Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach.
Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach.
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DOI：
10.1016/j.neuroimage.2014.06.052
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发表时间：
2014-11-01
期刊：
影响因子：
5.7
通讯作者：
Caffo BS
中科院分区：
文献类型：
--
作者：
Lindquist MA;Xu Y;Nebel MB;Caffo BS
迄今为止，大多数功能性磁共振成像（fMRI）研究都假设来自不同脑区的时间序列之间的功能连接（FC）在时间上是恒定的。然而，最近，人们对在fMRI实验中量化FC可能的动态变化越来越感兴趣，因为人们认为这可能提供对大脑网络基本工作的洞察。在这项工作中，我们专注于估计从大脑的两个不同区域提取的时间过程之间的成对相关性的动态行为的具体问题。我们批评常用的滑动窗口技术，并讨论了一些替代方法，用于金融文献中的波动模型，也可以证明在神经成像设置有用。特别是，我们专注于动态条件相关性（DCC）模型，它提供了一个基于模型的方法来估计动态相关性。我们调查了一系列模拟研究中的几种技术的特性，发现DCC在检测相关性的动态变化时，在灵敏度和特异性之间实现了最佳的总体平衡。我们还调查了它的可扩展性超出了双变量的情况下，证明其效用研究两个以上的大脑区域之间的动态相关性。最后，我们说明了它的性能在一个应用程序中测试-重测静息状态的功能磁共振成像数据。
To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-windows technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.
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