Hi Donna, 

Thanks for the explanation.

So you are saying that Caret cannot create these permuted f-maps, since
the statistical model needs to be applied to create the f-map, but also
to create the permuted f-maps. I would have to create them myself using
the statistical model, right? 
How are the permuted f-maps created? What permutation is used?

Thanks again, 
Casper
  

-----Oorspronkelijk bericht-----
Van: [email protected]
[mailto:[email protected]] Namens Donna Dierker
Verzonden: maandag 16 november 2009 17:00
Aan: Caret, SureFit, and SuMS software users
Onderwerp: Re: [caret-users] multiple comparison correction on SUMA data

On 11/16/2009 05:27 AM, Oers, C.A.M. van wrote:
> Hi,
>  
> Although I saw a post by Donna Dierker regarding the subject, I am 
> afraid I still have a question.
>  
> I have created SUMA files using a mixed model analysis. The resulting 
> files have an individual statistical threshold (and coefficient). I 
> would like to use Caret to do a multiple comparison correction with a 
> cluster size threshold.
I'm not sure how straightforward this will be, due to the randomization
component.  The caret_command utility has these features:

      caret_command -metric-statistics-anova-one-way
      caret_command -metric-statistics-anova-two-way
      caret_command -metric-statistics-coordinate-difference
      caret_command -metric-statistics-interhemispheric-clusters
      caret_command -metric-statistics-kruskal-wallis
      caret_command -metric-statistics-levene-map
      caret_command -metric-statistics-normalization
      caret_command -metric-statistics-one-sample-t-test
      caret_command -metric-statistics-paired-t-test
      caret_command -metric-statistics-shuffled-cross-correlation-map
      caret_command -metric-statistics-shuffled-t-map
      caret_command -metric-statistics-subtract-group-average
      caret_command -metric-statistics-t-map
      caret_command -metric-statistics-two-sample-t-test
      caret_command -metric-statistics-z-map

Enter the command for the help for that feature, or enter "caret_command
-help-ful > caret_command.txt" to get a text file with all the help.

Let's choose the case of a two-sample-t-test.  In this case, your
caret_command line will look like so:

DISTCOL=1
ITERATIONS=2500
THRESHNEG=-2.68
THRESHPOS=2.68
P_VALUE=0.05
VAR_SMOOTH_ITERATIONS=0
VAR_SMOOTH_STRENGTH=0.0
DO_TMAP_DOF=true
DO_TMAP_PVALUE=true
THREADS=4

FIDUCIAL=Human.PALS_B12.LEFT_AVG_B1-12.FIDUCIAL.clean.align.73730.coord
OPENTOPO=Human.sphere_6.LEFT_HEM_OPEN.73730.topo
DISTMETRIC=Human.PALS_B12.B1-12_LEFT_DISTORTION-vs-AVG-FIDUCIAL_ONLY.737
30.metric
COMPOSITE_IN_A=Composite.DEPTH.LEFT.KIDS.73730.surface_shape
COMPOSITE_IN_B=Composite.DEPTH.LEFT.ADULTS.73730.surface_shape
OUT_PREFIX=KIDS29vADULTS23_Depth.LEFT
COMMAND="caret_command -metric-statistics-two-sample-t-test NO_TRANSFORM
UNPOOLED"
COMMAND="$COMMAND $COMPOSITE_IN_A $COMPOSITE_IN_B"
COMMAND="$COMMAND $FIDUCIAL $OPENTOPO $DISTMETRIC $DISTCOL $OUT_PREFIX"
COMMAND="$COMMAND $ITERATIONS $THRESHNEG $THRESHPOS $P_VALUE"
COMMAND="$COMMAND $VAR_SMOOTH_ITERATIONS $VAR_SMOOTH_STRENGTH"
COMMAND="$COMMAND $DO_TMAP_DOF $DO_TMAP_PVALUE $THREADS"
$COMMAND

These sample scripts (e.g., tstt.sh and gen_composites.sh) are here:

http://brainmap.wustl.edu/pub/donna/WUSTL/BURTON/SCRIPTS/SBM/
login pub
password download

But note that this takes two composite shape files (imagine your 1D
files concatenated together in one file, with one 1D column per subject,
with the node number preceding all os them).  I don't think generating
composites is hard (see below about adding header).

Rather, the tricky bit here is that you already have your stat -- not
raw scores/activations.  The example above uses sulcal depth scores, but
this could be any measure.  But if it is already a stat, then it's less
clear to me how to randomize it.  With the two-sample t-test, a t-map is
generated using the two groups' composites.  Then, the composites are
combined and group memberships randomized to create permuted t-maps.  
The max size of each permuted map is stored to create a distribution,
and the size of the 95th percentile is used as the significance cut-off.

If you've got stats already, how are we randomizing?

If you can create your own permuted t- or f-maps, as well as your own
real t- or f-map, then there is a newer caret_stats feature that can
take these real and permuted maps and do either cluster operations or
TFCE on them.

But in your case, it isn't clear to me where/how the randomizing comes
in.

>  
> How can I convert a 1D surface file into a metric or surface_shape
file?
This part is easy:

1.  Add a node number in column 1, using matlab or some other tool.

2.  Slap on a header as shown in
http://brainvis.wustl.edu/CaretHelpAccount/caret5_help/file_formats/file
_formats.html#metricFile:

metric-version 2
tag-number-of-nodes 71723
tag-number-of-columns 2
tag-title untitled
tag-column-name 0 Depth
tag-column-name 1 Smoothed Depth
tag-column-color-mapping 0 -1.000000 1.000000 tag-BEGIN-DATA 0 1.803019
-0.045549
1 1.704132 -0.007309
2 1.523951 0.026103
...
71722 .822899  0.112485

If you can generate metric/surface_shape files from all your 1D files,
then it's not hard to generate a composite using something like the
gen_composites.sh script mentioned above).
> Which way to perform the correction?
>  
> I appreciate the help,
> Casper
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