motion_subj_censor.1D and outcount.run.1D

Dear all,

I’m trying to understand what the y-axis is exactly in these two graphs. I understand that in motion_subj_censor.1D, the TRs with “0” in the y-axis are those that have been censored and removed, is that correct?
Also, I understand that outcount.run.1D shows the number of outliers per TR, but what does a 0.015 value in the y-axis mean, for example?

Thank you for any input!

Hi-

I would check out the AFNI Bootcamp slide presentation that discusses the various uses of alignment (afni14*pdf), in particular how alignment (and outlier fraction within a volume) is used for censoring; if you don’t already have it on your computer, you can get the presentation by running the following:


afni_open -aw afni14_alignment.pdf

See slides 40 and following. Formulas are given.

For motion censoring, we combine the 6 motion parameter time series into a single Enorm (=Euclidean norm) of motion time series-- it describes the differential motion from volume to volume across time, roughly in units of mm. You, the user, set a threshold for which to censor: if Enorm is goes above that value (say, 0.2), then both time points used to estimate it (since it is a difference-based quantity) are censored.

The outlier count shows the fraction of voxels within an EPI brain mask that are outliers of their time series (before doing any alignment). If you have a lot of outliers in a volume, odds are motion or something else bad happened there. Again, you can apply a censor criterion to this time series: if the outlier fraction is >0.05, that means that more than 5% of voxels in the mask were outliery, and you might want to censor it (for example).

–pt