Subject: | | Re: Both inclusive and exclusive mask? |
From: | | [log in to unmask][log in to unmask]> wrote:
> Dear Adnan, > > If you’re comparing between groups, you don’t need the individual > subjects’ estimates. Only the group average estimate from each group. It is > fine if those averages is driven by a subset of subjects in each group. > > > > Best, > > P > > > > *From:* SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] *On > Behalf Of *adnan alahmadi > *Sent:* 05 November 2015 11:00 > *To:* [log in to unmask] > *Subject:* Re: [SPM] DCM BMA > > > > Dear Peter, > > > > Yes it does and make sense but the main question is that whether it is > correct if I can compare say the A(1,3) connections among or between > subjects although sometimes it is zeros in some subjects ? > > > > Many thanks > > > > Adnan > > > > On 5 November 2015 at 10:55, Zeidman, Peter <[log in to unmask]> > wrote: > > Hi Adnan, > > Presumably for subject 1 the only model(s) contributing to the average had > connection A(1,3) enabled whereas for subject 2 the only model(s) > contributing to the average had connection A(3,3) enabled. Does that fit > with what you expect? > > > > Best, > > Peter > > > > *From:* SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] *On > Behalf Of *adnan alahmadi > *Sent:* 04 November 2015 13:14 > *To:* [log in to unmask] > *Subject:* [SPM] DCM BMA > > > > Hi all. > > > > What I understand the best approach to compare or investigate DCM is the > use of BMA averaging. I have however couple of questions regarding this > option. > > > > Say for example I have two groups (or even one group). > > > > When I compare the models using compare, I should switch on the BMA option > and then say choose family: winning family. > > > > Then the models are compared, no problem so far and I did this for both > groups separately. > > > > So if I want to compare connections parameters between say two groups or > within a group I should go to Group1.BMS.DCM.rfx.bma.mEps where I can find > parameters of each subjects for all matrices. > > > > Say for example I look at the A matrix for subjects within a group or > between groups. Sometimes I get missing values or zeros in some subjects > and sometimes they are not. or in other words, > > > > Sometimes A matrix for subjects 1 is > > > > 0 0 -0.4 > > 0 0 0 > > 0 0 0 > > > > While for subject 2 is > > 0 0 0 > > 0 0 0 > > 0 0 0.3 > > > > and so on. > > > > What is confusing is that why sometimes in the BMA there are zeros and > this is dependent on subjects? > > > > When I tried to investigate the winning model and force DCM to do BMA of > the winning model, I can see the values in the right place of connections, > or in othe2xr |
Reply-To: | | [log in to unmask] |
Date: | | Fri, 27 Nov 2015 10:04:24 -0500 |
Content-Type: | | text/plain |
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I suspect what you want to do for masking will be very tricky to do with the GUI. However, there is always another solution.
You can use the spm_mask function in Matlab. It is very simple if you have no coding/Matlab experience. Type help spm_mask and it will explain how to use it. Basically:
Spm_mask('tmap.img', 'mask.img', 3.5), where 3.5 is a positive t threshold used for masking. So this would make an inclusive mask of the mask file, which can be a t-map of the other contrast. Not that this changes and re-write the tmap.img file.
If you then use imcalc to multiple the excluvice mask by -1, you can use:
Spm_mask('tmap.img', 'mask2.img', -3.5)
Then it takes only voxels greater than -3.5 (remember by times -1's), meaning any voxels significant in mask2 will be excluded.
The down side is this approach only incorporates t-stat and does not in any ways include a cluster threshold, so for examples if you have a cluster of 3 voxels above 3.5 they will still be masked even though such a small cluster should probably not be considered significant.
Though if you happen to be any good at SPM coding you can work around that I believe fairly easily.
You can actually use a similar approach to do a masked conjunction of your t and f contrasts, though I am not certain that is always an appropriate thing to do. It may depend on the context; e.g. using a t-test for post-hoc examination of F tests results would be OK, and you could spm_mask the tmap based off the f map.
Best of luck,
Colin Hawco, PhD
Neuranalysis Consulting
Neuroimaging analysis and consultation
www.neuranalysis.com
[log in to unmask]
-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Sandra
Sent: November-27-15 2:29 AM
To: [log in to unmask]
Subject: [SPM] Both inclusive and exclusive mask?
Dear SPM experts,
is it possible to apply both an inclusive (for one contrast) and an exclusive mask (for another contrast) in the same step? Because via command window it's only possible to chose either an inclusive or exclusive mask?
Is it further possible to conduct a conjunction approach with different contrasts combining F- as well as T-contrasts? So far, I always get an error message if I try to make conjunction of F- and T-contrasts...
Thank you very much.
Sandra
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