使用时间导数评估大脑功能连接的利弊
On the pros and cons of using temporal derivatives to assess brain functional connectivity
摘要：
脑区之间的相关性研究是大尺度脑时空动力学分析的重要组成部分。特别是，新的方法，适合于提取相互关联的动态变化是必要的。在这里，我们仔细研究了最近报道的度量称为"时间导数的乘法"（MTD），这是基于每个时间序列的时间导数。正式比较的MTD公式与皮尔逊相关性的衍生工具显示只有微小的差异，我们发现在实践中可以忽略不计。滑动窗口皮尔逊相关的原始时间序列在几个固定和非固定的设置，包括一个现实的固定网络检测的比较，揭示了较低的灵敏度的衍生物的低频漂移和自相关，但也较低的信噪比。它并没有表明任何明显的数学优势，建议的度量常用的相关方法。
英文摘要：
The study of correlations between brain regions is an important chapter of the analysis of large-scale brain spatiotemporal dynamics. In particular, novel methods suited to extract dynamic changes in mutual correlations are needed. Here we scrutinize a recently reported metric dubbed "Multiplication of Temporal Derivatives" (MTD) which is based on the temporal derivative of each time series. The formal comparison of the MTD formula with the Pearson correlation of the derivatives reveals only minor differences, which we find negligible in practice. A comparison with the sliding window Pearson correlation of the raw time series in several stationary and non-stationary set-ups, including a realistic stationary network detection, reveals lower sensitivity of derivatives to low frequency drifts and to autocorrelations but also lower signal-to-noise ratio. It does not indicate any evident mathematical advantages of the proposed metric over commonly used correlation methods.
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