AFNI version info (afni -ver): AFNI_24.0.10 'Caracalla'
Hi,
I would like to compute signal percent change from a hypercapnia task. I've used the following 3dDeconvolve command:
3dDeconvolve -overwrite -input "${inputFile}" \
-mask "${mask}" \
-polort 1 -jobs 12 \
-num_stimts 1 \
-stim_label 1 ${TASK} -stim_times_AM1 1 OnsetsGasChallenge.1D 'dmUBLOCK' \
-tout -fout \
-bucket "test_bucket.spm.OLS.nii.gz" \
-x1D "test.spm.xmat.1D" -xjpeg "test.spm.xmat.jpg"
where the paradigm consists on 5 min rest, 5 min block of hypercapnia and 5 min rest, so the .1D with the onset times for -stim_times_AM1 is "300:300".
My question is: are the values in the #X_coeff bricks in percent signal change (multiplied by 100)? If they are not, how can I compute them?
Thank you in advance!
Hello,
just pinning this post again for attention.
Looking forward to your advice.
Inés
Hi-
If you were using afni_proc.py with the scale block, in the step before regression your time series data would be scaled per-voxel, which we thinks makes the most sense for scaling:
- Chen G, Taylor PA, Cox RW (2017). Is the statistic value all we should care about in neuroimaging? Neuroimage. 147:952-959. doi:10.1016/j.neuroimage.2016.09.066
Is the statistic value all we should care about in neuroimaging? - PubMed
- Reynolds RC, Glen DR, Chen G, Saad ZS, Cox RW, Taylor PA (2024). Processing, evaluating and understanding FMRI data with afni_proc.py. arXiv:2406.05248 [q-bio.NC]
[2406.05248] Processing, evaluating and understanding FMRI data with afni_proc.py
- Taylor PA, Chen G, Glen DR, Rajendra JK, Reynolds RC, Cox RW (2018). FMRI processing with AFNI: Some comments and corrections on ‘Exploring the Impact of Analysis Software on Task fMRI Results’. bioRxiv 308643; doi:10.1101/308643
https://www.biorxiv.org/content/10.1101/308643v1.abstract
The formula for scaling the time series is:
3dTstat -prefix DSET_MEAN DSET_TIME_SERIES_IN
3dcalc \
-a DSET_TIME_SERIES_IN \
-b DSET_MEAN \
-c DSET_MASK_EPI_EXTENTS \
-expr 'c * min(200, a/b*100)*step(a)*step(b)' \
-prefix DSET_TIME_SERIES_OUT_SCALED
The output time series has every voxel mean scaled to 100, and fluctuations represent local BOLD % signal change (with min at 0 and max at 200, bounding noise voxels).
Also, here's a note about Duration modulation (DM) blocks (3dDeconvolve).
--pt