Sorry Jorge to write you again,I keep getting an error when trying to install
the freesurfer-Darwin-OSX-dev.dmg, "image not recognised".Do I supposed to
install it from matlab?Thank,Pablo
Date: Thu, 10 Sep 2015 13:44:36 +0000
From: jbernal0...@yahoo.es
To: 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>
Para: "freesurfer@nmr.mgh.harvard.edu" <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
To: 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) 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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addressed. If you believe this e-mail was sent to you in error and the e-mail
contains patient information, please contact the Partners Compliance HelpLine at
http://www.partners.org/complianceline . If the e-mail was sent to you in error
but does not contain patient information, please contact the sender and properly
dispose of the e-mail. _______________________________________________
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addressed. If you believe this e-mail was sent to you in error and the e-mail
contains patient information, please contact the Partners Compliance HelpLine at
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