Fiber tractography based on diffusion-weighted MRI provides a non-invasive characterization of the structural connectivity of the human brain at the macroscopic level. Quantification of structural connectivity strength is challenging and mainly reduced to "streamline counting" methods. These are however highly dependent on the topology of the connectome and the particular specifications for seeding and filtering, which limits their intra-subject reproducibility across repeated measurements and, in consequence, also confines their validity. Here we propose a novel method for increasing the intra-subject reproducibility of quantitative estimates of structural connectivity strength. To this end, the connectome is described by a large matrix in positional-orientational space and reduced by Principal Component Analysis to obtain the main connectivity "modes". It was found that the proposed method is quite robust to structural variability of the data.

中文翻译：

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通过主成分分析对结构连通性进行稳健的个体内估计

基于扩散加权 MRI 的纤维束成像在宏观层面上提供了人脑结构连通性的非侵入性表征。结构连接强度的量化具有挑战性，主要减少为“简化计数”方法。然而，这些高度依赖于连接组的拓扑结构以及播种和过滤的特定规范，这限制了它们在重复测量中的受试者内可重复性，因此也限制了它们的有效性。在这里，我们提出了一种新方法，用于提高结构连接强度定量估计的受试者内可重复性。为此，连接组由位置-方向空间中的大矩阵描述，并通过主成分分析减少以获得主要连接“模式”。发现所提出的方法对数据的结构可变性非常稳健。