Background

Dynamic functional connectivity enables us to study brain connectivity occurring at different frequencies. Techniques like sliding window correlation allow for the estimation of time varying connectivity and its frequency spectrum content. Since correlation is equal to the cosine of the phase (cos $\theta$) between activation amplitudes of two brain regions, we assume that phase is the relevant functional connectivity feature and leave out any contamination from activation amplitudes.

New method

First, this work studies the conditions by which time varying correlation can be separated from nuisance activation amplitudes that are not phase related. Second, we propose the filtered sliding window correlation to perform time varying estimation of cosine-phase (cos $\theta$ (t)) and nuisance filtering in one single step.

Results

Mathematical models predict the correlation frequencies that should be filtered out to avoid overlap with the activation amplitude spectra. Filtered sliding window correlation excluded nuisance frequencies with an accurate estimation of time varying correlation. Real data outcomes empirically suggest that fMRI frequencies of interest extend up to 0.05 Hz.

Comparison with existing methods

Compared with sliding window methods, the filtered sliding window correlation achieves better estimation for frequencies of interest.

Conclusions

The filtered sliding window correlation approach allows controlling for nuisance frequencies unrelated to time varying phase estimation.

中文翻译：

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动态功能连接中的滤波相关性和允许的频谱。

背景

动态功能连接使我们能够研究以不同频率发生的大脑连接。诸如滑动窗口相关性之类的技术允许估计时变连通性及其频谱内容。由于相关性等于相位的余弦 (cos$\theta$) 在两个大脑区域的激活幅度之间，我们假设相位是相关的功能连接特征，并排除激活幅度的任何污染。

新方法

首先，这项工作研究了可以将时变相关性与非相位相关的干扰激活幅度分开的条件。其次，我们提出了滤波滑动窗口相关性来执行余弦相位（cos$\theta$ (t)) 和干扰过滤一步完成。

结果

数学模型预测应该过滤掉以避免与激活幅度谱重叠的相关频率。过滤的滑动窗口相关性通过对时变相关性的准确估计排除了干扰频率。实际数据结果凭经验表明感兴趣的 fMRI 频率扩展到 0.05 Hz。

与现有方法的比较

与滑动窗口方法相比，过滤的滑动窗口相关性可以更好地估计感兴趣的频率。

结论

滤波滑动窗口相关方法允许控制与时变相位估计无关的干扰频率。