Johnstone Tom, Ores Walsh Kathleen S, Greischar Larry L, Alexander Andrew L, Fox Andrew S, Davidson Richard J, Oakes Terrence R
Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, Wisconsin 53705, USA.
Hum Brain Mapp. 2006 Oct;27(10):779-88. doi: 10.1002/hbm.20219.
The impact of using motion estimates as covariates of no interest was examined in general linear modeling (GLM) of both block design and rapid event-related functional magnetic resonance imaging (fMRI) data. The purpose of motion correction is to identify and eliminate artifacts caused by task-correlated motion while maximizing sensitivity to true activations. To optimize this process, a combination of motion correction approaches was applied to data from 33 subjects performing both a block-design and an event-related fMRI experiment, including analysis: (1) without motion correction; (2) with motion correction alone; (3) with motion-corrected data and motion covariates included in the GLM; and (4) with non-motion-corrected data and motion covariates included in the GLM. Inclusion of covariates was found to be generally useful for increasing the sensitivity of GLM results in the analysis of event-related data. When motion parameters were included in the GLM for event-related data, it made little difference if motion correction was actually applied to the data. For the block design, inclusion of motion covariates had a deleterious impact on GLM sensitivity when even moderate correlation existed between motion and the experimental design. Based on these results, we present a general strategy for block designs, event-related designs, and hybrid designs to identify and eliminate probable motion artifacts while maximizing sensitivity to true activations.
在块设计和快速事件相关功能磁共振成像（fMRI）数据的一般线性模型（GLM）中，研究了将运动估计用作无关协变量的影响。运动校正的目的是识别并消除由任务相关运动引起的伪影，同时最大限度地提高对真实激活的敏感性。为了优化此过程，将多种运动校正方法组合应用于33名同时进行块设计和事件相关fMRI实验的受试者的数据，包括以下分析：（1）不进行运动校正；（2）仅进行运动校正；（3）使用运动校正后的数据并将运动协变量纳入GLM；以及（4）使用未进行运动校正的数据并将运动协变量纳入GLM。发现在事件相关数据的分析中，纳入协变量通常有助于提高GLM结果的敏感性。对于事件相关数据，当将运动参数纳入GLM时，是否实际对数据应用运动校正影响不大。对于块设计，当运动与实验设计之间即使存在适度相关性时，纳入运动协变量也会对GLM敏感性产生有害影响。基于这些结果，我们提出了一种适用于块设计、事件相关设计和混合设计的通用策略，以识别并消除可能的运动伪影，同时最大限度地提高对真实激活的敏感性。
