Jo Hang Joon, Gotts Stephen J, Reynolds Richard C, Bandettini Peter A, Martin Alex, Cox Robert W, Saad Ziad S
Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892-1148, USA.
Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
J Appl Math. 2013 May 21;2013. doi: 10.1155/2013/935154.
Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We first replicate the previously reported head-motion bias on correlation coefficients using data generously contributed by Power et al. (2012). We then show that while motion can be the source of artifact in correlations, the distance-dependent bias-taken to be a manifestation of the motion effect on correlation-is exacerbated by the use of GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that WMe, as subset of the ANATICOR discussed by Jo et al. (2010), denoising approach results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.
静息态功能磁共振成像（RS fMRI）的伪影来源可能源于头部运动、生理因素和硬件。在这些来源中，运动受到了相当多的关注，并且发现它会根据区域之间的距离差异对相关性产生偏差，从而引发破坏效应。许多校正方法依赖于识别和剔除高运动时间点，并使用全脑平均时间序列作为干扰回归变量对数据进行正交化处理（全局信号回归，GSReg）。我们首先使用Power等人（2012年）慷慨提供的数据，重现了先前报道的头部运动对相关系数的偏差。然后我们表明，虽然运动可能是相关性伪影的来源，但通过使用GSReg，距离依赖性偏差（被视为运动对相关性影响的一种表现）会加剧。换句话说，GSReg后获得的相关性估计更容易受到运动的影响，进而受到剔除水平的影响。更一般地说，运动对相关性估计的影响取决于导致相关性估计的预处理步骤，某些方法的表现明显比其他方法差。为此，我们考虑了各种用于RS fMRI预处理的模型，并表明WMe作为Jo等人（2010年）讨论的ANATICOR的一个子集，去噪方法对运动的敏感性最小，并进而降低了相关性结果对剔除的依赖性。
