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 you only have very few subjects). Then set the
intercept as random effect and test for a slope effect (2nd column). You
can also play around with setting slope as random effect, but with only
7 subjects, not sure. You can do a model comparison as described on our
wiki.
3. Not sure if I would include gender (unless there is good reason for
that). You are looking at within subject slopes. I don't think you need
to demean age.
4. does not matter. If you look at the betas they will be in unit/days
or unit/years (or week , month) depending on your decision.
The danger with such a small group is that outliers will affect your
results a lot (they severely violate the assumption of gaussian noise)
and can produce spurious effects.
Best, Martin
On 10/11/2015 04:31 PM, Matthieu Vanhoutte wrote:
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"
<matthieuvanhou...@gmail.com <mailto:matthieuvanhou...@gmail.com>> a
écrit :
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 see the significant change of FA according to time. Will
LME study allow me to get this change and how to define contrast
to assess it ? Are the results of this longitudinal study
directionals, i.e. is it possible to establish an increase or
decrease of FA in significant regions according to time ?
3) Should the covariates (age, gender...) be demeaned or centered ?
4) Has the accurate time variable to be in years absolutely or
could it be also in days ?
Best regards,
Matthieu
2015-09-29 17:11 GMT+02:00 Martin Reuter
<mreu...@nmr.mgh.harvard.edu <mailto:mreu...@nmr.mgh.harvard.edu>>:
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 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 lme_mass_fit_vw functions
in my mass univariate case ? If yes, how do I have to format
my 3D FA volume as input of the LME toolbox ?
Best regards,
Matthieu
-------------------------------------
Matthieu Vanhoutte, MSc
Research Engineer - Department of Neuroradiology
Regional University Hospital, Lille, France
2015-09-29 16:34 GMT+02:00 Martin Reuter
<mreu...@nmr.mgh.harvard.edu
<mailto:mreu...@nmr.mgh.harvard.edu>>:
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, Martin
On 09/29/2015 09:11 AM, Matthieu Vanhoutte wrote:
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
2015-09-29 15:04 GMT+02:00 Martin Reuter
<mreu...@nmr.mgh.harvard.edu
<mailto:mreu...@nmr.mgh.harvard.edu>>:
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
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 format,
could it be in good way for LME ?
Thanks for helping !
Best regards,
Matthieu
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-- Martin Reuter, PhD Assistant Professor of Radiology,
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Imaging Massachusetts General Hospital Research
Affiliate, CSAIL, MIT Phone: +1-617-724-5652
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-- Martin Reuter, PhD Assistant Professor of Radiology,
Harvard Medical School Assistant Professor of Neurology,
Harvard Medical School A.A.Martinos Center for Biomedical
Imaging Massachusetts General Hospital Research Affiliate,
CSAIL, MIT Phone: +1-617-724-5652 <tel:%2B1-617-724-5652> Web
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Assistant Professor of Neurology, Harvard Medical School
A.A.Martinos Center for Biomedical Imaging
Massachusetts General Hospital
Research Affiliate, CSAIL, MIT
Phone: +1-617-724-5652
Web : http://reuter.mit.edu
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