Hi Cyril,
Thanks for your reply. Some follow-up q's:

> -----Original Message-----
> From: Cyril Pernet
>
> > Basically, I set up my simulated activation map (40x40x40 voxels) as a
> > checker board pattern of 10x10x10 ON/OFF voxels (value=100, 0
> respectively)
> > and use it to modulate the intensity of the simulated BOLD signal (a
> > regular-spaced stimulus train (impulse) convolved with the canonical hrfs
> > viz. spm_hrf). I then added some AR(1) noise (sigma^2 = 0.01) and a
> linear
> > drift to the data.
> >
> > When I run this data-set through SPM (canonical hrfs with no derivatives,
> p
> > < 0.01, no correction) I get the original region as a very strong
> > activation plus an additional cube-shaped regions of low activation.
> Given
> > the regular structure of this spurious regions - it does not seem likely
> to
> > be a Type I error.
> right here I can't help - I cannot see how you get something outside your
> 10x10x10 area specially since you did'nt smooth the data - did you create
> the data + noise then convolve or create the simulated data and add noise
> on the top? maybe there is something here to look at ??


I create the simulated data by convolving a regularly spaced impulse train (delta's spaced at 4s) with the hrf and then modulating it with a 3D activation map (alternating pattern of 100 and 0 valued voxels), and then adding noise to the result. The noise std-dev is really low - just high enough so that SPM does not give a "divide-by-zero" error when computing the t-scores. Then at each voxel I add an offset of 200 - to deal with the fact that SPM performs some kind of global normalization and thresholding.

The algorithm for normalization seems to be
1) compute a global mean
2) discard all voxels with intensity < global_mean/8
3) recomputed the new global mean and normalize wrt to this.

>
> > Also if I examine the beta_0001 image, corresponding to the stimulus
> > regressor, the values vary between 1-10 (while the original map was
> 0/100),
> > and while the shape of the high-intensity region is approximately
> correct,
> > it is fairly blurred.
> > Given that I'm not smoothing my data-sets or doing any other kind of
> > pre-processing why do I observe these effects. Also, why doesn't the beta
> > map reflect the original intensity of the activation pattern ?
> here I've an idea :-)
> your model is y=BX+e with X standing for your on/off 'activation' pattern
> and the grand mean ; therefore on non simulated data for a voxel you would
> look at variations + or - around the grand mean 50 ; since you convolved
> the data this value isn't 50 anymore

I'm not sure I totally understand what you are saying, but do you think the global-normalization is the cause of the problem?
Also - there is a further offset of 200 (a background value, to speak) - so the grand mean would be approx 200.

> in addiiotn to this there is the normalization factor ; what did you choose
> when you set up your model? for instance the grand mean overall voxels i.e.
> 6300 voxels at 0 + 1000 voxels at various values up to 100 (the hrf) would
> give a grand mean of maybe 1.7 or so. Again this will change the values of
> betas. Note that the ratio between the two regressors, if you modeled the
> on and off, should be closer to what you expect.
> I can see different possibilities for the values you obtain ; but none of
> them would account for activation outside your area ..
> hope this helps

I don't have two regressors - just one regressor modulated with values of 100 and 0. So comparing the ratio is not really feasible.
Would it be better to use two regressors modeling two orthogonal conditions, rather than one regressor modeling a single condition with and without activation ?
 
Thanks,
-firdaus