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

• Published in: NeuroImage (Orlando, Fla.) Vol. 84; pp. 320 - 341
• Main Authors: Power, Jonathan D., Mitra, Anish, Laumann, Timothy O., Snyder, Abraham Z., Schlaggar, Bradley L., Petersen, Steven E.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.01.2014; Elsevier; Elsevier Limited
• Subjects: Adolescent; Adult; Algorithms; Biological and medical sciences; Brain - physiology; Brain Mapping - methods; Conflicts of interest; Female; Functional connectivity; Fundamental and applied biological sciences. Psychology; Head Movements - physiology; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Male; Methods; Motion; Movement; MRI; Pattern Recognition, Automated - methods; Reproducibility of Results; Rest - physiology; Resting state; Sensitivity and Specificity; Subtraction Technique; Vertebrates: nervous system and sense organs; Young Adult; Functional connectivity; Motion; Resting state; Movement; MRI; Artifact; Nuclear magnetic resonance imaging; Functional imaging
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.08.048

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 10s 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. •Motion-related signal changes are varied and can persist >10s after motion ceases.•Such signal changes are often shared across almost all brain voxels.•Within-subject correction strategies can eliminate motion-related group differences.•Examines the linearity of motion's influence on resting state correlations