You have a few options. You can download an existing Matlab toolbox, such as the Conn toolbox, which has some of this functionality built in.
Personally I tend to code these things myself. You can grab the nifti toolbox for Matlab (off NITRICS), which will allow you to load your images as Matlab variables (I find it a bit easier to use than SPM's functions).
Then loop through voxels, run regress in matlab on the time series, and save the residuals into a new data structure.
If you want to do a really good job removing motion effects, I would suggest using the Pythagorean transform of the motion parameters (Damion Faire did some good work on this), of if you are more ambitious use a voxel-specific set of regressors as done by Ted Satterthwaite
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3811142/
Which really seems to make a difference in removing motion effects, especially for analysis between groups where motion may differ, a major confound in most analyses.
Good luck!
Colin Hawco, PhD
Neuranalysis Consulting
Neuroimaging analysis and consultation
www.neuranalysis.com
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-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Priya Aggarwal
Sent: January-06-16 12:18 PM
To: [log in to unmask]
Subject: [SPM] linear regression of cofounds
Hi everyone,
I want to regress out various cofounds (such as realignment parameters and its derivative, white matter signal etc.) from my regional mean fMRI time series. This linear regression is essential step in resting state fMRI functional connectivity analysis.
Can anyone please help me how can i do it?
Thank you for this help!
regards,
Priya
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