Hi AFNI experts,
I am using afni_proc.py program to do pre-processing steps on fmri resting data in anesthesia state, and i found that one of the data is excluded because of censoring too many time points, but after checking 6-d head motion curves by 1d_plot, it is showed as follow, this participant in anesthesia state did not have motion larger than one voxel. I thought it is causing by breathing machine, and do anyone think if it is a valid data or not?
Is it possible that the censoring is coming from the outliers and not the motion?
Thanks for your reply. In this case, I did not add censor_outliers option, so it could be sure that they are censored out by head motion.
What is the max displacement for your data? There should be more info in the summary saved after the afni_proc script is executed.
How many time points?
Thanks for your attention. As for the max displacement, it is not larger than 3, which is the resolution of voxels. And this dataset got 240 time points in total, but it had been censored out above half time points.
That I-S oscillation in the motion parameter time series looks like like it tends to peak around 0.3 or more. If you are using the sample censor limit of 0.3, I am not surprised that half of the time points are getting censored out.
The more important question is regarding the effect of the breathing machine on the data. Is the machine actually making the subject’s brain move that much, or is there something more subtle going on?
Look at the original data and slowly step through time. Does it look like the whole brain is going up and down by so much? Maybe, maybe not.
Thanks for your advice, I will check the data step by step.