These tests were inspired by the work of Benjamin Risk, particularly his illustrations of banding and poor middle-of-the-brain sensitivity in the residuals (e.g., slides 28 and 29 of his OHBM talk). He mentioned that you can see some of the same patterns in simple standard deviation (stdev) images, which are in this post.
Here is a typical example of what I've been seeing with raw (not preprocessed) images. The HCP and MB8 scans are of the same person. The HCP scan is of the WM_RL task; the other two are of one of our tasks and the runs are about 12 minutes in duration (much longer than the HCP task). These MB8 and MB4 runs have low overall motion.
Looking closely, horizontal bands are visible in the HCP and MB8 images (marked with green arrows). None of the MB4 images I've checked have such banding (though all the HCP ones I've looked at do); sensible anatomy (big vessels, brain edges, etc.) is brightest at MB4, as it should be.
Here are the same runs again, after preprocessing (HCP pipelines minimal preprocessing pipelines), first with all three at a low color threshold (800), second, with all three at a high color threshold (2000).
The HCP run is "washed out" at the 800 threshold with much higher standard deviation in the middle of the brain. Increasing the color scaling makes some anatomy visible in the HCP stdev map, but not as much as the others, and with a hint of the banding (marked with green arrows; easier to see in 3d). The MB4 and MB8 runs don't have as dramatic a "wash out" at any color scaling, with more anatomic structure visible at MB8 and especially MB4.The horizontal banding is still somewhat visible in the MB8 run (marked with an arrow), and the MB4 run has much lower stdev at the tip of the frontal lobe (marked with a green arc). tSNR versions of these images are below the jump.
These patterns are in line with Todd et. al (2017), as well as Benjamin Risk's residual maps. I'm encouraged that it looks like we're getting better signal-to-noise with our MB4 scans (though will be investigating the frontal dropout). Other metrics (innovation variance? GLM residuals?) may be even more useful for exploring these patterns. I suspect that some of the difference between the HCP and MB8 runs above may be due to the longer MB8 runs, but haven't confirmed.
tSNR (mean / standard deviation) versions of the runs:
sample afni commands to generate the images:
./3dTstat -stdev -prefix /scratch2/198855_3T_tfMRI_WM_RL_sd.nii.gz /data/hcp-zfs/OpenAccess/1200subject/198855/unprocessed/3T/tfMRI_WM_RL/198855_3T_tfMRI_WM_RL.nii.gz ./3dTstat -mean -prefix /scratch2/198855_3T_tfMRI_WM_RL_mean.nii.gz /data/hcp-zfs/OpenAccess/1200subject/198855/unprocessed/3T/tfMRI_WM_RL/198855_3T_tfMRI_WM_RL.nii.gz ./3dcalc -a /scratch2/198855_3T_tfMRI_WM_RL_mean.nii.gz -b /scratch2/198855_3T_tfMRI_WM_RL_sd.nii.gz -expr 'a/b' -prefix /scratch2/198855_3T_tfMRI_WM_RL_tsnr.nii.gz -fscale