Hi Susie,
I had a look at why you find these rather odd smoothness estimates.
I assume that you were doing a VBM study?
The smoothness estimator fails for very low probability voxels, i.e.,
voxels where the estimated probability for being grey matter is
extremely small. These are usually 'edge of the brain' voxels.
In SPM, the 2nd-level model interface was designed with having
functional images in mind. When you're doing VBM you have to modify one
of the default masking options: you should change the explicit, absolute
threshold to something a little bit greater than 0, say 0.05.
Also, John recommends the 0.05 threshold in his 2001 Neuroimage VBM
paper, because low-intensity voxels might not follow a normal distribution.
All the best, Stefan
> Hi Will
>
>
> >> load SPM
> >> SPM.xVol
>
> ans =
>
> XYZ: [3x692811 double]
> M: [4x4 double]
> iM: [4x4 double]
> DIM: [3x1 double]
> FWHM: [1.0882e-04 2.7407e-06 4.7969e-07]
> R: [1 2.2577e+08 6.7308e+15 4.6652e+21]
> S: 692811
> VRpv: [1x1 struct]
>
> I guess that means spm has estimated the smoothness wrongly! Is it
> something I am doing wrong, or something that can be sorted in spm?
>
> Thanks for all your help.
>
> Susie
>
>
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