Khalilian Maedeh, Kazemi Kamran, Fouladivanda Mahshid, Makki Malek, Helfroush Mohammad Sadegh, Aarabi Ardalan
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran.
Laboratory of Functional Neuroscience and Pathologies (LNFP), University Research Center (CURS), University Hospital, 80054 Amiens, France.
Diagnostics (Basel). 2021 May 27;11(6):970. doi: 10.3390/diagnostics11060970.
The majority of network studies of human brain structural connectivity are based on single-shell diffusion-weighted imaging (DWI) data. Recent advances in imaging hardware and software capabilities have made it possible to acquire multishell (b-values) high-quality data required for better characterization of white-matter crossing-fiber microstructures. The purpose of this study was to investigate the extent to which brain structural organization and network topology are affected by the choice of diffusion magnetic resonance imaging (MRI) acquisition strategy and parcellation scale. We performed graph-theoretical network analysis using DWI data from 35 Human Connectome Project subjects. Our study compared four single-shell (b = 1000, 3000, 5000, 10,000 s/mm) and multishell sampling schemes and six parcellation scales (68, 200, 400, 600, 800, 1000 nodes) using five graph metrics, including small-worldness, clustering coefficient, characteristic path length, modularity and global efficiency. Rich-club analysis was also performed to explore the rich-club organization of brain structural networks. Our results showed that the parcellation scale and imaging protocol have significant effects on the network attributes, with the parcellation scale having a substantially larger effect. Regardless of the parcellation scale, the brain structural networks exhibited a rich-club organization with similar cortical distributions across the parcellation scales involving at least 400 nodes. Compared to single b-value diffusion acquisitions, the deterministic tractography using multishell diffusion imaging data consisting of shells with b-values higher than 5000 s/mm resulted in significantly improved fiber-tracking results at the locations where fiber bundles cross each other. Brain structural networks constructed using the multishell acquisition scheme including high b-values also exhibited significantly shorter characteristic path lengths, higher global efficiency and lower modularity. Our results showed that both parcellation scale and sampling protocol can significantly impact the rich-club organization of brain structural networks. Therefore, caution should be taken concerning the reproducibility of connectivity results with regard to the parcellation scale and sampling scheme.
大多数关于人类脑结构连通性的网络研究都是基于单壳扩散加权成像（DWI）数据。成像硬件和软件功能的最新进展使得获取多壳（b值）高质量数据成为可能，这些数据对于更好地表征白质交叉纤维微结构是必需的。本研究的目的是探讨扩散磁共振成像（MRI）采集策略和脑区划分尺度的选择对脑结构组织和网络拓扑的影响程度。我们使用来自35名人类连接组计划受试者的DWI数据进行了图论网络分析。我们的研究比较了四种单壳（b = 1000、3000、5000、10000 s/mm²）和多壳采样方案，以及六种脑区划分尺度（68、200、400、600、800、1000个节点），使用了五种图指标，包括小世界特性、聚类系数、特征路径长度、模块度和全局效率。还进行了富俱乐部分析以探索脑结构网络的富俱乐部组织。我们的结果表明，脑区划分尺度和成像方案对网络属性有显著影响，其中脑区划分尺度的影响要大得多。无论脑区划分尺度如何，脑结构网络都呈现出富俱乐部组织，在至少包含400个节点的脑区划分尺度上具有相似的皮质分布。与单b值扩散采集相比，使用由b值高于5000 s/mm²的壳组成的多壳扩散成像数据进行的确定性纤维束成像，在纤维束相互交叉的位置产生了显著改善的纤维追踪结果。使用包括高b值的多壳采集方案构建的脑结构网络也表现出显著更短的特征路径长度、更高的全局效率和更低的模块度。我们的结果表明，脑区划分尺度和采样方案都能显著影响脑结构网络的富俱乐部组织。因此，在考虑脑区划分尺度和采样方案的连通性结果的可重复性时应谨慎。
