Estimation and Removal of Residual Motion Artifact in Retrospectively Motion-Corrected fMRI Data: A Comparison of Intervolume and Intravolume Motion Using Gold Standard Simulated Motion Data

Residual head motion artifact in motion-corrected resting-state (rs-) functional MRI (fMRI) and fMRI datasets reduces the temporal signal-to-noise ratio and leaves non-neuronal signal components in the data, which can induce false findings in these studies. While various residual motion nuisance regressors have been proposed to regress out residual motion artifact after motion correction, these validations have typically been conducted empirically in in vivo data, since realistic head motion–corrupted MR data are not available. Here, we generated motion-corrupted MR data by altering imaging plane coordinates before each volume and slice acquisition from an ex vivo brain phantom using the simulated prospective acquisition correction (SIMPACE) sequence. Testing SIMPACE motion-corrupted data with various intervolume motion patterns, we first investigated the mechanism of the residual motion signal after motion correction and also proposed a voxel-wise motion nuisance regressor, called the partial volume (PV) regressor. We also modified the slice-oriented motion-correction method (SLOMOCO) pipeline with 6 volume-wise rigid intervolume motion parameters (Vol-mopa), 6 slice-wise rigid intravolume motion parameters (Sli-mopa), and the proposed PV motion nuisance regressor. We then compared the residual signal after application of the modified SLOMOCO (mSLOMOCO) pipeline with two other methods: intervolume motion-correction method (VOLMOCO), and the original SLOMOCO (oSLOMOCO). We found that mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors outperformed both VOLMOCO with 6 Vol-mopa and PV regressors and oSLOMOCO with 14 voxel-wise regressors. In tests of the 10 different motion patterns of SIMPACE datasets with 1× and 2× amplified intravolume motion, mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors pipeline produced the average standard deviation (SD) of the residual time series signals in the gray matter (GM) smaller by 29% (1× amplified intravolume motion) and 45% (2× amplified intravolume motion) than VOLMOCO with 6 Vol-mopa and PV regressors pipeline. Also, mSLOMOCO with 12 Vol-/Sli-mopa and PV regressors pipeline outperformed oSLOMOCO with 14 voxelwise regressors pipeline, generating the average SD in GM smaller by 28% (1× amplified intravolume motion) and 31% (2× amplified intravolume motion) than oSLOMOCO with 14 voxel-wise regressors pipeline. The novel PV regressor also effectively reduced residual motion artifact as a motion nuisance regressor after both VOLMOCO and mSLOMOCO.