AFNI Message Board

Dear AFNI users-

We are very pleased to announce that the new AFNI Message Board framework is up! Please join us at:

https://discuss.afni.nimh.nih.gov

Existing user accounts have been migrated, so returning users can login by requesting a password reset. New users can create accounts, as well, through a standard account creation process. Please note that these setup emails might initially go to spam folders (esp. for NIH users!), so please check those locations in the beginning.

The current Message Board discussion threads have been migrated to the new framework. The current Message Board will remain visible, but read-only, for a little while.

Sincerely, AFNI HQ

History of AFNI updates  

|
May 06, 2022 07:23AM
Hi pt,
Sorry for the late reply, just realized somehow my reply wasn't posted.. And thanks for the clarification.
I indeed didn't use a residuals as an input. The input dataset I used is the result of the bold signal that our algorithm extracted from the original fMRI signal. I wonder whether it's reasonable to use this program to estimate the smoothness on this sort of data. I also sent you in private message a link of google drive that has this dataset.

This is the exact command I used:
Quote

3dFWHMx -detrend 2 -mask task_motor_GM_mask_re+orig.BRIK.gz -dset sub-012_task-stroop_acq-ME5_negDelta_R2_star_valid.nii.gz -acf sub-012_task-motor_negDelta_R2_star >> output
(I also used mask of the grey matter, because we think the signal should have more correlation within the grey matter)

And this is the output I got.
Quote

++ 3dFWHMx: AFNI version=AFNI_21.1.13 (Jun 21 2021) [64-bit]
++ Authored by: The Bob
++ Number of voxels in mask = 42353
*+ WARNING: removed 1978 voxels from mask because they are constant in time
++ detrending start: 7 baseline funcs, 180 time points
+ detrending done (0.00 CPU s thus far)
++ start ACF calculations out to radius = 10.78 mm
** ERROR: calfun[1]=nan --> 0
** ERROR: calfun[2]=nan --> 0
** calfun error: 0.5 -nan -nan
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** calfun error: 0.174175 -nan -nan
** calfun error: 0.578101 -nan -nan
** calfun error: 0.512978 -nan -nan
** calfun error: 0.477525 -nan -nan
** calfun error: 0.335081 -nan -nan
** calfun error: 0.33583 -nan -nan
** calfun error: 0.273389 -nan -nan
** calfun error: 0.181513 -nan -nan
** calfun error: 0.71641 -nan -nan
** calfun error: 0.294007 -nan -nan
** calfun error: 0.58882 -nan -nan
** calfun error: 0.0863528 -nan -nan
** calfun error: 0.736319 -nan -nan
** calfun error: 0.00924387 -nan -nan
** calfun error: 0.614497 -nan -nan
** calfun error: 0.652714 -nan -nan
** calfun error: 0.123942 -nan -nan
** calfun error: 0.224788 -nan -nan
** calfun error: 0.881058 -nan -nan
** calfun error: 0.457333 -nan -nan
** calfun error: 0.502193 -nan -nan
** calfun error: 0.899085 -nan -nan
** calfun error: 0.581293 -nan -nan
** calfun error: 0.570047 -nan -nan
** calfun error: 0.20683 -nan -nan
** calfun error: 0.322771 -nan -nan
** calfun error: 0.848737 -nan -nan
** calfun error: 0.547163 -nan -nan
** calfun error: 0.325978 -nan -nan
** calfun error: 0.0441218 -nan -nan
** calfun error: 0.281868 -nan -nan
** calfun error: 0.397327 -nan -nan
** calfun error: 0.080625 -nan -nan
** calfun error: 0.270315 -nan -nan
** calfun error: 0.280573 -nan -nan
** calfun error: 0.881108 -nan -nan
** calfun error: 0.513374 -nan -nan
** calfun error: 0.235958 -nan -nan
** calfun error: 0.446922 -nan -nan
** calfun error: 0.665299 -nan -nan
** calfun error: 0.818381 -nan -nan
** calfun error: 0.823295 -nan -nan
** calfun error: 0.258861 -nan -nan
** calfun error: 0.0432392 -nan -nan
** calfun error: 0.487497 -nan -nan
** calfun error: 0.611275 -nan -nan
** calfun error: 0.536781 -nan -nan
** calfun error: 0.736921 -nan -nan
** calfun error: 0.756628 -nan -nan
** calfun error: 0.303757 -nan -nan
** calfun error: 0.464821 -nan -nan
** calfun error: 0.910727 -nan -nan
** calfun error: 0.626032 -nan -nan
** calfun error: 0.586256 -nan -nan
** calfun error: 0.207976 -nan -nan
** calfun error: 0.223704 -nan -nan
** calfun error: 0.415171 -nan -nan
** calfun error: 0.702904 -nan -nan
** calfun error: 0.587506 -nan -nan
** calfun error: 0.430245 -nan -nan
** calfun error: 0.198843 -nan -nan
** calfun error: 0.414961 -nan -nan
** calfun error: 0.522641 -nan -nan
** calfun error: 0.47101 -nan -nan
** calfun error: 0.543195 -nan -nan
** calfun error: 0.405183 -nan -nan
** calfun error: 0.89818 -nan -nan
** calfun error: 0.299442 -nan -nan
** calfun error: 0.963985 -nan -nan
** calfun error: 0.0712973 -nan -nan
** calfun error: 0.424314 -nan -nan
** calfun error: 0.0809394 -nan -nan
** calfun error: 0.605887 -nan -nan
** calfun error: 0.377424 -nan -nan
** calfun error: 0.375948 -nan -nan
** calfun error: 0.759903 -nan -nan
** calfun error: 0.736869 -nan -nan
** calfun error: 0.609803 -nan -nan
** calfun error: 0.650161 -nan -nan
** calfun error: 0.772724 -nan -nan
** calfun error: 0.144644 -nan -nan
** calfun error: 0.0638747 -nan -nan
** calfun error: 0.238442 -nan -nan
** calfun error: 0.494171 -nan -nan
** calfun error: 0.687252 -nan -nan
** calfun error: 0.62956 -nan -nan
** calfun error: 0.146227 -nan -nan
** calfun error: 0.0604715 -nan -nan
** calfun error: 0.531892 -nan -nan
** calfun error: 0.848379 -nan -nan
** calfun error: 0.775491 -nan -nan
** calfun error: 0.048124 -nan -nan
** calfun error: 0.113735 -nan -nan
** calfun error: 0.396366 -nan -nan
** calfun error: 0.963007 -nan -nan
** calfun error: 0.372676 -nan -nan
** calfun error: 0.686703 -nan -nan
** calfun error: 0.829091 -nan -nan
** calfun error: 0.151971 -nan -nan
** calfun error: 0.975385 -nan -nan
** calfun error: 0.715677 -nan -nan
** calfun error: 0.506699 -nan -nan
** calfun error: 0.0694739 -nan -nan
** calfun error: 0.891099 -nan -nan
** calfun error: 0.0435461 -nan -nan
** calfun error: 0.0347871 -nan -nan
** calfun error: 0.130425 -nan -nan
** calfun error: 0.118446 -nan -nan
** calfun error: 0.219661 -nan -nan
** calfun error: 0.611 -nan -nan
** calfun error: 0.590625 -nan -nan
** calfun error: 0.608408 -nan -nan
** calfun error: 0.265953 -nan -nan
** calfun error: 0.78722 -nan -nan
** calfun error: 0.404545 -nan -nan
** calfun error: 0.0777802 -nan -nan
** calfun error: 0.462125 -nan -nan
** calfun error: 0.457497 -nan -nan
** calfun error: 0.3227 -nan -nan
** calfun error: 0.666429 -nan -nan
** calfun error: 0.0652796 -nan -nan
** calfun error: 0.264879 -nan -nan
** calfun error: 0.459242 -nan -nan
** calfun error: 0.1035 -nan -nan
** calfun error: 0.949271 -nan -nan
** calfun error: 0.131041 -nan -nan
** calfun error: 0.507417 -nan -nan
** calfun error: 0.12789 -nan -nan
** calfun error: 0.0824071 -nan -nan
** calfun error: 0.822152 -nan -nan
** calfun error: 0.105807 -nan -nan
** calfun error: 0.889957 -nan -nan
** calfun error: 0.0260475 -nan -nan
** calfun error: 0.700329 -nan -nan
** calfun error: 0.663898 -nan -nan
** calfun error: 0.33825 -nan -nan
** calfun error: 0.0529286 -nan -nan
** calfun error: 0.572073 -nan -nan
** calfun error: 0.821531 -nan -nan
** calfun error: 0.122197 -nan -nan
** calfun error: 0.295808 -nan -nan
** calfun error: 0.791438 -nan -nan
** calfun error: 0.208397 -nan -nan
** calfun error: 0.778501 -nan -nan
** calfun error: 0.916229 -nan -nan
** calfun error: 0.378687 -nan -nan
** calfun error: 0.472189 -nan -nan
** calfun error: 0.354295 -nan -nan
** calfun error: 0.773147 -nan -nan
** calfun error: 0.49172 -nan -nan
** calfun error: 0.24154 -nan -nan
** calfun error: 0.378748 -nan -nan
** calfun error: 0.597846 -nan -nan
** calfun error: 0.0505326 -nan -nan
** calfun error: 0.22891 -nan -nan
** calfun error: 0.281344 -nan -nan
** calfun error: 0.872457 -nan -nan
** calfun error: 0.199892 -nan -nan
** calfun error: 0.894457 -nan -nan
** calfun error: 0.0239702 -nan -nan
** calfun error: 0.596616 -nan -nan
** calfun error: 0.215822 -nan -nan
** calfun error: 0.768214 -nan -nan
** calfun error: 0.203238 -nan -nan
** calfun error: 0.437941 -nan -nan
** calfun error: 0.579581 -nan -nan
** calfun error: 0.251026 -nan -nan
** calfun error: 0.633517 -nan -nan
** calfun error: 0.707309 -nan -nan
** calfun error: 0.240631 -nan -nan
** calfun error: 0.401416 -nan -nan
** calfun error: 0.169393 -nan -nan
** calfun error: 0.843076 -nan -nan
** calfun error: 0.49657 -nan -nan
** calfun error: 0.343405 -nan -nan
** calfun error: 0.806705 -nan -nan
** calfun error: 0.393859 -nan -nan
** calfun error: 0.243727 -nan -nan
** calfun error: 0.0810603 -nan -nan
** calfun error: 0.563173 -nan -nan
** ERROR: Powell: nothing survived 1st round sad smiley
+ ACF done (0.00 CPU s thus far)
*+ WARNING: Zeroed 29 float errors while writing 1D file sub-012_task-motor_negDelta_R2_star
++ ACF 1D file [radius ACF mixed_model gaussian_NEWmodel] written to sub-012_task-motor_negDelta_R2_star
++ 1dplot: AFNI version=AFNI_21.1.13 (Jun 21 2021) [64-bit]
++ Authored by: RWC et al.
pnmtopng: 31 colors found
+ and 1dplot-ed to file sub-012_task-motor_negDelta_R2_star.png

Also, I used this same command except for removing the -detrend option and it ran successfully.

Zixun
Subject Author Posted

-detrend option in 3dFWHMx vs 3dDetrend

Zixun March 30, 2022 02:55AM

Re: -detrend option in 3dFWHMx vs 3dDetrend

ptaylor April 06, 2022 09:56PM

Re: -detrend option in 3dFWHMx vs 3dDetrend

Zixun May 06, 2022 07:23AM

Re: -detrend option in 3dFWHMx vs 3dDetrend

ptaylor May 10, 2022 10:41AM