Hi Chistophe,

I'm not surprised by your results as it is very likely that DIQ and F1 have non-zero means. If you were to demean them, then the no-variable analysis would be the same as the with-variable analysis. I actually just sent an email to the Freesurfer list on this topic of demeaning, and I'm copying the list on this response.

The important idea here is the difference between computing/testing the mean response and computing the intercept. The intercept is only meaningful when a continuous variable is present. When you do a statistical test, the results will be different for two possible reasons:

1. The statistical efficiencies are different (due to correlations caused by the non-zero mean of the continuous variable), which will reduce significance. This is always the case.

2. The values being tested (ie, the mean vs the intercept) are possibly different. I say "possibly" here because, scientifically speaking, there may be no effect of your continuous variable, in which case it would not add to the overall mean.

In my opinion, the most appropriate way to analyze the data is to leave the means in your continuous variables (ie, do not demean). Here's why. When you believe that a variable is important scientifically, you posit a model with a population effect (parameterized by an intercept) and the continuous variable (parameterized by a slope). BOTH OF THESE PARAMETERS ARE INDEPENDENT OF THE SAMPLE YOU HAVE CHOSEN. Therefore, when you perform statistical tests on these parameters, your results are independent of your sample -- very important when doing science! In contrast, the mean of your sample may be dependent on the sample you have chosen, and so statistical tests may only then apply to your sample.

For example, if age adds to the hemodynamic response (HRF), older subjects will have a larger amplitude to their HRF. Let's say you perform an experiment on a group of subjects and find that their mean HRF is significantly different than 0. Someone else tries to replicate your experiment and can't. Upon further examination, your sample was somewhat older than the other which caused your sample to have a higher mean and so achieve significance. When both sets are reanalyzed using age as a nuisance variable, the results are the same.

This does not necessarily mean you should not demean your variables, but you just have to be careful what conclusions you draw from it. Note: this applies to variables and regressors -- don't demean your observed raw data (unless you know what you are doing).

doug

Christophe Destrieux wrote:

Hi Doug

We are using mri_glmfit to study the influence on 2 factors (DIQ anf F1) on activations observed within a group of autistic patients.

We used a fsgdf with only one class (autists), and 2 variables : DIQ and F1, the matrix was computed using DOSS

We used the following contrast :

• "0 1 0", to study the influence of DIQ on activations
• "0 0 1", to study the influence of F1 on activations

We thought that the contrast 1 0 0 would represent activations of the whole group independently of F1 or DIQ ;

For comparison purposes we also computed the same map with one class but without any variable ; We assumed that the contrast that should represent activation of the whole group should then be "1"

We expected the same results in both analysis, but in fac maps are very different : they look ok with the second analysis (1class, 0 variable, contrast "1"), but were almost empty with the first one (1 class, 2 variables, contrast "1 0 0")

There is probably something wrong but we cant find out where

thanks

PS the message is CC to Frederic Andersson who joined the lab a few weeks ago ; he is engineer and he will probably bug you from time to time : )

cheers