Tomo -
I don't know whether this will help you, but you can use ImCalc
to perform the type of masking you want directly at the second
level of analysis (given that SPM only allows you to mask contrasts
within one analysis).
For example, to mask contrasts across two orthogonal one-sample
t-tests, select the maskee spmT (i1) and the mask spmT (i2) contrasts,
and evaluate the ImCalc expression:
i1.*(i2>T)
where T is the T-threshold for the mask. This is one-tailed inclusive
masking and will give 0's for voxels not surviving the mask threshold.
I'm not sure I believe in exclusive masking (you can't prove the null
hypothesis), but in your case, you could try something like:
i1.*(abs(i2)<T)
If you really want to set zeros voxels to NaN, you could try something
like:
i1.*(abs(i2)<T) - 9999*(abs(i2)>=T)
and then use ImCalc a second time on the resulting image with:
i1(find(i1)==-9999)=NaN
Note however that you must think carefully about these image alterations
on statistic values....
Rik
tomokada wrote:
>
> Dear Dr. Friston,
>
> > Although this is an ingenious use of ImCalc I do not think it is
> > necessary. You can address these sorts of questions using masking at
> > the level of SPM display. If you want to show regions that activate
> > more in A than B, but only in voxels where B is not less than C, then
> > simply mask [exclusively] the contrast A - B with C - B. Do this in
> > the results section.
>
> When I do this, SPM{T}-filtered*.img has almost the same value with spmT_*.img
> at 0 or higher, however, is it acceptable to use this spmT_*.img equivalent for
> the second level analysis in Basic Statistics ??
> And I still have voxel value of 0 outside the brain instead of NaN. In
> spm_write_plane.m, I deleted the line 'A(find(isnan(A))) = 0;' still, 0 thing
> continues. Is there any way to cope with this ?
>
> Best Regards - Tomo
>
> Tomohisa Okada, NIPS
> [log in to unmask]
> [log in to unmask]
> phone +81-776-61-3111*2338
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