I am going to work on digesting the rest of your email. But, I am
still unclear on the varcope files. Do those give me an estimate on
the variance of the parameter estimates? Are they similar to standard
errors?
Any papers that I could look at as examples would be greatly
appreciated.
Darren
On Saturday, July 5, 2003, at 01:26 AM, Joseph Devlin wrote:
> Hi Darren,
>
> Now, my understanding is that each voxel of the cope1.hdr file
> contains the Beta estimate for the effect of that contrast. So, are
> the values within groups comparable? Across groups? Is this a
> measure of activation for the group under the conditions?
>
>
> That's just about right. The cope images actually contain values
> which are the linear Contrasts Of Parameter Estimates (hence cope).
> Each parameter estimate is a beta so for a 1 -1 contrast, the cope
> would be beta(1) - beta(2).
>
> THe raw numbers themselves are not actually meaningful and may not be
> directly comparable across groups (or indeed, across individuals). If
> the design matrices are all identical across session, subject, and
> group then yes, the raw numbers should be directly comparable. If
> there are timing differences or if you model only correct responses or
> admit any kind of variability like that, then no, they are not
> compatible. For these values to be meaningful and comparable they
> really should be converted into percent signal change and this depends
> on the precise design matrix per session. The following fragment of
> ksh script will basically do what you want (per session, per subject
> rather than extracting a single value at the top level). THe
> advantage here is exactly what Hauke mentioned, namely that you get
> the distribution rather than a single summary statistic:
>
> #!/bin/ksh
>
> #
> # This script retrives the mean parameter estimates from a standard
> space
> # ROI and the baseline signal in the whole brain for computing mean
> # effect sizes in a region.
> #
>
> #
> # Specify the path for the subject directories
> #
> ORIG_PATH=~/scratch
>
> #
> # Specify the subject directories
> #
> DIRS="6235 6236 6247 6248 6301 6302 6314 6334 6335 6346 6347 6348"
>
> #
> # Specify the sessions
> #
> SESS="session_A+ session_B+"
>
> #
> # Specify the PEs you want to retrieve.
> #
> FILES="pe1 pe3 pe5 pe7 pe9 pe11 pe13 pe15"
>
> #
> # Adjust the header to reflect your individual conditions
> #
> print "SESSION UNREL FORM MORPH SEM IDENT NON-ID
> UNR-NON NON BASELINE"
>
> #
> # Specify the anatomical mask of the ROI. The mask should be in
> standard
> # space for the following code to work without modification.
> #
> MASK=~/masks/left_pars_triangularis
>
> #---------------------------------------------------------
> # Shouldn't need to change anything below this point
> #----------------------------------------------------------
> TMP=/tmp/$$
>
>
> for D in $DIRS; do
> for S in $SESS; do
> print -n "${D}/${S} "
>
> for F in $FILES; do
> # Put the EPI image into standard space
> flirt -in $ORIG_PATH/$D/$S.feat/stats/$F \
> -ref /usr/local/fsl/etc/standard/avg152T1_brain -applyxfm \
> -init $ORIG_PATH/$D/$S.feat/example_func2standard.xfm \
> -out $TMP
>
> # Mask it and compute the mean PE in the mask
> avwmaths $TMP -mas $MASK $TMP
> MEAN=$(avwstats $TMP -M)
>
> # Adjust the signal to reflect percent signal change
> COLUMN=${F#pe}
> DESIGN=$(awk 'BEGIN { column = '"$COLUMN"' ; mn = 0; mx = 0 } \
> matrix == 1 { if ($column < mn) mn=$column; \
> if ($column > mx) mx=$column }\
> /Matrix/ { matrix = 1 }\
> END { printf("%0.3f\n", mx-mn ) }'
> $ORIG_PATH/$D/$S.feat/design.mat)
> awk 'BEGIN {printf("%0.3f ", '"$MEAN"' * '"$DESIGN"') }'
> $ORIG_PATH/$D/$S.feat/design.mat
> done
>
> # Finally, compute the baseline signal over the whole brain for
> this mask.
> # Normally it is around 10000.
> print $(avwstats $ORIG_PATH/$D/$S.feat/filtered_func_data -M)
> done
> done
> rm $TMP.img $TMP.hdr
>
> A couple points to note. First, this script is specific to parameter
> estimates -- not copes. The reason is because the pes get adjusted
> according to the peak height difference in the relevant column of the
> design matrix which is only meaningful for pes. I'll return to copes
> in a second.
>
> Next, the values themselves still aren't percent signal change. They
> need to be divided by the final column (which is a value typically
> around 10000) and then multiplied by 100 to change into a percentage.
> Once you've done that, it's easy to get cope values. For a (1 -1)
> contrast I would simply create a new column where I subtract column 2
> from column 1 per session, per subject and then compute the mean and
> standard error mean (or whatever) over the contrast.
>
> And just to briefly return to another point Hauke raised -- the
> negative COPE values. If these really are beta rather than copes,
> there is nothing intrinsically wrong with negative values as long as
> you are comparing them and the contrast is positive. If, however, the
> contrast is negative (and this looks to be the case if the number you
> provided came from cope images), then you are talking about
> "deactivations" rather than "activations". If these are copes and you
> see activation in the amygdala for your contrast of interest, that
> probably means that your mask is somewhat inconsistent with your
> region of activation (either much larger or spatially displaced).
>
> Hope this is some help.
>
>
>
> Joe
|
