global efficiency, characteristic path length, modularity and clustering coefficient on filtered connectomes obtained with the spherical deconvolution informed filtering of tractograms and using the convex optimization modelling for microstructure informed tractography. The analysis is performed on both healthy subjects and patients affected by traumatic brain injury and with an assessment of the robustness of the computed graph-theoretical measures with respect to density-based thresholding of the connectome.Our results demonstrate that tractography filtering techniques change the topology of brain networks, and thus alter network metrics both in the pathological and the healthy cases. Moreover, the measures are shown to be robust to density-based thresholding.The present work highlights how the inclusion of tractography filtering techniques in connectomic pipelines requires extra caution as they systematically change the network topology both in healthy subjects and patients affected by traumatic brain injury. Finally, the practice of low-to-moderate density-based thresholding of the connectomes is confirmed to have negligible effects on the topological analysis.
使用非侵入性技术估计脑结构网络（即连接组）为大规模研究大脑功能和结构打开了大门，揭示了神经疾病与脑网络拓扑变化之间的联系。本研究旨在评估在结构连接组学流程中使用纤维束成像滤波技术时，通过扩散磁共振成像非侵入性估计的结构连接组拓扑是否以及如何受到影响。此外，这项工作还研究了滤波后连接组的拓扑描述符对基于密度阈值化这一常见做法的稳健性。我们研究了通过纤维束的球面反卷积信息滤波以及使用微观结构信息纤维束成像的凸优化建模获得的滤波后连接组的全局效率、特征路径长度、模块性和聚类系数的变化。分析在健康受试者和创伤性脑损伤患者中均进行，并评估了计算得到的图论测量相对于连接组基于密度阈值化的稳健性。我们的结果表明，纤维束成像滤波技术会改变脑网络的拓扑结构，从而在病理和健康情况下都会改变网络指标。此外，这些测量结果显示对基于密度的阈值化具有稳健性。本研究强调，在连接组学流程中纳入纤维束成像滤波技术需要格外谨慎，因为它们会系统性地改变健康受试者和创伤性脑损伤患者的网络拓扑结构。最后，证实对连接组进行低至中等密度的基于阈值化的做法对拓扑分析的影响可忽略不计。
