Motion-corrected independent component analysis for robust functional magnetic resonance imaging

Head movement during fMRI data collection can result in confusing artifacts when estimating task-related brain activity. We propose an improved version of motion-corrected independent component analysis (MCICA), which mitigates the motion effects of the fMRI time-series by maximizing the entropy difference between the observed fMRI data and a nonlinear function of the derived ICA components. Specifically, the improved MCICA algorithm operates on all timepoints, removing the requirement for the existence of enough motionless timepoints in the time-series and the need to detect motion-corrupted timepoints. Simulations demonstrate that MCICA is robust to activity level and the results are more accurate than cubic interpolation, even when the displacement is known. In real data from a motor fMRI experiment, preprocessing the data with MCICA resulted in the emergence of activity in the primary motor and supplementary motor cortices, and the mutual information between all subsequent volumes and the first one was increased.