External Email - Use Caution

Dear FreeSurfer users,

I am not sure whether my question was posted, so I will just ask it again.

I am interested in cortical thickness differences between preterm (PT) and fullterm (FT) individuals. I have used DODS with mri_glmfit and wanted to regress out scanner (4 sites) and sex in the analysis, using the attached FSGD file. Both of the variables are discrete and I have found different information on how to include discrete variables into the model.

I have followed Andrew Jahn's blog at first who includes discrete variables in the variable list just as continuous ones and specifies them with values of 0 or 1 (https://secure-web.cisco.com/1mCX947TtGflWAdnM3CtHlWXx5Aa4vkeu9B25dEdIVjqujq...). I have adapted this procedure and have specified my gender values as 1 and 2 (simply because it was stated like this in the participants information sheet I have received).

Recently, I have read a post on the mail archive, where Doug states: "you should not code gender and handedness as a continuous variables. In doing this, you are saying that you expect right handers to have 2x the thickness as left handers (or females to have 2x the thickness as males)" (https://secure-web.cisco.com/1A4RpPVoEFVSno401uhBm0BawiKw27S9-5VpEg7YyqN3rIy...). When following this argument, this would also hold for continuous variables, i.e., a patient aged 50 would have 2x the thickness of a patient aged 25.

So my question is how to correctly deal with discrete variables and why you have suggested to code gender as classes instead of variables. What is the actual difference? How does the model treat classes and variables?

Cheers and many thanks in advance,

Melissa Thalhammer