The --cache is completely independent of --qcache (it was an unfortunate
name for me to give it). The --cache means that the simulations have
already been run. Think about a statistics text book that has a table of
t-values, DOF, and corresponding p-values. I created a similar table
that we distribute with FreeSurfer so that you don't have to run the
simulation yourself.
doug
On 7/10/13 11:52 PM, jh kim wrote:
Dear Prof. Greve
Thanks for the quick responses.
My questions are now resolved.
Here is my last question regarding permutation testing.
I cannot figure out what the '--cache' flag stands for.
I've already done my data using recon-all with '-qcache' flag.
Should I use '--cache' flag option in mri_glmfit-sim command?
Thank you in advance for your help.
Kim
2013/7/11 Douglas N Greve <gr...@nmr.mgh.harvard.edu
<mailto:gr...@nmr.mgh.harvard.edu>>
On 07/09/2013 10:07 PM, jh kim wrote:
Dear Prof. Greve
I have additional (and probably silly) questions.
1) Is there much difference between result of monte-carlo
null-z simulation (QDEC GUI) analysis and that of clusterwise
correction for multiple comparisons using 10000 permutations?
I'm guessing similar results between both methods; however I'm
wondering whether significant clusters from the former
processing might disappeared when using the latter process, or
vice versa?
When you say permutation, do you mean the permutation method where
you permute your own data? It is hard to say whether they will
give the same result. I usually find permutation to give less
significant results (which may just be more accurate).
2) Does QDEC analysis automatically produce *.glmdir files for
each contrast? If not, how can I make *.glmdir from the QDEC
(not using the mri_glmfit command)?
It creates a glmdir for the whole analysis. Within the glmdir, it
creates a folder for each contrast
doug
Thank you for your kindness
Kim
2013/7/9 Douglas N Greve <gr...@nmr.mgh.harvard.edu
<mailto:gr...@nmr.mgh.harvard.edu>
<mailto:gr...@nmr.mgh.harvard.edu
<mailto:gr...@nmr.mgh.harvard.edu>>>
On 07/07/2013 01:36 PM, jh kim wrote:
> Dear FreeSurfer experts...
>
> Please be patient with a newbie's questions.
> For the first time, I'm trying to analyze between-group
differences in
> cortical thickness (controls vs. patients) by using FS
5.1.0 and
QDEC.
> I've read through the online manual and mailing lists;
however, I
> cannot figure out the following queries.
>
> 1) I selected 'group' (con, pat) as a fixed factor
(gender is not
> applicable because all are females), and demeaned age as a
continuous
> covariate. After analyzing using DODS option, several
questions are
> shown in the Display GUI panel. Does the question "Does
the average
> thickness differ between con and pat?" mean
between-group difference
> of cortical thickness after *controlling for the effect
of age*?
Yes, it controls for all your other factors.
>
> 2) Should I include average cortical thickness of the
left or right
> cortex (calculated from the command,
mris_anatomical_stats -l
> lh.cortex.label -f subid.txt subid lh)as a nuisance
variable? I know
> this issue has been addressed many times before, and it
is generally
> recommended not to include as a nuisance factor.
However, in case of
> statistically significant difference in mean cortical
thickness
of one
> hemisphere (unpaired two sample t-test by SPSS), is it
still not
> necessary to include it as a nuisance factor? From the
similar
points
> of view, how about volume and surface area analyses
(include or not
> total cortical volume and total surface area as a
nuisance factor,
> respectively) ?
I don't have a strong opinion about removing the global mean
thickness,
but I think others (Mike Harms?) recommend to do so. For
volume and
surface area, you probably want to normalize by the ICV.
>
> 3) I found several significant clusters that are
corrected for
> multiple comparisons using Monte Carlo simulation
(corrected P <
0.01)
> in the bottom of Display panel. I assume that this
procedures take
> 10,000 permutations by default. Am I right?
It is 10k iterations of generating white noise (not a
permutation test
unless you specified that explicitly).
>
> 4) Last question, how can I extract individual value
(cortical
> thickness per subject) from the significant clusters? I
want to do
> this 'all-at-once'. Please let me know the command.
There should already be a file in the output folder called
something.y.ocn.dat that has a row for each input subject
and a column
for each cluster.
doug
>
> Apology for bothering you.
> Any comments would be greatly appreciated.
>
> Thanks.
> Kim
>
>
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