My experiment is a ROI localization experiment. In the quality control file generated by afni_proc.py, I don’t understand what’s the meaning of " check : statistics vol " in the qc file. Can someone explain it to me in detail ? Or where can I find more detail about qc? Attached to this post is a screenshot, which is called “image1”
And I also want to know what conditions will lead to the following situation. The brain is covered with spots and the image is very blurred. This is the screenshot “image2” in attach file. Has anyone run into this issue before?
Thanks a lot,
Thanks for checking out the afni_proc.py QC (APQC) stuff. I am in the process of building a help page about it (and actually, there is a newer version of it in the distribution now, with additional features+images).
I guess you have a task data set-- there will be a basic F-stat calculated for whatever you are modeling, testing the significance of your overall regression model (quoting from the 3dDeconvolve help). THis is not a stat of any of your specific, individual glts or anything-- at some point, we will probably add in that functionality.
This is supposed to give you a sense of your model+data being in unison. It might help trouble shoot very noisy data, or stimulus timing files mistakes (wrong units, wrong file, etc.). The threshold is set to be the 90th %ile of the F-stat distribution of values iwthin the brain-- so you are seeing (within the brain) the top 10% of values in opaque coloration; values below that threshold can still be show, but they have increasing transparency (-> so you don’t lose all the information of the sub-threshold parts).
If you have a strong visual+audio set of tasks, for example, you might expect bright clusters in the visual and auditory cortical regions-- that would mean things are likely well for your data+model. If you see lots of speckles, and no real clusters in expected areas, there might be a problem with the stimulus timing file, or there might be lots of motion, or… something else (maybe the subject fell asleep and didn’t do the task?). Other plots in the APQC should help discern the nature of the issue. However, note that FMRI dsets are veeery noisy in general, so that might also just be part of hte issue.
Hope that is useful.
Thank you for your patience! I’m a freshman in fMRI and may often ask some silly questions! The APQC helped me a lot in checking the data.
Thanks again for your generous help!
We have added some more detailed documentation online now:
That would probably be useful, if you update to the latest version of AFNI for processing (built last night).
I just tried the pythonic version of the html review and like it very much. The matplotlib based graphs are so much nicer. The HTML review is a really great addition to afni.
A few suggestions:
Currently you only show the alignment of EPI and T1 in standard space. Would it be possible to show, above the standard space alignment, the alignment of the EPI and T1 in original space images as well? I ask because I recently had alignment problems (turned out to be a scriptign error on my part). I had to fire up afni to view the original space EPI and T1 data. Diagnosing the problem would have been trivial had the original space and standard space alignment been showed one row apart.
The regressors plot are shown without the labels supplied to afni_proc via the -regress_stim_labels option, would it be possible to get the regressor labels in these graphs?
Do you plan on adding a way to replace the task : task_name at the top with a user settable value?
Those are useful suggestions. Some of them are, in fact, already in the pipeline. Please see the helpdocs here, where some of the things that will be coming are listed, too:
Regressors will be labelled, yep, as will the task name. Basically, we just need to put in the functionality to build the dictionaries to pass those along.
In terms of EPI-anat alignment, at the moment, I think that will likely stay in its current form, when using a reference template (and esp. with -volreg_tlrc_warp). The raw EPI (-> volreg base dset) and anat volumes will be shown in their own native/orig spaces, though.