Dear experts,
I have collected the data of two groups of participants (patients - controls). Firstly, I analyzed the data without global signal regression, and there were some clusters remaining (all Patients > controls). Then, I analyzed the data with global signal regression, but there were only a remaining cluster (Patients < controls). I am really puzzeled about the very different results with and withough GSR (number of cluters and the valence of the clusters). And the questions such as GSR and head motion made me feel somewhat depressed since I entered the field of resting-state fMRI. I am really appreciated if you tell me some ways to deal with different results with and without GSR in this case.
Thanks in advance,
Vincent
Hi Vincent,
Hi Vincent,
Yes, we all feel depressed as this field is still suffering so many methodological issues -- and even for preprocessing, there are so many debating topics!!! However, this makes us have a job, which makes us can feed ourselves, right? ;)
For paper reviewing wise, if you go ahead without Global Signal Regression (GSR), almost all the reviewers will not give you a hard time on this issue.
But if you go ahead with GSR, then most reviewers will give you a really hard time.
Thus, I will recommend you go ahead without GSR to reduce your headache. Although I do think GSR has its value in my own perspective, and I may put them into supplementary results.
Hope this helps.
Best,
Chao-Gan
Dear Dr. Yan,
Dear Dr. Yan,
Thank you for your encouragement and helpful advice!
Luckily, the results are much better without GSR in this case. However, just in case, I wonder how to respond if reviewer asks me the reasons that I did not do GSR? Is it OK to cite some papers that suggest not to do GSR (e.g., Murphy et al., 2009)?
Best,
Vincent
Hi Vincent,
Hi Vincent,
Yes, you can cite that one. You can also cite Saad's work:
Saad, Z.S., Gotts, S.J., Murphy, K., Chen, G., Jo, H.J., Martin, A., Cox, R.W., 2012. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect 2, 25-32.
Saad, Z., Reynolds, R.C., Jo, H.J., Gotts, S.J., Chen, G., Martin, A., Cox, R., 2013. Correcting Brain-Wide Correlation Differences in Resting-State FMRI. Brain Connect 3, 339-352.
Now I am seriously kidding: please show me the proof that you have participated the social media event "The Wrath of TRN" to have your next question answered. ;)
Best,
Chao-Gan
Dear Dr. Yan,
Dear Dr. Yan,
Thank you for your rapid and helpful reply.
For "The Wrath of TRN", of course, I have shared the website to my Google+, and the proof is as follows:)
Best,
Vincent
Thanks, Vincent!
Thanks, Vincent!
I appreciate that!
Here is Fun post you should not miss: http://rfmri.org/comment/3239#comment-3239
R U ASH FAN?
Dear Vincent & Dear Yan
Dear Vincent & Dear Yan
Please let me share some background (be cautious with a Rookie :P)
About GSR, even with the opposite, when GSR it's your unique option you can check this paper Chai et al., 2011 Neuroimage. http://www.sciencedirect.com/science/article/pii/S1053811911009657
Because till I know Murphy, 2009 said that you must take care your results when there are anti-correlated networks.
Murphy = "These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step."
Chai = "Our results suggest that anticorrelations observed in resting-state connectivity are not an artifact introduced by global signal regression and might have biological origins"
In my case, my data looks better with GSR, personally I preffer it.
So, this is a House Of Cards.
Thank you all,
EDIT: I've realized that my answer it could be confusing. Chai et al., used the aCompCor method to defend their job and say the question above. It's important to know that you can find and a version of CompCor in DPARSFA/dpabi.
Re: [RFMRI] How to deal with
Re: [RFMRI] How to deal with
Re: [RFMRI] How to deal with