Hello JorgeTrying to set up for running Linear Mixed Effects. My design has 3 groups (HC, PT relatives and PT) and therefore I am not using QDEC as suggested in the tutorial. My first question: Are there any changes from the matlab commands you suggest in the tutorial if using fsgd rather than Qdec?About my design matrix you suggested below to use each family as the same subject. So I will have family_1 as subject_1, family_2 as subject_2... In my fsgd I need to define at the top the classes and in my matrix these will be the families (family_1, ...). However I also have a number of discrete variables which I define within the classes when running my glm, so I am wondering does this mean that I will be defining my classes as follow?class HC_Female_Family1class PT_Female_Family1class HC_Male_Family2etc Many thanks,Pablo
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
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 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
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: 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
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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