Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea.
Hum Brain Mapp. 2019 Aug 1;40(11):3321-3337. doi: 10.1002/hbm.24600. Epub 2019 Apr 19.
A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross-covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.
功能磁共振成像 (fMRI) 的典型时间序列表现出自相关，即时间序列的样本是相关的。此外，时间滤波是功能磁共振图像预处理的关键步骤之一，它会产生自身的自相关。在进行 fMRI 连接分析时，自相关的影响在很大程度上被忽略了。最近，自相关已经通过方差校正方法得到了解决，这些方法对采样率很敏感。在本文中，我们旨在研究采样率对方差校正方法的影响。为此，我们首先推导出了一个广义表达式，用于表示样本 Pearson 相关系数 (SPCC) 的方差，该表达式与时间序列的自相关和互协方差函数有关，还与采样率和滤波器截止频率有关。通过模拟，我们说明了对于固定采样率，方差校正的重要性。使用真实的静息态 fMRI 数据集，我们证明了具有更高采样率的数据更容易出现假阳性，这与现有的经验报告一致。我们还通过单个体结果进一步证明，对于具有更高采样率的数据，方差校正策略恢复了真实连接的完整性。
