Oakes T R, Johnstone T, Ores Walsh K S, Greischar L L, Alexander A L, Fox A S, Davidson R J
Waisman Laboratory for Brain Imaging, University of Wisconsin-Madison, WI 53705, USA.
Neuroimage. 2005 Nov 15;28(3):529-43. doi: 10.1016/j.neuroimage.2005.05.058. Epub 2005 Aug 15.
Motion correction of fMRI data is a widely used step prior to data analysis. In this study, a comparison of the motion correction tools provided by several leading fMRI analysis software packages was performed, including AFNI, AIR, BrainVoyager, FSL, and SPM2. Comparisons were performed using data from typical human studies as well as phantom data. The identical reconstruction, preprocessing, and analysis steps were used on every data set, except that motion correction was performed using various configurations from each software package. Each package was studied using default parameters, as well as parameters optimized for speed and accuracy. Forty subjects performed a Go/No-go task (an event-related design that investigates inhibitory motor response) and an N-back task (a block-design paradigm investigating working memory). The human data were analyzed by extracting a set of general linear model (GLM)-derived activation results and comparing the effect of motion correction on thresholded activation cluster size and maximum t value. In addition, a series of simulated phantom data sets were created with known activation locations, magnitudes, and realistic motion. Results from the phantom data indicate that AFNI and SPM2 yield the most accurate motion estimation parameters, while AFNI's interpolation algorithm introduces the least smoothing. AFNI is also the fastest of the packages tested. However, these advantages did not produce noticeably better activation results in motion-corrected data from typical human fMRI experiments. Although differences in performance between packages were apparent in the human data, no single software package produced dramatically better results than the others. The "accurate" parameters showed virtually no improvement in cluster t values compared to the standard parameters. While the "fast" parameters did not result in a substantial increase in speed, they did not degrade the cluster results very much either. The phantom and human data indicate that motion correction can be a valuable step in the data processing chain, yielding improvements of up to 20% in the magnitude and up to 100% in the cluster size of detected activations, but the choice of software package does not substantially affect this improvement.
功能磁共振成像（fMRI）数据的运动校正是数据分析之前广泛使用的一个步骤。在本研究中，对几个领先的fMRI分析软件包提供的运动校正工具进行了比较，包括AFNI、AIR、BrainVoyager、FSL和SPM2。使用来自典型人体研究的数据以及模拟数据进行了比较。对每个数据集都使用了相同的重建、预处理和分析步骤，只是使用每个软件包的各种配置进行运动校正。每个软件包都使用默认参数以及针对速度和准确性进行优化的参数进行了研究。40名受试者执行了一个Go/No-go任务（一种研究抑制性运动反应的事件相关设计）和一个N-back任务（一种研究工作记忆的组块设计范式）。通过提取一组从通用线性模型（GLM）得出的激活结果，并比较运动校正对阈值化激活簇大小和最大t值的影响，对人体数据进行了分析。此外，创建了一系列具有已知激活位置、幅度和逼真运动的模拟数据组。模拟数据的结果表明，AFNI和SPM2产生的运动估计参数最准确，而AFNI的插值算法引入的平滑最少。AFNI也是所测试软件包中速度最快的。然而，在典型人体fMRI实验的运动校正数据中，这些优势并没有产生明显更好的激活结果。虽然软件包之间在人体数据中的性能差异很明显，但没有一个软件包产生的结果比其他软件包显著更好。与标准参数相比，“准确”参数在簇t值上几乎没有改善。虽然“快速”参数没有导致速度大幅提高，但它们也没有使簇结果大幅下降。模拟和人体数据表明，运动校正在数据处理链中可能是一个有价值的步骤，在检测到的激活幅度上可提高多达20%，在簇大小上可提高多达100%，但软件包的选择并不会对这种改善产生实质性影响。
