Hi Steve
many thanks for your help. I would like to ask a couple of follow-ups here
and would be grateful for any reply:
Wrt to FE:
Doing fixed effects analysis, I have to calculate the requested "contrast"
or mean effect from the copes:
e.g. avwmerge -t copes cope1 ... copeN
avwmaths copes -Tmean copes
and get the adjusted variance image:
avwmerge -t varcopes varcope1 ... varcopeN
avwmaths varcopes -Tmean -div <Number of varcopes> varcopes
Is this true only for t-contrasts? How would I do FE with F-tests?
Wrt to DOFs:
Reading the technical reports, I actually thought flame is doing some
trick to preserve more dof's. However, this is not the case? My dof's at
any level would always be the number of input images minus number of EVs?
Wrt to demeaning of EVs:
It is not completely clear to me why one have to demean an EV. Say I have
a behavioral covariate 3 2 1. Demeaning it would change its values to 1 0 -
1. However, this should mean that voxels of the second image could take
any value (and not in between the first and third image) to get a good fit
of this EV. Am I missing something?
Wrt to flame:
As I am scripting all of my analysis, I noted one option that seems not to
be documented: "fixed effects". Is this already working? I guess Hauke
already asked: How to input F-tests into flame?
Wrt to contrast masking:
How do I implement this from the command line? Is it simply using avwmaths
with the -mas option?
And finally concerning my own study design:
I have 4 subjects each with 3 sessions per day and 6 repetitions over a
period of 6 weeks. I want to analyze the effect of a behavioral covariate
which has been measured weekly (same day as functional scans). Having only
one value of the covariate but 3 intra-day sessions I thought of putting
these together using fixed effects analysis. Finally I wanted to put the
(FE) copes/varcopes to a second but not third level for the following
reason:
- I do NOT want to generalize results to the population my subjects came
from
- it seems necessary to include inter-day variance
- the covariate for one subject is all zero
- I am having much more dof's
Using a design like:
subject1-week1: 1 0 0 0 -3
subject1-week2: 1 0 0 0 -1
...
subject2-week1: 0 1 0 0 2
subject2-week2: 0 1 0 0 1
...
subject4-week6: 0 0 0 1 -5
with the first 4 colums being subject EVs and the last colums being the
behavioral covariate, do I violate any statistical assumption (multiple
sessions/subjects - at least not possible using SPM)? Would it be wise to
define seperate groups for each subject (to get seperate variance
estimates)?
And finally, say something special has happened in one but none of the
other measurements and I want to analyze where in the brain it came from ;-
)) should this EV really look like -1 for each image but 23 for the
outstanding event (with 24 measurements in total)?
Thanks for all your help.
Best, Thomas
|