| I'm currently running a VBM analysis of AD and Controls using brain
| extraction, modulation etc. When I come to the basic models stage I
| select the following
| no grand mean scaling
| threshold masking NONE
| implicit mask (ignore zero's) YES
| explicity mask images NO
| Global calculation OMIT
|
| When I look at the mean images of AD's and Controls the mask on the AD's
| appears larger than that on the Controls (see attached- left images AD,
| right images Controls).
| Should I be using a mask in VBM? and If I should can anyone explain my
| the mask appears different on the two groups when there is only one
| mask.img.
You should use masking with VBM, but I can't explain why the mask appears
larger on one group than the other.
Incidentally, I would expect to see apparently more grey matter around
the corpus callosum of the alzheimers group. This reflects a real difference
in ventricular size, rather than grey matter. Because of the high contrast
between brain tissue and CSF, the spatial normalisation works very hard
at decreasing the sizes of extra large ventricles. It is unable to do this
without shrinking all the tissue in that region, which has the effect of
pulling in grey matter towards the centre of the brain. We have started
to get around this problem by spatially normalising based only on the
grey matter in the images (by segmenting and matching to the grey matter
probability images).
|
| Also can anyone explain why when you look at the mask images you get
| grey areas at the boundaries as well as just black (0) and white (1).
This is because the Display utility shows the images with trilinear
interpolation by default. The voxels at the edges are grey because
they are a weighted average of zeros and ones. Try switching to
nearest neighbour.
Best regards,
-John
|