I have a block design that runs for 11 minutes and participants are asked to perform this scan twice within one scan - this is to give them a mental break. While in the scanner the participants are asked to perform three different tasks depending on the block (3 trials each at 36.4 seconds for each scan). My question is what is the general recommendation for combining and analyzing two data sets?
Should I concatenate the two and treat it as one data set? Do I concatenate after preprocessing has been completed for each individual scan, or do I concatenate and then preprocess? I’ve notices there is a difference in the Bold signal scale between the two scans in some instances.
Should I analyze the two separately and average the beta coefficient maps?
Should I average the two time series and then analyze the results?
Any other recommendations? Is there a resource that you can point me towards that deals with this question?
To your specific questions on the fairly abstract description of your task:
Do not do #1 without some hefty processing (e.g. converting to signal change, detrending, censoring, many more to list)
This is a potentially useful way to go
Unless you have a specific “wouldn’t this be great for methods” question, I wouldn’t advise on #3. The fundamentals of measuring “activation” in a block design using a “let’s average some runs” mentality are often better addressed using regression.
My main recommendation is to use afni_proc.py to process the two runs at the same time. This will take care of the concerns in #1, give you the power to do #2, and essentially do #3. Using afni_proc.py, you could also setup GLT “contrasts” to compare run 1 and run 2 if that’s of interest.
Thank you for your response! A more specific description of the task is the participants are asked to track a sine wave using either their left hand, right hand, or both hands. The block design reads as follows:
(Rest, Left, Rest, Right, Rest, Both, Rest) x 3
I would like to use afni_proc, but as an extra step I’ve been using SLice-Oriented MOtion COrrection (SLOMOCO) right after running 3dVolreg because I’m scanning participants with stroke and have found this works very well. Because of this, I’ve been having a hard time figuring out where to input the the SLOMOCO output to continue with the rest of the pipeline. If you could offer any advice, that would be great.
My advice would be to write two afni_proc commands:
Take the first few steps, then run SLOMOCO on the data
Another afni_proc command that would take the outputs of SLOMOCO and runs the rest of the pipeline.
If you get stuck, please post your commands.
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National Institute of Mental Health (NIMH) is part of the National Institutes of
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