Hello all,
As I'm working on the preprocessing/denoising methods of my paper, I came across a term that I'm unfamiliar with and very little leads as to what it means. In two recent denoising strategies papers (references below), one of the denoising strategies is "demeaning & detrending". I've come to understand that detrending is an option that can be done in CONN's denoising tab, but I am unfamiliar with what "demeaning" means. Initial guesses were that it is the same as global signal regression, but that is classified as its own strategy in the two papers. Would anyone happen to know what this "demeaning" is referring to, what it tries to do, and how it is done?
I'm very much looking forward to your response, thank you!
Sincerely,
Billy
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., ... & Gur, R. C. (2017). Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage, 154, 174-187.
Satterthwaite, T. D., Ciric, R., Roalf, D. R., Davatzikos, C., Bassett, D. S., & Wolf, D. H. (2019). Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies. Human brain mapping, 40(7), 2033-2051.
As I'm working on the preprocessing/denoising methods of my paper, I came across a term that I'm unfamiliar with and very little leads as to what it means. In two recent denoising strategies papers (references below), one of the denoising strategies is "demeaning & detrending". I've come to understand that detrending is an option that can be done in CONN's denoising tab, but I am unfamiliar with what "demeaning" means. Initial guesses were that it is the same as global signal regression, but that is classified as its own strategy in the two papers. Would anyone happen to know what this "demeaning" is referring to, what it tries to do, and how it is done?
I'm very much looking forward to your response, thank you!
Sincerely,
Billy
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., ... & Gur, R. C. (2017). Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage, 154, 174-187.
Satterthwaite, T. D., Ciric, R., Roalf, D. R., Davatzikos, C., Bassett, D. S., & Wolf, D. H. (2019). Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies. Human brain mapping, 40(7), 2033-2051.