External Email - Use Caution Hi Douglas,
Sorry I didn't find an easy way to include previous email chain. Now I just copied all of them below, so that it's easy to follow. Thanks, Xiao <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from :%22Douglas+N.+Greve%22> Douglas N. Greve <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date :20200507> Thu, 07 May 2020 14:56:08 -0700 sorry, please include the previous email chain so that I know what you are referring to On 5/7/2020 5:33 PM, Xiaojiang Yang wrote: External Email - Use Caution Douglas, Just as I mentioned in the initial email, subjects in the healthy group have different ages. Because average cortical thickness is believed to be declined as the age gets older, I want to "normalize" the group by multiplying each subject's thickness with an age-dependent scalar (pre-calculated constant). Suppose I take age 30 as the "standard" age, then subjects with ages greater than 30 will have scalars greater than 1 (depend on age); subjects with ages smaller than 30 will have scalars less than 1. In this way, all subjects in the group looks like to be at the same age (30). Of course, I also suspect that there is no need to do the above mentioned normalization if I use mri_glmfit. But my initial intention was using non-linear scalars, and mri_glmft can only use linear fit. Please give me your opinion. Thank you! Xiao <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from :%22Douglas+N.+Greve%22> Douglas N. Greve <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date :20200507> Thu, 07 May 2020 14:06:27 -0700 I don't know what weight is. Can you elaborate? On 5/7/2020 4:54 PM, Xiaojiang Yang wrote: External Email - Use Caution Hi Douglas, Thank you very much! Could you please help me with more detailed information? 1)Should I use "Variables age" or "Variables age weight" in the fsgd file? If I can use either of them, which one is better? I don't know what weight is. Can you elaborate? 2)For the contrast file, the content "1 -1 0" you mentioned, it corresponds to the "Variables age" fsgd file, correct? Yes 3)If I use the "Variables age weight" fsgd file, what will be the content of the contrast file? Is it "1 -1 0 0"? Yes Thank you again! Xiao <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=from :%22Douglas+N.+Greve%22> Douglas N. Greve <https://www.mail-archive.com/search?l=freesurfer@nmr.mgh.harvard.edu&q=date :20200507> Thu, 07 May 2020 11:55:27 -0700 I think it will work properly if you use the raw data as input and create an FSGD file with two classes: (1) the individual subject and (2) healthy subjects. Also include age as a covariate. Use DOSS instead of DODS. Then set your contrast to be 1 -1 0 and run mri_glmfit. I think that should properly account for age. On 5/7/2020 2:44 PM, Xiaojiang Yang wrote: External Email - Use Caution Dear FS experts, I have a group of healthy subjects. Given a new individual subject, I want to compare its cortical thickness to the healthy group so that I can find where the thickness is abnormal (thickening or thinning). I use fsaverage as the template subject to calculate mean and std of the healthy group. Since subjects in the healthy group have different ages, I do a "normalization" process to all the subjects in the group BEFORE calculating the mean and std. Thus the normalized healthy group can be regarded as all subjects having the same "standard" age. The normalization process is just multiplying some pre-obtained scale (varied by age, but not linearly ) to the file *?h.thickness.fwhm0.fsaverage.mgh* of each subject that are already calculated by "recon-all --qcache" command. So the mean and std of the group are actually weighted in the sense of Freesurfer's recon-all results. My questions are: 1)If I do vertex-wise t-tests by simply comparing individual subject's thickness to the group using mris_calc but WITHOUT using mri_glmfit, can I still use mri_glmfit-sim to do multiple comparisons correction? If yes, how? 2)If the answer is no for the above question, how should I use mris_glmft to implement my above thoughts so that I can use use mri_glmfit-sim? Specifically, how can I use the weighted mean for the group? 3)Take a step back, if I consider the weight is linearly correlated with the age, how to design the fsdg and contrast file? Thank you very much! Xiao
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