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Hi Firdaus
> 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 ??
> 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
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
cyril