> I'm using Featquery to compute percent signal change from various
> regions of
> interest. I've first run Featquery with the default settings, which
> were to
> use the inverse of non-linear registration from highres2standard and
> inverse of the affine transformation from example_func2highres.
> However, I
> noticed that several subjects (I still need to check the others), the
> resulting mask transformed from standard to native EPI space is
> small, 2 or 3 voxels at best, sometimes empty, while the volume of
> the mask
> in standard space is 528 mm2 (66 voxels). I checked the
> example_func2standard registration and it looks fine.
> To compare, I manually registered the standard mask to EPI space
> using only
> the inverse of the affine transformations from example_func2highres
> highres2standard, using the default interpolation method and
> post-interpolation threshold value. Now, I obtain a 72 voxel mask
> (2812 mm2)
> that is even bigger than the original one in standard space...
> At this point I'm a bit confused as to why the mask obtained with
> "back-registration" is so big (might need to adjust the post-
> threshold?), but what I really don't get is why the results between
> the 2
> registration methods are so different.
> Any hint as to what I might have done wrong would be greatly
I must admit to being confused too. I have had a look at the scripts,
and right now I don't have an idea so I would probably need to have a
look at your data.
Could you please tar up your
example_func image file
image file with mask in standard space
highres2standard_warp image file
highres2standard_warp_inv image file
image file with mask after transformation into example_func space
and then upload them to http://www.fmrib.ox.ac.uk/cgi-bin/upload.cgi
and send me the reference number?