mris_preproc might work. You could also use mri_concat (mris_preproc is
a front end for mri_concat)
On 03/03/2015 06:19 PM, Janosch Linkersdörfer wrote:
>
> Am 03.03.2015 um 14:42 schrieb Douglas N Greve :
>
>> Some of this Martin will have to comment on, but my comments below
>>
>> On 03/03/201
Am 03.03.2015 um 14:42 schrieb Douglas N Greve :
>
> Some of this Martin will have to comment on, but my comments below
>
> On 03/03/2015 12:35 PM, Janosch Linkersdörfer wrote:
>> Hey Doug,
>>
>> thanks again! That means, I would do something like:
>>
>> 1.) construct longitudinal qdec table
Some of this Martin will have to comment on, but my comments below
On 03/03/2015 12:35 PM, Janosch Linkersdörfer wrote:
> Hey Doug,
>
> thanks again! That means, I would do something like:
>
> 1.) construct longitudinal qdec table
> - only use fsid, fsid-base, and years/age columns
> - substract
Hey Doug,
thanks again! That means, I would do something like:
1.) construct longitudinal qdec table
- only use fsid, fsid-base, and years/age columns
- substract the average age of the one-time point subjects from years/age value
for every subject at every time point
2.) run long_mris_slopes
-
I don't know, esp since it is an approximation to begin with. An
alternative is to take the offsets from your multi-time-point subjects
and the single maps from your subjects with one time point and run that
through the one-sample-group-mean (--osgm in mri_glmfit). If you go this
route, then y
Hi Doug,
thank you very much for your answer!
Am 02.03.2015 um 11:30 schrieb Douglas N Greve :
>
> You would do the long analysis using a random effects analysis.
OK, so basically do 2-stage modeling
(https://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalTwoStageModel), right?
> For each sub
You would do the long analysis using a random effects analysis. For each
subject you can get a slope (this won't work if the subject only has 1
time point), then concatenate the slopes into a file and run mri_glmfit,
then follow the procedures from the archive email you reference.
doug
On 02/2
Hi Doug and others,
I would like apply (Monte Carlo simulation) cluster correction (as opposed to
the implemented vertex-wise FDR correction) on the results from a longitudinal
study I analyzed using the LME toolbox. The design is unbalanced (different
number of time points, from 1 to 4, per su
Thanks, Doug!
I looked at the examples on the wiki, and the Paired Analysis wiki page as well.
You mentioned I should include the categorical and continuous variables in the
fsgd file for the mri_glmfit. It seems that one of my analyses includes few
subjects, and then one of the class end up lac
On 04/15/2014 10:34 PM, Pedro Rosa wrote:
> Hi, Doug.
> Thanks a lot!
> The command would look like this: mri_glmfit-sim --glmdir
> lh.thickness.Sch.glmdir --sim mc-z 1 2 teste --sim-sign abs
> --overwrite --cache 1.3 abs
> It takes only a few seconds to run.
The command is fine, but remove
Hi, Doug.
Thanks a lot!
The command would look like this: mri_glmfit-sim --glmdir
lh.thickness.Sch.glmdir --sim mc-z 1 2 teste --sim-sign abs --overwrite
--cache 1.3 abs
It takes only a few seconds to run.
I have a few questions about how to run it:
1) How the single entry for each subject
On 04/14/2014 10:35 AM, Pedro Rosa wrote:
> Dear Doug and Jorge,
> I tried what you suggested and I think it work, although I have some
> concerns.
> I am working with a longitudinal study with two time-points for all
> subjects, three categorical variables (group, substance abuse /
> dependenc
Dear Doug and Jorge,
I tried what you suggested and I think it work, although I have some concerns.I am working with a longitudinal study with two time-points for all subjects, three categorical variables (group, substance abuse / dependence and
Thanks, Doug!
Should I run the mri_preproc and and smooth the output using mri_surf2surf
with, let’s say, 10mm, and than run the LME normally in MatLab?
Would this be problematic with a different smoothing procedure in mri_glmfit?
How will mri_glmfit deal with the longitudinal design? Does this
I think I would just run mri_glmfit on your data to get the proper
directly structure and estimate of FWHM, then copy the sig file from the
mixed fx analysis into the glmfit folder for one of the contrasts. Then
run mri_glmfit-sim.
doug
On 3/29/14 10:29 AM, Pedro Rosa wrote:
Dear Doug and
Dear Doug and Jorge,
Thank you very much for your help.
I found another message in the list
(https://mail.nmr.mgh.harvard.edu/pipermail//freesurfer/2013-November/034649.html)
in which you suggested a way of using MC in mri_glmfit-sim by creating “fake
files”, which would not be read by the scrip
Jorge, do you output the FWHM?
doug
On 03/27/2014 03:14 PM, jorge luis wrote:
> Hi Pedro
>
> Sorry, right now the only multiple comparisons corrections implemented
> in lme are the original Benjamini and Hochberg (1995) FDR procedure
> (lme_mass_FDR) and a more recent and powerful two-stage FDR
Hi Pedro
Sorry, right now the only multiple comparisons corrections implemented in lme
are the original Benjamini and Hochberg (1995) FDR procedure (lme_mass_FDR) and
a more recent and powerful two-stage FDR procedure (lme_mass_FDR2):
Benjamini, Y., Krieger, A.M., Yekutieli, D. (2006). Adaptive
Dear Doug,
Thank you very much!
I will try what you suggested, although I am not sure if Jorge's stream
outputs the FMHM, or if I would need to run the statistics from the beggining
using in the terminal, and not in MatLab.
Do you think Jorge could comment on this issue?
Regards,
Pedro Rosa.
On
In theory, it should be possible. I have not used Jorge's stream, so I
don't know that much about it. Does it save an estimate of the FWHM? If
so, then you can run mri_surfcluster passing it the p-value (ie,
-log10(p)) map, the FWHM, the mask, and a voxel-wise threshold. This is
what mri_glmfi
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