PDF(Pubmed)
Abstract：
Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.
摘要：
近年来，在人类中使用扩散MRI绘制连接体图的数量激增，与此同时，处理和分析选择也有类似的增加。然而，这些不同的步骤及其效果很少被系统地比较。这里,在健康的年轻成年人群(n=294)中，我们描述了一系列分析管道对人类连接体一个被广泛研究的特性的影响:它的程度分布。我们评估了40条管道的效果(比较了划分的常见选择，流线播种,纤维束成像算法，和流线传播约束）和高度连接的集线器区域上的44个组代表连接体重建方案。我们发现，管道之间的枢纽位置变化很大。分区的选择对集线器架构有重大影响，和枢纽连通性与大多数评估管道的区域表面积高度相关（69％的管道中ρ>0.70），特别是在使用加权网络时。总的来说,我们的结果表明，在处理扩散MRI数据时需要谨慎的决策，并仔细考虑不同的处理选择如何影响连接体组织。
