have you run Featquery_gui to obtain these masks? If so, did you keep
the settings for
"Do not binarise mask (allow weighting)"
"Change post-interpolation thresholding of mask"
constant for the two cases?
When I look at your masks in native space it appears that the "linear"
mask hasn't been thresholded at all (i.e. it contains a range of
values 0 < v <= 1) which when all counted amounts to 72 voxels. The
non-linear mask on the other hand seems to have been thresholded at a
very high level, containing a single non-zero voxel with the intensity
If on the other hand I do a non-linear transform of your standard-
space mask into native space I get a similarish set of voxels to what
you have in in your linearly transformed mask. If I then threshold
both at a 0.5 level I end up with 6 non-zero voxels for the linear
case and 8 voxels for the non-linear case. I.e. a fairly reasonable
Also, the only remaining voxel in your non-linear mask coincides with
the voxel with the highest intensity after my non-linear transform
prior to thresholding.
Hence, is looks as if the difference is due to on the one had no post-
interpolation thresholding at all (your linear mask) and on the other
hand very severe post-interpolation thresholding (thr>0.99) for your
Can you please try to run Featquery_gui again, making sure to keep
these settings constant, and see if you get the same results?
On 14 Aug 2009, at 13:23, Stéphane Jacobs wrote:
> Hi Jesper,
> Thanks a lot for your response and your help. The reference number
> for the upload is 622827.
> I have included the 2 versions of the mask in example_func space,
> obtained with the linear and non-linear registrations.
> Stéphane Jacobs - Chercheur post-doctorant / Post-doctoral researcher
> Espace et Action - Inserm U864
> 16 avenue du Doyen Lépine
> 69676 Bron Cedex, France
> Téléphone / Phone: (+33) (0)4-72-91-34-38
> Jesper Andersson a écrit :
>> Dear Stephane,
>>> 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 the
>>> inverse of the affine transformation from example_func2highres.
>>> However, I
>>> noticed that several subjects (I still need to check the others),
>>> 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 and
>>> 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?