Hi all,
in order to perform correlation analysis between different ROI's timeseries within a group of 20 healthy subjects, I've been a little confused on how to proceed.
Reading previous posts in the forum and some papers, I understand that to perform nuisance signal regression I have to:
1- For each individual, remove the signal associated with several nuisance covariates (specifically white matter, CSF, global signal, and six motion parameters) using multiple regression analysis.
In order to do this using first-level Feat, after pre-stats and registration steps, in stats I have to:
-load filtered_func_data as input
-Full Model Setup:load every single nuisance(text files)as “custom” 1-column regressors(with no convolution, turning off temporal filtering and temporal derivative)
- What about contrast? Should I create one single contrast with all ones? [1 1 1 1 1 1 1 1 1], since I'm interested in the residual image (that should represent the corrected signal)?
2- This nuisance signal regression step should therefore produce a 4D residual file (res4d.nii) for each participant from which I should extract mean time series from each ROI of interest.
Are these steps correct? Some authors (Kelly et al., 2009) before extracting mean time series perform a voxelwise scaling on the 4D residuals, dividing each voxel’s time series by its SD. "Performing this step ensures that the ensuing FC estimates represent partial correlations rather than regression parameter estimates (e.g., Vincent et al., 2006), and removes potential between-condition differences in the magnitude of BOLD fluctuations (Sorg et al., 2007)." What do you think about it?
3- Moreover, since we acquired three resting state scans for each participant, is it correct to average the 3 final mean time series of each ROI I get from each partecipant?
Or should I average with a higher level FIXED effect analysis for each partecipant the output of these first level analyses and get a single res4d for each subject?
Any help whould be greatly appreciated,
Claudia
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