Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research and Poitras Center for Affective Disorders Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Neuroimage. 2012 Jan 16;59(2):1420-8. doi: 10.1016/j.neuroimage.2011.08.048. Epub 2011 Aug 26.
Anticorrelated relationships in spontaneous signal fluctuation have been previously observed in resting-state functional magnetic resonance imaging (fMRI). In particular, it was proposed that there exists two systems in the brain that are intrinsically organized into anticorrelated networks, the default mode network, which usually exhibits task-related deactivations, and the task-positive network, which usually exhibits task-related activations during tasks that demands external attention. However, it is currently under debate whether the anticorrelations observed in resting state fMRI were valid or were instead artificially introduced by global signal regression, a common preprocessing technique to remove physiological and other noise in resting-state fMRI signal. We examined positive and negative correlations in resting-state connectivity using two different preprocessing methods: a component base noise reduction method (CompCor, Behzadi et al., 2007), in which principal components from noise regions-of-interest were removed, and the global signal regression method. Robust anticorrelations between a default mode network seed region in the medial prefrontal cortex and regions of the task-positive network were observed under both methods. Specificity of the anticorrelations was similar between the two methods. Specificity and sensitivity for positive correlations were higher under CompCor compared to the global regression method. Our results suggest that anticorrelations observed in resting-state connectivity are not an artifact introduced by global signal regression and might have biological origins, and that the CompCor method can be used to examine valid anticorrelations during rest.
在静息态功能磁共振成像（fMRI）中，先前已经观察到自发信号波动中的反相关关系。特别是，有人提出大脑中存在两个系统，它们内在地组织成反相关网络，即默认模式网络，通常在与任务相关的去激活时表现出活动；而任务正网络，通常在需要外部注意力的任务中表现出与任务相关的激活。然而，目前仍存在争议的是，静息态 fMRI 中观察到的反相关是否是有效的，还是被全局信号回归这种常见的预处理技术人为引入的，全局信号回归是一种去除静息态 fMRI 信号中生理和其他噪声的常用预处理技术。我们使用两种不同的预处理方法来检查静息状态连接中的正相关和负相关：一种是基于组件的降噪方法（CompCor，Behzadi 等人，2007 年），其中去除了来自噪声感兴趣区域的主成分；另一种是全局信号回归方法。在这两种方法下，都观察到内侧前额叶皮质中的默认模式网络种子区域与任务正网络区域之间存在稳健的反相关。两种方法之间的反相关特异性相似。与全局回归方法相比，CompCor 下的正相关特异性和敏感性更高。我们的结果表明，静息状态连接中观察到的反相关不是全局信号回归引入的伪影，可能具有生物学起源，并且 CompCor 方法可用于在休息时检查有效的反相关。
