Department of Psychiatry, University of Wisconsin Madison, Madison, WI 53719, USA.
Neuroimage. 2012 Aug 15;62(2):864-70. doi: 10.1016/j.neuroimage.2012.01.016. Epub 2012 Jan 8.
Functional connectivity between different brain regions can be estimated from MRI data by computing the temporal correlation of low frequency (<0.1Hz) fluctuations in the MRI signal. These correlated fluctuations occur even when the subject is "at rest" (not asked to perform any particular task) and result from spontaneous neuronal activity synchronized within multiple distinct networks of brain regions. This estimate of connectivity, however, can be influenced by physiological noise, such as cardiac and respiratory fluctuations. This brief review looks at the effect of physiological noise on estimates of resting-state functional connectivity, discusses ways to remove physiological noise, and provides a personal recollection of the early developments in these approaches. This review also discusses the importance of physiological noise correction and provides a summary of evidence demonstrating that functional connectivity does have a neuronal underpinning and cannot purely be the result of physiological noise.
不同脑区之间的功能连接可以通过计算 MRI 信号中低频(<0.1Hz)波动的时间相关性从 MRI 数据中估计。这些相关的波动即使在受试者“休息”(不要求执行任何特定任务)时也会发生,并且是由多个不同的脑区网络中的自发神经元活动同步产生的。然而,这种连接的估计可能会受到生理噪声的影响,例如心脏和呼吸波动。这篇简短的综述探讨了生理噪声对静息状态功能连接估计的影响,讨论了去除生理噪声的方法,并提供了对这些方法早期发展的个人回忆。这篇综述还讨论了生理噪声校正的重要性,并总结了证明功能连接确实有神经元基础,而不能纯粹是生理噪声结果的证据。