Hi all,

my question is related to simple correlation analysis in resting state. I'm still learning to perform this type of analysis, and I find this topic quite trivial, so I want to make sure I'm getting everything right.
I want to use a bandpass filter (0.01 - 0.10 Hz), and I have basically two solutions:
A) Run deconvolution using only nuisance regressors, and filter the residual. Then, extract the activity from the seed and run the regression analysis on the filtered residual, using seed activity as the only regressor in the model;
B) Filter the data BEFORE the model. In order to avoid the reintroduction of nuisance-related variations into frequencies previously suppressed by the bandpass filter, both dataset and nuisance regressors need to be filtered. Then, run a single model, which should include both nuisance regressors and activity extracted from the seed.

Both procedures are used in literature, but results are not identical, and I wonder why. Furthermore, I'd like to know if one procedure is more advisable than another.

Hope the question is clear.
Thanks in advance,
Simone


Additional informations.
Datasets have been preprocessed (slice-time corrected, deobliqued, despiked, motion corrected, co-registered and normalized to a Talairach space, spatially filtered with a gaussian filter of 6mm FWHM).
Motion parameters (6), white matter signal, and cerebro-spinal fluid signal have been extracted, to use them as nuisance regressors.