Sensitivity and Uniformity in Detecting Motion Artifacts

Abstract

Removing artifacts due to head motion is a preprocessing procedure necessary for any fMRI analysis. In fMRI tool boxes, there have been standard algorithms for correcting motion artifacts. However, those tool boxes fail to indicate the extent to which the correction has been successfully done. Without knowing motion contamination especially after correction, the subsequent analysis using averaged fMRI data across subjects could be misleading. In this study, we proposed seven summary indices for measuring motion artifacts. The indices can be applied after motion correction by the image registration algorithms. In the simulation studies, we analyzed a real fMRI data set using a statistical method and estimated the brain activation maps. The real image data were then randomly shifted or rotated to simulate different degrees of head motion. The data contaminated by random motion were then corrected using the SPM image coregistration algorithms. The indices of motion contamination were computed using the corrected images. The corrected images were then analyzed again using the same statistical method. The consistency between the brain activation maps based on real data and those based on simulated data was used as a standard to evaluate the usefulness of the proposed seven indices. The results show that some indices are informative with regards to the degree of motion contamination in preprocessed fMRI data.