Thank you Martin.
My intention was not to enquire about advice on the statistical model
but instead on how to run the matlab commad with two experimental groups.
My decision is to test group by age interaction for each group. The
way to proceed for this would be to create two separate matrices?
Thanks
Pablo
------------------------------------------------------------------------
To: freesurfer@nmr.mgh.harvard.edu
From: mreu...@nmr.mgh.harvard.edu
Date: Tue, 3 Nov 2015 12:10:17 -0500
Subject: Re: [Freesurfer] A mixed effect model approach in within
subject dataset {Disarmed}
Dear Pablo,
maybe what you want is a group x age interaction for each of those
groups? (so 1x5 and 2x5)?
Not sure. It is your hypothesis, your model and we usually don't give
advise on model building. Since you have a special case (within family
analysis) I would recommend you try to get some advise from a
bio-statistician, if unsure what you are doing.
Best, Martin
On 11/03/2015 12:01 PM, pablo najt wrote:
Dear Martin and FS experts.
I have a quick query about how to obtain my design matrix of
/group by age/ from the following matlab command listed in the lme
wiki. In the wiki example the matrix is created from variables 1
and 2 (group by time).
X = [ones(length(M),1) M M(:,1).*M(:,2);
In my case I have a combination of variables 1 and 2 for group
(relatives of PT and PT) and 5 (age).
I would really appreciate if you could kindly advice on how to
adapt the command above to my design.
Thanks
Pablo
------------------------------------------------------------------------
To: freesurfer@nmr.mgh.harvard.edu
<mailto:freesurfer@nmr.mgh.harvard.edu>
From: mreu...@nmr.mgh.harvard.edu <mailto:mreu...@nmr.mgh.harvard.edu>
Date: Tue, 27 Oct 2015 17:13:51 -0400
Subject: Re: [Freesurfer] A mixed effect model approach in within
subject dataset {Disarmed}
Hi Pablo,
the sortData function sorts the rows so that entries from the same
subject (in your case same family) are blocked and that within
each block the time variable (2nd parameter specifies which column
that is in your M matrix, in your case the first =1) is increasing.
It is important, when creating your design matrix X, that ordering
agrees with Y. That is guaranteed if you generate X from M (which
is ordered like Y after the sort command).
Best, Martin
On 10/27/2015 01:32 PM, pablo najt wrote:
Thank you for your input.
I noticed that if I follow literally all the steps in the
wiki, my data which is ordered by variable 'family' (instead
of subjects, in my case is number of members belonging to e.g.
family_1) is shuffled. This happens after I run the command
sortData below. Especially I noticed that ni and X do not
match sID.
It would be really helpful to know what is this command doing.
I am wondering whether my data differs in number of columns or
else and because of this I end with a shuffled data. Any
suggestion or tips to figure what could be happening?
Thanks
Pablo
[M, Y, ni] = sortData(M,1,Y,sID)
------------------------------------------------------------------------
To: freesurfer@nmr.mgh.harvard.edu
<mailto:freesurfer@nmr.mgh.harvard.edu>
From: mreu...@nmr.mgh.harvard.edu
<mailto:mreu...@nmr.mgh.harvard.edu>
Date: Wed, 14 Oct 2015 10:54:41 -0400
Subject: Re: [Freesurfer] A mixed effect model approach in
within subject dataset {Disarmed}
Hi Pablo,
you should run something like this to get the ni:
[M,Y,ni] = sortData(M,1,Y,sID); # (sorts the data)
see
https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels
hope that helps, Martin
On 10/14/2015 10:43 AM, pablo najt wrote:
Dear FS experts.
I have query about a relating to a previous email
(below). I am aiming to run a LME analysis on
cross-sectional data from different families and have
variable 'family' (number of families) as my NI vector.
My design has three groups and therefore I am not able to
use qdec. I am running the matlab commands below and
finding some difficulty would really appreciate if you
could help out.
Thanks
Pablo
Start analysis as follows:
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');
%---------------------I input the concatenated .mgh
image from preproc and
mris_surf2surf-----------------------------------------------------------------------%
3-Fit a vertex-wise lme model with random effects.:
lhstats = lme_mass_fit_vw(X, [1 2], lhY, ni, lhcortex);
Here I am getting the following problems:
%-------------------- If I use number of families as
my ni get the
following------------------------------------------------------------------------------------------------%
lhstats = lme_mass_fit_vw(X, [1 2], lhY, 82,
lhcortex);
Error using lme_mass_fit (line 108)
The total number of measurements, indicated by
sum(ni), mustbe the same as the number of rows
of the design
matrix X
Error in lme_mass_fit_vw (line 73)
[stats1,st1] =
lme_mass_fit(X,[],Xrows,Zcols,Y,ni,prs,e);
My matrix is organised in "family", "group", Sex" and
"age" columns".
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');
------------------------------------------------------------------------
Date: Thu, 10 Sep 2015 13:44:36 +0000 From:
jbernal0...@yahoo.es <mailto:jbernal0...@yahoo.es> To:
freesurfer@nmr.mgh.harvard.edu
<mailto:freesurfer@nmr.mgh.harvard.edu> Subject: Re:
[Freesurfer] A mixed effect model approach in within
subject dataset
Hi Pablo
I think you can use LME to analyze your data by ordering
the rows of your design matrix appropriately. You can
consider all subjects belonging to the same family as if
they were a single subject in a longitudinal analysis. You
can put in your design matrix all subjects belonging to
family1 first, then all subjects belonging to family 2 and
so on. Then the 'ni' required by lme_mass_fit_vw is a
vector with the number of subjects in each family as its
entries (ordered according to your design matrix). So the
length of the 'ni' vector is equal to the number of
different families in your data.
Now you can go further and additionally order the rows of
your design matrix within each family by age. This will
allow you to test the effect of age within family.
When choosing the random effects for your statistical
model remember that a random effect can only be the
intercept term or any covariate that varies within family.
For example you can compare a model with a single random
effect for the intercept term against the same model but
considering both the intercept term and age as random effects.
Hope that helps
Cheers
-Jorge
------------------------------------------------------------------------
*De:* pablo najt <pablon...@hotmail.com>
<mailto:pablon...@hotmail.com> *Para:*
"freesurfer@nmr.mgh.harvard.edu"
<mailto:freesurfer@nmr.mgh.harvard.edu>
<freesurfer@nmr.mgh.harvard.edu>
<mailto:freesurfer@nmr.mgh.harvard.edu> *Enviado:*
Jueves 10 de septiembre de 2015 8:07 *Asunto:*
[Freesurfer] A mixed effect model approach in within
subject dataset
Dear Freesurfer users,
I wanted to enquire if anyone had successfully been
able to implement Bernal's Linear Mixed Effects (LME)
Models in cross-section dataset *not longitudinal*
(please see previous thread below). I am willing to
perform a LME (3 groups (HC, PT and
Unaffected_relatives) and 3 covariates (sex, age, and
family) with "family" variable been a within-subject
factor. LME will allow to control for the
non-independence of data contributed by patients and
relatives from the same families.
Thanks in advance!
Pablo
From: michaelnot...@hotmail.com
<mailto:michaelnot...@hotmail.com> To:
freesurfer@nmr.mgh.harvard.edu
<mailto:freesurfer@nmr.mgh.harvard.edu> Date: Wed, 19
Feb 2014 13:10:09 +0100 Subject: [Freesurfer] Analysis
of structural data acquired from multiple sites by
using a mixed effect model approach
Hi everybody, I want to compare the surface data of 3
groups (GroupA, GroupB and Controlls) but have the
problem that they were acquired from 4 different
scanner sites. As I can see it, there are three ways
how I could tackle this problem: 1. I could use
mri_glmfit and create a qdec table / fsgd-file with 12
classes: Class GroupA_site1; Class GroupA_site2,...
And then use the contrasts [0.25 0.25 0.25 0.25 0 0 0
0 -0.25 -0.25 -0.25 -0.25] to compare GroupA to the
Controlls. My Problem with this approach is, that the
sites don't contribute the same amount of subjects to
the analysis. I'm not sure if this could be handled by
simply using a weighted contrast. Meaning, if Site1
and Site2 had twice as many subjects than Site3 and
Site4, I could modify the contrast to [0.33 0.33 0.17
0.17 0 0 0 0 -0.33 -0.33 -0.17 -0.17]. 2. I could
create dummy variables to account for the variability
between sites. In this case, I only need to specify 3
classes (Class GroupA; Class GroupB; Class Controlls)
in my fsgd-file. And I use a design matrix that has 4
dummy variables at the end, which specify to which
site a subject belongs. This approach might work, but
I'm not confident that it is the right one. 3. I could
use a mixed effect model approach and specify site as
a random effect. If I understand it correctly, the
mixed effect model approach would be the best one, as
it accounts for the variability within sites. Is that
correct or are there other issues/better approaches? I
tried to implement a mixed effect model by using
Bernal's Linear Mixed Effects (LME) Models
(http://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels)
<http://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels%29>
but run into some problems. I'm not sure if LME can
only be applied on longitudinal data or if my
implementation is wrong. I have a design matrix X that
specifies the characteristics of each subject per row
as follows: Intercept GroupA GroupB Controll
Age IQ Site1 Site2 Site3 Site4 1 1 0 0
11.1 99 0 0 1 0 1 0 1 0 11.1 101 0 0 1 0 1
1 0 0 11.4 95 1 0 0 0 1 0 0 1 12.4 100 1 0
0 0 ... As I have no repeated measures, 'ni' is just a
vector with length X containing '1's. If I do now the
vertex-wise linear mixed-effects estimation, I get the
following output: >> stats = lme_mass_fit_vw(X,[7 8 9
10],Y,ni,lhcortex); Starting matlabpool using the
'local' profile ... connected to 8 workers. Starting
model fitting at each location ... Location 24994:
Index exceeds matrix dimensions. Location 24994:
Algorithm did not converge. Initial and final
likelihoods: -10000000000, -10000000000. Location
62484: Index exceeds matrix dimensions. Location
62484: Algorithm did not converge. Initial and final
likelihoods: -10000000000, -10000000000. ... I've
checked the matrix dimensions of X, Y, ni and lhcortex
and compared them to the LME mass_univariate example
stored in ADNI_Long_50sMCI_vs_50cMCI.mat but couldn't
find any divergence. Has anybody encountered similar
problems? Is my approach of specifying 'ni' as a
vector of'1's even legitimate? Thanks, Michael
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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
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A.A.Martinos Center for Biomedical Imaging
Massachusetts General Hospital
Research Affiliate, CSAIL, MIT
Phone: +1-617-724-5652
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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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