Model-based attenuation of movement artifacts in fMRI

Behavioral analysis of multi-joint arm reaching has allowed important advances in understanding the control of voluntary movements. Complementing this analysis with functional magnetic resonance imaging (fMRI) would give insight into the neural mechanisms behind this control. However, fMRI is very sensitive to artifacts created by head motion and magnetic field deformation caused by the moving limbs. It is thus necessary to attenuate these motion artifacts in order to obtain correct activation patterns. Most algorithms in literature were designed for slow changes of head position over several brain scans and are not very effective on data when the movement is of duration below the resolution of a brain scan. This paper introduces a simple model-based method to remove motion artifacts during short duration movements. The proposed algorithm can account for head movement and field deformations due to movements within and outside of the scanner's field of view. It uses information from the experimental design and subject kinematics to focus the artifact attenuation in time and space and minimize the loss of uncorrupted data. Applications of the algorithm on arm reaching experimental data obtained with blocked and event-related designs demonstrate attenuation of motion artifacts with minimal effect on the brain activations.