A popular solution to control for edge density variability in structural brain network analysis is to threshold the networks to a fixed density across all subjects. However, it remains unclear how this type of thresholding affects the basic network architecture in terms of edge weights, hub location, and hub connectivity and, especially, how it affects the sensitivity to detect disease-related abnormalities. We investigated these two questions in a cohort of patients with cerebral small vessel disease and age-matched controls. Brain networks were reconstructed from diffusion magnetic resonance imaging data using deterministic fiber tractography. Networks were thresholded to a fixed density by removing edges with the lowest number of streamlines. We compared edge length (mm), fractional anisotropy (FA), proportion of hub connections, and hub location between the unthresholded and the thresholded networks of each subject. Moreover, we compared weighted graph measures of global and local connectivity obtained from the (un)thresholded networks between patients and controls. We performed these analyses over a range of densities (2-20%). Results indicate that fixed-density thresholding disproportionally removes edges composed of long streamlines, but is independent of FA. The edges removed were not preferentially connected to hub or nonhub nodes. Over half of the original hubs were reproducible when networks were thresholded to a density ≥10%. Furthermore, the between-group differences in graph measures observed in the unthresholded network remained present after thresholding, irrespective of the chosen density. We therefore conclude that moderate fixed-density thresholds can successfully be applied to control for the effects of density in structural brain network analysis.

中文翻译：

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固定密度阈值对结构性脑网络的影响：在脑小血管疾病的演示。

控制结构性大脑网络分析中边缘密度变异性的一种流行解决方案是将所有对象的网络阈值固定为固定密度。但是，目前尚不清楚这种阈值如何在边缘权重，集线器位置和集线器连接性方面影响基本网络体系结构，尤其是如何影响检测与疾病相关的异常的敏感性。我们在一组脑小血管疾病和年龄匹配的对照患者中调查了这两个问题。使用确定性纤维束摄影术从扩散磁共振成像数据重建脑网络。通过移除流线数最少的边缘，将网络阈值固定为固定密度。我们比较了边长（mm），分数各向异性（FA），轮毂连接的比例，以及每个主题的非阈值网络和阈值网络之间的中心位置。此外，我们比较了从患者和对照之间的（非）阈值网络获得的全局和局部连通性的加权图度量。我们在一定密度范围内（2-20％）进行了这些分析。结果表明，固定密度阈值处理不成比例地删除了由长流线组成的边缘，但与FA无关。删除的边缘未优先连接到集线器或非集线器节点。当网络的阈值密度≥10％时，超过一半的原始集线器是可重现的。此外，在阈值化之后，无论选择的密度如何，在未阈值网络中观察到的图形度量的组间差异仍然存在。