Principle components analysis (PCA) can be used to detect repeating co-variant patterns of resting-state dynamic functional connectivity (DFC) of brain networks, accompanied with sliding-window technique. However, the robustness of PCA-based DFC-state extraction (DFC-PCA) is poorly studied. We investigated the reliability of PCA results and improved the robustness of DFC-PCA for a limited sample size. We first established how PCA-based DFC results varied with sample size and PC order in five rounds of bootstrapping with different sample sizes. The consistency across trials increased with increasing sample size and/or decreasing PC order. We then developed a framework based on PC matching and reordering to obtain a more reliable estimation of co-variant DFC patterns. With either the identical template generated by the surrogate dataset itself or with the external template obtained from existing results, the perceptual hash algorithm was used to reorder PCs according to their patterns. After order correction, reliable results were obtained by averaging across trials within each surrogate dataset. This newly developed framework allowed simultaneous measurement and improvement of DFC-PCA. This consistency could also be used as a criterion for PC selection and interpretation to support the reliability and validity of the conclusion.

主成分分析（PCA）可以用于检测脑网络的静止状态动态功能连接（DFC）的重复协变量模式，以及滑动窗口技术。但是，基于PCA的DFC状态提取（DFC-PCA）的鲁棒性研究很少。我们调查了PCA结果的可靠性，并在有限的样本量下提高了DFC-PCA的鲁棒性。我们首先建立了基于PCA的DFC结果如何随样本大小和PC顺序在五轮不同样本大小的自举中变化。随着样本数量的增加和/或PC订单数量的减少，各个试验的一致性也有所提高。然后，我们开发了基于PC匹配和重新排序的框架，以获得对协变量DFC模式的更可靠估计。无论是由替代数据集本身生成的相同模板，还是从现有结果中获取的外部模板，都使用感知哈希算法根据PC的模式对PC进行重新排序。订单更正后，通过对每个替代数据集中各个试验的平均值得出可靠的结果。这个新开发的框架允许同时测量和改进DFC-PCA。这种一致性也可以用作PC选择和解释的标准，以支持结论的可靠性和有效性。这个新开发的框架允许同时测量和改进DFC-PCA。这种一致性也可以用作PC选择和解释的标准，以支持结论的可靠性和有效性。这个新开发的框架允许同时测量和改进DFC-PCA。这种一致性也可以用作PC选择和解释的标准，以支持结论的可靠性和有效性。