-detrend option in 3dFWHMx vs 3dDetrend

Zixun · March 30, 2022, 6:55am

Hello,

I’m reading the notes of 3dFWHMx, which says
‘If you don’t want to go to that much trouble, use ‘-detrend’ to approximately subtract out the anatomical spatial structure, OR use the output of 3dDetrend for the same purpose.’

I tried both option on my dataset. First I using 3dDetrend -polort 2 -prefix det dset, and using the output as input for 3dFWHMx. The programs ran successfully. But then I tried using detrend option directly, i.e., 3dFWHMx -detrend 2 -mask aaa -acf bbb -input dset. The program failed the model fitting and showed a list of calfun error.
The output plots are also quite different. I paste their google drivelink as follows, if you are interested, please take a look.
https://drive.google.com/file/d/1d8QfbvLRvvcosUxJR8iftFicV-YtuZfT/view?usp=sharing
https://drive.google.com/file/d/1cM02JCWkNI8x_mUN6_FHiBdfeahhJYyS/view?usp=sharing

So, my question is the value following -detrend in 3dFWHMx and that following -polort 2 in 3dDetrend a different parameter? If so, what’s the difference and is there any explanation for the shape of the plot from 3dFWHMx -detrend 2?

Best regard,
Zixun

ptaylor · April 7, 2022, 1:56am

Hi-

This is interesting. I did a little bit of example testing and had an illuminating discussion with The Bob (with whom all discussions are illuminating) about this.

Can I ask what dataset you are using?

3dFWHMx is basically meant to be run on residuals—things that should be pretty deeply detrended already. This is because the predominant usage of this program is to estimate spatial distribution of noise in the data—and residuals are typically the approximation to noise that FMRI produces.

3dFWHMx’s detrending is different than 3dDetrend’s; as the help notes elsewhere, under the “-detrend …” option:


**N.B.: This is the same detrending as done in 3dDespike;
                using 2*q+3 basis functions for q > 0.

3dDespike uses trig functions to remove spikes/detrend (I think it might be something like a finite order approximation to Fourier expansion, with a bit of extra polynomial fitting).

Different detrending will affect 3dFWHMx results, but if you are inputting residuals, the differences should be pretty minimal (hence why I asked what you were inputting, above).

I’m not sure what the calfun error is. If you post the full output (along with input description), that might help.

–pt

Zixun · May 6, 2022, 11:23am

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:

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.

++ 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
** calfun error: 0.901839 -nan -nan
** calfun error: 0.0870421 -nan -nan
** calfun error: 0.628958 -nan -nan
** calfun error: 0.730271 -nan -nan
** calfun error: 0.347628 -nan -nan
** calfun error: 0.928938 -nan -nan
** calfun error: 0.49169 -nan -nan
** calfun error: 0.0139185 -nan -nan
** calfun error: 0.278045 -nan -nan
** calfun error: 0.300115 -nan -nan
** calfun error: 0.653391 -nan -nan
** calfun error: 0.315777 -nan -nan
** calfun error: 0.590435 -nan -nan
** calfun error: 0.442586 -nan -nan
** calfun error: 0.643492 -nan -nan
** calfun error: 0.279189 -nan -nan
** calfun error: 0.217996 -nan -nan
** calfun error: 0.790897 -nan -nan
** calfun error: 0.136375 -nan -nan
** calfun error: 0.633498 -nan -nan
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** calfun error: 0.943952 -nan -nan
** calfun error: 0.0503887 -nan -nan
** calfun error: 0.887808 -nan -nan
** calfun error: 0.316153 -nan -nan
** calfun error: 0.326987 -nan -nan
** calfun error: 0.452858 -nan -nan
** calfun error: 0.225948 -nan -nan
** calfun error: 0.554309 -nan -nan
** calfun error: 0.0954439 -nan -nan
** calfun error: 0.482207 -nan -nan
** calfun error: 0.953995 -nan -nan
** calfun error: 0.0293619 -nan -nan
** calfun error: 0.532326 -nan -nan
** calfun error: 0.589774 -nan -nan
** calfun error: 0.644046 -nan -nan
** calfun error: 0.421759 -nan -nan
** calfun error: 0.735954 -nan -nan
** calfun error: 0.859767 -nan -nan
** calfun error: 0.89774 -nan -nan
** calfun error: 0.0896146 -nan -nan
** calfun error: 0.427703 -nan -nan
** calfun error: 0.372699 -nan -nan
** calfun error: 0.35428 -nan -nan
** calfun error: 0.952295 -nan -nan
** calfun error: 0.164019 -nan -nan
** calfun error: 0.719498 -nan -nan
** calfun error: 0.828976 -nan -nan
** calfun error: 0.706215 -nan -nan
** calfun error: 0.701248 -nan -nan
** calfun error: 0.923176 -nan -nan
** calfun error: 0.960699 -nan -nan
** calfun error: 0.867074 -nan -nan
** calfun error: 0.46137 -nan -nan
** calfun error: 0.455997 -nan -nan
** calfun error: 0.697449 -nan -nan
** 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
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

ptaylor · May 10, 2022, 2:41pm

Hi, Zixun-

OK, thanks for sharing the data.

A couple things to note: The EPI dataset has a wide range of values, from -637911 to 314724, but it also includes supertiny, nonzero values, of order 10**-28 and 10**-31. The mask that is being used with it also doesn’t seem to perfectly fit the data.

The large range of values isn’t really a problem, but I am surprised to see it. The source of the calfun errors is having entire time series that have supertiny nonzero values—the mask doesn’t remove those. Also, automasking didn’t work well with this dset because it has a somewhat filamentary spatial structure. So, to generate a mask where time series were non-zero and non-supertiny, I did the following:


3dcalc                                                                       \
    -a          DSET_EPI'[0]'                                                \
    -expr       'step(a-0.001)+step(-a-0.001)'                               \
    -prefix     mask_estimate2.nii.gz                                        \
    -overwrite

(Note that you could adjust the windowing around zero, which is masking out anything that has magnitude <0.001.)

After doing that, I reran your 3dFWHMx command:


3dFWHMx                                                                      \
    -detrend  2                                                              \
    -mask  mask_estimate2.nii.gz \
    -dset     sub-012_task-motor_acq-ME5_negDelta_R2_star.nii.gz             \
    -acf      sub-012_task-motor_negDelta_R2_star    >> output3dFWHMx

… and there were no calfun errors. Therefore, those were a result of the supertiny nonzero values, or quasi-zero time series, being present in the non-masked region.

The resulting ACF image looked pretty reasonable, I would say. Though, I will note again the caveat that this program was designed for working on residual datasets.

–pt