Global and System-Specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks
Global and System-Specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks
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DOI：
10.1089/brain.2011.0065
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发表时间：
2011-12-01
影响因子：
3.4
通讯作者：
Shmuel, Amir
中科院分区：
文献类型：
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作者：
Carbonell, Felix;Bellec, Pierre;Shmuel, Amir
全局平均信号（GAS）对基于功能磁共振成像（fMRI）的静息态功能连接的影响是一个持续争论的问题。全球平均波动增加了功能系统之间的相关性，超出了反映其特定功能连接性的相关性。因此，去除 GAS 是促进观察网络特定功能连接性的常见做法。该策略依赖于线性加性模型的隐含假设，根据该模型，全局波动（无论其来源如何）和网络特定波动是叠加的。然而，去除 GAS 会在功能系统之间引入虚假的负相关性，从而使人们对先前关于默认模式和任务正网络波动之间负相关性的发现的有效性产生疑问。在这里，我们提出了一种估计全球波动的替代方法，不受 GAS 相关并发症的影响。主成分分析应用于静息态功能磁共振成像时间序列。全局信号效应估计量被定义为与 GAS 相关性最好的主成分 (PC)。我们提出的基于 PC 的全局效应估计器与 GAS 之间的平均相关系数为 0.97 +/- 0.05，表明我们的估计器成功逼近了 GAS。在 68 次运行中的 66 次中，与 GAS 表现出最高相关性的 PC 是第一台 PC。由于 PC 是正交的，因此我们的方法提供了全局波动的估计量，该估计量与其余的网络特定波动不相关。此外，与 GAS 的回归不同，基于 PC 的全局效应估计器的回归不会引入超出假设加性模型允许的基于种子的相关值减少的虚假反相关。在将这个基于 PC 的估计器从原始时间序列中回归出来后，我们观察到默认模式和任务正网络中的静息状态波动之间存在强大的反相关性。我们得出的结论是，静息态全局波动和网络特定波动是不相关的，支持静息态线性相加模型。此外，我们得出的结论是，默认模式和任务正向网络的网络特定静息状态波动显示出无伪影的反相关性。
The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97 +/- 0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations.