Thanks you for the replies.Jorge and FS experts,I have run the analysis and
first double checked that the sum of vectors of (ni) is equal to the number of
rows in (X). Both are 140 which is the number of my subjects.The analysis gave
the following 'error'(?) below:I looked up a previous thread coming across
this. At that case you recommendedWould you recommend this afain>
Aproximate percentage of fitted locations: 100%Warning: matlabpool will be
removed in a future release.To query the size of an already started parallel
pool, query the 'NumWorkers'property of the pool.To check if a pool is already
started use 'isempty(gcp('nocreate'))'. Warning: matlabpool will be removed in
a future release.To shutdown a parallel pool use 'delete(gcp('nocreate'))'
instead. Parallel pool using the 'local' profile is shutting down.
Summary:Algorithm did not converge at 90.0637 percent of the total number of
locations.Total elapsed time is 550.1023 minutes.
Also almost all the time the screen showed the following message:144114:
Algorithm did not converge. Initial and final likelihoods: -38.3408-1.5708i,
-241.4153-1.570796i.Location 144113: Algorithm did not converge. Initial and
final likelihoods: -5.5424-1.5708i, -133.8004-1.570796i.Location 144112:
Algorithm did not converge. Initial and final likelihoods: -7.7571-1.5708i,
-319.1378-1.570796i.Location 144111: Algorithm did not converge. Initial and
final likelihoods: -16.8597-1.5708i, 0.74448.Aproximate percentage of fitted
locations: 100%
So my two questions are:1. Is this problematic?2. Are there any fixes to this
issue?Thank you,Pablo
Date: Thu, 15 Oct 2015 13:38:22 +0000
From: jbernal0...@yahoo.es
To: freesurfer@nmr.mgh.harvard.edu
CC: pablon...@hotmail.com
Subject: Re: [Freesurfer] A mixed effect model approach in within subject
dataset {Disarmed}
Hi Pablo
The error you are getting is because in your Matlab setup you can only request
a maximum of 4 matlab parallel workers and by default lme requests 8. So you
just need to modify your command like this:
lhstats = lme_mass_fit_vw(X, [1 2], lhY, ni, lhcortex, [], 4);
Please make sure that sum(ni) and length(X) give the same value before running
the above command.
Cheers-Jorge
De: pablo najt <pablon...@hotmail.com>
Para: Freesurfer support list <freesurfer@nmr.mgh.harvard.edu>
Enviado: Jueves 15 de octubre de 2015 7:18
Asunto: Re: [Freesurfer] A mixed effect model approach in within subject
dataset {Disarmed}
Thank you Martin.I am trying to run the following command line and get the
error below. Would you have a suggestion to overcome this issue?Just in case I
am also including a snapshot of my loaded variables at the bottom.Many
thanks,Pablo>> lhstats = lme_mass_fit_vw(X, [1 2], lhY, ni, lhcortex);Warning:
matlabpool will be removed in a future release.To query the size of an already
started parallel pool, query the 'NumWorkers'property of the pool.To check if a
pool is already started use 'isempty(gcp('nocreate'))'. Warning: matlabpool
will be removed in a future release.To query the size of an already started
parallel pool, query the 'NumWorkers'property of the pool.To check if a pool is
already started use 'isempty(gcp('nocreate'))'. Warning: matlabpool will be
removed in a future release.Use parpool instead. Starting matlabpool using the
'local' profile ... Error using matlabpool (line 148)Failed to start a parallel
pool. (For information in addition to the causing error,validate the profile
'local' in the Cluster Profile Manager.)Error in lme_mass_fit (line 128)
matlabpool(prs);Error in lme_mass_fit_vw (line 73)[stats1,st1] =
lme_mass_fit(X,[],Xrows,Zcols,Y,ni,prs,e);Caused by: Error using
parallel.internal.pool.InteractiveClient/start (line 330) Failed to start
pool. Error using parallel.Job/submit (line 304) You requested a
minimum of 8 workers, but the cluster "local" has the NumWorkers
property set to allow a maximum of 4 workers. To run a communicating job
on more workers than this (up to a maximum of 512 for the Local
cluster), increase the value of the NumWorkers property for the cluster.
The default value of NumWorkers for a Local cluster is the number of
cores on the local machine.
To: freesur...@nmr.mgh.harvard.eduFrom: mreu...@nmr.mgh.harvard.eduDate: Wed,
14 Oct 2015 10:54:41 -0400Subject: 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
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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--
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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