Murphy Kevin, Birn Rasmus M, Handwerker Daniel A, Jones Tyler B, Bandettini Peter A
Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20892-1148, USA.
Neuroimage. 2009 Feb 1;44(3):893-905. doi: 10.1016/j.neuroimage.2008.09.036. Epub 2008 Oct 11.
Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.
功能磁共振成像（fMRI）信号中的低频波动已被用于绘制大脑中几个一致的静息态网络。以后扣带回皮质作为种子区域，功能连接分析不仅发现默认模式网络中存在正相关，而且在另一个与注意力过程相关的静息态网络中存在负相关。其解释是，人类大脑本质上被组织成动态的、反相关的功能网络。血氧水平依赖（BOLD）信号的全局变化通常被视为干扰效应，并且通常使用一般线性模型（GLM）技术将其去除。这种全局信号回归方法已被证明在标准的fMRI分析中会引入负激活测量值。本文的主题是，在功能连接分析中，这样一种校正技术是否可能是反相关静息态网络的原因。在这里我们表明，在全局信号回归之后，与一个种子体素的相关值之和必须为负值。模拟还表明，区域之间的小相位差异会导致虚假的负相关值。屏气和视觉任务相结合的实验表明，全局信号和局部信号的相对相位会影响连接性测量值，并且在实验中，全局信号回归会导致以零为中心的钟形相关值分布。最后，对静息态数据中负相关网络的分析表明，全局信号回归很可能是反相关的原因。当将全局信号回归作为初始处理步骤时，这些结果对大脑中负相关区域的解释提出了质疑。
