Thomas Nichols wrote:
> OK... we're on the same page; the dox you were referring to:
>
> % For instance,
> % the "Multi-subj: cond x subj interaction & covariates" design can be
> % used to write out an image of the activation for each subject. A
> % simple t-test on these activation images then turns out to be
> % equivalent to a mixed-effects analysis with random subject and
> % subject by condition interaction effects, inferring for the
> % population based on this sample of subjects (strictly speaking the
> % design would have to be balanced, with equal numbers of scans per
> % condition per subject, and also only two conditions per subject). For
> % further details, see spm_RandFX.man.
>
> I understand this as such: Use the
> 'Multi-subj: cond x subj interaction & covariates'
> at the 'first level' to produce an intrasubject contrast for each
> subject, for each contrast of interest. That is, each subject will
> have a complete set of contrasts of interest. Then simply submit
> each set of contrasts (consisting of nSubj images) to a one sample
> t-test... et volia, you're done.
This is what we did, but of course, there are 3 stages: (1) fMRI model for
each subjects' data separately, to produce cond* file, (2) Multi-subj: cond
x subj interaction & covariates to create con* files, (3) t-test on con*
files.
>
>
> *But*, as I see it, you don't need to do the
> 'Multi-subj: cond x subj interaction & covariates'
> because you
> > fit separate fMRI model to each subject, which produced files
> > cond_1.img through cond_12.img for each subject.
>
> Now, you don't have contrast images, you have condition images.
> If you had followed the recommended path, you would have created
> all the possible *intrasubject* contrast images within each of your
> fMRI models.
>
> However, you should get the exact same result (check it once) just
> by directly submitting your condition images to paired t tests.
> The "official" path goes through the contrast images prevent
> people from doing wacky things (SPM99 contrasts must be estimable).
> But if you know what you're doing there's nothing wrong with
> working with the condition images.
>
> To be specific: Say one contrast of interest is
> -1 1 0 0 0 0 0 0 0 0 0 0
> Then to get the RFX result for this run I'd do "Paired t-test" from
> the "Basic Models" button, number of pairs is the number of subjects,
> then you'd enter for each subject their cond_001.img and cond_002.img
> images. Then you'll have to use the contrast manager to define
> two contrasts, [-1 1] and [1 -1].
>
> Does this make sense (to all)?
This seemed to work, but ONLY with the type of example you gave, where we
are comparing two conditions. I found no way to use the Paired t-test to
perform a contrast of the mean of 6 conditions with the mean of the other 6.
I also tried doing the simple PET Multi-subject: conditions & covariates on
the 15 subjects by 12 cond* files from stage 1 combined into two conditions
(6 "scans" each). This gave results that seemed obviously wrong and had the
df for the fixed effects model, which are MUCH greater than the correct df
(14) for the RFX model.
Jonathan
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