Dear Karina,
as Torben already mentioned, it would be very helpful to see the batch
file used to generate the group SPM.mat.
I do not fully understand whether there might be differences in the
data you entered into your group analysis. Did you specify the same
first level design twice, or did you model the first level in different
ways (onsets, parametric modulators, inclusion/exclusion of movement
parameters, basis function set, high pass filter, time series data)? If
your first level design matrix was specified exactly the same, did you
take the same (sub)set of contrasts to the group level? The contrast
numbers seem to differ, but are the contrast weights the same?
Best
Volkmar
Am Sonntag, den 06.05.2018, 12:56 -0500 schrieb Karina Quevedo:
> Dear Torben, Volkmar and Manfred,
> Please see attached in a zip drive the screen shot of the matrix and
> covariance structure and the design matrix information. SPM12 is
> taking different decisions for the covariance structure for two sets
> of images that we are fairly certain that we calculated in the same
> way in parallel to check our process.
> We are going to run the 1st level images a third time.
>
> Torben we know that the second level job batches have not been
> specified in the same way already but happy to shared them with you.
>
> Essentially we ran the same batch (Batch 1) but uploaded a second
> different set of 1st level contrast images for QA (which we derived
> with the same fmri structure to begin with) and SPM took different
> decisions automatically regarding the var-covar structure.
>
> If we did something wrong we just want to know what it is and why the
> difference?
> Warmly?
> Karina
>
> On Sun, May 6, 2018 at 5:29 AM, Torben Lund <[log in to unmask]>
> wrote:
> > Dear Karina
> >
> > if you could share the two batch jobs then we could confirm, that
> > they have been indeed been specified the same way. If they have
> > been specified the same way I think there is a problem with SPM,
> > but I don’t really think this is the case.
> >
> >
> > Best
> > Torben
> >
> >
> >
> > > Den 5. maj 2018 kl. 21.41 skrev Karina Quevedo <[log in to unmask]>
> > > :
> > >
> > > Dear Volkmar, Torben and Manfred,
> > > We specify the same settings for variance in both models but we
> > > did used different images for estimating the two different
> > > models. However see the details before about how we estimated the
> > > two sets of images. (GRACIAS!)
> > >
> > > We are analyzing a complex set of data that has the following
> > > design structure,
> > >
> > > Pre: Participants see their own face and a stranger's face across
> > > 3 emotions (happy, neutral and sad)
> > >
> > > Intervention: Participants attempt to change their Bilateral
> > > Amygdala and Hippocampus (ROI) activity via a neurofeedback
> > > procedure:
> > >
> > > Condition 1: See the Self face and increase ROI.
> > > Condition 2: See the Other face and count backwards.
> > >
> > > Post: Participants see their own face and a stranger's face
> > > across 3 emotions (happy, neutral and sad).
> > >
> > > We are interested in analyzing the data Pre versus Post, and we
> > > used a Full Factorial Design with 3 factors: Time, Face and
> > > Emotion.
> > >
> > > The columns in the attached matrixes are as follows 1 Face a=
> > > self, b= other, 2 Emotion a=happy, b=neutral, c=sad.
> > > Column 1: Pre, 1a, 2a
> > > Column 2: Pre, 1a, 2b
> > > Column 3: Pre, 1a, 2c
> > > Column 4: Pre. 1b, 2a
> > > Column 5: Pre, 1b, 2b
> > > Column 6: Pre, 1b, 2c
> > > Column 7: Post, 1a, 2a
> > > Column 8: Post, 1a, 2b
> > > Column 9: Post, 1a, 2c
> > > Column 10: Post. 1b, 2a
> > > Column 11: Post, 1b, 2b
> > > Column 12: Post, 1b, 2c
> > >
> > > The first level analysis contrasts that we used to create these
> > > second level full factorial batches are coming from the same
> > > source in terms of pre-processing files.
> > >
> > > The only difference is that we calculated the first level
> > > contrast twice (using the same pre-processing data and initial
> > > fmri factorial specification ) and for one of the sets of first
> > > level analysis batches (after using the same initial fmri
> > > factorial design specification) we calculated more follow up t
> > > contrasts. That was all.
> > >
> > > However when we run two parallel second level analysis with those
> > > two sets of first level contrasts we are getting very different
> > > results because SPM 12 seems to be choosing to model the data
> > > differently in terms of covariance structure:
> > >
> > > > Batch 1 in the figure we attach is the batch using images
> > > > derived from 1st level batches where we calculated less t
> > > > contrasts. SPM created a covariance matrix that assumes higher
> > > > collinearity between column 1 an column 7 (i.e. Pre Happy Self
> > > > Face, and Post Happy Self Face). It gives a value of cosine =
> > > > -0.75.
> > > >
> > > > Batch 2 in the figure we attach is the batch using images
> > > > derived from 1st level batches where we calculated more t
> > > > contrasts. Using the same initial Batch 1 but loading the
> > > > different images, it seems that SPM 12 created a different
> > > > covariance structure. Cosine = -0.08 (more orthogonal compared
> > > > to Batch 1)
> > >
> > >
> > > In addition to that, we had also looked into the SPM.mat file for
> > > both of the second level batches. We took a screenshot of the
> > > SPM.xX.W values for both batches. If you would like, we could
> > > send you the SPM.mat batches for you to look at.
> > >
> > > Our questions are:
> > > 1. Why is SPM 12 making such different decisions with regards to
> > > covariance structures?
> > >
> > > 2. Given our design which model is more appropriate Batch 1 or
> > > Batch 2?
> > >
> > > 3. We cannot run a flexible factorial design without loosing one
> > > of our conditions (in order to model participants). Is that still
> > > ok?
> > >
> > > One of our colleges is adamant about running a flexible factorial
> > > but that means that we will lose either emotion or self in the
> > > design, We think that the best model is Batch 1 because it
> > > assumes more collinearity between the Pre and the Post
> > > conditions, what is your opinion?
> > >
> > > Warmly yours,
> > > Karina
> > > <design matrix information SPM_xX_W.jpg>
> > > <matrix and covariate structures.jpg>
> > >
> > >
> > > On Wed, May 2, 2018 at 4:37 AM, Volkmar Glauche <volkmar.glauche@
> > > uniklinik-freiburg.de> wrote:
> > > > Dear Karina,
> > > >
> > > > as Torben said, something seems to be different. Did you really
> > > > specify the same settings for variance ((un)equal) and
> > > > independence (yes/no) in both models? Did you use the same
> > > > factors when specifying the models? Are both models estimated
> > > > on the same set of images? Since non-sphericity estimation
> > > > depends on the data, different input data may yield different
> > > > non-sphericity estimates.
> > > > You may also want to use "Review Design" to look at the effects
> > > > of model estimation. You will find a "Design" menu in the
> > > > interactive window, where you can explore the design matrix,
> > > > files and factors and covariance structure. When looking at the
> > > > covariance structure, a multi-page image matrix will be
> > > > displayed. On the first page, you will find the estimated
> > > > covariance and on the following pages the individual covariance
> > > > components for which parameters have been estimated.
> > > >
> > > > Best,
> > > > Volkmar
> > >
> > >
> > >
> > > --
> > > Karina Quevedo, Ph.D., L.P.
> > > Assistant Professor. Division of Child and Adolescent Psychiatry
> > > Room F260
> > > University of Minnesota. Department of Psychiatry
> > > 2450 Riverside Avenue, Mpls, MN 55454
> > > 612-273-9761 Fax: 612-273-9779
> > > [log in to unmask]
> > >
>
>
>
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