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: mreuter@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
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: mreuter@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).In my case I have a combination of variables 1 and 2 for group (relatives of PT and PT) and 5 (age).X = [ones(length(M),1) M M(:,1).*M(:,2);
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
From: mreuter@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?ThanksPablo
[M, Y, ni] = sortData(M,1,Y,sID)
To: freesurfer@nmr.mgh.harvard.edu
From: mreuter@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.ThanksPablo
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');
3-Fit a vertex-wise lme model with random effects.:%---------------------I input the concatenated .mgh image from preproc and mris_surf2surf-----------------------------------------------------------------------%
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 designmatrix XError in lme_mass_fit_vw (line 73)[stats1,st1] = lme_mass_fit(X,[],Xrows,Zcols,Y,ni,prs,e);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');My matrix is organised in "family", "group", Sex" and "age" columns".
Date: Thu, 10 Sep 2015 13:44:36 +0000 From: jbernal0019@yahoo.es To: freesurfer@nmr.mgh.harvard.edu Subject: Re: [Freesurfer] A mixed effect model approach in within subject dataset_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is 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 MailScanner has detected a possible fraud attempt from "www.partners.org" claiming to be 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.Hi PabloI 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 helpsCheers-Jorge
De: pablo najt <pablonajt@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_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is 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 MailScanner has detected a possible fraud attempt from "www.partners.org" claiming to be 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.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!PabloFrom: michaelnotter@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_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is 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 MailScanner has detected a possible fraud attempt from "www.partners.org" claiming to be 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.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_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer-- 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_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is 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 MailScanner has detected a possible fraud attempt from "www.partners.org" claiming to be 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._______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer-- 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_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is 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 MailScanner has detected a possible fraud attempt from "www.partners.org" claiming to be 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._______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
-- 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_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is 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.
_______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer