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

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

2014 January ; 84 ; . doi:10.1016/j.neuroimage.2013.08.048 | Jonathan D Power, Anish Mitra, Timothy O Laumann, Abraham Z Snyder, Bradley L Schlaggar, and Steven E Petersen
This paper examines the impact of head motion on resting state functional connectivity (RSFC) in fMRI data. The authors find that motion-induced signal changes are complex, variable, and often persist for more than 10 seconds after motion ceases. These changes increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not effectively removed by various motion-based regressors but are reduced by global signal regression. The paper also links several data quality measures to motion, signal intensity changes, and RSFC correlations, suggesting that improvements in these measures may be cosmetic rather than true corrections. A censoring-based artifact removal strategy is proposed, which reduces group differences due to motion to chance levels. The authors discuss the conditions under which group-level regressions can and cannot correct motion-related effects. The study uses 160 healthy subjects from four cohorts, including adult and child groups, to demonstrate the effectiveness of the proposed methods.This paper examines the impact of head motion on resting state functional connectivity (RSFC) in fMRI data. The authors find that motion-induced signal changes are complex, variable, and often persist for more than 10 seconds after motion ceases. These changes increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not effectively removed by various motion-based regressors but are reduced by global signal regression. The paper also links several data quality measures to motion, signal intensity changes, and RSFC correlations, suggesting that improvements in these measures may be cosmetic rather than true corrections. A censoring-based artifact removal strategy is proposed, which reduces group differences due to motion to chance levels. The authors discuss the conditions under which group-level regressions can and cannot correct motion-related effects. The study uses 160 healthy subjects from four cohorts, including adult and child groups, to demonstrate the effectiveness of the proposed methods.
Reach us at info@study.space
Understanding Methods to detect%2C characterize%2C and remove motion artifact in resting state fMRI