The (in)stability of functional brain network measures across thresholds.
The (in)stability of functional brain network measures across thresholds.
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DOI：
10.1016/j.neuroimage.2015.05.046
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发表时间：
2015-09
期刊：
影响因子：
5.7
通讯作者：
Constable RT
中科院分区：
文献类型：
--
作者：
Garrison KA;Scheinost D;Finn ES;Shen X;Constable RT
大脑的大规模组织具有复杂网络的特征，可以使用图论的网络测量来量化。然而，许多网络测量被设计为在二进制图上计算，而功能性大脑组织通常从大脑区域之间的时间信号的相关性的连续测量中推断。使用从功能连通性数据导出的二进制图时，数据保持是必要的步骤。然而，目前还没有关于使用什么阈值的共识，并且网络测量和组对比在阈值之间可能不稳定。然而，全脑网络分析正在广泛应用，其结果通常以任意阈值或阈值范围报告。本研究旨在评估在一个大型静息状态功能连接数据集中跨阈值网络测量的稳定性。在绝对（基于相关性）和比例（基于稀疏性）阈值上评估网络测量，并在性别和年龄组之间进行比较。总体而言，网络措施被认为是不稳定的绝对阈值。例如，给定网络测量中的组差异的方向可以取决于阈值而改变。网络措施被认为是更稳定的比例阈值。这些结果表明，在将阈值应用于功能连接数据和解释二进制图模型的结果时，应谨慎使用。
The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. However, many network measures were designed to be calculated on binary graphs, whereas functional brain organization is typically inferred from a continuous measure of correlations in temporal signal between brain regions. Thresholding is a necessary step to use binary graphs derived from functional connectivity data. However, there is no current consensus on what threshold to use, and network measures and group contrasts may be unstable across thresholds. Nevertheless, whole-brain network analyses are being applied widely with findings typically reported at an arbitrary threshold or range of thresholds. This study sought to evaluate the stability of network measures across thresholds in a large resting state functional connectivity dataset. Network measures were evaluated across absolute (correlation-based) and proportional (sparsity-based) thresholds, and compared between sex and age groups. Overall, network measures were found to be unstable across absolute thresholds. For example, the direction of group differences in a given network measure may change depending on the threshold. Network measures were found to be more stable across proportional thresholds. These results demonstrate that caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.