During the past ten years, dynamic functional connectivity (FC) has been extensively studied using the sliding-window method. A fixed window-size is usually selected heuristically, since no consensus exists yet on choice of the optimal window-size. Furthermore, without a known ground-truth, the validity of the computed dynamic FC remains unclear and questionable. In this study, we computed single-scale time-dependent (SSTD) window-sizes for the sliding-window method. SSTD window-sizes were based on the frequency content at every time point of a time series and were computed without any prior information. Therefore, they were time-dependent and data-driven. Using simulated sinusoidal time series with frequency shifts, we demonstrated that SSTD window-sizes captured the time-dependent period (inverse of frequency) information at every time point. We further validated the dynamic FC values computed with SSTD window-sizes with both a classification analysis using fMRI data with a low sampling rate and a regression analysis using fMRI data with a high sampling rate. Specifically, we achieved both a higher classification accuracy in predicting cognitive impairment status in fighters and a larger explained behavioral variance in healthy young adults when using dynamic FC matrices computed with SSTD window-sizes as features, as compared to using dynamic FC matrices computed with the conventional fixed window-sizes. Overall, our study computed and validated SSTD window-sizes in the sliding-window method for dynamic FC analysis. Our results demonstrate that dynamic FC matrices computed with SSTD window-sizes can capture more temporal dynamic information related to behavior and cognitive function.

中文翻译：

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滑动窗口动态功能连接分析中的单尺度时间相关窗口大小：验证研究

在过去的十年中，使用滑动窗口方法对动态功能连接（FC）进行了广泛的研究。固定的窗口大小通常是启发式选择的，因为对于最佳窗口大小的选择尚无共识。此外，由于没有已知的地面实况，计算出的动态 FC 的有效性仍然不清楚和有问题。在这项研究中，我们计算了滑动窗口方法的单尺度时间相关 (SSTD) 窗口大小。SSTD 窗口大小基于时间序列每个时间点的频率内容，并且在没有任何先验信息的情况下计算。因此，它们依赖于时间和数据驱动。使用具有频移的模拟正弦时间序列，我们证明了 SSTD 窗口大小在每个时间点捕获了与时间相关的周期（频率的倒数）信息。我们通过使用低采样率 fMRI 数据的分类分析和使用高采样率 fMRI 数据的回归分析进一步验证了使用 SSTD 窗口大小计算的动态 FC 值。具体来说，与使用以 SSTD 窗口大小计算的动态 FC 矩阵作为特征相比，我们在预测战士的认知障碍状态方面实现了更高的分类准确度，并在健康的年轻人中实现了更大的解释行为方差。传统的固定窗口大小。总体而言，我们的研究计算并验证了用于动态 FC 分析的滑动窗口方法中的 SSTD 窗口大小。我们的结果表明，使用 SSTD 窗口大小计算的动态 FC 矩阵可以捕获更多与行为和认知功能相关的时间动态信息。