Hey Rick,

Thanks for your help with that, marrying the -1:0 resolved the problem.

In running afni_proc.py I included the lines "regress_censor_motion 0.3" and "regress_censor_outliers 0.1"
I'm now confused interpreting the output of the @ss_review_basic for one of my subjects. I'll paste the relevant info for my question just below:


TRs removed (per run) : 0
motion limit : 0.3
num TRs above mot limit : 103
average motion (per TR) : 0.229305
average censored motion : 0.0916467
max motion displacement : 7.61252
max censored displacement : 4.93871
outlier limit : 0.1
average outlier frac (TR) : 0.0191761
num TRs above out limit : 41
num runs found : 2
num TRs per run : 287 291
TRs total (uncensored) : 578

TRs censored : 148
censor fraction : 0.256055
num regs of interest : 4
num TRs per stim : 305 0 311 0
num TRs censored per stim : 72 0 81 0
fraction TRs censored : 0.236 0.000 0.260 0.000


So the numbers seem discrepant and I'm confused why. I should think that the total number of TRs that are censored would equal the number of TRs above the outlier limit (41) plus the number of TRs above the motion limit (103) = 144. However, 144 is different than the number of TRs the review claims to have censored, which is 148.
Furthermore, the number of TRs censored per stim (72, 0, 81, 0 = 153) does not add up to the total number of claimed censored TRs either.

Lastly, the afni_proc.py help page claims that "By default, the TR prior to the large motion derivative will also be censored. To turn off that behavior, use -regress_censor_prev with parameter 'no'."
I did not include this additional command, so why wouldn't the number of total censored TRs be the num of TRs above the motion limit (103) doubled (206) and then added to number of TRs above outlier limit (41), for grand total of 247?

I recognize that this subject's data is terrible with a lot of motion and I'll exclude them anyway, but I wanted to run the script on sucky data so I could understand what was happening with the censoring process.

Please forgive my ignorance and/or poor interpretation. Thank you for all your help, it is making learning how to use afni much less painful.