Methods to detect, characterize, and remove motion artifact in resting state fMRI

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• 作者：Jonathan D. Power ; Anish Mitra ; Timothy O. Laumann ; Abraham Z. Snyder ; Bradley L. Schlaggar ; Steven E. Petersen
• 关键词：Resting state ; Functional connectivity ; MRI ; Artifact ; Motion ; Movement
• 刊名：NeuroImage
• 出版年：1 January, 2014
• 年：2014
• 卷：84
• 期：Complete
• 页码：320-341
• 全文大小：5035 K

文摘

Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10 s after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.