Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation.
Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation.
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DOI：
10.1016/j.neuroimage.2016.04.047
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发表时间：
2016-07-15
期刊：
影响因子：
5.7
通讯作者：
Henson RN
中科院分区：
文献类型：
--
作者：
Geerligs L;Cam-Can;Henson RN
对全脑功能连通性或结构协方差的研究通常使用皮尔逊相关系数等衡量标准，该系数适用于感兴趣区域(ROI)内体素的平均数据。然而，当那些ROI内存在不均质性时，例如，表现出不同的功能连通性或结构协方差模式的子区域，跨体素的平均可能导致有偏向的连通性估计。这里，我们提出了一种新的基于“距离相关性”的度量：高维向量的多元相关性检验，它同时考虑了线性相关性和非线性相关性。我们使用模拟来展示在面对非均匀感兴趣区时，距离相关性如何优于皮尔逊相关性。为了在真实数据上评估这一新措施，我们使用了来自剑桥老龄化和神经科学中心(CaM-CAN)项目的214名参与者的两次会议的静息状态fMRI扫描和T1结构扫描。皮尔逊相关性和距离相关性在功能连通性和结构协方差方面都显示出相似的平均连通性模式。然而，距离相关性被显示为1)跨会话更可靠，2)参与者之间更相似，以及3)对不同的ROI集合更稳健。此外，我们发现距离相关的功能连通性和结构协方差估计之间的相似性比皮尔逊相关性更高。我们还探索了不同的前处理选项和运动伪影对功能连接性的相对影响。由于距离相关性易于实现和快速计算，因此它是研究基于ROI的全脑连接模式的一种很有前途的替代方法，适用于功能数据和结构数据。我们引入了距离相关性作为一种新的基于ROI的连通性度量。它可用于功能连通性和结构协方差分析。仿真结果表明，距离相关可以解决非均匀感兴趣区域的问题。在实际数据中，距离相关比皮尔逊相关更可靠、更稳健。距离相关性改进了不同ROI定义之间的一致性。
Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on “distance correlation”; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data. We introduce distance correlation as a new measure of ROI-based connectivity. It can be used for functional connectivity and structural covariance analyses. Simulations show that distance correlation copes with inhomogeneous ROIs. In real data, distance correlation is more reliable and robust than Pearson correlation. Distance correlation improves correspondence between different ROI definitions.