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help > RE: Correction for global signal
Sep 9, 2016 02:09 PM | Alfonso Nieto-Castanon - Boston University
RE: Correction for global signal
Dear Sascha,
I do not recommend using GSR because: a) it provides a less comprehensive correction compared to aCompCor (it does not really account for potential spatial differences in the way physiological effects are expressed in your data); and b) it can introduce artifactual anticorrelations in your results (see Murphy et al. 2009). If you really want to use GSR then, given that you are already deciding to live with the (b) issues above, I would probably recommend to still use aCompCor as well (keeping WM/CSF principal components) to at least address the (a) issues above. Yes, in that case the global signal will be partially redundant with the components extracted from the WM and CSF areas, but it will still introduce some new aspect to the regressed effects (unfortunately, that new aspect that is being introduced will be the portion of the global signal that is not already present in the WM and CSF components, which most likely represents "true" neural-source signal, so, again, perhaps not such a great idea to use GSR....)
Hope this helps
Alfonso
Originally posted by Sascha Froelich:
I do not recommend using GSR because: a) it provides a less comprehensive correction compared to aCompCor (it does not really account for potential spatial differences in the way physiological effects are expressed in your data); and b) it can introduce artifactual anticorrelations in your results (see Murphy et al. 2009). If you really want to use GSR then, given that you are already deciding to live with the (b) issues above, I would probably recommend to still use aCompCor as well (keeping WM/CSF principal components) to at least address the (a) issues above. Yes, in that case the global signal will be partially redundant with the components extracted from the WM and CSF areas, but it will still introduce some new aspect to the regressed effects (unfortunately, that new aspect that is being introduced will be the portion of the global signal that is not already present in the WM and CSF components, which most likely represents "true" neural-source signal, so, again, perhaps not such a great idea to use GSR....)
Hope this helps
Alfonso
Originally posted by Sascha Froelich:
Dear Alfonso,
when you say adding a new ROi that encompasses the whole brain would be a way to approximate global signal regression, would you remove the WM and CSF ROIs from the confounds list or not? If not, wouldn't that be redundant somehow?
Cheers,
Sascha
Originally posted by Alfonso Nieto-Castanon:
when you say adding a new ROi that encompasses the whole brain would be a way to approximate global signal regression, would you remove the WM and CSF ROIs from the confounds list or not? If not, wouldn't that be redundant somehow?
Cheers,
Sascha
Originally posted by Alfonso Nieto-Castanon:
Dear
Natalia,
By default the conn toolbox uses CompCor, instead of global signal regression, to address potential subject-movement and physiological confounding effects without the risk of artificially introducing anticorrelations into your functional connectivity estimates, so generally I would not recommend using global signal regression instead. See this thread (https://www.nitrc.org/forum/forum.php?th...), or this reference (Chai X.J., Nieto-Castanon A., Ongur D., Whitfield-Gabrieli S. (2012) Anticorrelations in resting state networks without global signal regression. NeuroImage 59(2):1420-1428) for more details about this issue.
That said, if you still want to regress out the global signal you may do so (approximately) by entering the 'grey-matter' ROI into the 'confounds' list in the 'Preprocessing' tab (and probably removing the White-matter and CSF ROIs if you do not want to use the CompCor strategy), or (perhaps more precisely) by adding a new ROI in the 'Setup->ROIs' tab that encompass the entire brain (e.g. brainmask.nii if you are working on normalized volumes) and entering this ROI instead into the 'confounds' list in the 'Preprocessing' tab.
Hope this helps
Alfonso
Originally posted by Natalia Yakunina:
By default the conn toolbox uses CompCor, instead of global signal regression, to address potential subject-movement and physiological confounding effects without the risk of artificially introducing anticorrelations into your functional connectivity estimates, so generally I would not recommend using global signal regression instead. See this thread (https://www.nitrc.org/forum/forum.php?th...), or this reference (Chai X.J., Nieto-Castanon A., Ongur D., Whitfield-Gabrieli S. (2012) Anticorrelations in resting state networks without global signal regression. NeuroImage 59(2):1420-1428) for more details about this issue.
That said, if you still want to regress out the global signal you may do so (approximately) by entering the 'grey-matter' ROI into the 'confounds' list in the 'Preprocessing' tab (and probably removing the White-matter and CSF ROIs if you do not want to use the CompCor strategy), or (perhaps more precisely) by adding a new ROI in the 'Setup->ROIs' tab that encompass the entire brain (e.g. brainmask.nii if you are working on normalized volumes) and entering this ROI instead into the 'confounds' list in the 'Preprocessing' tab.
Hope this helps
Alfonso
Originally posted by Natalia Yakunina:
Dear all,
Is there a way to regress against the global signal in CONN?
Thank you!
Best,
Natalia
Is there a way to regress against the global signal in CONN?
Thank you!
Best,
Natalia
Threaded View
| Title | Author | Date |
|---|---|---|
| Natalia Yakunina | Jul 15, 2013 | |
| Alfonso Nieto-Castanon | Jul 18, 2013 | |
| Sascha Froelich | Sep 9, 2016 | |
| Nobody | Nov 20, 2020 | |
| Alfonso Nieto-Castanon | Sep 9, 2016 | |
| Ben R | Apr 8, 2020 | |
| Scott Burwell | Sep 9, 2016 | |
| Natalia Yakunina | Jul 23, 2013 | |
| Jeff Browndyke | Aug 29, 2015 | |
| Alfonso Nieto-Castanon | Aug 31, 2015 | |
| Jeff Browndyke | Aug 31, 2015 | |
| Jeff Browndyke | Aug 28, 2015 | |
| Alfonso Nieto-Castanon | Aug 28, 2015 | |
| Jeff Browndyke | Aug 29, 2015 | |