Hi Matthieu,
1. The spatiotemporal approach is designed for surfaces, not for voxel
images, so regular mass univariate (vw).
2. Yes, that can be done with LME. 1 group is a simple design: column of
1, colum of time_from_baseline, other covariates such as age (I would
not include too many as
Hello Martin,
Many thanks for your answer !
Indeed, I didn't add age and gender as covariates according the fact of my
small number of patients group. Moreover, I couldn't put the slope as a
random effect because of no convergence problem...
Concerning the convergence, at the end of the
Hi Matthieu,
1. I think it is OK, maybe Jorge can comment, if not.
2. Not really. You could do a linear fit within each subject separately
(at each location) and then do a Wilcoxon Ranksum (or a Signed Rank )
Test. The non-parametric testing will help with potential outliers, the
problem
: Matthieu Vanhoutte <matthieuvanhou...@gmail.com>
Para: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Enviado: Domingo 11 de octubre de 2015 16:31
Asunto: Re: [Freesurfer] LME toolbox for longitudinal FA volume maps ?
Dear experts,Could anyone please help me ? My group ha
Dear experts,
Could anyone please help me ? My group has small size (7 subjects) with non
equally spaced different timepoints (from 2 up to 4).
Best regards,
Matthieu
Le 30 sept. 2015 13:57, "Matthieu Vanhoutte"
a écrit :
> Hi Martin,
>
> Thank you for helping !
>
Hello Martin,
Could you please find any time to answer me ?
Many thanks !
Best regards,
Matthieu
Le 30 sept. 2015 13:57, "Matthieu Vanhoutte"
a écrit :
> Hi Martin,
>
> Thank you for helping !
>
> 1) What should I use for the parameter estimation: "mass
Hi Martin,
Thank you for helping !
1) What should I use for the parameter estimation: "mass univariate" or Novel
mass-univariate tools (spatiotemporal models)" ?
2) I have one group of subjects with different timepoints (non equally
spaced and different numbers of timepoints), and would like to
Hi Matthieu,
if all your images are perfectly registered, you can do LME on a
voxel-by-voxel basis, just as if you had hippocampal volume or any other
ROI measure.
Best, Martin
On 09/29/2015 06:46 AM, Matthieu Vanhoutte wrote:
Dear experts,
I would like to know if it is possible to make a
Para: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Enviado: Martes 29 de septiembre de 2015 9:04
Asunto: Re: [Freesurfer] LME toolbox for longitudinal FA volume maps ?
Hi Matthieu,
if all your images are perfectly registered, you can do LME on a
voxel-by-voxel basis, ju
Hi Martin,
Thank you for your answer.
However, I would like to consider FA maps as a mass-univariate problem and
not a univariate voxel-by-voxel as hippocampal volume. Indeed, my voxels
aren't independant, are they ? So, how to correct for multiple comparisons
then ?
Best regards,
Matthieu
Dear experts,
I would like to know if it is possible to make a longitudinal study with
LME toolbox from volume FA maps registered in a common space ?
I don't have T1 images so the recon-all process couldn't be processed. But
if I put my 3D FA volume of dim = [nx,ny,nz] in a 1D nx*ny*nz voxels
Hi Matthieu,
yes, multiple comparisons are a problem for any mass univariate
approach. You can use the FDR2 correction (in the lme matlab tools)
which is less conservative than standard FDR. You can also work in
specific ROI's and average there, to reduce the number of comparisons.
Best,
Hi Martin,
I understand for the multiple comparisons problem. But if I consider a
voxel-by-voxel analysis just as hippocampal volume (lme_fit_FS for
estimation), how could I consider a correction for multiple comparisons in
this univariate case ?
Shouldn't I use the lme_mass_fit or
Hi Matthieu,
use lme_mass_fit_vw and the y is a simple vector. I would mask the image
before doing this and only pass the brain voxels (to increase speed and
reduce comparisons).
Best, Martin
On 09/29/2015 10:43 AM, Matthieu Vanhoutte wrote:
Hi Martin,
I understand for the multiple
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