Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.
Department of Radiology, Xinqiao Hospital, Chongqing, China.
Brain Behav. 2020 Aug;10(8):2336-2351. doi: 10.1002/brb3.1705. Epub 2020 Jul 2.
Graph metrics have been proposed as potential biomarkers for diagnosis in clinical work. However, before it can be applied in a clinical setting, their reproducibility should be evaluated.
This study systematically investigated the effect of two denoising pipelines and different whole-brain network constructions on reproducibility of subject-specific graph measures. We used the multi-session fMRI dataset from the Brain Genomics Superstruct Project consisting of 69 healthy young adults.
In binary networks, the test-retest variability for global measures was large at low density irrespective of the denoising strategy or the type of correlation. Weighted networks showed very low test-retest values (and thus a good reproducibility) for global graph measures irrespective of the strategy used. Comparing the test-retest values for different strategies, there were significant main effects of the type of correlation (Pearson correlation vs. partial correlation), the (partial) correlation value (absolute vs. positive vs. negative), and weight calculation (based on the raw (partial) correlation values vs. based on transformed Z-values). There was also a significant interaction effect between type of correlation and weight calculation. Similarly as for the binary networks, there was no main effect of the denoising pipeline.
Our results demonstrated that normalized global graph measures based on a weighted network using the absolute (partial) correlation as weight were reproducible. The denoising pipeline and the granularity of the whole-brain parcellation used to define the nodes were not critical for the reproducibility of normalized graph measures.
图论指标已被提出作为临床工作中诊断的潜在生物标志物。然而，在将其应用于临床环境之前，应评估其可重复性。
本研究系统地研究了两种去噪策略和不同的全脑网络构建对个体特定图测重现性的影响。我们使用了来自脑基因组超结构项目的多会话 fMRI 数据集，其中包含 69 名健康的年轻成年人。
在二值网络中，无论去噪策略或相关类型如何，全局测量的测试-重测变异性在低密度时都很大。加权网络显示出非常低的测试-重测值（因此具有良好的可重复性），而全局图测值不受所使用的策略的影响。比较不同策略的测试-重测值，相关类型（皮尔逊相关与部分相关）、（部分）相关值（绝对值与正相关与负相关）和权重计算（基于原始（部分）相关值与基于转换 Z 值）均存在显著的主效应。相关类型和权重计算之间也存在显著的交互效应。与二值网络类似，去噪策略没有主要影响。
我们的结果表明，基于绝对（部分）相关作为权重的加权网络的归一化全局图测值具有可重复性。去噪策略和用于定义节点的全脑分割的粒度对于归一化图测值的可重复性不是关键。
