Hi Matthieu

The easiest for you is to follow these steps:

1-Read your label eg.:

lhcortex = fs_read_label('freesurfer/subjects/fsaverage/label/lh.cortex.label');

2-Read the data file eg.:

[lhY, lhmri] = fs_read_Y('lh.thickness.mgh');

3-Fit a vertex-wise lme model with random effects for the intercept term and time covariate eg.:

lhstats = lme_mass_fit_vw(X, [1 2], lhY, ni, lhcortex);

Here you will need to first create your design matrix X in Matlab. The first column is all 1s (ones) and the second column is the time covariate.

4-Perform vertex-wise inference eg.:

CM.C = [your contrast matrix];

F_lhstats = lme_mass_F(lhstats, CM);

5-Save results eg.:

fs_write_fstats(F_lhstats, lhmri,' sig.mgh', 'sig');

Please read the header of the matlab .m files to understand the meaning of each script input. Also read carefully the paper: Bernal-Rusiel, et. al ( 2012). Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models. Neuroimage 66, 249–260.

The time variable can be in years or days, whatever makes sense for your study. Your results are going to be expressed in terms of the choosen unit of measurement.

Cheers

-Jorge

De: Matthieu Vanhoutte <matthieuvanhoutte@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 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" <matthieuvanhoutte@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 <mreuter@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 <mreuter@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 <mreuter@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, 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
Web  : http://reuter.mit.edu 
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The information in this e-mail is intended only for the person to whom it is
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_______________________________________________
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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
Web  : http://reuter.mit.edu 
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The information in this e-mail is intended only for the person to whom it is
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The information in this e-mail is intended only for the person to whom it is
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contains patient information, please contact the Partners Compliance HelpLine at
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