Hi,
On 10 May 2007, at 22:25, Xiaoyun Liang wrote:
> Hi,
> I have a problem now. When I computed the percent signal change
> using
> featquery for event-related data, I found some results are strange.
> For
> example, in one ROI, the max psc is 9.32, and the min psc is -17.
> So I tried
> to find what is the problem with it, and I found the value of the
> correponding voxels is very low, about 5000. But, actually, values
> of most
> voxels should be around 10000. What's the problem?
The 4D dataset is scaled to be 10000 on average, but there is
variability around that (otherwise the timeseries analysis would be
slightly pointless.....)
So for example voxels at the edge of the brain can have much lower
values (unless you make the thresholding in the preprocessing a lot
more aggressive) and hence you can get very high % signal change -
this is an obvious limitation and danger of quantifying effects via %
signal change.
Cheers.
> In my research, I defined ROIs, such as FFA, Amygdala, OFC
> etc., by
> using random-effects group activation with all faces vs. baseline.
> And I
> created spheres using coordinate of peak activation in the group
> activation
> as center. Then I intersected the sphere with group level
> activated map and
> obtained the functional mask. Then I applied the same mask to
> individual
> subject for specific ROI, and computed percent siganl change(PSC) with
> several contrasts.
> Thanks a lot!
>
>
> Xiaoyun
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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
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