Dear Joćo,
Depends on what particular set of p-values you are interested in...
(which I think is why SPM shows the (unique) t-values instead, as you
say).
It's easy to convert a t-map to a map of uncorrected voxel-wise
p-values (I think both Volkmar's Volumes toolbox and Christian's VBM
toolboxes have this functionality, or you can do it with imcalc).
It's also easy to convert the above uncorrected p-map to a voxel-wise
FDR p-map (or q-map), though it was a bit slow with spm_P_FDR last
time I tried (you've just reminded that I have a much faster version
of this, that I should probably inlclude in a future update...).
RFT voxel-wise FWE p-values are fairly easy to get from spm_P, though
if I remember correctly, you can get NaNs (perhaps even errors) with
very small t-values, which might need sorting out before you saved the
image for later visualisation. This might have been fixed since the
last time I tried though.
Cluster-wise p-values are not so easy to get, and would require a bit
of coding. Also, they would not show you any information about
relative signal within the clusters, whereas SPM's use of voxel-wise
t-values within significant clusters gives you a little extra
information.
Finally, note that overlaying p-values in e.g. MRIcroN will probably
not look good, firstly because more significant values are smaller,
whereas most colour-maps expect larger=better, and secondly because
the difference between 0.1 and 0.01 will probably end up being a very
small difference in the colour-map, while it's actually a very
important difference. One way to deal with this is to use
abs(log10(p)) instead of p, then e.g. 0.1 maps to 1, 0.01 maps to 2,
and the overlays look reasonably nice. The only complication with this
is that 0.05 maps to 1.301, so if this is the alpha-level you are
interested in, labelling the colour-bar with this value might look a
bit messy.
All things considered, it's probably not worth all this trouble, as
the thing you are usually interested in is which blobs survive a
particular significance threshold. There's also an argument that the
best thing you could look at within these blobs is the raw contrast
image, rather than the t-values or (any of) the p-values:
http://www.fil.ion.ucl.ac.uk/spm/ext/#MASCOI
Best,
Ged
On 6 May 2010 12:11, Joćo Duarte <[log in to unmask]> wrote:
> Dear Ged,
>
> thank you very much. It was easy...
> By the way, as far as I understand, the colorbar displayed is the one with
> the T values, right? Is it possibe to show a colorbar with p-values instead?
>
> Thanks.
>
> Regards,
>
> Joćo
>
> On Thu, May 6, 2010 at 11:42 AM, DRC SPM <[log in to unmask]> wrote:
>>
>> Dear Joćo,
>>
>> When you've got the glass brain results up in SPM, click the "save"
>> button near the bottom right of the interactive window, and enter a
>> filename for the output image. You should then be able to load this
>> thresholded t-map as an overlay in MRIcroN etc.
>>
>> Note that you can display the blobs overlaid on an image in SPM itself
>> too, just click the "overlays..." menu, and pick "sections" and then
>> select an image you want to overlay onto (e.g. an average image,
>> created by warping your original images with the same transformations
>> used to create your VBM data, and then using imcalc with expression
>> mean(X) and the data matrix (dmtx) flag true). If you've used DARTEL,
>> the simplest thing to overlay onto is the GM of the final template,
>> e.g. Template_6.nii.
>>
>> Hope that helps,
>> Ged
>>
>> 2010/5/6 Joćo Duarte <[log in to unmask]>:
>> > Dear SPMers,
>> >
>> > how can I display the map of significant blobs that is output of VBM
>> > analysis in SPM8, using for example MRIcroN?
>> >
>> > Thanks in advance.
>> >
>> > Regards,
>> >
>> > Joćo
>> >
>
>
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