Motion artifacts correction in AFNI

AFNI Message Board
1

Hi AFNI experts,

Recently I got the following comments on my manuscript regarding the AFNI preprocessing. Please help to answer these comments:

Following is the detail of the preprocessing steps:

Every single EPI volume was co-registered to the corresponding anatomical image of the subject and mapped to Talairach coordinates space with TT_N27+tlrc template. We exclude first six images from each EPI volume to achieve the MR steady state. In addition, slice timing was corrected. We censored time points based on their number of outliers, and head motion magnitude were cutout. Slice alignment was applied by using the local Pearson’s correlation (lpc) cost function. The correction of head motion along-with averaging the EPI volumes was performed to obtain a mean functional image. The dataset was already reoriented to RPI in the ADHD-200 dataset repository. Each EPI volume undergoes the linear multiple regression to regress the motion derivatives and effects of the white matter and cerebrospinal fluids. Spatial smoothing was performed by using a Gaussian kernel with a blur size of 6mm full-width half maximum (FWHM). A run of 10,000 Monte Carlo simulations was conducted with AlphaSim program. The cluster size of 10 voxels was determined at a family-wise error corrected with p < 0.000001 for the problem of multiple comparisons. As a result, the cluster consisting of lower than 10 voxels were excluded from the analysis.

Comments:

  1. A lot of studies have demonstrated that the pre-processing steps used by the authors still might not fully take care of motion artifacts and we know that there are strong group-level differences in motion parameters.

  2. My main comments to the paper are that the authors do not show how the results could be affected by artifacts and noise in the data that clearly affect global connectivity results.

waiting for the reply,

Thank you

2

I don't know the details of your study nor your analysis, so I can only make some general comment.

  1. A lot of studies have demonstrated that the pre-processing steps used by the authors still might not fully take
    care of motion artifacts and we know that there are strong group-level differences in motion parameters.

One possibility is to control the motion effect at the group level by using those motion parameters as an explanatory variable (i.e., covariate).

  1. My main comments to the paper are that the authors do not show how the results could be affected
    by artifacts and noise in the data that clearly affect global connectivity results.

To address that concern, you may perform another analysis without controlling for the effects of those artifacts and noise, and see how they differ.