Trouble at rest: how correlation patterns and group differences become distorted after global signal regression.
Trouble at rest: how correlation patterns and group differences become distorted after global signal regression.
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DOI：
10.1089/brain.2012.0080
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发表时间：
2012
影响因子：
3.4
通讯作者：
Cox RW
中科院分区：
文献类型：
--
作者：
Saad ZS;Gotts SJ;Murphy K;Chen G;Jo HJ;Martin A;Cox RW
静息状态功能磁共振成像（RS-FMRI）有望揭示大脑功能连接，而不需要针对特定大脑系统的特定任务。即使缺乏传统推断的特定候选目标，RS-FMRI也被用于发现人群之间的差异。然而，RS-FMRI的问题在于缺乏对噪声和信号构成的定义。RS-FMRI易于获取，但不便于分析或推断。在这篇评论中，我们讨论了一个尽管对RS-FMRI推断有重大影响但仍被忽视的问题：全局信号回归（GSReg）——在整个大脑中投射平均信号的做法——可以以显著改变相关模式的方式改变静息状态相关性，从而得出关于大脑功能连接的结论。虽然证明GSReg负偏倚相关性，该方法仍然被广泛使用。我们重新审视这个问题，认为gsregg的问题不仅仅是负偏倚或负相关的可解释性。它的使用可以从根本上改变一个群体内部的区域间相关性，或者群体之间的差异。我们使用了一个说明性模型来清楚地表达我们的反对意见，并推导出公式来形式化我们的结论。我们希望这将创造一个明确的背景，以便提出反驳意见。我们得出结论，GSReg不应该用于RS-FMRI研究，因为GSReg在不同区域的相关性偏差不同，这取决于潜在的真实区域间相关性结构。GSReg可以改变局部和长期的相关性，潜在地将潜在的群体差异传播到可能从未有过的地区。结论也适用于用在网络区域上聚合的信号分解来代替GSReg去噪，因为它们不能将感兴趣的信号从噪声中分离出来。在对相关图进行分组比较时，我们谈到了仔细计算干扰参数的必要性。
Resting-State Functional Magnetic Resonance Imaging (RS-FMRI) holds the promise of revealing brain functional connectivity without requiring specific tasks targeting particular brain systems. RS-FMRI is being used to find differences between populations even when a specific candidate target for traditional inferences is lacking. However, the problem with RS-FMRI is a lacking definition of what constitutes noise and signal. RS-FMRI is easy to acquire but not to analyze or draw inferences from. In this commentary we discuss a problem that is still treated lightly despite its significant impact on RS-FMRI inferences: Global Signal Regression (GSReg) – the practice of projecting out signal averaged over the entire brain – can change resting state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness. Although demonstrated GSReg negatively biases correlations, the approach remains in wide use. We revisit this issue to argue the problem with GSReg is more than negative bias or the interpretability of negative correlations. Its usage can fundamentally alter inter-regional correlations within a group, or their differences between groups. We used an illustrative model to clearly convey our objections and derived equations formalizing our conclusions. We hope this creates a clear context in which counterarguments can be made. We conclude that GSReg should not be used when studying RS-FMRI because GSReg biases correlations differently in different regions depending on the underlying true inter-regional correlation structure. GSReg can alter local and long-range correlations, potentially spreading underlying group differences to regions that may never have had any. Conclusions also apply to substitutions of GSReg for denoising with decompositions of signals aggregated over the network’s regions to the extent they cannot separate signals of interest from noise. We touch on the need for careful accounting of nuisance parameters when making group comparisons of correlation maps.