Covariance shrinkage can assess and improve functional connectomes.
Covariance shrinkage can assess and improve functional connectomes.
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DOI：
10.1016/j.neuroimage.2022.119229
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发表时间：
2022-08-01
期刊：
影响因子：
5.7
通讯作者：
Habes, Mohamad
中科院分区：
文献类型：
--
作者：
Honnorat, Nicolas;Habes, Mohamad
来自静息态功能 MRI 扫描的连接体显着受益于专用 fMRI 运动校正和去噪算法的开发。但它们基于经验相关性，在高维低样本量设置中可能会产生不可靠的结果。一系列统计估计器，即协方差收缩方法，可以缓解这个问题。不幸的是，这些方法很少用于纠正功能性连接体，并且迄今为止还没有进行广泛的实验来比较可用于此任务的收缩方法。在这项工作中，我们建议通过处理由人类连接组项目提供的一千个高分辨率静息态 fMRI 扫描组成的基准数据集来解决这个问题，以比较五种著名的协方差收缩方法在不同空间分辨率和扫描持续时间下产生可靠的功能连接组的能力：Ledoit 和 Wolf 引入的先驱线性协方差收缩方法、Oracle 近似收缩、QuEST 方法、NERCOME 方法以及最近的分析近似QuEST 方法。我们的实验表明，所有协方差收缩方法都能显着改善源自短功能磁共振成像扫描的功能连接体。 Oracle Approximating Shrinkage 和 QuEST 方法产生了最佳结果。最后，我们提供了可用于设计和分析功能磁共振成像研究的收缩强度图表。这些图表表明，稀疏连接体很难通过短功能磁共振成像扫描来估计，并且它们描述了一系列不应计算动态功能连接的设置。
Connectomes derived from resting-state functional MRI scans have significantly benefited from the development of dedicated fMRI motion correction and denoising algorithms. But they are based on empirical correlations that can produce unreliable results in high dimension low sample size settings. A family of statistical estimators, the covariance shrinkage methods, could mitigate this issue. Unfortunately, these methods have rarely been used to correct functional connectomes and no extensive experiment has been conducted so far to compare the shrinkage methods available for this task. In this work, we propose to fix this issue by processing a benchmark dataset made of a thousand high-resolution resting-state fMRI scans provided by the Human Connectome Project to compare the ability of five prominent covariance shrinkage methods to produce reliable functional connectomes at different spatial resolutions and scans duration: the pioneer linear covariance shrinkage method introduced by Ledoit and Wolf, the Oracle Approximating Shrinkage, the QuEST method, the NERCOME method, and a recent analytical approximation of the QuEST approach. Our experiments establish that all covariance shrinkage methods significantly improve functional connectomes derived from short fMRI scans. The Oracle Approximating Shrinkage and the QuEST method produced the best results. Lastly, we present shrinkage intensity charts that can be used for designing and analyzing fMRI studies. These charts indicate that sparse connectomes are difficult to estimate from short fMRI scans, and they describe a range of settings where dynamic functional connectivity should not be computed.
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