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Subject:

AnCoVa Design

From:

Wei Wen <[log in to unmask]>

Reply-To:

Wei Wen <[log in to unmask]>

Date:

Fri, 15 Dec 2000 09:18:58 +1100

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (109 lines)

Dear Stefan Kiebel, Alex Gamma and other SPMers,

I read the original message from Alex and the reply from Stefan (Nov.
2000) on ANCOVA, but my question is a bit different:
//////////////////////////////////////////////////////////////////////////////
> I'm puzzled about this: I have a global volume effect in a PET study
> despite using global normalization of the data. My design is
multi-subject
> (n=10), 2 conditions, 1 scan per condition (the scan is an average
scan of
> two replications of each condition), no covariates. I use Grand Mean
> scaling to 50 and 0.8 as the threshold for masking. I tried global
> normalization using AnCOVA or proportional scaling: in both cases,
SPM96-
> and SPM99- analyses, respectively, result in a global decrease (only
one
> big blob at p=0,001 uncorrected) for one contrast, while there is no
effect
> for the opposite contrast.
>
> I always assumed that global normalization should make this very case
> impossible. Any explanations or suggestions?

Do you mean that you are surprised that you can't see any increases with

p(peak height) < 0.001? Although I can't really come up with any
intelligent idea (maybe Jesper could?) under which configurations you
could see something as you observed, I would look at the t-map in the
display tool. Here you see the negative and positive peaks at one glance

and if there is some spatial structure, you'll see it. Displaying the
t-map and the functional or structural image using 'checkreg' might also

be quite useful.

Hope this helps a tiny bit, Stefan
//////////////////////////////////////////////////////////////////////////////

My problem:

I was doing analysis on a group of 8 subjects who had a scan (SPECT)
each before and after treatment. I used "Population mean effect: 2
conditions 1 scan/cond (paired t-test)". I guess this is the right
model. But according to the discussion outlined in the lecture notes
Chapeter 3 by Holmes and Friston, this model suffers from the problem of
so-called "weighted" regression. I was then trying to use AnCOVa and see
what would happen.  And I found that I could only use "compare
population: 1 scan/subject (AnCOVA)" but not "multi-subj: conditions and
covariates".
What I tried to use:
Multi-subjects conditions and covariates: I put 2 condtions, no
covariate, no nuisance but in the end the report said: 2 condition, + 0
covariate, + 8 block, + 8 nuisance: 18 total, having 16 degrees of
freedom leaving 0 degrees of freedom from 16 images. Since there is no
degree of freedom left and SPM couldn't carry on.

I have noticed that Alex Gamma had 2 scans for each condition which is
different from my case: 1 scan for each condition. Therefore, Alex could
carry on with this design, but not me.  Therefore, my question is:  Is
it correct to say that I cannot use this design (multi-subject:
conditions and covariates) since I have only one scan for each
condition? Besides the paired t-test and compare population mean
(AnCoVa), anything else I could use? I found that I am getting different
results from paired t-test and compare
population mean (AnCoVa) (I used Ancova for the global normalization).
If these 2 models are both OK, which results should I trust more? Back
to the # of nuisance: I input the nuisance as 0, how come it came out as
8? (since there were 8 subjects and each one of them having a different
residual? Is the error N(0, sigma) called nuisance here? And 8 subjects
having 8 different this type of error? {N(0, sigma_k) k=1,2...,8}. If my
last question is correct, then if I use "comparing population mean 2
effects: 1 scan / condition" how come I only got one nuisance? Are all
the subjects effectly taken as a single subject in this computational
model?

One more thing, Alex, I have also noticed that you used an average of 2
replications of each condition, which means that you used only one scan
for each condition.It sounded that your design was the same as mine: one
SPECT scan for each condition, no covariates, no nuisance etc. using
AnCova for the
global normalization and therefore, you would have zero degree of
freedom
left. How did you carry out the computation then? In my case, SPM
refused to go on, which makes sense?

Many thanks in expectation.

Wei Wen
--
_____________________________________________________

             | Wei Wen, PhD
             | Neuropsychiatric Institute
             | ( Rm 15A, McNevin Dickson Building )
   _--_|\    | Prince of Wales Hospital
  /      \   | Randwick, NSW 2031, Australia
  \_.--\_* <-- (Sydney)
        v    |
             | email:   [log in to unmask]
             | Voice:   +61-2-93823730
             | Fax:     +61-2-93823774
_____________________________________________________
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