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Alex,

(a) I would make sure that the seed time-course regressor is orthogonal to the nuisance covariates. To do this, simply use the regress function in matlab with your seed as the DV and the nusiance variables as the IV, the residuals are what aren't explained by the nusiance regressors. Use the residuals instead of the timecourse.

(b) your contrasts are fine; however, the contrasts represent the slope of the relationship between the seed region and other voxels. If you want the correlation coefficient, then you need to convert the T-statistic to an R-squared value AND for group analysis convert that to a normal distribution using the Fisher Z-transform.

(c) if you haven't subtracted the global mean from your data, you won't see the anti-correlations and need higher group thresholds to limit the regions that you find.

On Fri, May 22, 2009 at 5:05 AM, Alex Fornito <[log in to unmask]">[log in to unmask]> wrote:

Hi all,
I'm relatively new to spm and would like some clarification re: how multiple
regression is implemented.
I am trying to set up a 1st level design, which is a seed-based correlation
analysis of resting-state data. As such, there is no design matrix (in the
sense of a task regressor), and so instead I have created a matrix of 10
timecourse regressors, which I have loaded in using the multiple regression
option.
The first column in the matrix is the timecourse extracted from a seed
region, and is the regressor of interest. The other 9 columns in the matrix
are nuisance covariates (e.g., timecourses from white matter, csf, movement
parameters, etc).
After estimation, I'm presuming the contrast [1 0 0 0 0 0 0 0 0 0] will
return all voxels positively correlated with the seed timecourse, whereas
[-1 0 0 0 0 0 0 0 0 0] will return those negative correlated with the seed.

My questions are:
(a) does this sound like the correct way to set up the analysis?

(b) would the results returned by the contrasts defined above be
automatically corrected for shared variance with the other 9 nuisance
regressors, or do they require some kind of contrast weighting as well to
ensure they are covaried for?

Thanks for your help,
Alex