I'm not sure that the EV correlation values are easily obtained with FSL, however they are easily calculated by loading the design.mat file in excel or matlab..

Timing variations of the order of 0-2 seconds should generally be interpreted with caution, taking into account possible differences in the timing and shape of the regional HRF from the modelled HRF. In my experience, preparatory effects are often assessed over somewhat longer durations, with experiments designed to extend and modulate the preparatory phase. While not strictly physically impossible, combinations of overlapping regressors can combine to model unexpected and strange signals, which also need to be carefully interpreted. Visualising average responses can give a better idea of the pattern of response underlying the fit.

Cheers,

On 28 April 2010 19:58, Michael Harms <[log in to unmask]> wrote:

Someone more an expert on FSL than myself probably knows where the
actual numerical values of the covariance/correlation matrix of the
regressors can be found.
As you noted, you can always just compute the correlations yourself from
the columns of the design.mat file.

cheers,
-MH

On Wed, 2010-04-28 at 21:01 +0200, Cornelius Werner wrote:
> Hello Mike,
>
> thanks for your reply and sorry for the delay. Is there an easy way to get the correlation of the regressors? I.e., are the numerical values of the design_cov.png somewhere inside the feat-directory? The alternative would be probably to calculate a Pearson's r from the respective columns in the design.mat file. Right?
>
> Thanks a million!
> Cornelius
>
>
> Am 23.04.2010 um 14:17 schrieb Michael Harms:
>
> > Hello Cornelius,
> > The negative beta weight at t-1 means that in order to best fit the
> > actual signal something had to be subtracted from the response estimated
> > by the t-2 beta weight (and vice verse for the region with the inverse
> > pattern). What you are observing is the model simply finding the best
> > fit to the actual response, given the regressors that you've supplied.
> > It is very difficult to meaningfully interpret the individual beta
> > weights of highly correlated regressors -- the results are unstable and
> > can be changed dramatically by just subtle changes in the underlying
> > data. What is the correlation of t-2, t-1, and t0 regressors?
> >
> > cheers,
> > -Mike H.
> >
> > On Fri, 2010-04-23 at 11:34 +0200, Cornelius Werner wrote:
> >> Dear list,
> >>
> >> I am somewhat puzzled by a review I got and really can't wrap my head
> >> around this. Perhaps this is because I am just a physician - maybe you
> >> can help me see the error of my ways :-)
> >>
> >> We performed an fMRI experiment by scanning a patient population. They
> >> exhibited frequent spontaneous behavior, which we recorded and entered
> >> into the GLM as the first regressor. As we were interested in
> >> preparatory brain activity, we added two more regressors, preceding
> >> the behavioral regressor by one and two seconds, respectively, each
> >> with one second duration. Events were frequent enough and jittered
> >> enough to allow for some sort of rapid event-related design.
> >> This of course resulted in a design with three quite correlated
> >> regressors (lets say t-2, t-1 and t0). Thus, we did not model
> >> derivatives. The design matrix itself was still judged to be estimable
> >> by FSL. While I am aware that using such a setup in a whole-brain
> >> analysis in theory can yield strange results, we did obtain quite
> >> reasonable activations, corrected for multiple comparisons with voxel
> >> z>2.0 and p=0.05. Our results are anatomically meaningful and
> >> replicate (in part) previous observations.
> >>
> >> Crucially, in one of these activations, we see a significant positive
> >> beta weight at t-2, a negative beta weight at t-1, and no effect at
> >> t0. Another region shows the inverse pattern. One reviewer pointed out
> >> that, according to the signal properties of the HRF, such a signal
> >> could not be possibly observed. This is what astounded me somewhat. I
> >> was of the opinion that the BOLD effect is at least assumed to be
> >> additive. Isn't that the prerequisite for linear contrasts? Our
> >> methods folks couldn't help me out on this, so I am really interested
> >> in a clarification on this matter.
> >>
> >> Thank you very much!
> >> Cornelius
> >>